• We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
Logo of amjphAmerican Journal of Public Health Web SiteAmerican Public Health Association Web SiteSubmissionsSubscriptionsAbout Us
Am J Public Health. 2007 May; 97(5): 887–894.
PMCID: PMC1854865

Associations Between Lifestyle and Depressed Mood: Longitudinal Results From the Maastricht Aging Study

Abstract

Objectives. We examined whether healthy lifestyles are associated with absence of depressed mood.

Methods. A sample of 1169 adult participants in the Maastricht Aging Study provided baseline and 6-year follow-up data on smoking, alcohol use, physical exercise, body mass index, and mood. We examined associations between lifestyles and depressed mood using longitudinal analyses controlling for baseline depressive symptoms and covariates.

Results. Reports of excessive alcohol use at baseline predicted depressed mood at follow-up (relative risk [RR] = 2.48; 95% confidence interval [CI] = 1.08, 5.69), and reports of more than 30 minutes of physical exercise per day at baseline were associated with an absence of depressed mood at follow-up (RR=0.52; 95% CI=0.29, 0.92). Reports of being engaged in physical exercise throughout the 6-year follow-up period were also associated with absence of depressed mood (RR=0.56; 95% CI=0.34, 0.93).

Conclusions. In this relatively healthy population sample, certain lifestyles either predicted or protected against depressed mood. Adopting or maintaining healthy lifestyles might be a starting point in preventing or treating depressed mood over time.

Depressed mood is presumed to be caused by a variety of physical, psychological, and socioenvironmental factors.1 For example, unhealthy lifestyles such as smoking, excessive alcohol use, low levels of physical exercise, or being overweight or obese may provoke chronic diseases2,3 or worsen one’s health status over time.4 Chronic diseases frequently coincide with increased symptoms of depression,5 and feelings of depression may in turn result in unhealthy lifestyles.6 Potentially intensifying this “downward spiral,” unhealthy lifestyles might elicit or exacerbate feelings of depression,7,8 and depression may subsequently provoke or worsen the consequences associated with chronic diseases.9,10 However, it is not unequivocally clear how unhealthy lifestyles and the emergence of depressed mood (i.e., a clinically relevant level of depressive symptoms11) are associated over time.

Although research has consistently established that there is a cross-sectional association between smoking and depressed mood,1214 little evidence is available regarding whether there is a longitudinal association, that is, whether smoking precedes or follows depressed mood. It has been shown that, in general, heavy alcohol use is associated with depressed mood.15,16 Moreover, depressed mood is more often secondary to alcoholism than primary (i.e., clinicians more often treat individuals with alcoholism who have also developed depressed mood as a secondary reason for treatment than vice versa).15 Physical activity seems to help counteract prevalent depressive symptoms and protect against subsequent depression, but longitudinal studies are necessary to further unravel this association.17,18 The relation between being overweight or obese and being depressed is controversial; different studies have revealed negative, positive, and no associations between these conditions.1922

If healthy lifestyles are associated with the absence of depressed mood or protect against the emergence of depressed mood, this common and debilitating condition might be prevented or treated in the future through promoting healthy lifestyles. We sought to determine whether healthy lifestyles are associated, over time, with absence of depressed mood in the general population.

METHODS

Design and Study Population

We used data from the longitudinal Maastricht Aging Study, an ongoing investigation examining determinants of normal cognitive aging. Questionnaires were sent to 3449 individuals, aged 24 to 81 years, who were free of medical conditions that interfered with their normal cognitive functioning at their entry into the study. This population was drawn from the Registration Network Family Practices,23,24 a primary care research sampling frame consisting of 9919 individuals whose native language is Dutch. Our randomly recruited sample was stratified according to general ability level (defined as level of occupational achievement, including degree of complexity associated with professional occupations and knowledge and experience required25); women and older individuals were oversampled to ensure adequate representation of these groups in follow-up measurements.

