healthy lifestyle research paper topics

• Research article
• Open access
• Published: 29 September 2022

A healthy lifestyle is positively associated with mental health and well-being and core markers in ageing

• Pauline Hautekiet   ORCID: orcid.org/0000-0003-3805-3004 1 , 2 ,
• Nelly D. Saenen 1 , 2 ,
• Dries S. Martens 2 ,
• Margot Debay 2 ,
• Johan Van der Heyden 3 ,
• Tim S. Nawrot 2 , 4 &
• Eva M. De Clercq 1  

BMC Medicine volume  20 , Article number:  328 ( 2022 ) Cite this article

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Studies often evaluate mental health and well-being in association with individual health behaviours although evaluating multiple health behaviours that co-occur in real life may reveal important insights into the overall association. Also, the underlying pathways of how lifestyle might affect our health are still under debate. Here, we studied the mediation of different health behaviours or lifestyle factors on mental health and its effect on core markers of ageing: telomere length (TL) and mitochondrial DNA content (mtDNAc).

In this study, 6054 adults from the 2018 Belgian Health Interview Survey (BHIS) were included. Mental health and well-being outcomes included psychological and severe psychological distress, vitality, life satisfaction, self-perceived health, depressive and generalised anxiety disorder and suicidal ideation. A lifestyle score integrating diet, physical activity, smoking status, alcohol consumption and BMI was created and validated. On a subset of 739 participants, leucocyte TL and mtDNAc were assessed using qPCR. Generalised linear mixed models were used while adjusting for a priori chosen covariates.

The average age (SD) of the study population was 49.9 (17.5) years, and 48.8% were men. A one-point increment in the lifestyle score was associated with lower odds (ranging from 0.56 to 0.74) for all studied mental health outcomes and with a 1.74% (95% CI: 0.11, 3.40%) longer TL and 4.07% (95% CI: 2.01, 6.17%) higher mtDNAc. Psychological distress and suicidal ideation were associated with a lower mtDNAc of − 4.62% (95% CI: − 8.85, − 0.20%) and − 7.83% (95% CI: − 14.77, − 0.34%), respectively. No associations were found between mental health and TL.

Conclusions

In this large-scale study, we showed the positive association between a healthy lifestyle and both biological ageing and different dimensions of mental health and well-being. We also indicated that living a healthy lifestyle contributes to more favourable biological ageing.

Peer Review reports

According to the World Health Organization (WHO), a healthy lifestyle is defined as “a way of living that lowers the risk of being seriously ill or dying early” [ 1 ]. Public health authorities emphasise the importance of a healthy lifestyle, but despite this, many individuals worldwide still live an unhealthy lifestyle [ 2 ]. In Europe, 26% of adults smoke [ 3 ], nearly half (46%) never exercise [ 4 ], 8.4% drink alcohol on a daily basis [ 5 ] and over half (51%) are overweight [ 5 ]. These unhealthy behaviours have been associated with adverse health outcomes like cardiovascular diseases [ 6 , 7 , 8 ], respiratory diseases [ 9 ], musculoskeletal diseases [ 10 ] and, to a lesser extent, mental disorders [ 11 , 12 ].

Even though the association between lifestyle and health outcomes has been extensively investigated, biological mechanisms explaining these observed associations are not yet fully understood. One potential mechanism that can be suggested is biological ageing. Both telomere length (TL) and mitochondrial DNA content (mtDNAc) are known biomarkers of ageing. Telomeres are the end caps of chromosomes and consist of multiple TTAGGG sequence repeats. They protect chromosomes from degradation and shorten with every cell division because of the “end-replication problem” [ 13 ]. Mitochondria are crucial to the cell as they are responsible for apoptosis, the control of cytosolic calcium levels and cell signalling [ 14 ]. Living a healthy lifestyle can be linked with healthy ageing as both TL and mtDNAc have been associated with health behaviours like obesity [ 15 ], diet [ 16 ], smoking [ 17 ] and alcohol abuse [ 18 ]. Furthermore, as biomarkers of ageing, both TL and mtDNAc have been associated with age-related diseases like Parkinson’s disease [ 19 ], coronary heart disease [ 20 ], atherosclerosis [ 21 ] and early mortality [ 22 ]. Also, early mortality and higher risks for the aforementioned age-related diseases are observed in psychiatric illnesses, and it is suggested that advanced biological ageing underlies these observations [ 23 ].

Multiple studies evaluated individual health behaviours, but research on the combination of these health behaviours is limited. As they often co-occur and may cause synergistic effects, assessing them in combination with each other rather than independently might better reflect the real-life situation [ 24 , 25 ]. Therefore, in a general adult population, we combined five commonly studied health behaviours including diet, smoking status, alcohol consumption, BMI and physical activity into one healthy lifestyle score to evaluate its association with mental health and well-being and biological ageing. Furthermore, we evaluated the association between the markers of biological ageing and mental health and well-being. We hypothesise that individuals living a healthy lifestyle have a better mental health status, a longer TL and a higher mtDNAc and that these biomarkers are positively associated with mental health and well-being.

Study population

In 2018, 11611 Belgian residents participated in the 2018 Belgian Health Interview Survey (BHIS). The sampling frame of the BHIS was the Belgian National Register, and participants were selected based on a multistage stratified sampling design including a geographical stratification and a selection of municipalities within provinces, of households within municipalities and of respondents within households [ 26 ]. The study population for this cross-sectional study included 6054 BHIS participants (see flowchart in Additional file 1 : Fig. S1) [ 27 , 28 , 29 , 30 , 31 ]. Minors (< 18 years) and participants not eligible to complete the mental health modules (participants who participated through a proxy respondent, i.e. a person of confidence filled out the survey) were excluded ( n  = 2172 and n  = 846, respectively). Furthermore, of the 8593 eligible participants, those with missing information to create the mental health indicators, the lifestyle score or the covariates used in this study were excluded ( n  = 1642, 788 and 109, respectively).

For the first time in 2018, a subset of 1184 BHIS participants contributed to the 2018 Belgian Health Examination Survey (BELHES). All BHIS participants were invited to participate except for minors (< 18 years), BHIS participants who participated through a proxy respondent and residents of the German Community of Belgium, the latter representing 1% of the Belgian population. Participants were recruited on a voluntary basis until the regional quotas were reached (450, 300 and 350 in respectively Flanders, Brussels Capital Region and Wallonia). These participants underwent a health examination, including anthropological measurements and completed an additional questionnaire. Also, blood and urine samples were collected. Of the 6054 included BHIS participants, 909 participated in the BELHES. Participants for whom we could not calculate both TL and mtDNAc were excluded ( n  = 170). More specifically, participants were excluded because they did not provide a blood sample ( n  = 91) or because they did not provide permission for DNA research ( n  = 32). Twenty samples were excluded from DNA extraction because either total blood volume was too low ( n  = 7), samples were clothed ( n  = 1) or tubes were broken due to freezing conditions ( n  = 12). Twenty-seven samples were excluded because they did not meet the biomarker quality control criteria (high technical variation in qPCR triplicates). This was not met for 3 TL samples, 20 mtDNAc samples and 4 samples for both biomarkers. For this subset, we ended up with a final number of 739 participants. Further in this paper, we refer to “the BHIS subset” for the BHIS participants ( n  = 6054) and the “BELHES subset” for the BELHES participants ( n  = 739).

As part of the BELHES, this project was approved by the Medical Ethics Committee of the University Hospital Ghent (registration number B670201834895). The project was carried out in line with the recommendations of the Belgian Privacy Commission. All participants have signed a consent form that was approved by the Medical Ethics Committee.

Health interview survey

The BHIS is a comprehensive survey which aims to gain insight into the health status of the Belgian population. The questions on the different dimensions of mental health and well-being were based on international standardised and validated questionnaires [ 32 ], and this resulted in eight mental health outcomes that were used in this study. Detailed information on each indicator score and its use is addressed in Additional file 1 : Table. S1. Firstly, the General Health Questionnaire (GHQ-12) provides the prevalence of psychological and severe psychological distress in the population [ 27 ]. On the total GHQ score, cut-off points of + 2 and + 4 were used to identify respectively psychological and severe psychological distress.

Secondly, we used two indicators for the positive dimensions of mental health: vitality and life satisfaction. Four questions of the short form health survey (SF-36) indicate the participant’s vital energy level [ 28 , 33 ]. We used a cut-off point to identify participants with an optimal vitality score, which is a score equal to or above one standard deviation above the mean, as used in previous studies [ 34 , 35 ]. Life satisfaction was measured by the Cantril Scale, which ranges from 0 to 10 [ 29 ]. A cut-off point of + 6 was used to indicate participants with high or medium life satisfaction versus low life satisfaction.

Thirdly, the question “How is your health in general? Is it very good, good, fair, bad or very bad?” was used to assess self-perceived health, also known as self-rated health. Based on WHO recommendations [ 36 ], the answer categories were dichotomised into “good to very good self-perceived health” and “very bad to fair self-perceived health”.

Fourthly, depressive and generalised anxiety disorders were defined using respectively the Patient Health Questionnaire (PHQ-9) and the Generalised Anxiety Disorder Questionnaire (GAD-7). We identified individuals who suffer from major depressive syndrome or any other type of depressive syndrome according to the criteria of the PHQ-9 [ 37 ]. A cut-off point of + 10 on the total sum of the GAD-7 score was used to indicate generalised anxiety disorder [ 31 ]. Additionally, a dichotomous question on suicidal ideation was used: “Have you ever seriously thought of ending your life?”; “If yes, did you have such thoughts in the past 12 months?”. Finally, the BHIS also includes personal, socio-economic and lifestyle information. The standardised Cronbach’s alpha coefficients for the PHQ-9, GHQ-12, GAD-7 and questions on vitality of the SF-36 ranged between 0.80 and 0.90.

Healthy lifestyle score

We developed a healthy lifestyle score based on five different health behaviours: body mass index (BMI), smoking status, physical activity, alcohol consumption and diet (Table 1 ). These health behaviours were defined as much as possible according to the existing guidelines for healthy living issued by the Belgian Superior Health Council [ 38 ] and the World Health Organisation [ 39 , 40 , 41 ]. Firstly, BMI was calculated as a person’s self-reported weight in kilogrammes divided by the square of the person’s self-reported height in metres (kg/m 2 ). BMI was classified into four categories: underweight (BMI < 18.5 kg/m 2 ), normal weight (BMI 18.5–24.9 kg/m 2 ), overweight (BMI 25.0–29.9 kg/m 2 ) and obese (BMI ≥ 30.0 kg/m 2 ). Due to a J-shaped association of BMI with the overall mortality and multiple specific causes of death, obesity and underweight were both classified as least healthy [ 42 ]. BMI was scored as follows: obese and underweight = 0, overweight = 1 and normal weight = 2.

Secondly, smoking status was divided into four categories. Participants were categorised as regular smokers if they smoked a minimum of 4 days per week or if they quit smoking less than 1 month before participation (= 0). Occasional smokers were defined as smoking more than once per month up to 3 days per week (= 1). Participants were classified as former smokers if they quit smoking at least 1 month before the questionnaire or if they smoked less than once a month (= 2). The final category included people who never smoked (= 3).

Thirdly, physical activity was assessed by the question: “What describes best your leisure time activities during the last year?”. Four categories were established and scored as follows: sedentary activities (= 0), light activities less than 4 h/week (= 1), light activities more than 4 h/week or recreational sport less than 4 h/week (= 2) and recreational sport more than 4 h or intense training (= 3). Fourthly, information on the number of alcoholic drinks per week was used to categorise alcohol consumption. The different categories were set from high to low alcohol consumption: 22 drinks or more/week (= 0), 15–21 drinks/week (= 1), 8–14 drinks/week (= 2), 1–7 drinks/week (= 3)and less than 1 drink/week (= 4).

Finally, in line with the research by Benetou et al., a diet score was calculated using the frequency of consuming fruit, vegetables, snacks and sodas [ 43 ]. For fruit as well as vegetable consumption, the frequency was scored as follows: never (= 0), < 1/week (= 1), 1–3/week (= 2), 4–6/week (= 3) and ≥ 1/day (= 4). The frequency of consuming snacks and sodas was scored as follows: never (= 4), < 1/week (= 3), 1–3/week (= 2), 4–6/week (= 1) and ≥ 1/day (= 0). The diet score was then divided into tertiles, in line with the research by Benetou et al. [ 43 ]. A diet score of 0–9 points was classified as the least healthy behaviour (= 0). A diet score ranging from 10 to 12 made up the middle category (= 1), and a score from 13 to 16 was classified as the healthiest behaviour (= 2).

All five previously described health behaviours were combined into one healthy lifestyle score (Table 1 ). The sum of the scores obtained for each health behaviour indicated the absolute lifestyle score. To calculate the relative lifestyle score, each absolute scored health behaviour was given equal weight by recalculating its maximum absolute score to a relative score of 1. The relative lifestyle scores were then summed up to achieve a final continuous lifestyle score, ranging from 0 to 5, with a higher score representing a healthier lifestyle.

Telomere length and mitochondrial DNA content assay

Blood samples were collected during the BELHES and centrifuged for 15 min at 3000 rpm before storage at − 80 °C. After extracting the buffy coat from the blood sample, DNA was isolated using the QIAgen Mini Kit (Qiagen, N.V.V Venlo, The Netherlands). The purity and quantity of the sample were measured with a NanoDrop spectrophotometer (ND-2000; Thermo Fisher Scientific, Wilmington, DE, USA). DNA integrity was assessed by agarose gel electrophoresis. To ensure a uniform DNA input of 6 ng for each qPCR reaction, samples were diluted and checked using the Quant-iT™ PicoGreen® dsDNA Assay Kit (Life Technologies, Europe).

Relative TL and mtDNAc were measured in triplicate using a previously described quantitative real-time PCR (qPCR) assay with minor modifications [ 44 , 45 ]. All reactions were performed on a 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) in a 384-well format. Used telomere, mtDNAc and single copy-gene reaction mixtures and PCR cycles are given in Additional file 1 : Text. S1. Reaction efficiency was assessed on each plate by using a 6-point serial dilution of pooled DNA. Efficiencies ranged from 90 to 100% for single-copy gene runs, 100 to 110% for telomere runs and 95 to 105% for mitochondrial DNA runs. Six inter-run calibrators (IRCs) were used to account for inter-run variability. Also, non-template controls were used in each run. Raw data were processed and normalised to the reference gene using the qBase plus software (Biogazelle, Zwijnaarde, Belgium), taking into account the run-to-run differences.

Leucocyte telomere length was expressed as the ratio of telomere copy number to single-copy gene number (T/S) relative to the mean T/S ratio of the entire study population. Leucocyte mtDNAc was expressed as the ratio of mtDNA copy number to single-copy gene number (M/S) relative to the mean M/S ratio of the entire study population. The reliability of our assay was assessed by calculating the interclass correlation coefficient (ICC) of the triplicate measures (T/S and M/S ratios and T, M and S separately) as proposed by the Telomere Research Network, using RStudio version 1.1.463 (RStudio PBC, Boston, MA, USA). The intra-plate ICCs of T/S ratios, TL runs, M/S ratios, mtDNAc runs and single-copy runs were respectively 0.804 ( p  < 0.0001), 0.907 ( p  < 0.0001), 0.815 ( p  < 0.0001), 0.916 ( p  < 0.0001) and 0.781 ( p  < 0.0001). Based on the IRCs, the inter-plate ICC was 0.714 ( p  < 0.0001) for TL and 0.762 ( p  < 0.0001) for mtDNAc.

