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Impact of Physical Activity on Psychological Distress: A Prospective Analysis of an Australian National Sample Francisco Perales, PhD, Jesus del Pozo-Cruz, PhD, and Borja del Pozo-Cruz, PhD
Moderate to vigorous physical activity (MVPA) is important to people’s lives, with the World Health Organization as well as national and international bodies recommending frequent participation in it.1,2 Recent analyses of Australian population-level data have endorsed this by showing that MVPA is independently associated not only with general and physical health but also with overall levels of mental health and self-reported life satisfaction.3 However, the relationships between MVPA and other facets of mental health, including levels of psychological distress, have not yet been well established. Psychological distress, understood as the experience of unpleasant feelings or emotions that affect day-to-day functioning, affects a sizable share of the population in developed countries such the United States, the United Kingdom, and Australia4---7 and is known to lead to more severe mental disorders and physical health issues.8,9 Consequently, the financial and human costs of psychological distress are nonnegligible, and gaining a deeper understanding of the factors that influence individuals’ distress levels is important for the development of efficient public health policies and the devising of effective palliative interventions. Emerging evidence of an association between MVPA and overall levels of mental health has suggested that associations between MVPA and psychological distress are also likely. Potential effects may run through known physiological, psychological, and social processes. From a physiological point of view, we know that MVPA enhances fitness levels, which in turn regulate physiological stress responses, such as reduced secretion of hormones and lowered blood pressure.10 From a psychological perspective, MVPA has been linked to reduced arousal and mood enhancement through cognitive distraction and biochemical changes, and to positive health behaviors during periods of high stress (e.g., a lower likelihood to smoke and eat unhealthily).11 Additionally, participation in
Objectives. We analyzed the individual-level associations between participation in moderate to vigorous physical activity (MVPA) and psychological distress levels using a large, nationally representative, longitudinal sample and multivariable panel regression models. Methods. We used 3 waves of panel data from the Household, Income and Labour Dynamics in Australia Survey, consisting of 34 000 observations from 17 000 individuals and covering 2007, 2009, and 2011. We used fixed-effects panel regression models accounting for observable and unobservable confounders to examine the relationships between the weekly frequency of MVPA and summary measures of psychological distress based on the Kessler Psychological Distress Scale. Results. We found substantial and highly statistically significant associations between the frequency of MVPA and different indicators of psychological distress. Frequent participation in MVPA reduces psychological distress and decreases the likelihood of falling into a high-risk category. Conclusions. Our findings underscore the importance of placing physical activity at the core of health promotion initiatives aimed at preventing and remedying psychological discomfort. (Am J Public Health. 2014;104:e91–e97. doi:10.2105/AJPH.2014.302169) MVPA tends to increase time spent outdoors, as well as the frequency and quality of social interactions and interpersonal relationships.12---14 As a result, we would expect MVPA to have the potential to enhance well-being by reducing psychological distress. Consistent with these theories, results from the limited body of existing empirical research have suggested that there are indeed negative associations between the frequency of MVPA and psychological distress levels. However, these findings have emerged almost exclusively from analyses of small nonprobability samples,15---21 and the few available studies based on nationally representative samples are cross-sectional (e.g., Scotland22 and Singapore23). The small, nonprobability nature of the samples used in these studies means that findings are tentative and cannot be generalized to the population as a whole. Their cross-sectional nature means that longitudinal regression techniques that enable more precise estimation of the associations of interest by examining within-individual change over time and minimizing omitted variable bias attributable to unobservable factors cannot be implemented.
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In fact, undertaking large-scale prospective analyses is often regarded as a necessary step forward in enhancing current knowledge of the associations between MVPA and psychological distress.6,23 In this article, we fill this gap and add to the literature by establishing the population-level associations between the weekly frequency of MVPA and self-reported levels of psychological distress with a nationally representative Australian panel data set and fixed-effect (FE) panel regression models.
METHODS Our data set of choice, the Household, Income and Labour Dynamics in Australia (HILDA) Survey, is a large-scale, nationally representative panel survey that collects annual information from the same respondents. Twelve waves of data covering 2001 to 2012 are currently available, with low attrition rates.24 This data set is very useful in examining the relationships between MVPA and psychological distress for 2 reasons: (1) it features a remarkably large sample that is representative of the
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Australian population, and (2) it contains repeated measures over time of the variables of interest (MVPA, psychological distress, and contextual factors), which allows for more elaborated statistical analysis via assessment of within-individual changes. Information on psychological distress has been collected in waves 7 (2007), 9 (2009), and 11 (2011) of the HILDA Survey. Hence, our analyses are restricted to these 3 time points.