Between 1993 and 1995 (baseline), 1823 respondents returned the questionnaire and underwent cognitive and physical examinations.26 Six years after completing their baseline assessments, these 1823 participants were invited to take part in follow-up examinations. A total of 294 respondents refused further participation, 116 had died, and 37 had been lost to follow-up; thus, 1376 participants underwent reassessments. Ultimately, 1169 (33.9%) of the 3449 respondents were included in our study sample (207 respondents were lost to subsequent analyses owing to incomplete data on relevant variables at baseline or follow-up).

Measures

Baseline depressive symptoms were assessed with the self-report Symptom Checklist 90 Depression Scale.27,28 The 16 items on this instrument are rated in 5 categories ranging from no complaint (1) to maximal complaints (5). Scores can range from 16 to 80, with higher scores indicating a higher number of depressive symptoms. At follow-up, we used the self-report Center for Epidemiologic Studies Depression Scale (CES-D),29 an instrument that has demonstrated good psychometric qualities in epidemiological studies involving older populations.30 The CES-D’s 20 items are rated in 4 categories ranging from no complaint (0) to maximal complaints (3). Scores can range from 0 to 60, again with higher scores indicating a higher number of depressive symptoms. We used a CES-D threshold score of 16 or above in screening for depressed mood.29 Strong correlations between these 2 depression instruments have been found,31 and the predictive validity of both scales has been reported elsewhere.32

On the basis of respondents’ reports of their current and former smoking behavior, they were grouped into the following categories at baseline and follow-up: current smoker (the reference category), former smoker, and never smoker. Transitions in smoking behavior over time were categorized as (1) respondent still does not smoke, (2) respondent quit smoking, (3) respondent initiated smoking, and (4) respondent still smokes (reference category).

Mean alcohol consumption at baseline and follow-up was calculated according to participants’ reports of the number of glasses of alcohol (representing approximately 10 g of alcohol in conformance with the unit of alcohol system) they drank per day on average (more than 10 glasses, between 7 and 10 glasses, between 3 and 6 glasses, 1 or 2 glasses, none) and the average number of days per week they consumed alcohol (every day, 5 or 6 days, 3 or 4 days, 1 or 2 days, less than 1 day). These measures were used to group participants into the following categories: nondrinkers (the reference category), those consuming up to 2 drinks per day on average, and those consuming 3 or more drinks per day on average (excessive alcohol use). Transitions in alcohol use over time were categorized as (1) respondent still drinks alcohol, (2) respondent initiated alcohol use, (3) respondent quit drinking alcohol, and (4) respondent still does not drink alcohol (reference category).

Mean numbers of minutes spent daily on physical exercise at baseline and follow-up were computed on the basis of the number of hours participants reported spending each week, on average, engaging in light activities such as ball sports, aerobic exercise, walking, and biking. These measures were used to group participants into the following categories: those not engaging in physical exercise (the reference category), those engaging in physical exercise for up to 30 minutes per day on average, and those engaging in physical exercise for more than 30 minutes per day on average. Transitions in physical exercise over time were categorized as (1) respondent still engages in physical exercise, (2) respondent initiated physical exercise, (3) respondent discontinued physical exercise, and (4) respondent still does not engage in physical exercise (reference category).

Members of the study staff assessed respondents’ body weight and height at baseline and follow-up. We used body mass index (weight in kilograms divided by height in meters squared) cutoff scores of 27.8 or above for men and 27.3 or above for women to distinguish between respondents who were (at least) overweight (the reference category) and those who were not.33 These values corresponded with weights that were roughly 20% or more above the desired weights listed in the 1983 Metropolitan Life Insurance Company tables. Transitions in overweight status over time were categorized as (1) respondent is still not overweight, (2) respondent is no longer overweight, (3) respondent became overweight, and (4) respondent is still overweight (reference category).