Statistical analysis

Statistical analyses were performed using the SAS software (version 9.4; SAS Institute Inc., Cary, NC, USA). We performed a log(10) transformation of the TL and mtDNAc data to reduce skewness and to better approximate a normal distribution. Three analyses were done: (1) In the BHIS subset ( n  = 6054), we evaluated the association between the lifestyle score and the mental health and well-being outcomes (separately). These results are presented as the odds ratio (95% CI) of having a mental health condition or disorder for a one-point increment in the lifestyle score. (2) In the BELHES subset ( n  = 739), we evaluated the association between the lifestyle score and both TL and mtDNAc (separately). These results are presented as the percentage difference in TL or mtDNAc (95% CI) for a one-point increment in the lifestyle score. (3) In the BELHES subset ( n  = 739), we evaluated the association between the mental health and well-being outcomes and both TL and mtDNAc (separately). These results are presented as the percentage difference in TL or mtDNAc (95% CI) when having a mental health condition or disorder compared with the healthy group.

For all three analyses, we performed multivariable linear mixed models (GLIMMIX; unstructured covariance matrix) taking into account a priori selected covariates including age (continuous), sex (male, female), region (Flanders, Brussels Capital Region, Wallonia), highest educational level of the household (up to lower secondary, higher secondary, college or university), country of birth (Belgium, EU, non-EU) and household type (single, one parent with child, couple without child, couple with child, others). To capture the non-linear effect of age, we included a quadratic term when the result of the analysis showed that both the linear and quadratic terms had a p -value < 0.1. For the two analyses on TL and mtDNAc, we additionally adjusted for the date of participation in the BELHES. As multiple members of one household participated, we added household numbers in the random statement.

Bivariate analyses evaluating the associations between the characteristics and TL, mtDNAc, the lifestyle score or psychological distress as a parameter of mental health and well-being are evaluated based on the same model. The chi-squared tests (categorical data) and t -tests (continuous data) were used to evaluate the characteristics of included and excluded participants. The lifestyle score was validated by creating a ROC curve and calculating the area under the curve (AUC) of the adjusted association between the lifestyle score and self-perceived health. Adjustments were made for age, sex, region, highest educational level of the household, country of birth and household type.

In a sensitivity analysis, to evaluate the robustness of our findings, we additionally adjusted our main models separately for perceived quality of social support (poor, moderate, strong) and chronic disease (suffering from any chronic disease or condition: yes, no). The third model, evaluating the biomarkers with the mental health outcomes, was also additionally adjusted for the lifestyle score.

Population characteristics

The characteristics of the BHIS and BELHES subset are presented in Table 2 . In the BHIS subset, 48.8% of the participants were men. The average age (SD) was 49.9 (17.5) years, and most participants were born in Belgium (79.5%). The highest educational level in the household was most often college or university degree (53.3%), and the most common household composition was couple with child(ren) (37.7%). The proportion of participants in different regions of Belgium, i.e. Flanders, Brussels Capital Region and Wallonia, was respectively 41.1%, 23.3% and 35.6%. For the BELHES subset, we found similar results except for region and education. We noticed more participants from Flanders and more participants with a high educational level in the household. The mean (SD) relative TL and mtDNAc were respectively 1.04 (0.23) and 1.03 (0.24). TL and mtDNAc were positively correlated (Spearman’s correlation = 0.21, p  < 0.0001).

We compared (1) the characteristics of the 6054 eligible BHIS participants that were included in the BHIS subset with the 2539 eligible participants that were excluded from the BHIS subset (Additional file 1 : Table S2) and (2) the 739 participants from the BHIS subset that were included in the BELHES subset with the 5315 participants that were excluded from the BELHES subset (Additional file 1 : Table S3). Except for sex and nationality in the latter, all other covariates showed differences between the included and excluded groups. On the other hand, population data from 2018 indicates that the average age (SD) of the adult Belgian population was 49.5 (18.9) with a distribution over Flanders, Brussels Capital Region and Wallonia of respectively 58.2%, 10.2% and 31.6% and that 48.7% were men. The distribution of our sample according to age and sex thus largely corresponds to the age and sex distribution of the adult Belgian population figures. The large difference in the regional distribution is due to the oversampling of the Brussels Capital Region in the BHIS.

Bivariate associations evaluating the characteristics with TL, mtDNAc, the lifestyle score or psychological distress as a parameter of mental health are presented in Additional file 1 : Table S4. Briefly, men had a − 6.41% (95% CI: − 9.10 to − 3.65%, p  < 0.0001) shorter TL, a − 8.03% (95% CI: − 11.00 to − 4.96%, p  < 0.0001) lower mtDNAc, lower odds of psychological distress (OR = 0.59, 95% CI: 0.53 to 0.66, p  < 0.0001) and a lifestyle score of − 0.28 (95% CI: − 0.32 to − 0.24, p  < 0.0001) points less compared with women. Furthermore, a 1-year increment in age was associated with a − 0.64% (− 0.73 to − 0.55%, p  < 0.0001) shorter TL and a − 0.19% (95% CI: − 0.31 to − 0.08%, p  = 0.00074) lower mtDNAc.

Mental health prevalence and lifestyle characteristics

Within the BHIS subset, 32.3% and 18.0% of the participants had respectively psychological and severe psychological distress. 86.7% had suboptimal vitality, 12.0% indicated low life satisfaction and 22.0% had very bad to fair self-perceived health. The prevalence of depressive and generalised anxiety disorders was respectively 9.0% and 10.8%, respectively. 4.4% of the participants indicated to have had suicidal thoughts in the past 12 months. Similar results were found for the BELHES subset (Table 3 ).

Within the BHIS subset, the average lifestyle score (SD) was 3.1 (0.9) (Table 4 ). A histogram of the lifestyle score is shown in Additional file 1 : Fig. S2. 16.6% were regular smokers, and 4.9% reported 22 alcoholic drinks per week or more. 29.7% reported that their main leisure time included mainly sedentary activities, and 18.6% were underweight or obese. 29.2% were classified as having an unhealthy diet score. The participants of the BELHES subset were slightly more active, but no other dissimilarities were found (Table 4 ). The ROC curve shows an area under the curve (AUC) of 0.74, indicating a 74% predictive accuracy for the lifestyle score as a self-perceived health predictor (Additional file 1 : Fig. S3).

Healthy lifestyle and mental health and well-being

Living a healthier lifestyle, indicated by having a higher lifestyle score, was associated with lower odds of all mental health and well-being outcomes (Table 5 ). After adjustment, a one-point increment in the lifestyle score was associated with lower odds of psychological (OR = 0.74, 95% CI: 0.69, 0.79) and severe psychological distress (OR = 0.69, 95% CI: 0.64, 0.75). Similarly, for the same increment, the odds of suboptimal vitality, low life satisfaction and very bad to fair self-perceived health were respectively 0.62 (95% CI: 0.56, 0.68), 0.62 (95% CI: 0.56, 0.68) and 0.56 (95% CI: 0.52, 0.61). Finally, the odds of having depressive disorder, generalised anxiety disorder or suicidal ideation were respectively 0.57 (95% CI: 0.51, 0.63), 0.63 (95% CI: 0.57, 0.69) and 0.63 (95% CI: 0.55, 0.72) for a one-point increment in the lifestyle score.

The biomarkers of ageing

After adjustment, living a healthy lifestyle was positively associated with both TL and mtDNAc (Table 6 ). A one-point increment in the lifestyle score was associated with a 1.74 (95% CI: 0.11, 3.40%, p  = 0.037) higher TL and a 4.07 (95% CI: 2.01, 6.17%, p  = 0.00012) higher mtDNAc.

People suffering from severe psychological distress had a − 4.62% (95% CI: − 8.85, − 0.20%, p  = 0.041) lower mtDNAc compared with those who did not suffer from severe psychological distress. Similarly, people with suicidal ideation had a − 7.83% (95% CI: − 14.77, − 0.34%, p  = 0.041) lower mtDNAc compared with those without suicidal ideation. No associations were found for the other mental health and well-being outcomes, and no associations were found between mental health and TL (Table 6 ).

Sensitivity analysis

Additional adjustment of the main analyses for perceived quality of social support, chronic disease or lifestyle score (in the association between the mental health outcomes and the biomarkers of ageing) did not strongly change the effect of our observations (Additional file 1 : Tables S5-S7). However, we noticed that most of the associations between severe psychological distress or suicidal ideation and mtDNAc showed marginally significant results.

In this study, we evaluated the associations between eight mental health and well-being outcomes, a healthy lifestyle score and 2 biomarkers of biological ageing: telomere length and mitochondrial DNA content. Having a healthy lifestyle was positively associated with all mental health and well-being indicators and the markers of biological ageing. Furthermore, having had suicidal ideation or suffering from severe psychological distress was associated with a lower mtDNAc. However, no association was found between mental health and TL.

In the first part of this research, we evaluated the association between lifestyle and mental health and well-being and showed that living a healthy lifestyle was positively associated with better mental health and well-being outcomes. Similar trends were found in previous studies for each of the health behaviours separately [ 11 , 12 , 46 , 47 , 48 ]. Although evaluating these health behaviours separately provides valuable information, assessing them in combination with each other rather than independently might better reflect the real-life situation as they often co-occur and may exert a synergistic effect on each other [ 24 , 25 , 49 ]. For example, 68% of the adults in England engaged in two or more unhealthy behaviours [ 25 ]. Especially, smoking status and alcohol consumption co-occurred, but half of the studies in the review by Noble et al. indicated clustering of all included health behaviours [ 24 ].

To date, the number of studies evaluating the combination of multiple health behaviours and mental health and well-being in adults is limited, and most of them use a different methodology to assess this association [ 50 , 51 , 52 , 53 , 54 , 55 , 56 ]. Firstly, differences are found between the included health behaviours. Most studies included the four “SNAP” risk factors, i.e. smoking, poor nutrition, excess alcohol consumption and physical inactivity. Other health behaviours that were sometimes included were BMI/obesity, sleep duration/quality and psychological distress [ 50 , 53 , 54 , 56 ]. Secondly, differences are found in the scoring of the health behaviours and the use of the lifestyle score. Whereas in this study the health behaviours were scored categorically, studies often dichotomised the health behaviours and/or the final lifestyle score [ 50 , 52 , 53 , 56 ]. Also, two studies performed clustering [ 54 , 55 ]. Health behaviours can cluster together at both ends of the risk spectrum, but less is known about the middle categories. This is avoided by using the cluster method where participants are clustered based on similar behaviours. On the other hand, a lifestyle score can be of better use and more easily be interpreted when aiming to compare healthy versus unhealthy lifestyles, as was the case for this study.

Despite these different methods, all previously mentioned studies show similar results. Together with our findings, which also support these results, this provides clear evidence that an unhealthy lifestyle is associated with poor mental health and well-being outcomes. Important to notice is that, like our research, most studies in this field have a cross-sectional design and are therefore not able to assume causality. Therefore, mental health might be the cause or the consequence of an unhealthy lifestyle. Further prospective and longitudinal studies are warranted to confirm the direction of the association.

Healthy lifestyle and biomarkers of ageing

How lifestyle affects our health is not yet fully understood. One possible pathway is through oxidative stress and biological ageing. An unhealthy lifestyle has been associated with an increase in oxidative stress [ 57 , 58 , 59 ], and in turn, higher concentrations of oxidative stress are known to negatively affect TL and mtDNAc [ 60 ]. In this study, we showed that living a healthy lifestyle was associated with a longer TL and a higher mtDNAc. Our results showed a stronger association of lifestyle with mtDNAc compared with TL. TL is strongly determined by TL at birth [ 61 ]. On the other hand, mtDNAc might be more variable in shorter time periods. Although mtDNAc and TL were strongly correlated, this could explain why lifestyle is more strongly associated with mtDNAc. However, we can only speculate about this, and further research is necessary to confirm our results.

Similar as for the association with mental health, in previous studies, the biomarkers have been associated with health behaviours separately rather than combined [ 62 , 63 , 64 , 65 ]. To our knowledge, we are the first to evaluate the associations between a healthy lifestyle score and mtDNAc. Our results are in line with our expectations. As TL and mtDNAc are known to be correlated [ 60 ], we would expect similar trends for both biomarkers. In the case of TL, few studies included a combined lifestyle score in association with this biomarker. Consistent with our results, in a study population of 1661 men, the sum score of a healthier lifestyle was correlated with a longer TL [ 66 ]. Similar results were found by Sun et al. where a combination of healthy lifestyles in a female study population was associated with a longer TL compared with the least healthy group [ 67 ]. Also, improvement in lifestyle has been associated with TL maintenance in the elderly at risk for dementia [ 68 ], and a lifestyle intervention programme was positively associated with leucocyte telomere length in children and adolescents [ 69 ]. These results suggest that on a biological level, a healthy lifestyle is associated with healthy ageing. Within this context, a study on adults aged 60 and older showed that maintaining a normal weight, not smoking and performing regular physical activity were associated with slower development of disability and a reduction in mortality [ 70 ]. Similarly, midlife lifestyle factors like non-smoking, higher levels of physical activity, non-obesity and good social support have been associated with successful ageing, 22 years later [ 71 ].

Mental health and well-being and biomarkers of ageing

Finally, we evaluated the association between the biomarkers of ageing and the mental health and well-being outcomes. The hypothesis that biological ageing is associated with mental health has been supported by observations showing that chronically stressed or psychiatrically ill persons have a higher risk for age-related diseases like dementia, diabetes and hypertension [ 23 , 72 , 73 ]. Important to notice is that, like our research, the majority of studies on this topic have a cross-sectional design and therefore are unable to identify causality. Therefore, it is currently unknown whether psychological diseases accelerate biological ageing or whether biological ageing precedes the onset of these diseases [ 74 ].

Our results showed a lower mtDNAc for individuals with suicidal ideation or severe psychological distress but not for any of the other mental health outcomes. Evidence on the association between mtDNAc and mental health is inconsistent. Women above 60 years old with depression had a significantly lower mtDNAc compared with the control group [ 75 ]. Furthermore, individuals with a low mtDNAc had poorer outcomes in terms of self-rated health [ 76 ]. In contrast, Otsuka et al. showed a higher peripheral blood mtDNAc in suicide completers [ 77 ], and studies on major depressive syndrome [ 78 ] and self-rated health [ 79 ] showed the same trend. Finally, Vyas et al. showed no significant association between mtDNAc and depression status in mid-life and older adults [ 80 ]. These differences might be due to the differences in study population and methods. For example, the two studies indicating lower mtDNAc in association with poor mental health both had an elderly study population, and one study population consisted of psychiatrically ill patients. Next to that, differences were found in the type of samples, mtDNAc assays and questionnaires or diagnostics. The inconsistency of these studies and our results calls for further research on this association and for standardisation of methods within studies to enable clear comparisons.