Measures The key independent variable was the weekly frequency of MVPA, as reported by survey participants. This variable contains responses to a question in a self-completion questionnaire: “In general, how often do you participate in moderate or intensive physical activity for at least 30 minutes? Moderate level physical activity will cause a slight increase in breathing and heart rate, such as brisk walking.” Respondents can choose 1 of the following 6 answers: “not at all,” “less than once a week,” “1 or 2 times a week,” “3 times a week,” “more than 3 times a week (but not every day),” and “every day.” We used this information to derive a set of dummy variables capturing the frequency of MVPA undertaken by survey participants. Our outcome of interest was self-reported levels of psychological distress, operationalized using the well-established Kessler Psychological Distress Scale.25,26 This scale consists of a battery of 10 questions designed to capture nonspecific psychological distress and measure depressive symptoms and anxiety disorders. Respondents are asked how often in the past 4 weeks they had experienced different feelings and emotions, including feeling 1. “tired for no good reason,” 2. “nervous,” 3. “so nervous that nothing could calm you down,” 4. “hopeless,” 5. “restless or fidgety,” 6. “so restless that you could not sit still,” 7. “depressed,” 8. “that everything was an effort,” 9. “so sad that nothing could cheer you up,” and 10. “worthless.” Possible responses are rated on a 5-point Likert scale (all the time, most of the time, some
of the time, a little of the time, and none of the time) and can be combined into more informative summary measures. First, reversing and adding scores for the 10 survey items gives an additive index ranging from 10 to 50 known as the K10. Second, the K10 scores can be used to separate the population into 4 risk groups: scores of 10 to 15 take the value 1 (“low risk”), scores of 16 to 21 take the value 2 (“moderate risk”), scores of 22 to 29 take the value 3 (“high risk”), and scores of 30 or higher take the value 4 (“high risk”).27 The resulting outcome variable is an ordered measure with scores ranging from 1 to 4.
Statistical Analyses To explore the association between the frequency of MVPA and psychological distress, we exploited the panel structure of the HILDA Survey data and estimated within-group FE panel regression models. These models use the repeated observations from the same individuals over time to account for unobserved person-specific factors that might confound the associations and minimize omitted variable bias. Therefore, FE models provide a better picture of the associations between MVPA and psychological distress than is possible in cross-sectional regression.28,29 Note that it is not possible to retrieve the effect of time-invariant explanatory factors such as gender and socioeconomic background on the outcome variable using FE models, although they are implicitly accounted for by the model. To model the first outcome variable, the K10 scale, we fit linear FE models for continuous dependent variables. These models estimate how deviations from individuals’ usual behavior and characteristics associate with deviations from their usual outcomes (captured by the individual mean scores in these over time). These take the form K 10 it K 10 i ¼ MVPAit MVPAi bþ Xit Xi c þ ðe it ei Þ where subscripts i and t denote individual and time; K10 is the measure of psychological distress of interest; MVPA is a set of dummy variables capturing the weekly frequency of physical activity; X is a vector of control variables; b and c are vectors of coefficients; and
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e is the usual stochastic error term in regression. The X vector of control variables includes variables known or suspected to influence psychological distress. These variables include the respondent’s age at last birthday measured in years; highest educational qualification ever attained (“university qualification,” “professional qualification,” “school year 12,” “below school year 12”); gross yearly personal income adjusted for inflation using Consumer Price Indices; respondent’s body mass index, calculated as mass in kilograms divided by the square of height in meters; whether the respondent lives alone (“yes,” “no”); whether the respondent currently smokes cigarettes (“yes,” “no”); the respondent’s frequency of sex-based excess alcohol drinking during the past 12 months (“sometimes (less than once a month),” “once a month,” “several times a month,” “never”); whether the respondent reports having a long-term health condition, impairment, or disability that restricts everyday activities (“yes,” “no”); the respondent’s employment status (“employed,” “unemployed,” “not in the labor force”); and the respondent’s number of total weekly work hours, measured as the sum of the usual weekly hours worked in all jobs and the usual weekly hours of domestic labor. To model the risk categories extracted from the K10, an FE model for ordered outcomes is required, but the literature has only recently provided solutions to achieve this. Two competing specifications have been proposed: the person-specific threshold, ordered FE logit model by Ferrer-i-Carbonell and Frijters30 and the blow-up and cluster, ordered FE logit model by Baetschmann et al.31 None of these is readily available in standard statistical packages, and we thus programmed them ourselves. Because there is not yet consensus as to which model is preferable, we fit and discuss the results of both estimation strategies. In broad terms, the person-specific thresholdordered FE logit strategy consists of creating a binary variable out of the original ordered variable using a person-specific threshold: Risk values that are equal or higher than the person-specific over-time mean take the value 1, and risk values that are lower than the person-specific over-time mean take the value 0. A binary logistic FE model (also known as a conditional logit model) is then fitted to the
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resulting dichotomous variable. In the blow-up and cluster, ordered FE logit strategy, one first creates an expanded data set by multiplying every data row as many times as there are potential dichotomizations of the original ordered variable and applying a different dichotomization to each set of duplicated observations. In our case, there were 3 possibilities: (1) risk level 1 takes the value 0, and risk levels 2 to 4 take the value 1; (2) risk levels 1 to 2 take the value 0, and risk levels 3 to 4 take the value 1; and (3) risk levels 1 to 3 take the value 0, and risk level 4 takes the value 1. Again, a binary logistic FE model is fitted to the resulting dichotomous variable, adjusting the standard errors to account for the artificial duplication of rows. For detailed, formal derivations of these estimators, see Ferrer-i-Carbonell and Frijters30 and Baetschmann et al.,31 respectively. Despite the complex properties of these methods, the estimated model parameters can be transformed into odds ratios and interpreted quite simply.