Previous research has shown that age, gender, marital status, educational level, ability to engage in instrumental activities of daily living (IADLs), and chronic disease are associated with both lifestyle3436 and depression.10,3739 We included these variables in our analyses as covariates. Data on marital status, age, gender, and educational level were obtained from the questionnaire completed by the respondents. Marital status was categorized as widowed, not married or no longer married, and married or living with a partner. Educational level was categorized as low (primary education at most), intermediate (junior vocational training), or high (senior vocational or academic training).

The questionnaire asked respondents to indicate whether they needed assistance in the following IADLs as a result of their physical condition: grocery shopping, housekeeping, preparing meals, maintaining personal hygiene, and getting dressed. If respondents answered no on these questions, their IADL status was not considered to be impaired. If they responded yes to 1 or more of these items, their IADL status was considered impaired. Finally, in an interview conducted by a trained research assistant, respondents were given the opportunity to indicate whether a medical doctor had ever diagnosed them with 1 or more of 37 chronic diseases.

Statistical Analyses

After comparing individuals who did not take part in the study or were lost to follow-up with study participants (using t tests and χ2 analyses), analyzing the characteristics of the sample at baseline, and comparing study variables at baseline and follow-up, we examined associations between sociodemographic variables and baseline lifestyle domains and follow-up depressive symptoms. In addition, we examined associations between transitions in lifestyle domains and depressed mood at follow-up. We used cross-sectional and longitudinal techniques in our analyses, specifically paired-samples t tests, χ2 analyses, and McNemar tests; analyses of variance; and multivariate logistic regression models in which transitions in the various lifestyle domains were independent variables and follow-up depressed mood was the outcome variable. SPSS software (SPSS Inc, Chicago, Ill) was used in analyzing the data. The unhealthy lifestyle components served as reference categories, and longitudinal analyses were adjusted for baseline depressive symptoms and covariates.

RESULTS

As can be seen in Table 1 [triangle], attrition analyses at baseline demonstrated that the 2280 individuals who either were lost to follow-up (n = 654) or did not take part in the study (n = 1626) were significantly older (mean = 55.1 years, SD = 17.6) than the 1169 study respondents (mean = 48.9 years, SD = 14.7). In addition, they were more likely to be female (55.8% vs 47.6%) or widowed (12.3% vs 5.0%), to be at low levels of education (51.6% vs 37.2%), to report impairments in IADLs (20.9% vs 7.4%), and to be overweight (39.8% vs 32.9%). Finally, they reported more chronic diseases (mean = 1.9, SD = 1.8, and mean = 1.3, SD = 1.3, respectively), fewer minutes of physical activity per day (mean = 13.6, SD = 27.3, and mean = 18.2, SD = 28.3, respectively), and more symptoms of depression (mean = 22.0, SD = 7.9, and mean = 20.5, SD = 6.1, respectively).

TABLE 1
Baseline Characteristics of Sample Participants and Comparisons on Key Variables at Baseline and Follow-Up: Maastricht Aging Study

Table 1 [triangle] also presents data on lifestyle at follow-up. Between baseline and follow-up, the percentage of respondents who reported smoking decreased significantly (from 26.7% to 22.0%). The percentage of respondents who reported not drinking alcohol at all increased significantly during this period (from 13.8% to 15.5%; Table 1 [triangle]), as did the percentage reporting that they consumed 3 or more drinks per day on average (from 6.8% to 8.3%).

The percentage of respondents who reported not engaging in physical exercise increased significantly from 49.4% at baseline to 62.8% at follow-up. In addition, average number of minutes spent daily on physical exercise decreased significantly from 18.2 (SD = 28.3) at baseline to 10.6 (SD = 20.5) at follow-up. Not only did the average body mass index in the sample exhibit a significant increase from 26.5 kg/m2 (SD = 4.1) to 27.0 kg/m2 (SD = 4.2) between baseline and follow-up, the percentage of overweight respondents also increased significantly (from 32.9% at baseline to 39.6% at follow-up). Finally, 14.0% of the respondents had a score of 16 or above (the threshold score for depressed mood) on the CES-D Scale at follow-up (Table 1 [triangle]).