As for TL, we did not find an association with any of the mental health and well-being outcomes. Previous studies in adults showed a lower TL in association with current but not remitted anxiety disorder [ 81 ], depressive [ 82 ] and major depressive disorder [ 73 , 83 ], childhood trauma [ 84 ] suicide [ 77 , 85 ], depressive symptoms in younger adults [ 86 ] and acculturative stress and postpartum depression in Latinx women [ 87 ]. Also, in a meta-analysis, psychiatric disorders overall were associated with a shorter leucocyte TL [ 88 ]. However, other studies failed to demonstrate an association between TL and mental health outcomes like major depressive disorder [ 89 ], late-life depression [ 90 ] and anxiety disorder [ 91 ]. Again, this could be due to a different method to assess the mental health outcomes, a different study design, uncontrolled confounding factors and the type of telomere assay. For example, a meta-analysis showed stronger associations with depression when using southern blot or FISH assay compared with qPCR to measure telomere length [ 92 ].

Strengths and limitations

An important strength of this study is the use of a validated lifestyle score that can easily be reproduced and used for other research on lifestyle. Secondly, we were able to use a large sample size for our analyses in the BHIS subset. Thirdly, by assessing multiple dimensions of mental health and well-being, we were able to give a comprehensive overview of the mental health status. To our knowledge, we are the first to evaluate the associations between a healthy lifestyle score and mtDNAc.

Our results should however be interpreted with consideration for some limitations. As mentioned before, the study has a cross-sectional design, and therefore, we cannot assume causality. Secondly, for the lifestyle score, we used self-reported data, which might not always represent the actual situation. For example, BMI values tend to be underestimated due to the overestimation of height and underestimation of weight [ 93 ], and also, smoking behaviour is often underestimated [ 94 ]. Also, equal weights were used for each of the health behaviours as no objective information was available on which weight should be given to a specific health behaviour. Thirdly, there is a distinct time lag between the completion of the BHIS questionnaire and the collection of the BELHES samples. The mean (SD) number of days is 52 (35). This is less than the period for suicidal ideation, assessed over the 12 previous months, but there might be a more limited overlap with the period for assessment of the other mental health variables, such as vitality and psychological distress, assessed over the last few weeks, and depressive and generalised anxiety disorders, assessed over the last 2 weeks. Fourthly, due to a non-response bias, the lowest socio-economic classes are less represented in our study population. This will not affect our dose–response associations but might affect the generalisability of our findings to the overall population. Finally, we do not have data on blood cell counts, which has been associated with mtDNAc [ 95 ].

In this large-scale study, we showed that living a healthy lifestyle was positively associated with mental health and well-being and, on a biological level, with a higher TL and mtDNAc, indicating healthy ageing. Furthermore, individuals with suicidal ideation or suffering from severe psychological distress had a lower mtDNAc. Our findings suggest that implementing strategies to incorporate healthy lifestyle changes in the public’s daily life could be beneficial for public health, and might offset the negative impact of environmental stressors. However, further studies are necessary to confirm our results and especially prospective and longitudinal studies are essential to determine causality of the associations.

Availability of data and materials

The dataset used for this study is available through a request to the Health Committee of the Data Protection Authority.

Abbreviations

Area under the curve

Body mass index

Confidence intervals

Generalised Anxiety Disorder Questionnaire

General Health Questionnaire

Inter-run calibrator

• Mitochondrial DNA content

Patient Health Questionnaire

Relative operating characteristic curve

Short Form Health Survey

• Telomere length

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Acknowledgements

We are grateful to all BHIS and BELHES participants for contributing to this study.

The HuBiHIS project is financed by Sciensano (PJ) N°: 1179–101. Dries Martens is a postdoctoral fellow of the Research Foundation—Flanders (FWO 12X9620N).

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Sciensano, Risk and Health Impact Assessment, Juliette Wytsmanstraat 14, 1050, Brussels, Belgium

Pauline Hautekiet, Nelly D. Saenen & Eva M. De Clercq

Centre for Environmental Sciences, Hasselt University, 3500, Hasselt, Belgium

Pauline Hautekiet, Nelly D. Saenen, Dries S. Martens, Margot Debay & Tim S. Nawrot

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TL, mtDNAc and single copy-gene reaction mixture and PCR cycling conditions. Table S1. The mental health indicators with their scores and uses. Table S2. Comparison of the characteristics of the 6,054 eligible BHIS participants that were included in the BHIS subset compared to the 1,838 eligible participants that were excluded from the BHIS subset. Table S3. Comparison of the characteristics of the 739 participants from the BHIS subset that were included in the BELHES subset compared to the 5,315 participants that were excluded from the BELHES subset. Table S4. Bivariate associations between the characteristics and telomere length (TL), mitochondrial DNA content (mtDNAc), the lifestyle score or psychological distress. Table S5. Results of the sensitivity analysis of the association between lifestyle and mental health. Table S6. Results of the sensitivity analysis of the association between lifestyle and the biomarkers of ageing. Table S7. Results of the sensitivity analysis of the association between mental health and the biomarkers of ageing. Fig. S1. Exclusion criteria. The BHIS subset consisted of 6,055 BHIS participants and the BELHES subset consisted of 739 BELHES participants. Fig. S2. Histogram of the lifestyle score. Fig. S3. Validation of the lifestyle score. ROC curve for the lifestyle score as a predictor for good to very good self-perceived health. The model was adjusted for age, sex, region, highest educational level in the household, household composition and country of birth.

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Hautekiet, P., Saenen, N.D., Martens, D.S. et al. A healthy lifestyle is positively associated with mental health and well-being and core markers in ageing. BMC Med 20 , 328 (2022). https://doi.org/10.1186/s12916-022-02524-9

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Association between healthy lifestyle practices and life purpose among a highly health-literate cohort: a cross-sectional study

• Nobutaka Hirooka 1 ,
• Takeru Kusano 1 ,
• Shunsuke Kinoshita 1 ,
• Ryutaro Aoyagi 1 &
• Nakamoto Hidetomo 1  

BMC Public Health volume  21 , Article number:  820 ( 2021 ) Cite this article

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The national health promotion program in the twenty-first century Japan (HJ21) correlates life purpose with disease prevention, facilitating the adoption of healthy lifestyles. However, the influence of clustered healthy lifestyle practices on life purpose, within the context of this national health campaign remains uninvestigated. This study assessed the association between such practices and life purpose, in line with the HJ21.

We performed a nationwide cross-sectional survey on certified specialists in health management. Participants’ demographic information, lifestyle, and purpose in life were measured using a validated tool. The cohort was median-split into two groups based on their clustered health-related lifestyle score. The values for health-related lifestyle and purpose were compared between the two groups and the correlation between health-related lifestyle and purpose in life was measured.

Data from 4820 participants were analyzed. The higher-scoring health-related lifestyle group showed a significantly higher life purpose than the lower group (35.3 vs 31.4; t  = 23.6, p  < 0.001). There was a significant association between the scores of clustered healthy lifestyle practices and life purpose ( r  = 0.401, p  < 0.001). The higher-scoring health-related lifestyle group achieved a higher life purpose than the lower-scoring group. This association between healthy lifestyle practices and life purpose denotes a positive and linear relationship.

Conclusions

Our results suggest that individuals who have a better health-related lifestyle gain a higher sense of life purpose. In other words, a healthy lifestyle predicts a purpose in life. Our findings posit that examining the causal relationship between healthy lifestyle and purpose in life may be a more efficient approach toward health promotion.

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Several studies have investigated the implications of life purpose, and literature has shown that a strong sense of purpose in life was positively associated with positive health outcomes [ 1 , 2 , 3 , 4 , 5 , 6 ]. Thus, having a sense of purpose in life is a vital component of human life. Due to a rapidly aging society in Japan, a national health promotion program in the twenty-first century—Health Japan twenty-first century (HJ21)—considers purpose in life as one of the major target goals of health promotion [ 7 ].

Purpose in life is defined as “a self-organizing life aim that stimulates goals” [ 1 ] and is known to promote healthy behaviors and give life meaning [ 8 , 9 ]. Ikigai is a Japanese word for what is considered an important factor for achieving better health and a fulfilling life [ 10 ]. Ikigai is defined as something to live for, exemplifying the joy and the goal of living [ 11 ]. Although Ikigai may not be fully comparable to purpose in life, it does contain the respective concept and plays a cardinal role in yielding positive health-related outcomes [ 12 ].

Notably, health outcomes associated with life purpose or Ikigai include physical [ 1 , 12 , 13 ] and mental health [ 3 , 13 ], quality of life [ 4 ], disease mortality [ 1 , 12 ], and longevity [ 12 ]. Possessing a strong sense of purpose in life has been associated with a lower risk of mortality and cardiovascular diseases [ 1 ] (relative risk: 0.83 and 0.83, respectively). The study concluded that purpose in life tends to yield health benefits. One of the mechanisms considered in the literature was the benefits associated with a healthy lifestyle. People who have adopted a higher purpose in life tend to follow healthier lifestyle practices, which may decrease the incidence of non-communicable chronic diseases, such as cardiovascular diseases or cancer.

Healthcare personnel are responsible for the health of their patients. Studies have shown that healthcare personnel are more likely to encourage healthy lifestyle behaviors among their patients if they engage in such behaviors themselves. Our study population comprises certified specialists in health management who routinely provide advice on health to individuals in their community. Investigating the relationship between lifestyle and purpose in life among healthcare personnel, our target population, is therefore of great scientific interest.

There is a hierarchy of causality among chronic diseases. Non-communicable diseases, such as diabetes, stroke, cancer, and coronary artery disease, have risk factors. In the case of risk factors, such as hypertension, smoking, dyslipidemia, hyperglycemia, studies typically signified proximal causes [ 14 , 15 ]. A healthy lifestyle is a central causality for these risk factors and thus basic lifestyle should be considered a fundamental and proximal risk factor for the aforementioned non-communicable diseases. Studies also highlight that healthy lifestyle practices prevent many similar chronic diseases [ 16 , 17 ], and that intervening to promote healthier lifestyle decreases mortality due to non-communicable diseases [ 18 , 19 ]. Hence, the notion that health benefits are brought through a healthy lifestyle may be supported if the lifestyle strongly correlates with purpose in life.

In this context, however, research exploring the association between purpose in life and healthy lifestyle practices remain scarce. Moreover, existing literature usually considers a single health behavior in relation to purpose in life. To determine the relationship between purpose in life and clustered health-related lifestyle—the fundamental and proximal cause of many health outcomes—the potential benefits of purpose in life towards disease prevention and health must be deciphered.

This study aimed to investigate the association between health-related lifestyles, in line with the HJ21, and purpose in life, measured with a validated tool to better understand the relational mechanisms.

Study design

The design was a cross-sectional study on a cohort of nationwide certified specialists in health management. We surveyed health-related lifestyles similar to those in the questionnaire used for the HJ21. Our questionnaire is based on the one of the oldest national health surveys around the world, the National Health and Nutrition Survey conducted by Japanese Government [ 20 ]. This survey is the oldest of all national health examination surveys currently conducted worldwide and serves as a comprehensive database for risk factors related to non-communicable diseases in Japan. The survey includes questions on demographic data and health-related habits, such as physical activity and exercise, nutrition and diet, smoking, stress, and alcohol intake. Purpose in life was measured with a validated tool in Japanese using the purposeful life scale (Ikigai-9) [ 21 ]. The ethics committee of the Saitama Medical University approved the study (ID: 896, 2018).

Participants

Study participants were certified specialists in health management who actively pursued professional growth provided by the Japanese Association of Preventive Medicine for Adult Disease [ 22 ]. This certification is sponsored by the Ministry of Education, Culture, Sports, Science and Technology, Japan. We excluded specialists who did not actively engage in continuing education or health promotion activities. These specialists are expected to engage the community and the society they live in to promote health and wellbeing. Specialists in health management are certified in multiple processes of study. Candidates study various aspects within the course, including health promotion, lifestyle-related diseases, mental health, nutrition, environment and health, physical activity and exercise, emergency medicine, life support, and health care system. To register, candidates must pass the final written examination. The Japanese Association of Preventive Medicine for Adult Disease encourages specialists to participate in numerous activities by facilitating health promotion workshops, speeches, and activities after registration. Among these individuals who met our inclusion criteria ( N  = 9149), 4820 agreed to participate in the survey.

Variables and measurements

Variables measured in this study were demographic characteristics; health-related habits, including physical activity and exercise, nutrition and diet, smoking, stress, and alcohol intake; and purpose in life. There were eleven health-related lifestyle questions, of which five were two-scaled (“Intention to maintain ideal weight,” “Exercise,” “Alcohol intake,” “Manage lifestyle to prevent disease,” and “Smoking”). For these items, a score of “1” was assigned for an unhealthy lifestyle and a score of “4” was assigned for a healthy lifestyle. The rest of the six health-related habits (“Reading nutritional information labels,” “Maintaining a balanced diet in daily life,” “Intention for exercise,” “Stress,” “Rest,” and “Sleep”) were to be answered on a four-point scale, from “4” (most favorable) to “1” (least favorable). Finally, we added the values of each answer to the questions on the health-related lifestyle of the participants as their clustered health-related lifestyle scores. To measure purpose in life, we used the Ikigai-9 scale, a validated tool to quantify purpose in life. The Ikigai-9 is a psychometric tool that measures across the dimensions of (1) optimistic and positive emotions toward life, (2) active and positive attitudes towards one’s life, and (3) acknowledgement of the meaning of one’s existence [ 23 ]. The Ikigai-9 scale consists of nine questions on various aspects of life purpose and each question must be answered on a five-point scale, from “1” (Strongly disagree) to “5” (Strongly agree). These variables and measurements were previously described elsewhere [ 24 ]. Considering the variables, age, weight, height, BMI, volume of alcohol intake, and purpose in life scores were numeric. Sex, healthy lifestyle, smoking, alcohol intake, and stress comprised either binary or ordinal data.

Descriptive statistics (i.e., mean, standard deviation, range) were used to describe participants’ characteristics. The cohort was divided into two groups (i.e., a higher and lower group, with a cut-off using the median score) based on the clustered health-related lifestyle scores. The correlations between age and lifestyle score and between age and purpose in life score were analyzed. The difference in the Ikigai-9 score between the two clustered health-related lifestyle score groups was investigated. Further, the effect size of the difference in Ikigai-9 score between the two groups was calculated with using Cohen’s d . The association between the clustered health-related lifestyle score and the Ikigai-9 score was also analyzed as a bivariate correlation and a correlation coefficient was calculated to see whether the health-related lifestyles accounted for life purpose. A multiple regression analysis was performed to determine the association between the clustered health-related lifestyle score and the purpose in life score, after controlling for age. All statistical tests were two-tailed and the software IBM SPSS Statistics (Version 26.0. Armonk, NY) was used for the analysis.