TABLE 1—Descriptive Statistics: Household, Income and Labour Dynamics in Australia Survey, 2007, 2009, and 2011 Variable
Mean (SD) or %
The analytical sample of the HILDA Survey in 2007, 2009, and 2011 consisted of 33 918 observations from 17 080 individuals, and thus respondents were observed 1.98 times on average. Descriptive statistics for all variables can be found in Table 1. In 10% of all observations, individuals reported doing no MVPA at all; in 15%, less than once a week; in 24%, once or twice a week; in 16%, 3 times a week; in 22%, more than 3 times a week (but not every day); and in 13%, every day. Raw relationships between this variable and indicators of psychological distress are presented in Figures 1 and 2. Mean responses to each of the 10 items constituting the K10 scale increased with the weekly frequency of MVPA (Figure 1; high scores mean absence of distress). Consequently, the overall scores in the K10 index and the prevalence of higher risk categories decreased with the frequency of MVPA (Figure 2; high scores represent worse outcomes). This is a preliminary indication that MVPA may influence psychological distress levels. More thorough examination of whether true relationships exist requires multivariable models that control for observable and unobservable
Max.
Frequency of moderate or intensive physical activity Not at all
10
0
1
< 1 time/wk
15
0
1
1 or 2 times/wk
24
0
1
3 times/wk > 3 times/wk (but not every day)
16 22
0 0
1 1
Every day K10 summary scale
13
0
1
15.5 (6.0)
10
50
K10 risk categories Low risk
65
0
1
Moderate risk
21
0
1
High risk
10
0
1
Very high risk Age, y
4 44.2 (18.1)
0 15
1 97
< year 12
24
0
1
Year 12
31
0
1
Professional qualification
16
0
1
Highest educational qualification
University qualification
RESULTS
Min.
29
0
Gross yearly personal income, $
88 000 (64 000)
0
1 550 000
Body mass index Long-term condition or disability
26.6 (5.7) 26
12 0
171 1
20
0
1
Sometimes (< 1 time/mo)
46
0
1
Once a month
24
0
1
Several times a month
10
0
1
Current smoker
1
Sex-based excessive alcohol drinking, past y
Never
20
0
1
15
0
1
Employed
66
0
1
Not in the labor force
30
0
1
3
0
1
35 (21)
0
160
Lives alone Employment status
Unemployed Weekly work hours, paid and unpaid
Note. K10 = Kessler Psychological Distress Scale; Min. = minimum; Max. = maximum. The sample size was n = 17 080, with 33 918 person-year observations.
confounding factors. FE panel regression models that can achieve this are presented in Table 2. Note that because these models are based on within-individual change over time, individuals who are observed only once do not contribute to model estimation. Similarly, those for whom no change in a certain explanatory variable is ever recorded do not contribute to the estimation of the model parameter associated with that variable.
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The first column of results in Table 2 shows the findings from a linear FE model of the K10 summary scale. The model coefficients in this specification give the expected change in the K10 index associated with a within-individual change in the explanatory variables. For MVPA, model coefficients give the difference in outcomes for the same individual at times when he or she falls into a given activity category and at times when he or she falls into the
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4.8 K10 distress markers
Mean Item Score (1–5)
Tired Nervous
4.4
So nervous... Hopeless Restless
4.0
So restless... Depressed All an effort
3.6
Nothing cheer up Worthless
ay yd er Ev
>3
tim
es
es tim 3
es 1–
2
tim
e nc 3
es tim 3
tim 2 1–
e nc 3
3
tim
es
es tim 2 1–
3 times/wk (but not every day)
–1.42*** (–1.72, –1.09) 0.52*** (0.43, 0.63) 0.59*** (0.51, 0.67)
Every day
–1.79*** (–2.14, –1.42) 0.46*** (0.37, 0.57) 0.53*** (0.45, 0.62)
Age, y
–0.01 (–0.05, 0.01)
1.00 (0.98, 1.02)
1.00 (0.99, 1.02)
1.00
1.00
Education < year 12 (Ref)
0.00
Year 12
–0.23 (–0.97, 0.74)
1.02 (0.62, 1.66)
1.05 (0.72, 1.53)
Professional qualification University qualification
–0.12 (–0.62, 0.66) –0.22 (–0.59, 0.31)
1.11 (0.80, 1.53) 1.07 (0.82, 1.40)
1.04 (0.80, 1.34) 0.97 (0.80, 1.18)
Gross personal income, y
–0.00 (–0.02, 0.01)
0.99 (0.98, 1.00)
0.99 (0.98, 1.00)
Body mass index
–0.01 (–0.03, 0.02)
1.00 (0.98, 1.01)
1.00 (0.99, 1.01)
Long-term condition Current smoker
0.90*** (0.68, 1.07)
1.52*** (1.33, 1.73) 1.42*** (1.30, 1.55)
0.50** (0.17, 0.84)
1.21* (1.00, 1.46)
1.20* (1.04, 1.39)
Sex-based excessive alcohol drinking (past year) Never (Ref)
0.00
1.00
1.00
Sometimes (< 1 time/month) Once a month
0.16 (–0.05, 0.36) 0.27 (–0.03, 0.55)
0.96 (0.84, 1.11) 1.12 (0.93, 1.36)
1.00 (0.90, 1.11) 1.12 (0.98, 1.28)
0.47** (0.14, 0.76)
1.28* (1.05, 1.55)
1.22** (1.05, 1.40)
0.68*** (0.27, 1.03)
1.20 (0.98, 1.46)
1.23** (1.05, 1.44)
Several times a month Lives alone Employment status Employed (Ref) Not in the labor force Unemployed Weekly work hours, paid and unpaid
0.00
1.00
1.00 1.05 (0.92, 1.20)
0.16 (–0.11, 0.47)
1.04 (0.87,1.24)
–0.07 (–0.60, 0.45)
0.99 (0.77, 1.26)
0.97 (0.81, 1.17)
–0.05 (–0.11, 0.01)
0.96 (0.93, 1.00)
0.98 (0.95, 1.00)
Note. BUC = blow-up and cluster estimation method; CI = confidence interval; K10 = Kessler Psychological Distress Scale; MVPA = moderate to vigorous physical activity; OR = odds ratio; PST = person-specific threshold estimation method. The sample size was n = 17 080, with 33 918 person-year observations. *P < .05; **P < .01; ***P < .001.