Results of univariate longitudinal analyses focusing on follow-up depressive symptoms, stratified according to baseline sociodemographic variables and lifestyle domains, are shown in Table 2 [triangle]. Respondents aged 65 through 81 years had higher follow-up mean depression scores than respondents in the other age categories. Also, women had significantly higher mean depression scores at follow-up than men, and respondents at low education levels had higher follow-up depression scores than respondents at intermediate or high education levels.

TABLE 2
CES-D Depression Scores at Follow-Up (n = 1169) and Comparisons Stratified by Baseline Sociodemographic and Lifestyle Variables: Maastricht Aging Study

In addition, respondents with impairments in IADLs at baseline had higher follow-up mean depression scores than respondents with no IADL impairments. Respondents reporting 2 or more chronic diseases at baseline had higher follow-up depression scores than respondents reporting no chronic diseases or no more than 1 chronic disease, and those who reported not engaging in physical exercise at baseline had higher follow-up scores than those in the other physical activity groups. Finally, respondents who were overweight at baseline had higher follow-up mean depression scores than respondents who were not overweight at baseline (Table 2 [triangle]).

Post hoc interaction analyses revealed that neither gender nor age had a modifying effect on the associations between the baseline lifestyle domains assessed and follow-up depressive symptoms (data not shown). Hence, analyses were not carried out separately for women and men or for different age groups.

Table 3 [triangle] shows relative risks (RRs) and 95% confidence intervals (CIs) derived from longitudinal multivariate logistic regression models with baseline lifestyle domains as determinants of follow-up depressed mood. In comparison with respondents who reported no alcohol use at baseline, those who reported excessive alcohol use at baseline were roughly 2.5 times as likely to be depressed at follow-up (RR = 2.48; 95% CI = 1.08, 5.69). Also, each glass of alcohol consumed on average per day at baseline was associated with a 17% increased risk of depressed mood at follow-up (RR = 1.17; 95% CI = 1.03, 1.32). Both of these analyses were adjusted for covariates (Table 3 [triangle]).

TABLE 3
Multivariate Logistic Regression Models for Baseline Lifestyle Domains as Determinants of Depressed Mood at Follow-Up (n = 1169)

Respondents who reported engaging in physical exercise for more than 30 minutes per day on average at baseline had a 48% lower risk of being depressed at follow-up than respondents who reported not engaging in physical exercise at baseline (RR = 0.52; 95% CI = 0.29, 0.92). Also, each minute of physical exercise per day reported at baseline was associated with a 1% decreased risk of depressed mood at follow-up (RR = 0.99; 95% CI = 0.98, 1.00). Both analyses were adjusted for covariates (Table 3 [triangle]).

Table 4 [triangle] shows relative risks and 95% confidence intervals derived from longitudinal multivariate logistic regression models with transitions in lifestyle domains as determinants of follow-up depressed mood. Analyses adjusted for covariates demonstrated that respondents who initiated alcohol use between baseline and follow-up were at lower risk of being depressed at follow-up than respondents who reported no alcohol use at baseline or follow-up (RR = 0.18; 95% CI = 0.04, 0.76; Table 4 [triangle]). Post hoc analyses revealed that none of the 47 respondents who initiated alcohol use between baseline and follow-up exceeded 11 alcoholic drinks per week on average (data not shown). Finally, analyses adjusted for covariates demonstrated that respondents who reported engaging in physical exercise at baseline as well as at follow-up decreased their risk of being depressed at follow-up by 44% compared with respondents who did not engage in physical exercise throughout the 6-year period (RR = 0.56; 95% CI = 0.34, 0.93; Table 4 [triangle]).