The demographic and health-related lifestyle characteristics of the study participants are shown in Table  1 . In total, 4820 certified specialists in health management were included in the analysis. There were 3190 women (66.2%) and 1630 men (33.8%). The mean ( SD ) age of all study participants was 55.4 (±12.2) years. The majority of the participants (85.0%) were non-obese and “intended to keep ideal weight” and “maintain a healthy lifestyle (82.6% and 89.2%, respectively) to prevent lifestyle-related disease,” such as obesity, metabolic syndrome, and cardiovascular disease. We also found that more than 80% of the study participants “read nutritional information labels” and more than 90% “maintained a balanced diet in daily life.” Regarding exercise and physical activity, more than 80% of the study participants “intended to exercise” and approximately 64% of them achieved the recommended levels. These findings reflected a low rate of obesity among the participants, which was 15.0% in the study. While most of the participants reported resting and sleeping adequately, the rate of taking on stress was high (74.4%). The descriptive analysis of the Ikigai-9 scores confirmed that it was normally distributed, based on the histogram and P-P plot.

Table  2 shows the demographics and healthy lifestyle practices for both the higher and lower clustered health-related lifestyle score groups. We found consistent favorable results in all measured health-related habits in the higher clustered health-related lifestyle score group. There was a significant difference in the scores of purpose in life between the higher group and the lower clustered health-related lifestyle score group ( t  = 23.6, p  < .0001). In the higher group, the average score of purpose in life was 35.3 (95% CI; [35.1–35.5]), while for the lower group, the average score for purpose in life was 31.4 (95% CI; [31.2–31.7]). The differences in the Ikigai-9 purpose in life scores of the two groups and its effect sizes (Cohen’s d) were 3.8 (95% CI; [3.5–4.2]) and 0.68, respectively. Moreover, there was a significant association between the clustered health-related lifestyle score and purpose in life score, r  = .401, p  < .001. The significance remained after controlling for age. Correlation between age and both lifestyle and purpose in life were significant (Pearson r  = 0.29 and 0.15, respectively; both p  < .05).

We found that the higher-scoring clustered health-related lifestyle group showed a statistically significant higher purpose in life than the lower-scoring clustered health-related lifestyle group. The study also highlighted a significant positive association between the clustered health-related lifestyle score and the Ikigai-9 score. To the best of our knowledge, this study was the first to show that a strong sense of purpose in life correlates with clustered health-related lifestyles in the context of a national health campaign. Several studies indicate a positive relationship between purpose in life and health-related lifestyles [ 1 , 25 , 26 , 27 ]. Furthermore, many publications reveal a correlation between a single healthy habit and purpose in life. Therefore, our findings—that affirm a positive relationship between purpose in life and clustered health-related lifestyle—are consistent with previously reported results and help broaden the evidence of this association.

Exploring the mechanistic link of purpose in life with a healthy lifestyle may help us understand this relationship. While studies highlight the positive relationship between purpose in life and health-related lifestyle, a few studies’ results are inconsistent with our findings. For example, an existing prospective study did not observe a positive association between purpose in life and healthy sleep patterns [ 28 ]. In other studies, the purpose of life was not associated with smoking [ 29 , 30 ]. Notably, the mechanistic link between health-related lifestyle and purpose in life has not been well examined. Hooker et al. proposed a hypothesized model linking purpose in life with health [ 31 ]. They summarized the relationship between life purpose and health outcomes by utilizing the concept of self-regulation. In the model, they proposed that life purpose influenced health through three self-regulatory processes and skills: stress-buffering, adaptive coping, and health behaviors. Health-related lifestyle, one of the self-regulatory processes, is the result of individuals setting goals, monitoring their progress, and using feedback to modify their lifestyle [ 31 ]. Thus, a purpose provides the foundation and motivation for engaging in a healthy lifestyle. Kim et al. also suggested that sense of purpose in life enhances the likelihood for engagement in restorative health-related lifestyle practices (e.g., physical activity, healthy sleep quality, use of preventive health care services) from cardiovascular disease to the indirect effect of behavior [ 32 ].

There is an alternative explanation for the mechanistic link between purpose in life and health-related lifestyle. A reverse causality model suggested that engaging in healthy lifestyle practices could predict a greater purpose in life [ 31 , 33 ]. Our results denoted that the group with a higher score in purpose in life performed healthier lifestyle practices and behaviors (Table 2 ), which can be supported by either of the hypothesized models. Age statistically significantly influenced both lifestyle and purpose in life in this study, while gender did not. However, age did not change overall relation between lifestyle and purpose in life. This infers that age may act as a moderator on the association. Further research is needed to clarify the mechanism and the directionality of the association, including any modifying factors. The mechanism to explain the causal relationship between life purpose and healthy lifestyle practices helped prepare for healthy aging by preventing diseases, increasing health longevity, and imbuing a health-oriented drive, which are the major goals of the HJ21.

Additionally, the difference in life purpose scores between the two groups (35.3 vs 31.4), shown in Table 2 , should be further explored, whilst we found a statistically significant difference and a correlation between healthy lifestyle practices and purpose in life. Rather than being a single concept, purpose in life has several elements and a more comprehensive construct. The majority of measurement tools concerned with meaning in life assess two distinct concepts: coherence and purpose [ 34 ]. Coherence is a sense of comprehensibility, or one’s life “making sense,” which is descriptive and value-neutral. Purpose means a sense of core goals, aims, and direction in one’s life, which is more evaluative and value-laden in concept. Ikigai is the Japanese concept meaning a sense of life worth living. The Ikigai-9 scale used in this study has three constructs for measuring the purpose in life; (1) optimistic and positive emotions toward life, (2) active and positive attitudes towards one’s life, and (3) acknowledgement of the meaning of one’s existence. The scale seems to measure more similarly to the purpose; however, the total score does not distinguish between the association of specific constructs and healthy lifestyle practices. Thus, further methodological sophistication regarding the evaluation of a specific concept encompassed within life purpose needs to be reached. This aspect broadens our understanding of purpose in life and its relation to health. This particular cohort of certified specialists shared many features of high health literacy through the process of professional development and certification, combined with life-long learning and activities related to their role as health management specialists. Further, health-related lifestyle practices mean that the certified specialists were far healthier than the national average. These characteristics represent an individual’s health literacy. Health literacy is considered to be an individuals’ capacity to obtain and understand basic health information and services and to make appropriate health-related decisions based on this information [ 35 ]. Therefore, health literacy is directly associated with disease mortality [ 36 ], overall health status [ 37 ], disease prevention [ 38 , 39 ], and health behaviors. These can be attributed to purpose in life [ 2 ].

Thus, health literacy and health-related lifestyle appear to have a similar relationship with disease prevention and better health outcomes. The mediating effect of health literacy on the relationship between healthy lifestyle and life purpose should be investigated. Such inquiries in a prospective cohort study can better explain the mechanism of the causal link between purpose in life, health-related lifestyle, and health literacy.

Limitations

There are several limitations to this study. First, all the measurements were self-reported, which can be a source of bias. Second, while the survey questionnaires are widely used in national health promotion, they have not been fully validated. Third, the real-life meaning of purpose in life has not been determined yet. The Ikigai-9 score, one of the tools used to measure the life purpose score, was validated in a small and limited population; however, the instrument may not capture it holistically. This limitation was implicated by the previously reported systematic review. Furthermore, Zheng et al. found variability in the strength of correlation among the questionnaire for quality of life, part of which included questions regarding a purposeful life [ 40 ]. Lastly, the correlational analysis did not include an adjustment for confounding factors other than age. Hence, little is known about factors influencing the relationship between a healthy lifestyle and purpose in life. We need to establish other potential influencing factors and determine which variables have mediating, moderating, and confounding effects on purpose in life to understand the causal relationship between healthy lifestyle practices and life purpose [ 41 ]. This exploration proposes a promising model for future intervention programs.

Despite these limitations, this study has several strengths. First, the study sample size, N  = 4820, was large and distributed throughout Japan. This aspect of the study increases generalizability. According to the previous review, numerous studies on purpose in life focused on older adults [ 42 ], whereas only a few were concerned with younger or middle-aged adults. In the present study, the majority of the participants were younger and middle-aged adults. Second, previous studies used relatively simple questions or did not employ validated tools to measure purpose in life. However, we used a validated tool, Ikigai-9, in this study. This aspect allows the study results to increase the reliability and validity of the measurement of purpose in life and also hold applicability in other studies. Lastly, study participants were certified specialists in health management who have shown high health literacy. This inclusion criterion provides guidance on improving healthy lifestyle practices through health literacy as an approach to health promotion.

In conclusion, a healthy lifestyle was found to be positively associated with purpose in life among a cohort of highly health-literate professionals. Healthcare personnel who receive specific training for health management may play important roles in promoting a population’s health and wellbeing. However, the mechanism to explain the relationship between purpose in life and health-related lifestyle remains unknown. Therefore, causal relations between improving healthier lifestyles and increasing purpose in life should be tested.

Availability of data and materials

The datasets used in the current study are available from the corresponding author upon reasonable request.

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All authors contributed to the study conception and design. Material preparation and data analysis were performed by Nobutaka Hirooka, Takeru Kusano, and Shunsuke Kinoshita. Nobutaka Hirooka, Shunsuke Kinoshita, and Ryutaro Aoyagi collected the data. Nobutaka Hirooka, Takeru Kusano, and Hidetomo Nakamoto interpreted the analysis. The first draft of the manuscript was written by Nobutaka Hirooka and all authors commented on drafted versions of the manuscript. All authors read and approved the final version of the manuscript.

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Hirooka, N., Kusano, T., Kinoshita, S. et al. Association between healthy lifestyle practices and life purpose among a highly health-literate cohort: a cross-sectional study. BMC Public Health 21 , 820 (2021). https://doi.org/10.1186/s12889-021-10905-7

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Introduction.

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Young people and healthy eating: a systematic review of research on barriers and facilitators

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J Shepherd, A Harden, R Rees, G Brunton, J Garcia, S Oliver, A Oakley, Young people and healthy eating: a systematic review of research on barriers and facilitators, Health Education Research , Volume 21, Issue 2, 2006, Pages 239–257, https://doi.org/10.1093/her/cyh060

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A systematic review was conducted to examine the barriers to, and facilitators of, healthy eating among young people (11–16 years). The review focused on the wider determinants of health, examining community- and society-level interventions. Seven outcome evaluations and eight studies of young people's views were included. The effectiveness of the interventions was mixed, with improvements in knowledge and increases in healthy eating but differences according to gender. Barriers to healthy eating included poor school meal provision and ease of access to, relative cheapness of and personal taste preferences for fast food. Facilitators included support from family, wider availability of healthy foods, desire to look after one's appearance and will-power. Friends and teachers were generally not a common source of information. Some of the barriers and facilitators identified by young people had been addressed by soundly evaluated effective interventions, but significant gaps were identified where no evaluated interventions appear to have been published (e.g. better labelling of food products), or where there were no methodologically sound evaluations. Rigorous evaluation is required particularly to assess the effectiveness of increasing the availability of affordable healthy food in the public and private spaces occupied by young people.

Healthy eating contributes to an overall sense of well-being, and is a cornerstone in the prevention of a number of conditions, including heart disease, diabetes, high blood pressure, stroke, cancer, dental caries and asthma. For children and young people, healthy eating is particularly important for healthy growth and cognitive development. Eating behaviours adopted during this period are likely to be maintained into adulthood, underscoring the importance of encouraging healthy eating as early as possible [ 1 ]. Guidelines recommend consumption of at least five portions of fruit and vegetables a day, reduced intakes of saturated fat and salt and increased consumption of complex carbohydrates [ 2, 3 ]. Yet average consumption of fruit and vegetables in the UK is only about three portions a day [ 4 ]. A survey of young people aged 11–16 years found that nearly one in five did not eat breakfast before going to school [ 5 ]. Recent figures also show alarming numbers of obese and overweight children and young people [ 6 ]. Discussion about how to tackle the ‘epidemic’ of obesity is currently high on the health policy agenda [ 7 ], and effective health promotion remains a key strategy [ 8–10 ].

Evidence for the effectiveness of interventions is therefore needed to support policy and practice. The aim of this paper is to report a systematic review of the literature on young people and healthy eating. The objectives were

(i) to undertake a ‘systematic mapping’ of research on the barriers to, and facilitators of, healthy eating among young people, especially those from socially excluded groups (e.g. low-income, ethnic minority—in accordance with government health policy);

(ii) to prioritize a subset of studies to systematically review ‘in-depth’;

(iii) to ‘synthesize’ what is known from these studies about the barriers to, and facilitators of, healthy eating with young people, and how these can be addressed and

(iv) to identify gaps in existing research evidence.

General approach

This study followed standard procedures for a systematic review [ 11, 12 ]. It also sought to develop a novel approach in three key areas.

First, it adopted a conceptual framework of ‘barriers’ to and ‘facilitators’ of health. Research findings about the barriers to, and facilitators of, healthy eating among young people can help in the development of potentially effective intervention strategies. Interventions can aim to modify or remove barriers and use or build upon existing facilitators. This framework has been successfully applied in other related systematic reviews in the area of healthy eating in children [ 13 ], physical activity with children [ 14 ] and young people [ 15 ] and mental health with young people [16; S. Oliver, A. Harden, R. Rees, J. Shepherd, G. Brunton and A. Oakley, manuscript in preparation].

Second, the review was carried out in two stages: a systematic search for, and mapping of, literature on healthy eating with young people, followed by an in-depth systematic review of the quality and findings of a subset of these studies. The rationale for a two-stage review to ensure the review was as relevant as possible to users. By mapping a broad area of evidence, the key characteristics of the extant literature can be identified and discussed with review users, with the aim of prioritizing the most relevant research areas for systematic in-depth analysis [ 17, 18 ].

Third, the review utilized a ‘mixed methods’ triangulatory approach. Data from effectiveness studies (‘outcome evaluations’, primarily quantitative data) were combined with data from studies which described young people's views of factors influencing their healthy eating in negative or positive ways (‘views’ studies, primarily qualitative). We also sought data on young people's perceptions of interventions when these had been collected alongside outcomes data in outcome evaluations. However, the main source of young people's views was surveys or interview-based studies that were conducted independently of intervention evaluation (‘non-intervention’ research). The purpose was to enable us to ascertain not just whether interventions are effective, but whether they address issues important to young people, using their views as a marker of appropriateness. Few systematic reviews have attempted to synthesize evidence from both intervention and non-intervention research: most have been restricted to outcome evaluations. This study therefore represents one of the few attempts that have been made to date to integrate different study designs into systematic reviews of effectiveness [ 19–22 ].

Literature searching

A highly sensitive search strategy was developed to locate potentially relevant studies. A wide range of terms for healthy eating (e.g. nutrition, food preferences, feeding behaviour, diets and health food) were combined with health promotion terms or general or specific terms for determinants of health or ill-health (e.g. health promotion, behaviour modification, at-risk-populations, sociocultural factors and poverty) and with terms for young people (e.g. adolescent, teenager, young adult and youth). A number of electronic bibliographic databases were searched, including Medline, EMBASE, The Cochrane Library, PsycINFO, ERIC, Social Science Citation Index, CINAHL, BiblioMap and HealthPromis. The searches covered the full range of publication years available in each database up to 2001 (when the review was completed).