our framework to confirm this are urgently required. j
About the Authors Francisco Perales is with the Institute for Social Science Research, The University of Queensland, Brisbane, Australia. Jesus del Pozo-Cruz is with the Department of Physical Education and Sports, University of Seville, Spain. Borja del Pozo-Cruz is with the Department of Sport and Exercise Sciences, University of Auckland, New Zealand. Correspondence should be sent to Borja del Pozo-Cruz, Department of Sport and Exercise Science, University of Auckland, Private Bag 92012, Auckland, New Zealand 1142 (e-mail: [email protected]). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link. This article was accepted June 27, 2014.
Contributors F. Perales conceptualized the study, conducted the data analysis, and drafted the article. J. del Pozo-Cruz critically reviewed and revised the article. B. del Pozo-Cruz provided conceptual guidance and critically reviewed the article. All authors approved the final version.
Acknowledgments We used unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Project was initiated and is funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). We are grateful to the Department of Sport and Exercise Science at the University of Auckland for
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Human Participant Protection The analyses in this study are based on publicly available secondary survey data collected by the Melbourne Institute at the University of Melbourne.
Frequency of MVPA Not at all (Ref)
supporting a research sabbatical for Jesus del Pozo-Cruz to participate in this project. Note. The findings and views reported in this article are those of the authors and should not be attributed to either DSS or the Melbourne Institute.
References 1. Haskell WL, Lee IM, Pate RR, et al. Physical activity and public health: updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc. 2007;39(8):1423---1434. 2. Sparling PB, Owen N, Lambert EV, Haskell WL. Promoting physical activity: the new imperative for public health. Health Educ Res. 2000;15(3):367---376. 3. Perales F, del Pozo-Cruz J, del Pozo-Cruz J, del Pozo-Cruz B. On the associations between physical activity and quality of life: findings from an Australian nationally representative panel survey. Qual Life Res. 2014;Epub ahead of print. 4. Pratt LA. Serious psychological distress, as measured by the K6, and mortality. Ann Epidemiol. 2009;19(3): 202---209. 5. Cvetkovski S, Reavley NJ, Jorm AF. The prevalence and correlates of psychological distress in Australian tertiary students compared to their community peers. Aust N Z J Psychiatry. 2012;46(5):457---467. 6. George ES, Jorm L, Kolt GS, Bambrick H, Lujic S. Physical activity and psychological distress in older men: findings from the New South Wales 45 and Up Study. J Aging Phys Act. 2012;20(3):300---316. 7. Russ TC, Stamatakis E, Hamer M, Starr JM, Kivimaki M, Batty GD. Association between psychological distress and mortality: individual participant pooled analysis of 10 prospective cohort studies. BMJ. 2012;345:e4933. 8. Puustinen PJ, Koponen H, Kautiainen H, Mantyselka P, Vanhala M. Psychological distress measured by the GHQ-12 and mortality: a prospective population-based study. Scand J Public Health. 2011;39(6):577---581. 9. Robinson KL, McBeth J, Macfarlane GJ. Psychological distress and premature mortality in the general population: a prospective study. Ann Epidemiol. 2004; 14(7):467---472. 10. Gerber M, Kellmann M, Hartmann T, Pühse U. Do exercise and fitness buffer against stress among Swiss police and emergency response service officers? Psychol Sport Exerc. 2010;11(4):286---294. 11. Gerber M, Puhse U. Do exercise and fitness protect against stress-induced health complaints? A review of the literature. Scand J Public Health. 2009;37(8):801---819. 12. Maturo CC, Cunningham SA. Influence of friends on children’s physical activity: a review. Am J Public Health. 2013;103(7):e23---e38. 13. Shelton RC, McNeill LH, Puleo E, et al. The association between social factors and physical activity among low-income adults living in public housing. Am J Public Health. 2011;101(11):2102---2110. 14. Blazer DG, Hybels CF. Origins of depression in later life. Psychol Med. 2005;35(9):1241---1252.
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15. Hung CT, Lai YH, Lee YH, Chang CL, Chen JS. The variance of physical activity and psychological distress of patient with early stage lung cancer through three months after tumor removal. Psychooncology. 2013; 22(supp 3):330---331. 16. Zhao G, Li CY, Li J, Balluz LS. Physical activity, psychological distress, and receipt of mental healthcare services among cancer survivors. J Cancer Surviv. 2013; 7(1):131---139. 17. Rees S, Silove D, Chey T, Steel Z, Bauman A, Phan T. Physical activity and psychological distress amongst Vietnamese living in the Mekong Delta. Aust N Z J Psychiatry. 2012;46(10):966---971. 18. Cairney J, Faulkner G, Veldhuizen S, Wade TJ. Changes over time in physical activity and psychological distress among older adults. Can J Psychiatry. 2009; 54(3):160---169. 19. Mummery K, Schofield G, Caperchione C. Physical activity dose-response effects on mental health status in older adults. Aust N Z J Public Health. 2004;28(2): 188---192.