TABLE 4
Multivariate Logistic Regression Models for Transitions in Lifestyle Domains Between Baseline and Follow-Up as Determinants of Depressed Mood at Follow-Up (n = 1169)

DISCUSSION

In assessing longitudinal associations between lifestyle domains and depressed mood, we found that excessive alcohol use at baseline (compared with abstinence) predicted depressed mood at follow-up and that engaging in more than 30 minutes of physical exercise on average per day at baseline (compared with not exercising) was associated with an absence of depressed mood at follow-up. In addition, we found that those who initiated alcohol use were at reduced odds of depressed mood at follow-up relative to steady non-drinkers and that those who persistently engaged in physical exercise were less likely to be depressed at follow-up than those who persistently did not engage in physical exercise.

Our results did not show any longitudinal associations between smoking behavior and depressed mood. The increase between baseline and follow-up in the percentage of respondents who reported never having smoked (Table 1 [triangle]) indicates some inconsistency in questionnaire responses over the 6-year period, and this may have diminished the reliability of our data on smoking behavior and weakened the observed associations between smoking behavior and depressive symptoms.

Our finding of a significant longitudinal predictive effect of excessive alcohol use at baseline on the presence of depressed mood at follow-up is in accord, to some extent, with the results of Aneshensel and Huba.8 In their study involving 742 adults in the Los Angeles metropolitan area, they found contradictory cross-sectional and longitudinal effects of alcohol use on depression. They inferred that high levels of alcohol use were associated with low scores for depressive symptoms (cross sectional) but that high initial levels of alcohol use were associated with subsequent increased depressive symptoms (longitudinal).

However, we are uncertain about the validity of the observed association between alcohol use initiation and absence of depressed mood at follow-up. Because of the relatively small size of the group initiating alcohol use (n = 47) compared with the other groups, the fact that nonrespondents reported more depressive symptoms at baseline than respondents, and the fact that none of the respondents initiating alcohol use between baseline and follow-up exceeded an average of 2 glasses of alcohol per day, we cannot rule out that this finding was a statistical artifact attributable to selection bias.

Our finding of a significant longitudinal protective effect of baseline physical exercise (at recommended levels) on subsequent depressed mood is in line with the results of Strawbridge and colleagues.17 In a 5-year follow-up investigation, they found that high levels of physical activity were associated with absence of depression and were protective against subsequent depression among 1947 community-dwelling adults aged 50 through 94 years. They used a measure of physical activity focusing on usual frequency (never, sometimes, often) of exercise, taking part in active sports, taking long walks, and swimming.

Furthermore, the protective effect of physical exercise on subsequent depression reported by Strawbridge et al.17 was confirmed in our analyses examining the effects of physical exercise behavior over time: Respondents who reported engaging in physical activity at both baseline and follow-up were at 44% lower risk of subsequent depression than those who reported not engaging in physical exercise at either baseline or follow-up. Thus, it can be cautiously suggested not only that physical exercise may be an effective element in the treatment of depression40 but that maintenance of regular physical exercise over a relatively long period of time may protect against the emergence of clinically relevant levels of depressive symptoms. The inhibitory effect of exercise on levels of inflammatory and cardiovascular risk factors may be part of the explanatory pathway through which exercise protects against depression,41 in that the presence of high levels of these risk factors has also been associated with the presence of high levels of depressive symptoms.42,43

This study was limited by the considerable attrition because of nonresponse and loss to follow-up. The 6-year follow-up period may have been a key source of selection bias, potentially resulting in the sample included here being less representative of the overall study population than desired. However, this is a frequently encountered problem in longitudinal aging studies and is difficult to avoid.10,44 In addition, according to Kempen and Van Sonderen, attrition has more adverse effects in the case of descriptive measurements than in the case of measures focusing on longitudinal associations, such as those used in our study.45

Bias may also have been introduced through the use of mainly self-report measures. In general, research involving the use of self-reported measures is inexpensive, easy to conduct, and not affected by interrater variability. A downside of such research, however, is the risk of inaccurate recollection of past events and response bias. These factors, if present, may have led to a certain degree of distortion of our findings.