Full reports of potentially relevant studies identified from the literature search were obtained and classified (e.g. in terms of specific topic area, context, characteristics of young people, research design and methodological attributes).

Inclusion screening

Inclusion criteria were developed and applied to each study. The first round of screening was to identify studies to populate the map. To be included, a study had to (i) focus on healthy eating; (ii) include young people aged 11–16 years; (iii) be about the promotion of healthy eating, and/or the barriers to, or facilitators of, healthy eating; (iv) be a relevant study type: (a) an outcome evaluation or (b) a non-intervention study (e.g. cohort or case control studies, or interview studies) conducted in the UK only (to maximize relevance to UK policy and practice) and (v) be published in the English language.

The results of the map, which are reported in greater detail elsewhere [ 23 ], were used to prioritize a subset of policy relevant studies for the in-depth systematic review.

A second round of inclusion screening was performed. As before, all studies had to have healthy eating as their main focus and include young people aged 11–16 years. In addition, outcome evaluations had toFor a non-intervention study to be included it had to

(i) use a comparison or control group; report pre- and post-intervention data and, if a non-randomized trial, equivalent on sociodemographic characteristics and pre-intervention outcome variables (demonstrating their ‘potential soundness’ in advance of further quality assessment);

(ii) report an intervention that aims to make a change at the community or society level and

(iii) measure behavioural and/or physical health status outcomes.

(i) examine young people's attitudes, opinions, beliefs, feelings, understanding or experiences about healthy eating (rather than solely examine health status, behaviour or factual knowledge);

(ii) access views about one or more of the following: young people's definitions of and/or ideas about healthy eating, factors influencing their own or other young people's healthy eating and whether and how young people think healthy eating can be promoted and

(iii) privilege young people's views—presenting views directly as data that are valuable and interesting in themselves, rather than only as a route to generating variables to be tested in a predictive or causal model.

Non-intervention studies published before 1990 were excluded in order to maximize the relevance of the review findings to current policy issues.

Data extraction and quality assessment

All studies meeting inclusion criteria underwent data extraction and quality assessment, using a standardized framework [ 24 ]. Data for each study were entered independently by two researchers into a specialized computer database [ 25 ] (the full and final data extraction and quality assessment judgement for each study in the in-depth systematic review can be viewed on the Internet by visiting http://eppi.ioe.ac.uk ).

Outcome evaluations were considered methodologically ‘sound’ if they reported:Only studies meeting these criteria were used to draw conclusions about effectiveness. The results of the studies which did not meet these quality criteria were judged unclear.

(i) a control or comparison group equivalent to the intervention group on sociodemographic characteristics and pre-intervention outcome variables.

(ii) pre-intervention data for all individuals or groups recruited into the evaluation;

(iii) post-intervention data for all individuals or groups recruited into the evaluation and

(iv) on all outcomes, as described in the aims of the intervention.

Non-intervention studies were assessed according to a total of seven criteria (common to sets of criteria proposed by four research groups for qualitative research [ 26–29 ]):

(i) an explicit account of theoretical framework and/or the inclusion of a literature review which outlined a rationale for the intervention;

(ii) clearly stated aims and objectives;

(iii) a clear description of context which includes detail on factors important for interpreting the results;

(iv) a clear description of the sample;

(v) a clear description of methodology, including systematic data collection methods;

(vi) analysis of the data by more than one researcher and

(vii) the inclusion of sufficient original data to mediate between data and interpretation.

Data synthesis

Three types of analyses were performed: (i) narrative synthesis of outcome evaluations, (ii) narrative synthesis of non-intervention studies and (iii) synthesis of intervention and non-intervention studies together.

For the last of these a matrix was constructed which laid out the barriers and facilitators identified by young people alongside descriptions of the interventions included in the in-depth systematic review of outcome evaluations. The matrix was stratified by four analytical themes to characterize the levels at which the barriers and facilitators appeared to be operating: the school, family and friends, the self and practical and material resources. This methodology is described further elsewhere [ 20, 22, 30 ].

From the matrix it is possible to see:

(i) where barriers have been modified and/or facilitators built upon by soundly evaluated interventions, and ‘promising’ interventions which need further, more rigorous, evaluation (matches) and

(ii) where barriers have not been modified and facilitators not built upon by any evaluated intervention, necessitating the development and rigorous evaluation of new interventions (gaps).

Figure 1 outlines the number of studies included at various stages of the review. Of the total of 7048 reports identified, 135 reports (describing 116 studies) met the first round of screening and were included in the descriptive map. The results of the map are reported in detail in a separate publication—see Shepherd et al. [ 23 ] (the report can be downloaded free of charge via http://eppi.ioe.ac.uk ). A subset of 22 outcome evaluations and 8 studies of young people's views met the criteria for the in-depth systematic review.

The review process.

Outcome evaluations

Of the 22 outcome evaluations, most were conducted in the United States ( n = 16) [ 31–45 ], two in Finland [ 46, 47 ], and one each in the UK [ 48 ], Norway [ 49 ], Denmark [ 50 ] and Australia [ 51 ]. In addition to the main focus on promoting healthy eating, they also addressed other related issues including cardiovascular disease in general, tobacco use, accidents, obesity, alcohol and illicit drug use. Most were based in primary or secondary school settings and were delivered by teachers. Interventions varied considerably in content. While many involved some form of information provision, over half ( n = 13) involved attempts to make structural changes to young people's physical environments; half ( n = 11) trained parents in or about nutrition, seven developed health-screening resources, five provided feedback to young people on biological measures and their behavioural risk status and three aimed to provide social support systems for young people or others in the community. Social learning theory was the most common theoretical framework used to develop these interventions. Only a minority of studies included young people who could be considered socially excluded ( n = 6), primarily young people from ethnic minorities (e.g. African Americans and Hispanics).

Following detailed data extraction and critical appraisal, only seven of the 22 outcome evaluations were judged to be methodologically sound. For the remainder of this section we only report the results of these seven. Four of the seven were from the United States, with one each from the UK, Norway and Finland. The studies varied in the comprehensiveness of their reporting of the characteristics of the young people (e.g. sociodemographic/economic status). Most were White, living in middle class urban areas. All attended secondary schools. Table I details the interventions in these sound studies. Generally, they were multicomponent interventions in which classroom activities were complemented with school-wide initiatives and activities in the home. All but one of the seven sound evaluations included and an integral evaluation of the intervention processes. Some studies report results according to demographic characteristics such as age and gender.

Soundly evaluated outcome evaluations: study characteristics (n = 7)

RCT = Randomized Controlled Trial; CT = controlled trial (no randomization); PE = process evaluation.

Separate evaluations of the same intervention in two populations in New York (the Bronx and Westchester County).

The UK-based intervention was an award scheme (the ‘Wessex Healthy Schools Award’) that sought to make health-promoting changes in school ethos, organizational functioning and curriculum [ 48 ]. Changes made in schools included the introduction of health education curricula, as well as the setting of targets in key health promotion areas (including healthy eating). Knowledge levels, which were high at baseline, changed little over the course of the intervention. Intervention schools performed better in terms of healthy food choices (on audit scores). The impact on measures of healthy eating such as choosing healthy snacks varied according to age and sex. The intervention only appeared possibly to be effective for young women in Year 11 (aged 15–16 years) on these measures (statistical significance not reported).

The ‘Know Your Body’ intervention, a cardiovascular risk reduction programme, was evaluated in two separate studies in two demographically different areas of New York (the Bronx and Westchester County) [ 45 ]. Lasting for 5 years it comprised teacher-led classroom education, parental involvement activities and risk factor examination in elementary and junior high schools. In the Bronx evaluation, statistically significant increases in knowledge were reported, but favourable changes in cholesterol levels and dietary fat were not significant. In the Westchester County evaluation, we judged the effects to be unclear due to shortcomings in methods reported.

A second US-based study, the 3-year ‘Gimme 5’ programme [ 40 ], focused on increasing consumption of fruits and vegetables through a school-wide media campaign, complemented by classroom activities, parental involvement and changes to nutritional content of school meals. The intervention was effective at increasing knowledge (particularly among young women). Effects were measured in terms of changes in knowledge scores between baseline and two follow-up periods. Differences between the intervention and comparison group were significant at both follow-ups. There was a significant increase in consumption of fruit and vegetables in the intervention group, although this was not sustained.

In the third US study, the ‘Slice of Life’ intervention, peer leaders taught 10 sessions covering the benefits of fitness, healthy diets and issues concerning weight control [ 41 ]. School functioning was also addressed by student recommendations to school administrators. For young women, there were statistically significant differences between intervention and comparison groups on healthy eating scores, salt consumption scores, making healthy food choices, knowledge of healthy food, reading food labels for salt and fat content and awareness of healthy eating. However, among young men differences were only significant for salt and knowledge scores. The process evaluation suggested that having peers deliver training was acceptable to students and the peer-trainers themselves.

A Norwegian study evaluated a similar intervention to the ‘Slice of Life’ programme, employing peer educators to lead classroom activities and small group discussions on nutrition [ 49 ]. Students also analysed the availability of healthy food in their social and home environment and used a computer program to analyse the nutritional status of foods. There were significant intervention effects for reported healthy eating behaviour (but not maintained by young men) and for knowledge (not young women).

The second ‘North Karelia Youth Study’ in Finland featured classroom educational activities, a community media campaign, health-screening activities, changes to school meals and a health education initiative in the parents' workplace [ 47 ]. It was judged to be effective for healthy eating behaviour, reducing systolic blood pressure and modifying fat content of school meals, but less so for reducing cholesterol levels and diastolic blood pressure.

The evidence from the well-designed evaluations of the effectiveness of healthy eating initiatives is therefore mixed. Interventions tend to be more effective among young women than young men.

Young people's views

Table II describes the key characteristics of the eight studies of young people's views. The most consistently reported characteristics of the young people were age, gender and social class. Socioeconomic status was mixed, and in the two studies reporting ethnicity, the young people participating were predominantly White. Most studies collected data in mainstream schools and may therefore not be applicable to young people who infrequently or never attend school.

Characteristics of young people's views studies (n = 8)

All eight studies asked young people about their perceptions of, or attitudes towards, healthy eating, while none explicitly asked them what prevents them from eating healthily. Only two studies asked them what they think helps them to eat healthy foods, and only one asked for their ideas about what could or should be done to promote nutrition.

Young people tended to talk about food in terms of what they liked and disliked, rather than what was healthy/unhealthy. Healthy foods were predominantly associated with parents/adults and the home, while ‘fast food’ was associated with pleasure, friendship and social environments. Links were also made between food and appearance, with fast food perceived as having negative consequences on weight and facial appearance (and therefore a rationale for eating healthier foods). Attitudes towards healthy eating were generally positive, and the importance of a healthy diet was acknowledged. However, personal preferences for fast foods on grounds of taste tended to dominate food choice. Young people particularly valued the ability to choose what they eat.

Despite not being explicitly asked about barriers, young people discussed factors inhibiting their ability to eat healthily. These included poor availability of healthy meals at school, healthy foods sometimes being expensive and wide availability of, and personal preferences for, fast foods. Things that young people thought should be done to facilitate healthy eating included reducing the price of healthy snacks and better availability of healthy foods at school, at take-aways and in vending machines. Will-power and encouragement from the family were commonly mentioned support mechanisms for healthy eating, while teachers and peers were the least commonly cited sources of information on nutrition. Ideas for promoting healthy eating included the provision of information on nutritional content of school meals (mentioned by young women particularly) and better food labelling in general.

Table III shows the synthesis matrix which juxtaposes barriers and facilitators alongside results of outcome evaluations. There were some matches but also significant gaps between, on the one hand, what young people say are barriers to healthy eating, what helps them and what could or should be done and, on the other, soundly evaluated interventions that address these issues.

Synthesis matrix

Key to young people's views studies: Y1 , Dennison and Shepherd [ 56 ]; Y2 , Harris [ 57 ]; Y3 , McDougall [ 58 ]; Y4 , Miles and Eid [ 59 ]; Y5 , Roberts et al. [ 60 ]; Y6 , Ross [ 61 ]; Y7 , Watt and Sheiham [ 62 ]; Y8 , Watt and Sheiham [ 63 ]. Key to intervention studies: OE1 , Baranowski et al. [ 31 ]; OE2 , Bush et al. [ 32 ]; OE3 , Coates et al. [ 33 ]; OE4 , Ellison et al. [ 34 ]; OE5 , Flores [ 36 ]; OE6 , Fitzgibbon et al. [ 35 ]; OE7 , Hopper et al. [ 64 ]; OE8 , Holund [ 50 ]; OE9 , Kelder et al. [ 38 ]; OE10 , Klepp and Wilhelmsen [ 49 ]; OE11 , Moon et al. [ 48 ]; OE12 , Nader et al. [ 39 ]; OE13 , Nicklas et al. [ 40 ]; OE14 , Perry et al. [ 41 ]; OE15 , Petchers et al. [ 42 ]; OE16 , Schinke et al. [ 43 ]; OE17 , Wagner et al. [ 44 ]; OE18 , Vandongen et al. [ 51 ]; OE19 , Vartiainen et al. [ 46 ]; OE20 , Vartiainen et al. [ 47 ]; OE21 , Walter I [ 45 ]; OE22 , Walter II [ 45 ]. OE10, OE11, OE13, OE14, OE20, OE21 and OE22 denote a sound outcome evaluation. OE21 and OE22 are separate evaluations of the same intervention. Due to methodological limitations, we have judged the effects of OE22 to be unclear. Y1 and Y2 do not appear in the synthesis matrix as they did not explicitly report barriers or facilitators, and it was not possible for us to infer potential barriers or facilitators. However, these two studies did report what young people understood by healthy eating, their perceptions, and their views and opinions on the importance of eating a healthy diet. OE2, OE12, OE16 and OE17 do not appear in the synthesis matrix as they did not address any of the barriers or facilitators.

In terms of the school environment, most of the barriers identified by young people appear to have been addressed. At least two sound outcome evaluations demonstrated the effectiveness of increasing the availability of healthy foods in the school canteen [ 40, 47 ]. Furthermore, despite the low status of teachers and peers as sources of nutritional information, several soundly evaluated studies showed that they can be employed effectively to deliver nutrition interventions.

Young people associated parents and the home environment with healthy eating, and half of the sound outcome evaluations involved parents in the education of young people about nutrition. However, problems were sometimes experienced in securing parental attendance at intervention activities (e.g. seminar evenings). Why friends were not a common source of information about good nutrition is not clear. However, if peer pressure to eat unhealthy foods is a likely explanation, then it has been addressed by the peer-led interventions in three sound outcome evaluations (generally effectively) [ 41, 47, 49 ] and two outcome evaluations which did not meet the quality criteria (effectiveness unclear) [ 33, 50 ].