Bonn, Germany: Institute for the Study of Labor; 2011. IZA Discussion Paper No. 5443. 32. Brown H, Roberts J. Exercising choice: the economic determinants of physical activity behaviour of an employed population. Soc Sci Med. 2011;73(3):383--390. 33. Ham SA, Ainsworth BE. Disparities in data on Healthy People 2010 physical activity objectives collected by accelerometry and self-report. Am J Public Health. 2010;100(suppl 1):S263---S268. 34. Crone D, Guy H. “I know it is only exercise, but to me it is something that keeps me going”: a qualitative approach to understanding mental health service users’ experiences of sports therapy. Int J Ment Health Nurs. 2008;17(3):197---207. 35. Martinsen EW. Physical activity in the prevention and treatment of anxiety and depression. Nord J Psychiatry. 2008;62(suppl 47):25---29.
20. Ayala GX, San Diego Prevention Research Center Team. Effects of a promotor-based intervention to promote physical activity: Familias Sanas y Activas. Am J Public Health. 2011;101(12):2261---2268. 21. Hurwitz EL, Morgenstern H, Chiao C. Effects of recreational physical activity and back exercises on low back pain and psychological distress: findings from the UCLA Low Back Pain Study. Am J Public Health. 2005; 95(10):1817---1824. 22. Hamer M, Stamatakis E, Steptoe A. Dose-response relationship between physical activity and mental health: the Scottish Health Survey. Br J Sports Med. 2009; 43(14):1111---1114. 23. Sloan RA, Sawada SS, Girdano D, Liu YT, Biddle SJH, Blair SN. Associations of sedentary behavior and physical activity with psychological distress: a crosssectional study from Singapore. BMC Public Health. 2013;13:885. 24. Watson N, Wooden M. The HILDA Survey: a case study in the design and development of a successful household panel study. Longitudinal and Life Course Studies. 2012;3(3):369---381. 25. Kessler RC, Andrews G, Colpe LJ, et al. Short screening scales to monitor population prevalences and trends in non-specific psychological distress. Psychol Med. 2002;32(6):959---976. 26. Andrews G, Slade T. Interpreting scores on the Kessler Psychological Distress Scale (K10). Aust N Z J Public Health. 2001;25(6):494---497. 27. Wooden M. Use of the Kessler Psychological Distress Scale in the HILDA Survey. Carlton, Victoria, Australia: Melbourne Institute of Applied Economic and Social Research; 2009. No. 2/09. 28. Wooldridge JM. Econometric Analysis of Cross Section and Panel Data. 2nd ed. London, UK: MIT Press; 2010. 29. Allison PD. Fixed Effects Regression Models. London, UK: Sage; 2009. 30. Ferrer-i-Carbonell A, Frijters P. How important is methodology for the estimates of the determinants of happiness? Econ J. 2004;114(497):641---659. 31. Baetschmann G, Staub KE, Winkelmann R. Consistent Estimation of the Fixed Effects Ordered Logit Model.
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Impact of Physical Activity on Psychological Distress: A Prospective Analysis of an Australian National Sample Francisco Perales, PhD, Jesus del Pozo-Cruz, PhD, and Borja del Pozo-Cruz, PhD
Moderate to vigorous physical activity (MVPA) is important to people’s lives, with the World Health Organization as well as national and international bodies recommending frequent participation in it.1,2 Recent analyses of Australian population-level data have endorsed this by showing that MVPA is independently associated not only with general and physical health but also with overall levels of mental health and self-reported life satisfaction.3 However, the relationships between MVPA and other facets of mental health, including levels of psychological distress, have not yet been well established. Psychological distress, understood as the experience of unpleasant feelings or emotions that affect day-to-day functioning, affects a sizable share of the population in developed countries such the United States, the United Kingdom, and Australia4---7 and is known to lead to more severe mental disorders and physical health issues.8,9 Consequently, the financial and human costs of psychological distress are nonnegligible, and gaining a deeper understanding of the factors that influence individuals’ distress levels is important for the development of efficient public health policies and the devising of effective palliative interventions. Emerging evidence of an association between MVPA and overall levels of mental health has suggested that associations between MVPA and psychological distress are also likely. Potential effects may run through known physiological, psychological, and social processes. From a physiological point of view, we know that MVPA enhances fitness levels, which in turn regulate physiological stress responses, such as reduced secretion of hormones and lowered blood pressure.10 From a psychological perspective, MVPA has been linked to reduced arousal and mood enhancement through cognitive distraction and biochemical changes, and to positive health behaviors during periods of high stress (e.g., a lower likelihood to smoke and eat unhealthily).11 Additionally, participation in
Objectives. We analyzed the individual-level associations between participation in moderate to vigorous physical activity (MVPA) and psychological distress levels using a large, nationally representative, longitudinal sample and multivariable panel regression models. Methods. We used 3 waves of panel data from the Household, Income and Labour Dynamics in Australia Survey, consisting of 34 000 observations from 17 000 individuals and covering 2007, 2009, and 2011. We used fixed-effects panel regression models accounting for observable and unobservable confounders to examine the relationships between the weekly frequency of MVPA and summary measures of psychological distress based on the Kessler Psychological Distress Scale. Results. We found substantial and highly statistically significant associations between the frequency of MVPA and different indicators of psychological distress. Frequent participation in MVPA reduces psychological distress and decreases the likelihood of falling into a high-risk category. Conclusions. Our findings underscore the importance of placing physical activity at the core of health promotion initiatives aimed at preventing and remedying psychological discomfort. (Am J Public Health. 2014;104:e91–e97. doi:10.2105/AJPH.2014.302169) MVPA tends to increase time spent outdoors, as well as the frequency and quality of social interactions and interpersonal relationships.12---14 As a result, we would expect MVPA to have the potential to enhance well-being by reducing psychological distress. Consistent with these theories, results from the limited body of existing empirical research have suggested that there are indeed negative associations between the frequency of MVPA and psychological distress levels. However, these findings have emerged almost exclusively from analyses of small nonprobability samples,15---21 and the few available studies based on nationally representative samples are cross-sectional (e.g., Scotland22 and Singapore23). The small, nonprobability nature of the samples used in these studies means that findings are tentative and cannot be generalized to the population as a whole. Their cross-sectional nature means that longitudinal regression techniques that enable more precise estimation of the associations of interest by examining within-individual change over time and minimizing omitted variable bias attributable to unobservable factors cannot be implemented.
December 2014, Vol 104, No. 12 | American Journal of Public Health
In fact, undertaking large-scale prospective analyses is often regarded as a necessary step forward in enhancing current knowledge of the associations between MVPA and psychological distress.6,23 In this article, we fill this gap and add to the literature by establishing the population-level associations between the weekly frequency of MVPA and self-reported levels of psychological distress with a nationally representative Australian panel data set and fixed-effect (FE) panel regression models.
METHODS Our data set of choice, the Household, Income and Labour Dynamics in Australia (HILDA) Survey, is a large-scale, nationally representative panel survey that collects annual information from the same respondents. Twelve waves of data covering 2001 to 2012 are currently available, with low attrition rates.24 This data set is very useful in examining the relationships between MVPA and psychological distress for 2 reasons: (1) it features a remarkably large sample that is representative of the
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Australian population, and (2) it contains repeated measures over time of the variables of interest (MVPA, psychological distress, and contextual factors), which allows for more elaborated statistical analysis via assessment of within-individual changes. Information on psychological distress has been collected in waves 7 (2007), 9 (2009), and 11 (2011) of the HILDA Survey. Hence, our analyses are restricted to these 3 time points.
Measures The key independent variable was the weekly frequency of MVPA, as reported by survey participants. This variable contains responses to a question in a self-completion questionnaire: “In general, how often do you participate in moderate or intensive physical activity for at least 30 minutes? Moderate level physical activity will cause a slight increase in breathing and heart rate, such as brisk walking.” Respondents can choose 1 of the following 6 answers: “not at all,” “less than once a week,” “1 or 2 times a week,” “3 times a week,” “more than 3 times a week (but not every day),” and “every day.” We used this information to derive a set of dummy variables capturing the frequency of MVPA undertaken by survey participants. Our outcome of interest was self-reported levels of psychological distress, operationalized using the well-established Kessler Psychological Distress Scale.25,26 This scale consists of a battery of 10 questions designed to capture nonspecific psychological distress and measure depressive symptoms and anxiety disorders. Respondents are asked how often in the past 4 weeks they had experienced different feelings and emotions, including feeling 1. “tired for no good reason,” 2. “nervous,” 3. “so nervous that nothing could calm you down,” 4. “hopeless,” 5. “restless or fidgety,” 6. “so restless that you could not sit still,” 7. “depressed,” 8. “that everything was an effort,” 9. “so sad that nothing could cheer you up,” and 10. “worthless.” Possible responses are rated on a 5-point Likert scale (all the time, most of the time, some
of the time, a little of the time, and none of the time) and can be combined into more informative summary measures. First, reversing and adding scores for the 10 survey items gives an additive index ranging from 10 to 50 known as the K10. Second, the K10 scores can be used to separate the population into 4 risk groups: scores of 10 to 15 take the value 1 (“low risk”), scores of 16 to 21 take the value 2 (“moderate risk”), scores of 22 to 29 take the value 3 (“high risk”), and scores of 30 or higher take the value 4 (“high risk”).27 The resulting outcome variable is an ordered measure with scores ranging from 1 to 4.
Statistical Analyses To explore the association between the frequency of MVPA and psychological distress, we exploited the panel structure of the HILDA Survey data and estimated within-group FE panel regression models. These models use the repeated observations from the same individuals over time to account for unobserved person-specific factors that might confound the associations and minimize omitted variable bias. Therefore, FE models provide a better picture of the associations between MVPA and psychological distress than is possible in cross-sectional regression.28,29 Note that it is not possible to retrieve the effect of time-invariant explanatory factors such as gender and socioeconomic background on the outcome variable using FE models, although they are implicitly accounted for by the model. To model the first outcome variable, the K10 scale, we fit linear FE models for continuous dependent variables. These models estimate how deviations from individuals’ usual behavior and characteristics associate with deviations from their usual outcomes (captured by the individual mean scores in these over time). These take the form K 10 it K 10 i ¼ MVPAit MVPAi bþ Xit Xi c þ ðe it ei Þ where subscripts i and t denote individual and time; K10 is the measure of psychological distress of interest; MVPA is a set of dummy variables capturing the weekly frequency of physical activity; X is a vector of control variables; b and c are vectors of coefficients; and
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e is the usual stochastic error term in regression. The X vector of control variables includes variables known or suspected to influence psychological distress. These variables include the respondent’s age at last birthday measured in years; highest educational qualification ever attained (“university qualification,” “professional qualification,” “school year 12,” “below school year 12”); gross yearly personal income adjusted for inflation using Consumer Price Indices; respondent’s body mass index, calculated as mass in kilograms divided by the square of height in meters; whether the respondent lives alone (“yes,” “no”); whether the respondent currently smokes cigarettes (“yes,” “no”); the respondent’s frequency of sex-based excess alcohol drinking during the past 12 months (“sometimes (less than once a month),” “once a month,” “several times a month,” “never”); whether the respondent reports having a long-term health condition, impairment, or disability that restricts everyday activities (“yes,” “no”); the respondent’s employment status (“employed,” “unemployed,” “not in the labor force”); and the respondent’s number of total weekly work hours, measured as the sum of the usual weekly hours worked in all jobs and the usual weekly hours of domestic labor. To model the risk categories extracted from the K10, an FE model for ordered outcomes is required, but the literature has only recently provided solutions to achieve this. Two competing specifications have been proposed: the person-specific threshold, ordered FE logit model by Ferrer-i-Carbonell and Frijters30 and the blow-up and cluster, ordered FE logit model by Baetschmann et al.31 None of these is readily available in standard statistical packages, and we thus programmed them ourselves. Because there is not yet consensus as to which model is preferable, we fit and discuss the results of both estimation strategies. In broad terms, the person-specific thresholdordered FE logit strategy consists of creating a binary variable out of the original ordered variable using a person-specific threshold: Risk values that are equal or higher than the person-specific over-time mean take the value 1, and risk values that are lower than the person-specific over-time mean take the value 0. A binary logistic FE model (also known as a conditional logit model) is then fitted to the
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resulting dichotomous variable. In the blow-up and cluster, ordered FE logit strategy, one first creates an expanded data set by multiplying every data row as many times as there are potential dichotomizations of the original ordered variable and applying a different dichotomization to each set of duplicated observations. In our case, there were 3 possibilities: (1) risk level 1 takes the value 0, and risk levels 2 to 4 take the value 1; (2) risk levels 1 to 2 take the value 0, and risk levels 3 to 4 take the value 1; and (3) risk levels 1 to 3 take the value 0, and risk level 4 takes the value 1. Again, a binary logistic FE model is fitted to the resulting dichotomous variable, adjusting the standard errors to account for the artificial duplication of rows. For detailed, formal derivations of these estimators, see Ferrer-i-Carbonell and Frijters30 and Baetschmann et al.,31 respectively. Despite the complex properties of these methods, the estimated model parameters can be transformed into odds ratios and interpreted quite simply.
TABLE 1—Descriptive Statistics: Household, Income and Labour Dynamics in Australia Survey, 2007, 2009, and 2011 Variable
Mean (SD) or %
The analytical sample of the HILDA Survey in 2007, 2009, and 2011 consisted of 33 918 observations from 17 080 individuals, and thus respondents were observed 1.98 times on average. Descriptive statistics for all variables can be found in Table 1. In 10% of all observations, individuals reported doing no MVPA at all; in 15%, less than once a week; in 24%, once or twice a week; in 16%, 3 times a week; in 22%, more than 3 times a week (but not every day); and in 13%, every day. Raw relationships between this variable and indicators of psychological distress are presented in Figures 1 and 2. Mean responses to each of the 10 items constituting the K10 scale increased with the weekly frequency of MVPA (Figure 1; high scores mean absence of distress). Consequently, the overall scores in the K10 index and the prevalence of higher risk categories decreased with the frequency of MVPA (Figure 2; high scores represent worse outcomes). This is a preliminary indication that MVPA may influence psychological distress levels. More thorough examination of whether true relationships exist requires multivariable models that control for observable and unobservable
Max.
Frequency of moderate or intensive physical activity Not at all
10
0
1
< 1 time/wk
15
0
1
1 or 2 times/wk
24
0
1
3 times/wk > 3 times/wk (but not every day)
16 22
0 0
1 1
Every day K10 summary scale
13
0
1
15.5 (6.0)
10
50
K10 risk categories Low risk
65
0
1
Moderate risk
21
0
1
High risk
10
0
1
Very high risk Age, y
4 44.2 (18.1)
0 15
1 97
< year 12
24
0
1
Year 12
31
0
1
Professional qualification
16
0
1
Highest educational qualification
University qualification
RESULTS
Min.
29
0
Gross yearly personal income, $
88 000 (64 000)
0
1 550 000
Body mass index Long-term condition or disability
26.6 (5.7) 26
12 0
171 1
20
0
1
Sometimes (< 1 time/mo)
46
0
1
Once a month
24
0
1
Several times a month
10
0
1
Current smoker
1
Sex-based excessive alcohol drinking, past y
Never
20
0
1
15
0
1
Employed
66
0
1
Not in the labor force
30
0
1
3
0
1
35 (21)
0
160
Lives alone Employment status
Unemployed Weekly work hours, paid and unpaid
Note. K10 = Kessler Psychological Distress Scale; Min. = minimum; Max. = maximum. The sample size was n = 17 080, with 33 918 person-year observations.
confounding factors. FE panel regression models that can achieve this are presented in Table 2. Note that because these models are based on within-individual change over time, individuals who are observed only once do not contribute to model estimation. Similarly, those for whom no change in a certain explanatory variable is ever recorded do not contribute to the estimation of the model parameter associated with that variable.
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The first column of results in Table 2 shows the findings from a linear FE model of the K10 summary scale. The model coefficients in this specification give the expected change in the K10 index associated with a within-individual change in the explanatory variables. For MVPA, model coefficients give the difference in outcomes for the same individual at times when he or she falls into a given activity category and at times when he or she falls into the
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4.8 K10 distress markers
Mean Item Score (1–5)
Tired Nervous
4.4
So nervous... Hopeless Restless
4.0
So restless... Depressed All an effort
3.6
Nothing cheer up Worthless
ay yd er Ev
>3
tim
es
es tim 3
es 1–
2
tim
e nc 3
es tim 3
tim 2 1–
e nc 3
3
tim
es
es tim 2 1–
3 times/wk (but not every day)
–1.42*** (–1.72, –1.09) 0.52*** (0.43, 0.63) 0.59*** (0.51, 0.67)
Every day
–1.79*** (–2.14, –1.42) 0.46*** (0.37, 0.57) 0.53*** (0.45, 0.62)
Age, y
–0.01 (–0.05, 0.01)
1.00 (0.98, 1.02)
1.00 (0.99, 1.02)
1.00
1.00
Education < year 12 (Ref)
0.00
Year 12
–0.23 (–0.97, 0.74)
1.02 (0.62, 1.66)
1.05 (0.72, 1.53)
Professional qualification University qualification
–0.12 (–0.62, 0.66) –0.22 (–0.59, 0.31)
1.11 (0.80, 1.53) 1.07 (0.82, 1.40)
1.04 (0.80, 1.34) 0.97 (0.80, 1.18)
Gross personal income, y
–0.00 (–0.02, 0.01)
0.99 (0.98, 1.00)
0.99 (0.98, 1.00)
Body mass index
–0.01 (–0.03, 0.02)
1.00 (0.98, 1.01)
1.00 (0.99, 1.01)
Long-term condition Current smoker
0.90*** (0.68, 1.07)
1.52*** (1.33, 1.73) 1.42*** (1.30, 1.55)
0.50** (0.17, 0.84)
1.21* (1.00, 1.46)
1.20* (1.04, 1.39)
Sex-based excessive alcohol drinking (past year) Never (Ref)
0.00
1.00
1.00
Sometimes (< 1 time/month) Once a month
0.16 (–0.05, 0.36) 0.27 (–0.03, 0.55)
0.96 (0.84, 1.11) 1.12 (0.93, 1.36)
1.00 (0.90, 1.11) 1.12 (0.98, 1.28)
0.47** (0.14, 0.76)
1.28* (1.05, 1.55)
1.22** (1.05, 1.40)
0.68*** (0.27, 1.03)
1.20 (0.98, 1.46)
1.23** (1.05, 1.44)
Several times a month Lives alone Employment status Employed (Ref) Not in the labor force Unemployed Weekly work hours, paid and unpaid
0.00
1.00
1.00 1.05 (0.92, 1.20)
0.16 (–0.11, 0.47)
1.04 (0.87,1.24)
–0.07 (–0.60, 0.45)
0.99 (0.77, 1.26)
0.97 (0.81, 1.17)
–0.05 (–0.11, 0.01)
0.96 (0.93, 1.00)
0.98 (0.95, 1.00)
Note. BUC = blow-up and cluster estimation method; CI = confidence interval; K10 = Kessler Psychological Distress Scale; MVPA = moderate to vigorous physical activity; OR = odds ratio; PST = person-specific threshold estimation method. The sample size was n = 17 080, with 33 918 person-year observations. *P < .05; **P < .01; ***P < .001.
our framework to confirm this are urgently required. j
About the Authors Francisco Perales is with the Institute for Social Science Research, The University of Queensland, Brisbane, Australia. Jesus del Pozo-Cruz is with the Department of Physical Education and Sports, University of Seville, Spain. Borja del Pozo-Cruz is with the Department of Sport and Exercise Sciences, University of Auckland, New Zealand. Correspondence should be sent to Borja del Pozo-Cruz, Department of Sport and Exercise Science, University of Auckland, Private Bag 92012, Auckland, New Zealand 1142 (e-mail: [email protected]). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link. This article was accepted June 27, 2014.
Contributors F. Perales conceptualized the study, conducted the data analysis, and drafted the article. J. del Pozo-Cruz critically reviewed and revised the article. B. del Pozo-Cruz provided conceptual guidance and critically reviewed the article. All authors approved the final version.
Acknowledgments We used unit record data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey. The HILDA Project was initiated and is funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). We are grateful to the Department of Sport and Exercise Science at the University of Auckland for
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Human Participant Protection The analyses in this study are based on publicly available secondary survey data collected by the Melbourne Institute at the University of Melbourne.
Frequency of MVPA Not at all (Ref)
supporting a research sabbatical for Jesus del Pozo-Cruz to participate in this project. Note. The findings and views reported in this article are those of the authors and should not be attributed to either DSS or the Melbourne Institute.
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