Finally, a pair of suboptimal conditions of our study need to be mentioned. First, CES-D scores were not available at baseline. Therefore, we used Symptom Checklist 90 Depression Scale baseline scores to control our statistical analyses for initial level of depressive symptoms. Because these 2 instruments have been shown to be valid and highly correlated,31 we believe that our analyses were adequately controlled; however, this may be open to debate. Second, we did not adjust for broad socioeconomic factors, such as unemployment, major life events, and work stressors, that might be associated with both unhealthy lifestyles and depression. Thus, control for educational level alone may not have been sufficient and could have resulted in residual confounding by these broader socioeconomic conditions. These limitations should be considered in interpreting our results and formulating public health recommendations.

We believe that our study involved a number of strengths as well. We comprehensively examined the effects of 4 lifestyle domains on depressive symptoms using longitudinal analysis techniques. The pivotal findings of this study were that (1) excessive alcohol use at baseline predicted depressed mood at follow-up; (2) physical exercise at recommended levels predicted absence of depressed mood at follow-up; and (3) physical exercise over a relatively long period of time was associated with absence of depressed mood.

Our results indicate that the potential downward spiral mentioned in the introduction might be halted through adoption or maintenance of healthy lifestyles, which could prevent the deterioration or even occurrence of depressed mood over time and, in turn, the worsening of chronic disease symptoms. In addition to the role of behavior change, adoption or maintenance of healthy lifestyles might be facilitated by the creation of health-promotive environments46 in both homes and workplaces (e.g., through offering only low-fat, high-fiber meals in company and school cafeterias or lowering sales taxes on memberships in health and physical fitness clubs), reducing barriers to engaging in healthy behaviors and motivating people to engage in these behaviors. Future research could assess the effects of implementing such health-promotive environments on lifestyle alterations and subsequent changes in both overall health and mental health.

Acknowledgments

This work was supported in part by a grant from the Dutch Ministries of Education and Health and Welfare, via the Steering Committee for Gerontological Research.

Human Participant Protection
This study was approved by the medical ethics committee of the University Hospital Maastricht. Participants provided written informed consent.

Notes

Peer Reviewed

Contributors
C. H. van Gool and G. I. J. M. Kempen formulated the hypothesis. C. H. van Gool analyzed the data, interpreted the findings, and drafted the article. G. I. J. M. Kempen, H. Bosma, M. P. J. van Boxtel, J. Jolles, and J. T. M. van Eijk assisted with interpretation of findings and critical revision of the article.