The fact that young people choose fast foods on grounds of taste has generally not been addressed by interventions, apart from one soundly evaluated effective intervention which included taste testings of fruit and vegetables [ 40 ]. Young people's concern over their appearance (which could be interpreted as both a barrier and a facilitator) has only been addressed in one of the sound outcome evaluations (which revealed an effective intervention) [ 41 ]. Will-power to eat healthy foods has only been examined in one outcome evaluation in the in-depth systematic review (judged to be sound and effective) (Walter I—Bronx evaluation) [ 45 ]. The need for information on nutrition was addressed by the majority of interventions in the in-depth systematic review. However, no studies were found which evaluated attempts to increase the nutritional content of school meals.

Barriers and facilitators relating to young people's practical and material resources were generally not addressed by interventions, soundly evaluated or otherwise. No studies were found which examined the effectiveness of interventions to lower the price of healthy foods. However, one soundly evaluated intervention was partially effective in increasing the availability of healthy snacks in community youth groups (Walter I—Bronx evaluation) [ 45 ]. At best, interventions have attempted to raise young people's awareness of environmental constraints on eating healthily, or encouraged them to lobby for increased availability of nutritious foods (in the case of the latter without reporting whether any changes have been effected as a result).

This review has systematically identified some of the barriers to, and facilitators of, healthy eating with young people, and illustrated to what extent they have been addressed by soundly evaluated effective interventions.

The evidence for effectiveness is mixed. Increases in knowledge of nutrition (measured in all but one study) were not consistent across studies, and changes in clinical risk factors (measured in two studies) varied, with one study detecting reductions in cholesterol and another detecting no change. Increases in reported healthy eating behaviour were observed, but mostly among young women revealing a distinct gender pattern in the findings. This was the case in four of the seven outcome evaluations (in which analysis was stratified by gender). The authors of one of the studies suggest that emphasis of the intervention on healthy weight management was more likely to appeal to young women. It was proposed that interventions directed at young men should stress the benefits of nutrition on strength, physical endurance and physical activity, particularly to appeal to those who exercise and play sports. Furthermore, age was a significant factor in determining effectiveness in one study [ 48 ]. Impact was greatest on young people in the 15- to 16-year age range (particularly for young women) in comparison with those aged 12–13 years, suggesting that dietary influences may vary with age. Tailoring the intervention to take account of age and gender is therefore crucial to ensure that interventions are as relevant and meaningful as possible.

Other systematic reviews of interventions to promote healthy eating (which included some of the studies with young people fitting the age range of this review) also show mixed results [ 52–55 ]. The findings of these reviews, while not being directly comparable in terms of conceptual framework, methods and age group, seem to offer some support for the findings of this review. The main message is that while there is some evidence to suggest effectiveness, the evidence base is limited. We have identified no comparable systematic reviews in this area.

Unlike other reviews, however, this study adopted a wider perspective through inclusion of studies of young people's views as well as effectiveness studies. A number of barriers to healthy eating were identified, including poor availability of healthy foods at school and in young people's social spaces, teachers and friends not always being a source of information/support for healthy eating, personal preferences for fast foods and healthy foods generally being expensive. Facilitating factors included information about nutritional content of foods/better labelling, parents and family members being supportive; healthy eating to improve or maintain one's personal appearance, will-power and better availability/lower pricing of healthy snacks.

Juxtaposing barriers and facilitators alongside effectiveness studies allowed us to examine the extent to which the needs of young people had been adequately addressed by evaluated interventions. To some extent they had. Most of the barriers and facilitators that related to the school and relationships with family and friends appear to have been taken into account by soundly evaluated interventions, although, as mentioned, their effectiveness varied. Many of the gaps tended to be in relation to young people as individuals (although our prioritization of interventions at the level of the community and society may have resulted in the exclusion of some of these interventions) and the wider determinants of health (‘practical and material resources’). Despite a wide search, we found few evaluations of strategies to improve nutritional labelling on foods particularly in schools or to increase the availability of affordable healthy foods particularly in settings where young people socialize. A number of initiatives are currently in place which may fill these gaps, but their effectiveness does not appear to have been reported yet. It is therefore crucial for any such schemes to be thoroughly evaluated and disseminated, at which point an updated systematic review would be timely.

This review is also constrained by the fact that its conclusions can only be supported by a relatively small proportion of the extant literature. Only seven of the 22 outcome evaluations identified were considered to be methodologically sound. As illustrated in Table III , a number of the remaining 15 interventions appear to modify barriers/build on facilitators but their results can only be judged unclear until more rigorous evaluation of these ‘promising’ interventions has been reported.

Finally, it is important to acknowledge that the majority of the outcome evaluations were conducted in the United States, and by virtue of the inclusion criteria, all the young people's views studies were UK based. The literature therefore might not be generalizable to other countries, where sociocultural values and socioeconomic circumstances may be quite different. Further evidence synthesis is needed on barriers to, and facilitators of, healthy eating and nutrition worldwide, particularly in developing countries.

The aim of this study was to survey what is known about the barriers to, and facilitators of, healthy eating among young people with a view to drawing out the implications for policy and practice. The review has mapped and quality screened the extant research in this area, and brought together the findings from evaluations of interventions aiming to promote healthy eating and studies which have elicited young people's views.

There has been much research activity in this area, yet it is disappointing that so few evaluation studies were methodologically strong enough to enable us to draw conclusions about effectiveness. There is some evidence to suggest that multicomponent school-based interventions can be effective, although effects tended to vary according to age and gender. Tailoring intervention messages accordingly is a promising approach which should therefore be evaluated. A key theme was the value young people place on choice and autonomy in relation to food. Increasing the provision and range of healthy, affordable snacks and meals in schools and social spaces will enable them to exercise their choice of healthier, tasty options.

We have identified that several barriers to, and facilitators of, healthy eating in young people have received little attention in evaluation research. Further work is needed to develop and evaluate interventions which modify or remove these barriers, and build on these facilitators. Further qualitative studies are also needed so that we can continue to listen to the views of young people. This is crucial if we are to develop and test meaningful, appropriate and effective health promotion strategies.

We would like to thank Chris Bonell and Dina Kiwan for undertaking data extraction. We would also like to acknowledge the invaluable help of Amanda Nicholas, James Thomas, Elaine Hogan, Sue Bowdler and Salma Master for support and helpful advice. The Department of Health, England, funds a specific programme of health promotion work at the EPPI-Centre. The views expressed in the report are those of the authors and not necessarily those of the Department of Health.

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Establishing healthy behaviors that stick

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By Mayo Clinic staff

Many people make healthy resolutions or set goals with the best intentions, only to see them fall short or break down completely over time. It's common to read about research and medical studies that promote a healthy lifestyle's psychological and physical benefits. Living a healthy lifestyle can even help prevent cancer . So why do you still fall short of your intentions to make healthy diet and lifestyle changes?

A few common reasons people tend to give up on changes to behaviors include:

• Perceived dislike of exercise Studies show that people overestimate how difficult it is to exercise. As a result, you may tend to give up before you even begin a new exercise program or training regimen.
• Toxic eating environment Quick, cheap and tempting food options are a constant pressure from a multibillion-dollar marketing industry. These highly targeted psychological messages may leave you wondering if you're in charge of your eating behaviors or, instead, are being conditioned to choose convenience over more nutritious options.
• Setting too many goals or creating an all-or-nothing plan People tend to change too many behaviors or routines at a time. Creating restrictive changes that lead to feelings of deprivation or lower mood can result in an "on or off" or "all or nothing" plan that can't be maintained.
• Consistency is complicated Whether you choose a lifelong goal or a temporary objective, staying motivated requires complex planning and follow-through. Establishing healthy behaviors that stick requires a different mindset and recognition that putting effort toward something important promotes an improved mood and well-being.

Tips to stay motivated

If you want to make your habits permanent, you need to:.

• Alter your mindset and challenge negative thoughts and beliefs.
• Anticipate lapses and recover quickly.
• Remind yourself that you deserve to feel good and that your plan will get you there.
• Start with one small change, celebrate success and add more changes over time.
• Use positive self-talk such as "I'm an exerciser" and "I'm someone who eats healthy options," to embed identity shifts into your plan.

Your thoughts determine how you feel about yourself, which affects your behavior, mood, interactions with others and progress toward your goals. When you identify positive thoughts, make sure to practice them.

Consider using this path to help spur on your healthy behaviors:

• Develop  positive and realistic goals  for yourself.
• Find multiple ways to remind yourself of your goal.
• Identify why you want to meet this goal.
• List the behaviors you feel are unhealthy.
• Select one of the identified behaviors that you would like to change.
• Brainstorm ways to change this behavior and start small.
• Devise a plan to promote this strategy.
• Identify potential obstacles that could interfere with your goal.
• Identify your options for support.
• Set a date for when you want to achieve your goal.
• Counter destructive thoughts with more constructive ones.
• Consider what you must do to maintain change when you complete your goal.
• Don't expect perfection; anticipate imperfection.
• Evaluate your successes when you reach your goal.
• Note how you feel now that you have worked to meet your goal.
• Select another goal and restart the process when you're ready.

Don't let a lapse keep you from your goal

A lapse is a slight error, slip or pause in progress most people face at some point during the journey. Relapse occurs when lapses string together and a person returns to their former behavior. Remember that a lapse is normal and doesn't always lead to a relapse. Anticipate that a setback can and will occur. Then, figure out which triggers led to the lapse.

Common triggers include:

• A certain time of day.
• A challenging life event.
• Negative emotions, boredom or a shift from your initial intentions.
• Particular foods and visual cues.
• People who have an influence on your life.
• Social events, celebrations or your customs.

Remember, the danger is not the slip but how you react to that lapse. — Lisa Hardesty, Ph.D.,  is a clinical psychologist at Mayo Clinic Health System in Mankato , Minnesota.

Learn more about healthy behaviors that can help prevent cancer by reading these articles:

• Cancer prevention: 7 tips to reduce your risk
• Excess body weight, alcohol and tobacco: How lifestyle can affect your cancer risk
• Is there a connection between ultraprocessed food and cancer?
• Plant-based diet is encouraged for people with cancer
• Reduce your risk of the 4 most common cancers

A version of this article was originally published on the Mayo Clinic Health System blog .

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Choose Your Test

Sat / act prep online guides and tips, 113 great research paper topics.

General Education

One of the hardest parts of writing a research paper can be just finding a good topic to write about. Fortunately we've done the hard work for you and have compiled a list of 113 interesting research paper topics. They've been organized into ten categories and cover a wide range of subjects so you can easily find the best topic for you.

In addition to the list of good research topics, we've included advice on what makes a good research paper topic and how you can use your topic to start writing a great paper.

What Makes a Good Research Paper Topic?

Not all research paper topics are created equal, and you want to make sure you choose a great topic before you start writing. Below are the three most important factors to consider to make sure you choose the best research paper topics.

#1: It's Something You're Interested In

A paper is always easier to write if you're interested in the topic, and you'll be more motivated to do in-depth research and write a paper that really covers the entire subject. Even if a certain research paper topic is getting a lot of buzz right now or other people seem interested in writing about it, don't feel tempted to make it your topic unless you genuinely have some sort of interest in it as well.

#2: There's Enough Information to Write a Paper

Even if you come up with the absolute best research paper topic and you're so excited to write about it, you won't be able to produce a good paper if there isn't enough research about the topic. This can happen for very specific or specialized topics, as well as topics that are too new to have enough research done on them at the moment. Easy research paper topics will always be topics with enough information to write a full-length paper.

Trying to write a research paper on a topic that doesn't have much research on it is incredibly hard, so before you decide on a topic, do a bit of preliminary searching and make sure you'll have all the information you need to write your paper.

#3: It Fits Your Teacher's Guidelines

Don't get so carried away looking at lists of research paper topics that you forget any requirements or restrictions your teacher may have put on research topic ideas. If you're writing a research paper on a health-related topic, deciding to write about the impact of rap on the music scene probably won't be allowed, but there may be some sort of leeway. For example, if you're really interested in current events but your teacher wants you to write a research paper on a history topic, you may be able to choose a topic that fits both categories, like exploring the relationship between the US and North Korea. No matter what, always get your research paper topic approved by your teacher first before you begin writing.

113 Good Research Paper Topics

Below are 113 good research topics to help you get you started on your paper. We've organized them into ten categories to make it easier to find the type of research paper topics you're looking for.

Arts/Culture

• Discuss the main differences in art from the Italian Renaissance and the Northern Renaissance .
• Analyze the impact a famous artist had on the world.
• How is sexism portrayed in different types of media (music, film, video games, etc.)? Has the amount/type of sexism changed over the years?
• How has the music of slaves brought over from Africa shaped modern American music?
• How has rap music evolved in the past decade?
• How has the portrayal of minorities in the media changed?

Current Events

• What have been the impacts of China's one child policy?
• How have the goals of feminists changed over the decades?
• How has the Trump presidency changed international relations?
• Analyze the history of the relationship between the United States and North Korea.
• What factors contributed to the current decline in the rate of unemployment?
• What have been the impacts of states which have increased their minimum wage?
• How do US immigration laws compare to immigration laws of other countries?
• How have the US's immigration laws changed in the past few years/decades?
• How has the Black Lives Matter movement affected discussions and view about racism in the US?
• What impact has the Affordable Care Act had on healthcare in the US?
• What factors contributed to the UK deciding to leave the EU (Brexit)?
• What factors contributed to China becoming an economic power?
• Discuss the history of Bitcoin or other cryptocurrencies  (some of which tokenize the S&P 500 Index on the blockchain) .
• Do students in schools that eliminate grades do better in college and their careers?
• Do students from wealthier backgrounds score higher on standardized tests?
• Do students who receive free meals at school get higher grades compared to when they weren't receiving a free meal?
• Do students who attend charter schools score higher on standardized tests than students in public schools?
• Do students learn better in same-sex classrooms?
• How does giving each student access to an iPad or laptop affect their studies?
• What are the benefits and drawbacks of the Montessori Method ?
• Do children who attend preschool do better in school later on?
• What was the impact of the No Child Left Behind act?
• How does the US education system compare to education systems in other countries?
• What impact does mandatory physical education classes have on students' health?
• Which methods are most effective at reducing bullying in schools?
• Do homeschoolers who attend college do as well as students who attended traditional schools?
• Does offering tenure increase or decrease quality of teaching?
• How does college debt affect future life choices of students?
• Should graduate students be able to form unions?

• What are different ways to lower gun-related deaths in the US?
• How and why have divorce rates changed over time?
• Is affirmative action still necessary in education and/or the workplace?
• Should physician-assisted suicide be legal?
• How has stem cell research impacted the medical field?
• How can human trafficking be reduced in the United States/world?
• Should people be able to donate organs in exchange for money?
• Which types of juvenile punishment have proven most effective at preventing future crimes?
• Has the increase in US airport security made passengers safer?
• Analyze the immigration policies of certain countries and how they are similar and different from one another.
• Several states have legalized recreational marijuana. What positive and negative impacts have they experienced as a result?
• Do tariffs increase the number of domestic jobs?
• Which prison reforms have proven most effective?
• Should governments be able to censor certain information on the internet?
• Which methods/programs have been most effective at reducing teen pregnancy?
• What are the benefits and drawbacks of the Keto diet?
• How effective are different exercise regimes for losing weight and maintaining weight loss?
• How do the healthcare plans of various countries differ from each other?
• What are the most effective ways to treat depression ?
• What are the pros and cons of genetically modified foods?
• Which methods are most effective for improving memory?
• What can be done to lower healthcare costs in the US?
• What factors contributed to the current opioid crisis?
• Analyze the history and impact of the HIV/AIDS epidemic .
• Are low-carbohydrate or low-fat diets more effective for weight loss?
• How much exercise should the average adult be getting each week?
• Which methods are most effective to get parents to vaccinate their children?
• What are the pros and cons of clean needle programs?
• How does stress affect the body?
• Discuss the history of the conflict between Israel and the Palestinians.
• What were the causes and effects of the Salem Witch Trials?
• Who was responsible for the Iran-Contra situation?
• How has New Orleans and the government's response to natural disasters changed since Hurricane Katrina?
• What events led to the fall of the Roman Empire?
• What were the impacts of British rule in India ?
• Was the atomic bombing of Hiroshima and Nagasaki necessary?
• What were the successes and failures of the women's suffrage movement in the United States?
• What were the causes of the Civil War?
• How did Abraham Lincoln's assassination impact the country and reconstruction after the Civil War?
• Which factors contributed to the colonies winning the American Revolution?
• What caused Hitler's rise to power?
• Discuss how a specific invention impacted history.
• What led to Cleopatra's fall as ruler of Egypt?
• How has Japan changed and evolved over the centuries?
• What were the causes of the Rwandan genocide ?

• Why did Martin Luther decide to split with the Catholic Church?
• Analyze the history and impact of a well-known cult (Jonestown, Manson family, etc.)
• How did the sexual abuse scandal impact how people view the Catholic Church?
• How has the Catholic church's power changed over the past decades/centuries?
• What are the causes behind the rise in atheism/ agnosticism in the United States?
• What were the influences in Siddhartha's life resulted in him becoming the Buddha?
• How has media portrayal of Islam/Muslims changed since September 11th?

Science/Environment

• How has the earth's climate changed in the past few decades?
• How has the use and elimination of DDT affected bird populations in the US?
• Analyze how the number and severity of natural disasters have increased in the past few decades.
• Analyze deforestation rates in a certain area or globally over a period of time.
• How have past oil spills changed regulations and cleanup methods?
• How has the Flint water crisis changed water regulation safety?
• What are the pros and cons of fracking?
• What impact has the Paris Climate Agreement had so far?
• What have NASA's biggest successes and failures been?
• How can we improve access to clean water around the world?
• Does ecotourism actually have a positive impact on the environment?
• Should the US rely on nuclear energy more?
• What can be done to save amphibian species currently at risk of extinction?
• What impact has climate change had on coral reefs?
• How are black holes created?
• Are teens who spend more time on social media more likely to suffer anxiety and/or depression?
• How will the loss of net neutrality affect internet users?
• Analyze the history and progress of self-driving vehicles.
• How has the use of drones changed surveillance and warfare methods?
• Has social media made people more or less connected?
• What progress has currently been made with artificial intelligence ?
• Do smartphones increase or decrease workplace productivity?
• What are the most effective ways to use technology in the classroom?
• How is Google search affecting our intelligence?
• When is the best age for a child to begin owning a smartphone?
• Has frequent texting reduced teen literacy rates?

How to Write a Great Research Paper

Even great research paper topics won't give you a great research paper if you don't hone your topic before and during the writing process. Follow these three tips to turn good research paper topics into great papers.

#1: Figure Out Your Thesis Early

Before you start writing a single word of your paper, you first need to know what your thesis will be. Your thesis is a statement that explains what you intend to prove/show in your paper. Every sentence in your research paper will relate back to your thesis, so you don't want to start writing without it!

As some examples, if you're writing a research paper on if students learn better in same-sex classrooms, your thesis might be "Research has shown that elementary-age students in same-sex classrooms score higher on standardized tests and report feeling more comfortable in the classroom."

If you're writing a paper on the causes of the Civil War, your thesis might be "While the dispute between the North and South over slavery is the most well-known cause of the Civil War, other key causes include differences in the economies of the North and South, states' rights, and territorial expansion."

#2: Back Every Statement Up With Research

Remember, this is a research paper you're writing, so you'll need to use lots of research to make your points. Every statement you give must be backed up with research, properly cited the way your teacher requested. You're allowed to include opinions of your own, but they must also be supported by the research you give.

#3: Do Your Research Before You Begin Writing

You don't want to start writing your research paper and then learn that there isn't enough research to back up the points you're making, or, even worse, that the research contradicts the points you're trying to make!

Get most of your research on your good research topics done before you begin writing. Then use the research you've collected to create a rough outline of what your paper will cover and the key points you're going to make. This will help keep your paper clear and organized, and it'll ensure you have enough research to produce a strong paper.

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Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

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Study helps explain why childhood maltreatment continues to impact on mental and physical health into adulthood

Childhood maltreatment can continue to have an impact long into adulthood because of how it effects an individual's risk of poor physical health and traumatic experiences many years later, a new study has found.

Individuals who experienced maltreatment in childhood -- such as emotional, physical and sexual abuse, or emotional and physical neglect -- are more likely to develop mental illness throughout their entire life, but it is not yet well understood why this risk persists many decades after maltreatment first took place.

In a study published in Proceedings of the National Academy of Sciences , scientists from the University of Cambridge and Leiden University found that adult brains continue to be affected by childhood maltreatment in adulthood because these experiences make individuals more likely to experience obesity, inflammation and traumatic events, all of which are risk factors for poor health and wellbeing, which in turn also affect brain structure and therefore brain health.

The researchers examined MRI brain scans from approximately 21,000 adult participants aged 40 to 70 years in UK Biobank, as well as information on body mass index (an indicator of metabolic health), CRP (a blood marker of inflammation) and experiences of childhood maltreatment and adult trauma.

Sofia Orellana, a PhD student at the Department of Psychiatry and Darwin College, University of Cambridge, said: "We've known for some time that people who experience abuse or neglect as a child can continue to experience mental health problems long into adulthood and that their experiences can also cause long term problems for the brain, the immune system and the metabolic system, which ultimately controls the health of your heart or your propensity to diabetes for instance. What hasn't been clear is how all these effects interact or reinforce each other."

Using a type of statistical modelling that allowed them to determine how these interactions work, the researchers confirmed that experiencing childhood maltreatment made individuals more likely to have an increased body mass index (or obesity) and experience greater rates of trauma in adulthood. Individuals with a history of maltreatment tended to show signs of dysfunction in their immune systems, and the researchers showed that this dysfunction is the product of obesity and repeated exposure to traumatic events.

Next, the researchers expanded their models to include MRI measures of the adult's brains and were able to show that widespread increases and decreases in brain thickness and volume associated with greater body mass index, inflammation and trauma were attributable to childhood maltreatment having made these factors more likely in the first place. These changes in brain structure likely mean that some form of physical damage is occurring to brain cells, affecting how they work and function.

Although there is more to do to understand how these effects operate at a cellular level in the brain, the researchers believe that their findings advance our understanding of how adverse events in childhood can contribute to life-long increased risk of brain and mind health disorders.

Professor Ed Bullmore from the Department of Psychiatry and an Honorary Fellow at Downing College, Cambridge, said: "Now that we have a better understanding of why childhood maltreatment has long term effects, we can potentially look for biomarkers -- biological red flags -- that indicate whether an individual is at increased risk of continuing problems. This could help us target early on those who most need help, and hopefully aid them in breaking this chain of ill health."

The research was supported by MQ: Transforming Mental Health, the Royal Society, Medical Research Council, National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre, the NIHR Applied Research Collaboration East of England, Girton College and Darwin College.

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Story Source:

Materials provided by University of Cambridge . The original text of this story is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License . Note: Content may be edited for style and length.

Journal Reference :

• Orellana, SC et al. Childhood maltreatment influences adult brain structure through its effects on immune, metabolic and psychosocial factors. . PNAS , 2024 DOI: 10.1073/pnas.230470412

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How can I plan what to eat or drink when I have diabetes?

How can physical activity help manage my diabetes, what can i do to reach or maintain a healthy weight, should i quit smoking, how can i take care of my mental health, clinical trials for healthy living with diabetes.

Healthy living is a way to manage diabetes . To have a healthy lifestyle, take steps now to plan healthy meals and snacks, do physical activities, get enough sleep, and quit smoking or using tobacco products.

Healthy living may help keep your body’s blood pressure , cholesterol , and blood glucose level, also called blood sugar level, in the range your primary health care professional recommends. Your primary health care professional may be a doctor, a physician assistant, or a nurse practitioner. Healthy living may also help prevent or delay health problems  from diabetes that can affect your heart, kidneys, eyes, brain, and other parts of your body.

Making lifestyle changes can be hard, but starting with small changes and building from there may benefit your health. You may want to get help from family, loved ones, friends, and other trusted people in your community. You can also get information from your health care professionals.

What you choose to eat, how much you eat, and when you eat are parts of a meal plan. Having healthy foods and drinks can help keep your blood glucose, blood pressure, and cholesterol levels in the ranges your health care professional recommends. If you have overweight or obesity, a healthy meal plan—along with regular physical activity, getting enough sleep, and other healthy behaviors—may help you reach and maintain a healthy weight. In some cases, health care professionals may also recommend diabetes medicines that may help you lose weight, or weight-loss surgery, also called metabolic and bariatric surgery.

Choose healthy foods and drinks

There is no right or wrong way to choose healthy foods and drinks that may help manage your diabetes. Healthy meal plans for people who have diabetes may include

• dairy or plant-based dairy products
• nonstarchy vegetables
• protein foods
• whole grains

Try to choose foods that include nutrients such as vitamins, calcium , fiber , and healthy fats . Also try to choose drinks with little or no added sugar , such as tap or bottled water, low-fat or non-fat milk, and unsweetened tea, coffee, or sparkling water.

Try to plan meals and snacks that have fewer

• foods high in saturated fat
• foods high in sodium, a mineral found in salt
• sugary foods , such as cookies and cakes, and sweet drinks, such as soda, juice, flavored coffee, and sports drinks

Your body turns carbohydrates , or carbs, from food into glucose, which can raise your blood glucose level. Some fruits, beans, and starchy vegetables—such as potatoes and corn—have more carbs than other foods. Keep carbs in mind when planning your meals.

You should also limit how much alcohol you drink. If you take insulin  or certain diabetes medicines , drinking alcohol can make your blood glucose level drop too low, which is called hypoglycemia . If you do drink alcohol, be sure to eat food when you drink and remember to check your blood glucose level after drinking. Talk with your health care team about your alcohol-drinking habits.

Find the best times to eat or drink

Talk with your health care professional or health care team about when you should eat or drink. The best time to have meals and snacks may depend on

• what medicines you take for diabetes
• what your level of physical activity or your work schedule is
• whether you have other health conditions or diseases

Ask your health care team if you should eat before, during, or after physical activity. Some diabetes medicines, such as sulfonylureas  or insulin, may make your blood glucose level drop too low during exercise or if you skip or delay a meal.

Plan how much to eat or drink

You may worry that having diabetes means giving up foods and drinks you enjoy. The good news is you can still have your favorite foods and drinks, but you might need to have them in smaller portions  or enjoy them less often.

For people who have diabetes, carb counting and the plate method are two common ways to plan how much to eat or drink. Talk with your health care professional or health care team to find a method that works for you.

Carb counting

Carbohydrate counting , or carb counting, means planning and keeping track of the amount of carbs you eat and drink in each meal or snack. Not all people with diabetes need to count carbs. However, if you take insulin, counting carbs can help you know how much insulin to take.

Plate method

The plate method helps you control portion sizes  without counting and measuring. This method divides a 9-inch plate into the following three sections to help you choose the types and amounts of foods to eat for each meal.

• Nonstarchy vegetables—such as leafy greens, peppers, carrots, or green beans—should make up half of your plate.
• Carb foods that are high in fiber—such as brown rice, whole grains, beans, or fruits—should make up one-quarter of your plate.
• Protein foods—such as lean meats, fish, dairy, or tofu or other soy products—should make up one quarter of your plate.

If you are not taking insulin, you may not need to count carbs when using the plate method.

Work with your health care team to create a meal plan that works for you. You may want to have a diabetes educator  or a registered dietitian  on your team. A registered dietitian can provide medical nutrition therapy , which includes counseling to help you create and follow a meal plan. Your health care team may be able to recommend other resources, such as a healthy lifestyle coach, to help you with making changes. Ask your health care team or your insurance company if your benefits include medical nutrition therapy or other diabetes care resources.

Talk with your health care professional before taking dietary supplements

There is no clear proof that specific foods, herbs, spices, or dietary supplements —such as vitamins or minerals—can help manage diabetes. Your health care professional may ask you to take vitamins or minerals if you can’t get enough from foods. Talk with your health care professional before you take any supplements, because some may cause side effects or affect how well your diabetes medicines work.

Research shows that regular physical activity helps people manage their diabetes and stay healthy. Benefits of physical activity may include

• lower blood glucose, blood pressure, and cholesterol levels
• better heart health
• healthier weight
• better mood and sleep
• better balance and memory

Talk with your health care professional before starting a new physical activity or changing how much physical activity you do. They may suggest types of activities based on your ability, schedule, meal plan, interests, and diabetes medicines. Your health care professional may also tell you the best times of day to be active or what to do if your blood glucose level goes out of the range recommended for you.

Do different types of physical activity

People with diabetes can be active, even if they take insulin or use technology such as insulin pumps .

Try to do different kinds of activities . While being more active may have more health benefits, any physical activity is better than none. Start slowly with activities you enjoy. You may be able to change your level of effort and try other activities over time. Having a friend or family member join you may help you stick to your routine.

The physical activities you do may need to be different if you are age 65 or older , are pregnant , or have a disability or health condition . Physical activities may also need to be different for children and teens . Ask your health care professional or health care team about activities that are safe for you.

Aerobic activities

Aerobic activities make you breathe harder and make your heart beat faster. You can try walking, dancing, wheelchair rolling, or swimming. Most adults should try to get at least 150 minutes of moderate-intensity physical activity each week. Aim to do 30 minutes a day on most days of the week. You don’t have to do all 30 minutes at one time. You can break up physical activity into small amounts during your day and still get the benefit. 1

Strength training or resistance training

Strength training or resistance training may make your muscles and bones stronger. You can try lifting weights or doing other exercises such as wall pushups or arm raises. Try to do this kind of training two times a week. 1

Balance and stretching activities

Balance and stretching activities may help you move better and have stronger muscles and bones. You may want to try standing on one leg or stretching your legs when sitting on the floor. Try to do these kinds of activities two or three times a week. 1

Some activities that need balance may be unsafe for people with nerve damage or vision problems caused by diabetes. Ask your health care professional or health care team about activities that are safe for you.

Stay safe during physical activity

Staying safe during physical activity is important. Here are some tips to keep in mind.

Drink liquids

Drinking liquids helps prevent dehydration , or the loss of too much water in your body. Drinking water is a way to stay hydrated. Sports drinks often have a lot of sugar and calories , and you don’t need them for most moderate physical activities.