References

1. Jorm AF. The epidemiology of depressive states in the elderly: implications for recognition, intervention and prevention. Soc Psychiatry Psychiatr Epidemiol. 1995;30:53–59. [PubMed]
2. Gohlke H. Lifestyle modification—is it worth it? Herz. 2004;29:139–144. [PubMed]
3. Meigs JB, Hu FB, Rifai N, et al. Biomarkers of endothelial dysfunction and risk of type 2 diabetes mellitus. JAMA. 2004;291:1978–1986. [PubMed]
4. Penninx BW, Leveille S, Ferrucci L, van Eijk JT, Guralnik JM. Exploring the effect of depression on physical disability: longitudinal evidence from the established populations for epidemiologic studies of the elderly. Am J Public Health. 1999;89:1346–1352. [PMC free article] [PubMed]
5. Bisschop MI, Kriegsman DM, Deeg DJ, et al. The longitudinal relation between chronic diseases and depression in older persons in the community: the Longitudinal Aging Study Amsterdam. J Clin Epidemiol. 2004;57:187–194. [PubMed]
6. van Gool CH, Kempen GI, Penninx BW, et al. Relationship between changes in depressive symptoms and unhealthy lifestyles in late middle aged and older persons: results from the Longitudinal Aging Study Amsterdam. Age Ageing. 2003;32:81–87. [PubMed]
7. National Academy on an Aging Society. Depression: a treatable disease. Available at: http://www.agingsociety.org/agingsociety/pdf/depression.pdf. Accessed April 5, 2006.
8. Aneshensel CS, Huba GJ. Depression, alcohol use, and smoking over 1 year: a four-wave longitudinal causal model. J Abnorm Psychol. 1983;92:134–150. [PubMed]
9. Depression Guideline Panel. Depression in Primary Care, Volume 1: Detection and Diagnosis. Rockville, Md: Agency for Health Care Policy and Research; 1993. AHCPR publication 93–0550.
10. van Gool CH, Kempen GI, Penninx BW, et al. Impact of depression on disablement in late middle aged and older persons: results from the Longitudinal Aging Study Amsterdam. Soc Sci Med. 2005;60:25–36. [PubMed]
11. Haringsma R, Engels GI, Beekman AT, Spinhoven P. The criterion validity of the Center for Epidemiological Studies Depression Scale (CES-D) in a sample of self-referred elders with depressive symptomatology. Int J Geriatr Psychiatry. 2004;19:558–563. [PubMed]
12. Anda RF, Williamson DF, Escobedo LG, et al. Depression and the dynamics of smoking: a national perspective. JAMA. 1990;264:1541–1545. [PubMed]
13. Carney RM, Rich MW, Tevelde A, et al. Major depressive disorder in coronary artery disease. Am J Cardiol. 1987;60:1273–1275. [PubMed]
14. Frederick T, Frerichs RR, Clark VA. Personal health habits and symptoms of depression at the community level. Prev Med. 1988;17:173–182. [PubMed]
15. Freed EX. Alcohol and mood: an updated review. Int J Addict. 1978;13:173–200. [PubMed]
16. Aneshensel CS. An application of log-linear models: the stress-buffering function of alcohol use. J Drug Educ. 1983;13:287–301.
17. Strawbridge WJ, Deleger S, Roberts RE, et al. Physical activity reduces the risk of subsequent depression for older adults. Am J Epidemiol. 2002;156: 328–334. [PubMed]
18. Goodwin RD. Association between physical activity and mental disorders among adults in the United States. Prev Med. 2003;36:698–703. [PubMed]
19. Bin Li Z, Yin Ho S, Man Chan W, et al. Obesity and depressive symptoms in Chinese elderly. Int J Geriatr Psychiatry. 2004;19:68–74. [PubMed]
20. Roberts RE, Strawbridge WJ, Deleger S, et al. Are the fat more jolly? Ann Behav Med. 2002;24: 169–180. [PubMed]
21. Ross CE. Overweight and depression. J Health Social Behav. 1994;35:63–79. [PubMed]
22. Carpenter KM, Hasin DS, Allison DB, et al. Relationships between obesity and DSM-IV major depressive disorder, suicide ideation, and suicide attempts: results from a general population study. Am J Public Health. 2000;90:251–257. [PMC free article] [PubMed]
23. Metsemakers JF, Höppener P, Knottnerus JA, et al. Computerized health information in the Netherlands: a registration network of family practices. Br J Gen Pract. 1992;42:102–106. [PMC free article] [PubMed]
24. Jolles J, Houx PJ, van Boxtel MP, et al., eds. Maastricht Aging Study: Determinants of Cognitive Aging. Maastricht, the Netherlands: Neuropsych Publishers; 1995.
25. Valentijn SA, van Boxtel MP, van Hooren SA, et al. Change in sensory functioning predicts change in cognitive functioning: results from a 6-year follow-up in the Maastricht Aging Study. J Am Geriatr Soc. 2005; 53:374–380. [PubMed]
26. van Boxtel MP, Buntinx F, Houx PJ, et al. The relation between morbidity and cognitive performance in a normal aging population. J Gerontol. 1998; 53A:M146–M154. [PubMed]
27. Derogatis LR, Lipman RS, Covi L. SCL-90: an outpatient psychiatric rating scale—preliminary report. Psychopharmacol Bull. 1973;9:13–27. [PubMed]
28. Arrindell WA, Ettema JHM. Dimensional structure, reliability and validity of the Dutch version of the Symptom Checklist (SCL-90). Ned Tijdschr Psychologie. 1981;43:381–387.
29. Radloff LS. The CES-D Scale: A self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1:385–401.
30. Beekman AT, Deeg DJ, Van Limbeek J, et al. Criterion validity of the Center for Epidemiologic Studies Depression Scale (CES-D): results from a community-based sample of older subjects in the Netherlands. Psychol Med. 1997;27:231–235. [PubMed]
31. Weissman MM, Sholomskas D, Pottenger M, Prusoff BA, Locke BZ. Assessing depressive symptoms in five psychiatric populations: a validation study. Am J Epidemiol. 1977;106:203–214. [PubMed]
32. Dohrenwend BP. Mental Illness in the United States: Epidemiological Estimates. New York, NY: Praeger; 1980.
33. Williamson DF, Kahn HS, Remington PL, et al. The 10-year incidence of overweight and major weight gain in US adults. Arch Intern Med. 1990;150: 665–672. [PubMed]
34. Enright PL, McBurnie MA, Bittner V, et al. The 6-min walk test: a quick measure of functional status in elderly adults. Chest. 2003;123:387–398. [PubMed]
35. Ruchlin HS. Prevalence and correlates of alcohol use among older adults. Prev Med. 1997;26:651–657. [PubMed]
36. Henderson PN, Rhoades D, Henderson JA, et al. Smoking cessation and its determinants among older American Indians: the Strong Heart Study. Ethn Dis. 2004;14:274–279. [PubMed]
37. Beekman AT, Penninx BW, Deeg DJ, et al. Depression and physical health in later life: results from the Longitudinal Aging Study Amsterdam (LASA). J Affect Disord. 1997;46:219–231. [PubMed]
38. Dong C, Sanchez LE, Price RA. Relationship of obesity to depression: a family-based study. Int J Obes Relat Metab Disord. 2004;28:790–795. [PubMed]
39. Schoevers RA, Beekman AT, Deeg DJ, Geerlings MI, Jonker C, Van Tilburg W. Risk factors for depression in later life: results of a prospective community based study (AMSTEL). J Affect Disord. 2000;59: 127–137. [PubMed]
40. Dunn AL, Trivedi MH, Kampert JB, Clark CG, Chambliss HO. Exercise treatment for depression: efficacy and dose response. Am J Prev Med. 2005;28:1–8. [PubMed]
41. Abramson JL, Vaccarino V. Relationship between physical activity and inflammation among apparently healthy middle-aged and older US adults. Arch Intern Med. 2002;162:1286–1292. [PubMed]
42. Danner M, Kasl SV, Abramson JL, Vaccarino V. Association between depression and elevated C-reactive protein. Psychosom Med. 2003;65:347–356. [PubMed]
43. Ford DE, Erlinger TP. Depression and C-reactive protein in US adults: data from the Third National Health and Nutrition Examination Survey. Arch Intern Med. 2004;164:1010–1014. [PubMed]
44. Koster A, Bosma H, van Lenthe FJ, Kempen GI, Mackenbach JP, van Eijk JT. The role of psychosocial factors in explaining socioeconomic differences in mobility decline in a chronically ill population: results from the GLOBE study. Soc Sci Med. 2005;61: 123–132. [PubMed]
45. Kempen GI, Van Sonderen E. Psychological attributes and changes in disability among low-functioning, older persons: does attrition affect the outcomes? J Clin Epidemiol. 2002;55:224–229. [PubMed]
46. Stokols D. Translating social ecological theory into guidelines for community health promotion. Am J Health Promotion. 1996;10:282–298. [PubMed]

Articles from American Journal of Public Health are provided here courtesy of American Public Health Association
PubReader format: click here to try

Formats:

Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...

Links

  • MedGen
    MedGen
    Related information in MedGen
  • PubMed
    PubMed
    PubMed citations for these articles

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...