Avoid low blood glucose

Check your blood glucose level before, during, and right after physical activity. Physical activity often lowers the level of glucose in your blood. Low blood glucose levels may last for hours or days after physical activity. You are most likely to have low blood glucose if you take insulin or some other diabetes medicines, such as sulfonylureas.

Ask your health care professional if you should take less insulin or eat carbs before, during, or after physical activity. Low blood glucose can be a serious medical emergency that must be treated right away. Take steps to protect yourself. You can learn how to treat low blood glucose , let other people know what to do if you need help, and use a medical alert bracelet.

Avoid high blood glucose and ketoacidosis

Taking less insulin before physical activity may help prevent low blood glucose, but it may also make you more likely to have high blood glucose. If your body does not have enough insulin, it can’t use glucose as a source of energy and will use fat instead. When your body uses fat for energy, your body makes chemicals called ketones .

High levels of ketones in your blood can lead to a condition called diabetic ketoacidosis (DKA) . DKA is a medical emergency that should be treated right away. DKA is most common in people with type 1 diabetes . Occasionally, DKA may affect people with type 2 diabetes  who have lost their ability to produce insulin. Ask your health care professional how much insulin you should take before physical activity, whether you need to test your urine for ketones, and what level of ketones is dangerous for you.

Take care of your feet

People with diabetes may have problems with their feet because high blood glucose levels can damage blood vessels and nerves. To help prevent foot problems, wear comfortable and supportive shoes and take care of your feet  before, during, and after physical activity.

If you have diabetes, managing your weight  may bring you several health benefits. Ask your health care professional or health care team if you are at a healthy weight  or if you should try to lose weight.

If you are an adult with overweight or obesity, work with your health care team to create a weight-loss plan. Losing 5% to 7% of your current weight may help you prevent or improve some health problems  and manage your blood glucose, cholesterol, and blood pressure levels. 2 If you are worried about your child’s weight  and they have diabetes, talk with their health care professional before your child starts a new weight-loss plan.

You may be able to reach and maintain a healthy weight by

• following a healthy meal plan
• consuming fewer calories
• being physically active
• getting 7 to 8 hours of sleep each night 3

If you have type 2 diabetes, your health care professional may recommend diabetes medicines that may help you lose weight.

Online tools such as the Body Weight Planner  may help you create eating and physical activity plans. You may want to talk with your health care professional about other options for managing your weight, including joining a weight-loss program  that can provide helpful information, support, and behavioral or lifestyle counseling. These options may have a cost, so make sure to check the details of the programs.

Your health care professional may recommend weight-loss surgery  if you aren’t able to reach a healthy weight with meal planning, physical activity, and taking diabetes medicines that help with weight loss.

If you are pregnant , trying to lose weight may not be healthy. However, you should ask your health care professional whether it makes sense to monitor or limit your weight gain during pregnancy.

Both diabetes and smoking —including using tobacco products and e-cigarettes—cause your blood vessels to narrow. Both diabetes and smoking increase your risk of having a heart attack or stroke , nerve damage , kidney disease , eye disease , or amputation . Secondhand smoke can also affect the health of your family or others who live with you.

If you smoke or use other tobacco products, stop. Ask for help . You don’t have to do it alone.

Feeling stressed, sad, or angry can be common for people with diabetes. Managing diabetes or learning to cope with new information about your health can be hard. People with chronic illnesses such as diabetes may develop anxiety or other mental health conditions .

Learn healthy ways to lower your stress , and ask for help from your health care team or a mental health professional. While it may be uncomfortable to talk about your feelings, finding a health care professional whom you trust and want to talk with may help you

• lower your feelings of stress, depression, or anxiety
• manage problems sleeping or remembering things
• see how diabetes affects your family, school, work, or financial situation

Ask your health care team for mental health resources for people with diabetes.

Sleeping too much or too little may raise your blood glucose levels. Your sleep habits may also affect your mental health and vice versa. People with diabetes and overweight or obesity can also have other health conditions that affect sleep, such as sleep apnea , which can raise your blood pressure and risk of heart disease.

NIDDK conducts and supports clinical trials in many diseases and conditions, including diabetes. The trials look to find new ways to prevent, detect, or treat disease and improve quality of life.

What are clinical trials for healthy living with diabetes?

Clinical trials—and other types of clinical studies —are part of medical research and involve people like you. When you volunteer to take part in a clinical study, you help health care professionals and researchers learn more about disease and improve health care for people in the future.

Researchers are studying many aspects of healthy living for people with diabetes, such as

• how changing when you eat may affect body weight and metabolism
• how less access to healthy foods may affect diabetes management, other health problems, and risk of dying
• whether low-carbohydrate meal plans can help lower blood glucose levels
• which diabetes medicines are more likely to help people lose weight

Find out if clinical trials are right for you .

Watch a video of NIDDK Director Dr. Griffin P. Rodgers explaining the importance of participating in clinical trials.

What clinical trials for healthy living with diabetes are looking for participants?

You can view a filtered list of clinical studies on healthy living with diabetes that are federally funded, open, and recruiting at www.ClinicalTrials.gov . You can expand or narrow the list to include clinical studies from industry, universities, and individuals; however, the National Institutes of Health does not review these studies and cannot ensure they are safe for you. Always talk with your primary health care professional before you participate in a clinical study.

This content is provided as a service of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), part of the National Institutes of Health. NIDDK translates and disseminates research findings to increase knowledge and understanding about health and disease among patients, health professionals, and the public. Content produced by NIDDK is carefully reviewed by NIDDK scientists and other experts.

NIDDK would like to thank: Elizabeth M. Venditti, Ph.D., University of Pittsburgh School of Medicine.

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Developmental Trajectories of Early Life Trauma

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Public Health Buckeyes: Angela Falconi

BSPH student combines passions for health care, policy

Falconi has been involved in CPH research and is an active member of Ohio State's Pilipino Student Association.

Meet Angela Falconi, a fourth-year student specializing in  environmental public health who aspires to advocate for others through public health policy.

What inspired you to pursue a public health education?

Growing up, I was surrounded by both medicine and public policy because of my parents. Since I was six, my father, a politician and elected official, had me act as his unofficial campaign staff—knocking on doors with him to speak to voters, sitting in on city council meetings and accompanying him to various events. My mother, a pediatric physician, inspired me to pursue a career in medicine by showing me the impact that she’s made on her patients and always encouraging me to learn more about the health care field. When choosing my major, it felt natural to me to combine policy and health into public health.

What public health topics are you passionate about?

“Your zip code determines your health.”

This is one of the most important phrases I have learned in my public health courses, and as a volunteer at Helping Hands Health and Wellness Center, a free clinic which provides health care services for the uninsured and underinsured. I see the realities of this phrase in the patients who I work with. 

As an aspiring elected official, I want to create health care reform which helps individuals the health care system has failed to provide with affordable service.

You spent last summer in Washington, D.C. interning in the U.S. Senate. What was that experience like?

I worked (there) through the IMPACT program, created by the US-Asia Institute in coordination with the Embassy for the Philippines for Filipino students interested in public policy. Working and living in D.C. was one of the best experiences I have had in my undergraduate career because I was able to learn about and research health care policy on the national stage, which is exactly what I hope to do in my future career.

What have you enjoyed most about being involved in research as a student?

I am a research assistant for the Consumer Access Project which utilizes a secret shopper survey of Affordable Care Act (ACA) insurance marketplace plan networks to study these barriers and inequities, including disparities related to race. I have loved getting to work with  Wendy Xu as she has helped me learn more about the research process as well as how everyday Americans experience the health care system.

What kind of extracurricular activities are you involved in?

The Pilipino Student Association (PSA) has been my home away from home since the start of my time at Ohio State. It has not only allowed me to learn more about my Filipino culture, but I met my best friend through this organization. I have been involved in PSA in numerous roles: culture night coordinator, vice-president internal, president and now dance leader. 

As dance leader, I lead PSA’s tinikling team. Tinikling is a dance which involves two people beating, sliding, and tapping two bamboo poles on the ground while two people dance above the sticks, trying not to get caught in between them. Our latest performance from PSA’s culture show “Barrio” was in October. I choreographed, taught and performed the modern part of this dance!

What are your goals for the future?

I hope to not only assist individual patients as a physician, but I also hope to help others on the national scale by being an advocate as an elected official. I hope to apply the experiences and lessons that I have learned from my time at Ohio State into my future career in the field of health policy.

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About The Ohio State University College of Public Health

The Ohio State University College of Public Health is a leader in educating students, creating new knowledge through research, and improving the livelihoods and well-being of people in Ohio and beyond. The College's divisions include biostatistics, environmental health sciences, epidemiology, health behavior and health promotion, and health services management and policy. It is ranked 29 th  among all colleges and programs of public health in the nation, and first in Ohio, by  U.S. News and World Report. Its specialty programs are also considered among the best in the country. The MHA program is ranked 8 th , the biostatistics specialty is ranked 22 nd , the epidemiology specialty is ranked 25 th and the health policy and management specialty is ranked 17 th .

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Fall 2024 CSCI Special Topics Courses

Cloud computing.

Meeting Time: 09:45 AM‑11:00 AM TTh  Instructor: Ali Anwar Course Description: Cloud computing serves many large-scale applications ranging from search engines like Google to social networking websites like Facebook to online stores like Amazon. More recently, cloud computing has emerged as an essential technology to enable emerging fields such as Artificial Intelligence (AI), the Internet of Things (IoT), and Machine Learning. The exponential growth of data availability and demands for security and speed has made the cloud computing paradigm necessary for reliable, financially economical, and scalable computation. The dynamicity and flexibility of Cloud computing have opened up many new forms of deploying applications on infrastructure that cloud service providers offer, such as renting of computation resources and serverless computing.    This course will cover the fundamentals of cloud services management and cloud software development, including but not limited to design patterns, application programming interfaces, and underlying middleware technologies. More specifically, we will cover the topics of cloud computing service models, data centers resource management, task scheduling, resource virtualization, SLAs, cloud security, software defined networks and storage, cloud storage, and programming models. We will also discuss data center design and management strategies, which enable the economic and technological benefits of cloud computing. Lastly, we will study cloud storage concepts like data distribution, durability, consistency, and redundancy. Registration Prerequisites: CS upper div, CompE upper div., EE upper div., EE grad, ITI upper div., Univ. honors student, or dept. permission; no cr for grads in CSci. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/6BvbUwEkBK41tPJ17 ).

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Machine learning for healthcare: concepts and applications.

Meeting Time: 11:15 AM‑12:30 PM TTh  Instructor: Yogatheesan Varatharajah Course Description: Machine Learning is transforming healthcare. This course will introduce students to a range of healthcare problems that can be tackled using machine learning, different health data modalities, relevant machine learning paradigms, and the unique challenges presented by healthcare applications. Applications we will cover include risk stratification, disease progression modeling, precision medicine, diagnosis, prognosis, subtype discovery, and improving clinical workflows. We will also cover research topics such as explainability, causality, trust, robustness, and fairness.

Registration Prerequisites: CSCI 5521 or equivalent. Complete the following Google form to request a permission number from the instructor ( https://forms.gle/z8X9pVZfCWMpQQ6o6  ).

Visualization with AI

Meeting Time: 04:00 PM‑05:15 PM TTh  Instructor: Qianwen Wang Course Description: This course aims to investigate how visualization techniques and AI technologies work together to enhance understanding, insights, or outcomes.

This is a seminar style course consisting of lectures, paper presentation, and interactive discussion of the selected papers. Students will also work on a group project where they propose a research idea, survey related studies, and present initial results.

This course will cover the application of visualization to better understand AI models and data, and the use of AI to improve visualization processes. Readings for the course cover papers from the top venues of AI, Visualization, and HCI, topics including AI explainability, reliability, and Human-AI collaboration.    This course is designed for PhD students, Masters students, and advanced undergraduates who want to dig into research.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/YTF5EZFUbQRJhHBYA  ). Although the class is primarily intended for PhD students, motivated juniors/seniors and MS students who are interested in this topic are welcome to apply, ensuring they detail their qualifications for the course.

Visualizations for Intelligent AR Systems

Meeting Time: 04:00 PM‑05:15 PM MW  Instructor: Zhu-Tian Chen Course Description: This course aims to explore the role of Data Visualization as a pivotal interface for enhancing human-data and human-AI interactions within Augmented Reality (AR) systems, thereby transforming a broad spectrum of activities in both professional and daily contexts. Structured as a seminar, the course consists of two main components: the theoretical and conceptual foundations delivered through lectures, paper readings, and discussions; and the hands-on experience gained through small assignments and group projects. This class is designed to be highly interactive, and AR devices will be provided to facilitate hands-on learning.    Participants will have the opportunity to experience AR systems, develop cutting-edge AR interfaces, explore AI integration, and apply human-centric design principles. The course is designed to advance students' technical skills in AR and AI, as well as their understanding of how these technologies can be leveraged to enrich human experiences across various domains. Students will be encouraged to create innovative projects with the potential for submission to research conferences.

Registration Prerequisites: Complete the following Google form to request a permission number from the instructor ( https://forms.gle/Y81FGaJivoqMQYtq5 ). Students are expected to have a solid foundation in either data visualization, computer graphics, computer vision, or HCI. Having expertise in all would be perfect! However, a robust interest and eagerness to delve into these subjects can be equally valuable, even though it means you need to learn some basic concepts independently.

Sustainable Computing: A Systems View

Meeting Time: 09:45 AM‑11:00 AM  Instructor: Abhishek Chandra Course Description: In recent years, there has been a dramatic increase in the pervasiveness, scale, and distribution of computing infrastructure: ranging from cloud, HPC systems, and data centers to edge computing and pervasive computing in the form of micro-data centers, mobile phones, sensors, and IoT devices embedded in the environment around us. The growing amount of computing, storage, and networking demand leads to increased energy usage, carbon emissions, and natural resource consumption. To reduce their environmental impact, there is a growing need to make computing systems sustainable. In this course, we will examine sustainable computing from a systems perspective. We will examine a number of questions:   • How can we design and build sustainable computing systems?   • How can we manage resources efficiently?   • What system software and algorithms can reduce computational needs?    Topics of interest would include:   • Sustainable system design and architectures   • Sustainability-aware systems software and management   • Sustainability in large-scale distributed computing (clouds, data centers, HPC)   • Sustainability in dispersed computing (edge, mobile computing, sensors/IoT)

Registration Prerequisites: This course is targeted towards students with a strong interest in computer systems (Operating Systems, Distributed Systems, Networking, Databases, etc.). Background in Operating Systems (Equivalent of CSCI 5103) and basic understanding of Computer Networking (Equivalent of CSCI 4211) is required.

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Where do you fit in the political typology?

Are you a faith and flag conservative progressive left or somewhere in between.

Take our quiz to find out which one of our nine political typology groups is your best match, compared with a nationally representative survey of more than 10,000 U.S. adults by Pew Research Center. You may find some of these questions are difficult to answer. That’s OK. In those cases, pick the answer that comes closest to your view, even if it isn’t exactly right.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .