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Mastery, physical activity and psychological distress in mid-aged adults Mastery, physical activity and psychological distress in mid-aged adults
Novic, Adam J.; Seib, Charrlotte; Burton, Nicola W.
2023-12-31 00:00:00
AUSTRALIAN JOURNAL OF PSYCHOLOGY 2023, VOL. 75, NO. 1, e2153623 https://doi.org/10.1080/00049530.2022.2153623 a,b,c c,d a,b,c Adam J. Novic , Charrlotte Seib and Nicola W. Burton a b School of Applied Psychology, Griffith University, Brisbane, Australia; Centre for Mental Health, Griffith University, Brisbane, Australia; c d Menzies Health Institute Queensland, Griffith University, Gold Coast, Australia; School of Nursing and Midwifery, Griffith University, Brisbane, Australia ABSTRACT ARTICLE HISTORY Received 3 November 2021 Objective: The objective was to investigate associations between mastery and physical activity Accepted 27 November 2022 with psychological distress in a population-based sample of mid-aged adults. Method: Self-reported measures of psychological distress, mastery and time spent in each of KEYWORDS walking, moderate and vigorous physical activity in the previous week were examined in Exercise; mental health; a cross-sectional sample of 7,146 adults aged 40–64 years (M = 53 years, SD = 6.5 years, 42.4% well-being; self-control; men). Generalized Linear Models were used to examine the inter-relationship between mastery mid-aged adults and physical activity with psychological distress. Results: In fully adjusted models, only mastery was significantly associated with psychological distress (β = − 0.12, SE = 0.01, p < .01). There was no significant interaction between mastery and physical activity on psychological distress. Conclusions: Mastery may be an important resource against psychological distress. A sense of control may therefore be a key component for psychotherapeutic interventions to mitigate distress in mid-aged adults. KEY POINTS What is already known about this topic: (1) Previous research indicates psychological distress is prevalent among Australian mid-aged adults. (2) Mastery and physical activity are resources shown to protect against psychological distress in mid-aged adults. (3) Longitudinal research with mid-aged adults has demonstrated a positive relationship between mastery and physical activity. What this topic adds: (1) The current study showed higher mastery was associated with lower psychological distress in a sample of mid-aged adults. (2) No relationship was observed between physical activity and distress or for an interaction between physical activity and mastery. (3) This evidence may inform the development of interventions to mitigate distress in mid- aged adults. Psychological distress is associated with significant to cost AUD$5.9 billion in relation to reduced produc- morbidity and premature death globally. Within tivity and increased disability support, even after Australia, approximately 2.4 million adults (i.e., 13%) accounting for diagnosed mental and somatic illness experience high/very high psychological distress (Hilton et al., 2010; Rai et al., 2012). More research is each year with mid-aged adults (45 to 64 years) the needed therefore, to identify modifiable factors asso- highest proportion of all age groups reporting very ciated with distress, to inform strategies to reduce this high levels of psychological distress (Australian burden. Bureau of Statistics, 2018). Psychological distress Mastery is a predictor of emotional well-being in increases risk of major chronic diseases such as arthri- mid-aged adults and can be an important psychologi- tis, cardiovascular disease, chronic obstructive pul- cal resource to protect against distress (Windsor & monary disorder, and has a positive dose-response Anstey, 2010). Mastery is a malleable self-concept cen- association with all-cause mortality (McLachlan & tral to managing existing stressors and is typically Gale, 2018; Russ et al., 2012). Psychological distress conceptualised as a sense of control – as opposed to causes significant economic burden and is estimated fatalistic rule – that one has over the important forces CONTACT Adam J. Novic adam.novic@griffithuni.edu.au © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. e2153623-2 A. J. NOVIC ET AL. that affect one’s life (Pearlin & Schooler, 1978). Mastery Although mastery and physical activity have has an inverse association with negative affect and separately been shown to be inversely associated a positive association with life satisfaction and positive with psychological distress, and have also been posi- affect (Windsor et al., 2009). For people in early mid- tively associated with each other, studies have not age adulthood, mastery has been shown to have investigated these variables concurrently as inde- a moderate negative correlation with anxiety pendent predictors of distress, or their possible (r = ‒0.43, p < .01) and depression (r = ‒0.51, p < .01) interaction in mid-aged adults. This study aimed to (Burns et al., 2011). explore cross-sectional evidence for the inter- Physical activity can also protect against psycholo- relationship between mastery and physical activity gical strains and stressors. People that are more fre- with psychological distress in mid-aged adults. quently physically activity are less likely to experience psychological distress than those who do no or little Methods (i.e., < 1/week) physical activity (Hamer et al., 2009; Perales et al., 2014). All forms of physical activity, This study presents a secondary data analysis of the whether light, moderate or vigorous have been HABITAT study, a multilevel longitudinal cohort study shown to have long-term protective effects against investigating physical activity, as well as potentially psychological distress (Sheikh et al., 2018). For those related psychological, social and environmental factors over the age of 45 years, engaging in physical activity in mid-aged and older men and women living in has been shown to reduce the odds of psychological Brisbane, Australia (Burton et al., 2009). The HABITAT distress (Plotnikoff et al., 2015). Meta analyses of pro- survey was awarded initial ethical clearance by the spective studies have shown that physical activity is QUT Human Research Ethics Committee in 2007 protective against incident depression (adjusted (ID3967 H). OR = 0.78, 95%CI: 0.70 to 0.87) and anxiety (adjusted The design and recruitment process have been OR = 0.66, 95% CI = 0.53–0.82) in adults (McDowell described in detail elsewhere (Burton et al., 2009). et al., 2019; Schuch et al., 2018). Briefly, a multi-stage probability sampling design, There may be an interrelationship between mastery which was used to obtain a stratified random sample and physical activity. Research has explored the med- of 200 neighbourhoods from Census Collection iating role of both mastery and physical activity on Districts (CCDs) ranked by the Index of Relative distress. Longitudinal research with an Australian sam- Socioeconomic Disadvantage, a score used to reflect ple of adults (n = 7,485) indicated a positive association attributes such as proportion of low-income families between mastery and physical activity, which in turn and individuals with low educational attainment and was associated with better physical and psychological workers in relatively unskilled occupations. From each health (Sargent-Cox et al., 2015). A greater sense of of the 200 neighbourhoods, an average of 85 house- mastery may, therefore, support adults to be physically holds with at least one person aged 40 to 65 years (as active and result in reductions in distress. Mastery has of March 2007) was identified using data from the also been hypothesised as a mechanism by which Australian Electoral Commission on people’s names, physical activity positively impacts on mental health address, and date of birth. The final stage of the sam- (Biddle, 1993). Mastery has also been shown as pling procedure involved randomly selecting one per- a mediator of the relationship between physical activ- son aged 40 to 65 years from each selected household ity and psychological distress in people aged 20 to 65 (i.e., 17,000 mid-aged adults) who were then invited to years, as well as a moderator of the relationship with participate by mail. inactive individuals reporting low mastery experien- Data were collected using structured self- cing the highest level of distress (Martin & Wade, completed mail questionnaires which were tailored 2000). However, among people aged over 65 years, to the local area and age of the cohort. Participants no association was demonstrated between mastery were mailed advanced notice of survey delivery and and distress in inactive individuals reporting high dis- reminders/thank you for completion and return. tress (Cairney et al., 2005). This suggests that an inter- People who did not respond were sent a replacement relationship between mastery and physical activity questionnaire after seven weeks of the initial mailout. may be less salient among older adults and prompts Each person was assigned a unique identification code further research with other age groups such as mid- which was printed on their questionnaire to enable aged adults. matching across survey waves. Overall, the HABITAT AUSTRALIAN JOURNAL OF PSYCHOLOGY e2153623-3 survey included five waves across a nine-year period, aged adults eligible to participate in wave 2 of the the first wave in 2007 (n = 11,035, 68.43% response HABITAT study. Longitudinal inconsistencies in the rate) and the last in 2016 (n = 5,187, 58.77% response dataset (e.g., changes in gender from baseline) were rate). When compared with 2006 census data, the 2007 reviewed with 172 cases excluded due to suspected HABITAT sample was broadly representative of the change in respondent across waves. The analysis com- wider Australian mid-aged population (Turrell et al., prised a final sample of 7,146 adults aged between 40 2010). and 64 years. The process for deriving the final sample is shown in Figure 1. Study sample Measures This analysis used 2009 HABITAT survey data as it included measures of psychological distress, mastery, The primary outcome was measured using the abbre- and physical activity. Only those respondents aged 40 viated Kessler 6 (K6) scale (Kessler et al., 2002). This six- to 64 years in 2009 were considered for the current item instrument assesses how often respondents study given the study focus on mid-aged adults. Socio- experienced anxio-depressive symptoms in the past demographic data on country of birth and highest month on a 5-point Likert scale ranging from 0 “none completed educational qualification were obtained of the time” to 4 “all of the time”. Items are summed from the 2007 baseline survey as these were not col- resulting in a total score ranging from 0 to 24, with lected in 2009. The analytical sample comprised 7,866 higher scores indicating more psychological distress. participants (72.65% response rate) from 10,828 mid- Previous work has classified scores of 5–12 as 17,000 people invited to participate Ineligible (873) Deceased/duplicate: 8 Not in HABITAT age range: 84 Physical/mental impairment: 36 Overseas/not at address: 745 16,127 people eligible to participate Wave 1 (2007) in wave 1 Non-response: 5, 092 Wave 1 participants (n = 11,035) Excluded (207) Deceased/duplicate: 46 Overseas/not at address: 35 Physical/mental impairment: 14 Lost to follow-up: 112 10,828 participants invited to wave 2 Wave 2 (2009) Non-response: 2, 962 Wave 2 participants (n = 7,866) Excluded (720) Not same participant: 172 Not in study age range: 548 Final analytical sample (n = 7,146) Figure 1. Flowchart of derived analytic sample. *Longitudinal inconsistencies in the dataset (e.g., changes in gender) were assessed. Only those that were indicated to represent a single respondent across waves were included in analyses. e2153623-4 A. J. NOVIC ET AL. moderate distress, and a score ≥13 as severe distress The distributions of key variables (sociodemographic (Prochaska et al., 2012). The K6 had acceptable internal variables, mastery, time in physical activity, psycholo- consistency in the analytic sample of the current study gical distress) were graphically inspected using fre- (Cronbach’s α = 0.87). quency distributions and normal P-P plots and were Mastery was assessed using the Pearlin Mastery summarised as means (standard deviations), medians Scale (PMS) (Pearlin & Schooler, 1978), a 7-item scale (interquartile range [IQR]), and proportions (number, that measures the extent to which an individual percentage). Bivariate associations between the key regards aspects of life as being under personal control (mastery, physical activity) and sociodemographic or fatalistically ruled. Respondents are asked to indi- (gender, employment status, education, health, coun- cate to what extent they agree with items using try of birth) study variables and the outcome of distress a 5-point Likert scale ranging from 1 “strongly disagree” were estimated using correlation coefficient for con- to 5 “strongly agree” and items are summed resulting in tinuous data and effect sizes derived from parametric a total score ranging from 7 to 35 with higher scores (independent samples t-tests and analysis of variance) indicating a greater sense of mastery. The PMS had and non-parametric (Mann Whitney-U and Kruskal a satisfactory internal consistency in the analytic sam- Wallis-H) methods, where appropriate. Bivariate ana- ple of the current study (Cronbach’s α = 0.81). lyses were conducted to explore multicollinearity Physical activity was assessed using items from the model assumptions among key predictor variables Active Australia Survey (AAS) (Australian Institute of and to determine inclusion in multivariable modelling, Health and Welfare, 2003) for participation in walking, with an alpha level of .05 required. moderate physical activity (e.g., gentle swimming, social Model assumptions were tested before undertaking tennis, golf), and vigorous physical activity (e.g., jogging, multivariate analyses. Normality assumption for ordin- aerobics, competitive tennis), excluding household and ary least squares (OLS) regression was assessed by gardening activity. Respondents report the duration of examining the regression standardised residual, result- each category of physical activity in the last week. The ing in no transformations. To assess for multicollinear- AAS items exhibit good reliability and acceptable validity ity among model predictors, variance inflation factors (Australian Institute of Health and Welfare, 2003; Brown (VIF) were estimated with the highest VIF value among et al., 2002) and have been recommended as suitable for predictors being 1.18 (health rating). A scatter plot of use in Australian population-based research (Brown et al., the regression standardised residual against the 2004). Total time in physical activity was a weighted cal- regression standardised predicted value indicated vio- culation computed by adding time spent in walking, lation of homoscedasticity; therefore, a generalised moderate physical activity, and two times the amount model was preferred to accommodate the nature of spent in vigorous physical activity given the higher inten- the data. sity (Australian Institute of Health and Welfare, 2003). Inspection of the Normal P-P plot suggests the out- Physical activity was truncated according to Active come variable was best represented by a gamma dis- Australia Survey guidelines to minimise errors due to tribution. A mixed effect model was considered to over-reporting (Australian Institute of Health and accommodate for non-independence of observations Welfare, 2003). through cluster random effects (i.e., neighbourhood Several covariates identified from previous research based on CCDs). Appropriateness of this type of mod- as potential confounders with psychological distress elling was determined by evaluating the intraclass (Enticott et al., 2016; McLachlan & Gale, 2018; Pearlin correlation (ICC) statistic and design effect statistic et al., 1981) were also considered in this analysis. These based on the simplest intercept-only (i.e., null) model were self-reported and included demographic variables (Hox et al., 2017). The obtained ICC was based on (age, gender, country of birth, and highest educational a gamma distribution with log link and indicated that qualification) and general health (rated as poor, fair, only 3.03% of the variation in psychological distress good, very good, or excellent). To avoid considerable scores lay between neighbourhoods, suggesting lim- discrepancy in group frequencies, three general health ited influence of clustering given a value less than 5% response groups were created by collapsing poor and (Heck et al., 2014; Nakagawa et al., 2017). Based on an fair responses, and very good and excellent responses. average of 11.79 observations in each cluster, the computed design effect was 1.33. With an average cluster size over 10, a design effect less than 2 is Statistical analyses suggested to limit negative bias of standard errors as All data were analysed using the Statistical Package for a result of nested data and thus providing further the Social Science (SPSS) version 26 (IBM Corp, 2019). evidence for independence of the data (Lai & Kwok, AUSTRALIAN JOURNAL OF PSYCHOLOGY e2153623-5 Table 1. Summary characteristics of analytic sample of adults 2015; Muthen & Satorra, 1995). A single-level model aged 40 to 64 years (n = 7,146). was preferred for parsimony given the near indepen- n (%) dence of observations and model variables structured Gender on the same level. The independent and combined Men 3,033 (42.4) contribution of mastery and time spent in physical Women 4,113 (57.6) Country of Birth activity (weighted) in predicting psychological distress Australia 5,449 (76.3) were assessed by examining main and interactive Other 1,666 (23.3) Education effects using a Generalized Linear Model (GzLM) to School (year 12) 2,655 (37.2) account for observed heteroscedasticity. Certificate/Diploma 2,076 (29.1) Bachelor/Postgraduate Degree 2,397 (33.5) A stepped model approach was used in the ana- Employment Status lyses. Three models were derived to examine the influ - In paid workforce 5,054 (70.7) Not in paid workforce 1,497 (20.9) ence of included covariates on model coefficients. The Living Arrangement first model included only the key predictor variables of Living alone 1,064 (14.9) physical activity and mastery. The second model Single parent with 1+ children 474 (6.6) Single with friends/relatives 315 (4.4) added sociodemographic covariates in addition to Couple with no children 1,979 (27.7) variables included in the first model. The final model Couple with 1+ children 2,873 (40.2) Other 176 (2.5) added the self-reported health covariate to variables in Reported Health Rating the second model. Fair 1,224 (17.1) Good 2,790 (39.0) Excellent 2,945 (41.2) total may differ due to missing data. Results Alternate living arrangement (e.g., couple residing with nephew). Missing data analysis Missing data analyses were performed identifying data 24–29). The median total time spent in physical to be missing completely at random (Little’s MCAR, χ activity was 240 weighted minutes/week (IQR: (15115) = 11956.06, p = 1.00). Overall, there was only 90–550). a small amount of missing data on variables: age and Bivariate analyses showed significant negative gender (0%), country of birth (0.4%), highest education associations between psychological distress and achieved (0.3%), health rating (2.6%), time spent in mastery (r (6918) = −.53, p < .01), and time spent in vigorous intensity physical activity (3.2%), time spent physical activity (r (6820) = −.08, p < .01), while posi- in moderate intensity physical activity (4.0%), time tive associations were found between mastery and spent walking (3.4%), mastery (2.4%), and psychologi- time spent in physical activity (r (6813) = .13, p cal distress (2.3%). As such, missing data for time spent < .01). Bivariate analyses of grouped data are pre- in physical activity was then managed according to sented in Table 2. published protocols and guidelines (Australian Regarding the primary outcome, results indicated Institute of Health and Welfare, 2003). Where partici- greater psychological distress among women (U = pants did not report time spent in either one or two 5753504.00, p = .02, Cohen’s d = 0.06), those not in physical activity intensities, these were replaced with the paid workforce (U = 3401645.50, p < .01, a zero (0) value and cases where no physical activity d = 0.09), those with school only level education 2 2 data was reported (2.5%) were excluded from analyses. (χ (2) = 6.40, p = .04, η <0.01), and those reporting 2 2 Cases with missing data on remaining variables were poor/fair general health (χ (2) = 558.94, p < .01, η = excluded from analyses. 0.08). Country of birth was not shown to be asso- ciated with psychological distress in this sample (U = 4256105.50, p = .24). Main analysis Table 3 presents the multivariate analysis. Summary characteristics of the study sample are Psychological distress was modelled as a function of displayed in Table 1. The average age of mastery and physical activity using a Gamma GzLM a participants was 53 years (SD = 6.50), and most with a log link function (to ensure positive fitted values). were female (57.6%), born in Australia (76.6%), in Models were run with and without covariates to deter- the paid workforce (77.1%), and residing with mine the influence of confounding factors on parameter a partner (67.9%). Respondents generally reported estimates. An inverse relationship was noted between low levels of psychological distress (median = 2, IQR: psychological distress and mastery (β = −0.13, SE = 0.01, 1–5) and high levels of mastery (median = 27, IQR: p < .01) which was unchanged following adjustment for e2153623-6 A. J. NOVIC ET AL. a b Table 2. Bivariable associations of grouped data between psychological distress , mastery and physical activity . Psychological distress Mastery Physical activity Median (IQR) Mean (SD) Median (IQR) Gender Men 2 (0–4)* 26.82 (4.51)* 300 (90–600)** Women 2 (1–5) 26.53 (4.58) 240 (90–480) Country of Birth Australia 2 (1–5) 26.68 (4.58) 240 (90–563) Other 2 (0–5) 26.58 (4.45) 240 (80–530) Education School (year 12) 2 (0–5)* 26.01 (4.65)** 210 (60–480)** Certificate/Diploma 2 (1–4) 26.73 (4.36) 270 (90–600) Bachelor/Postgraduate Degree 2 (1–4) 27.31 (4.52) 300 (120–600) Employment Status In paid workforce 2 (1–4)** 26.90 (4.40)** 240 (90–540) Not in paid workforce 2 (1–5) 25.83 (4.97) 240 (90–583) Living Arrangement Living alone 2 (1–6)** 25.98 (5.02)** 240 (70–540)** Single parent with 1+ children 3 (1–6) 25.81 (4.77) 210 (80–480) Single with friends/relatives 3 (1–5) 25.83 (4.60) 180 (73–435) Couple with no children 2 (0–4) 27.09 (4.36) 260 (90–600) Couple with 1+ children 2 (1–4) 26.94 (4.34) 270 (90–555) Other 3 (1–7) 24.90 (5.30) 180 (60–450) General Health Rating Fair/poor 4 (2–8)** 24.16 (4.90)** 120 (30–300)** Good 2 (1–5) 26.14 (4.22) 230 (80–480) Very good/Excellent 2 (0–3) 28.14 (4.16) 360 (135–720) a b Psychological distress was measured using the Kessler 6 (K6) scale (Kessler et al., 2002) ranging from 0–24; Mastery was measured using Pearlin Mastery Scale (PMS) (Pearlin & Schooler, 1978) ranging from 7–35; Physical activity was measured using the Active Australia Survey (AAS) (Australian Institute of Health and Welfare, 2003) and is reported as weighted mins per week. Alternate living arrangement (e.g., couple residing with nephew). The sample size was n = 7,146. * p < .05 ** p < .01. Table 3. Generalised linear models of mastery and physical activity adjusted for sociodemographic and health characteristics. Model 1 Model 2 Model 3 Variables β (SE) β (SE) β (SE) Mastery x Time in Physical Activity 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) Mastery −0.13 (0.01)** −0.13 (0.01)** −0.12 (0.01)** Time in Physical Activity 0.00 (0.00) 0.00 (0.00) 0.00 (0.00) Age - −0.02 (0.00)** −0.02 (0.00)** Gender Men - Ref Ref Women - 0.05 (0.04) 0.07 (0.04) Country of Birth Australia - Ref Ref Other - −0.01 (0.05) −0.01 (0.05) Education School - Ref Ref Certificate/Diploma - 0.03 (0.05) 0.05 (0.05) Bachelor/Postgraduate - 0.09 (0.05) 0.13 (0.05)* General Health Fair/Poor - - Ref Good - - −0.25 (0.06)** Very Good/Excellent - - −0.46 (0.06)** AIC 19986.24 19877.78 19331.84 BIC 20020.33 19945.91 19413.33 Model 1, unadjusted model; Model 2, adjusted for age, gender, country of birth, education; Model 3, adjusted for model 2 and general health. The sample size was n = 7,146. * p < .05 ** p < .01. socio-demographic characteristics (Model 2) and self- Discussion rated health status (Model 3). The fit statistics were Identifying potentially modifiable factors to mitigate compared, with the final adjusted model a good fit for distress can inform suitable interventions to reduce the data (χ (6565) = 6662.18, p = .20). AUSTRALIAN JOURNAL OF PSYCHOLOGY e2153623-7 associated burden. This study sought to assess the There was little evidence of an interrelationship inter-relationships of mastery and physical activity on between mastery and physical activity on psychologi- psychological distress in mid-aged adults. After adjust- cal distress in the current study. Given the reported ing for multiple potential confounders, our data indi- high levels of mastery in our study participants, this cated that mastery had a significant inverse association may suggest that there is no further protective effect with psychological distress in mid-aged adults. In both against distress from physical activity. Notably, our adjusted and unadjusted models, there was no evi- study did find a positive relationship between mastery dence for the combined effect of mastery and physical and time spent in physical activity, suggesting that activity on distress in the sample. these two factors may have a bidirectional relationship. Mastery was shown to have a strong negative associa- Previous research suggests that higher mastery is posi- tion with psychological distress in mid-aged adults and tively associated with higher physical activity levels, might therefore be a key resource to protect against which in turn is associated with better physical and distress. This result is consistent with previous longitudi- psychological health (Sargent-Cox et al., 2015). nal research suggesting increases in mastery over time are Another consideration is the frequency and intensity associated with corresponding increases in positive affect “dose” of physical activity, which has also emerged as in mid-aged adults (Windsor & Anstey, 2010). Mastery is an additional consideration when examining the rela- considered a part of one’s self-concept central to mana- tionship with psychological distress (Perales et al., ging stressors and can help regulate experiences of psy- 2014). The influence of dose of physical activity on chological distress (Pearlin & Schooler, 1978). Research mastery and distress may provide further insights for has also suggested mastery is one mechanism by which development of interventions to mitigate psychologi- exercise has a positive impact on psychological distress cal distress for mid-aged adults. (Biddle, 1993; Martin & Wade, 2000). Whilst our study offers important insights, some Contrary to previous findings (Plotnikoff et al., 2015; methodological limitations should be acknowledged. Schuch et al., 2018), although our results demon- The cross-sectional design limits inferences about the strated a significant bivariate association between phy- direction of observed relationships. It may be that sical activity and distress, they did not show the distress constrains participation in physical activity amount of time spent in physical activity to explain and lowers levels of mastery in mid-aged adults. The scores on psychological distress. This may reflect dif- survey method (self-report data) did not allow for col- ferences in the measurement of physical activity as lection of clinical indicators of health (e.g., blood pres- previous studies which have demonstrated sure, BMI) and may introduce a social desirability bias a relationship have assessed frequency of physical such that respondents answered questions about key activity (Hamer et al., 2009; Perales et al., 2014) while variables in a manner that would be viewed as favour- our study assessed time. A review of studies on physi- able. Among our participants, two-thirds were meeting cal activity and anxiety also demonstrated mixed evi- current physical activity guidelines of at least 150 dence of an association across studies with different mins/week (World Health Organization, 2010), approxi- measures of physical activity (e.g., frequency, intensity, mately half reported mastery in the top quartile of time categories, meeting physical activity recommen- scores, and three quarters had minimal levels of dis- dations) (McDowell et al., 2019). Results may also tress. The limited variability on the distress outcome reflect a bidirectional relationship between physical may have constrained evidence of inter relationships. activity and distress as previous research has observed However, the self-report measures used in the study a reciprocal relationship over time (Gucciardi et al., are well established and are widely used (Brown et al., 2020). Previous studies exploring the relationship 2004; Kessler et al., 2002; Pearlin et al., 1981) with between physical activity and distress have been con- generalisability aided by use of a multi-stage probabil- ducted with samples across broad age ranges, includ- ity sampling design. The large sample size also allowed ing younger and older adults (Hamer et al., 2009; us to explore a range of possible covariates in the Sheikh et al., 2018), have shown significant associa- analyses. tions with weekly engagement in, and intensity of, The results of our study highlight the potential physical activity and age. Differences in physical activ- importance of mastery as a protective psychological ity among people across the adult lifespan may poten- resource against distress. For mid-aged adults, a sense tially influence psychological outcomes for different of psychological control appears to relate to psycholo- age groups (McDowell et al., 2019). Therefore, other gical health more so than physical activity and could factors (e.g., general health) may be more important be included as a key component in psychotherapeutic influences on distress for mid-aged adults. interventions to mitigate distress. Contemporary e2153623-8 A. J. NOVIC ET AL. approaches, such as Acceptance and Commitment Australian Institute of Health and Welfare. (2003). The Active Australia Survey: A guide and manual for implementation, Therapy (ACT) may promote mastery through, for analysis and reporting. Australian Institute of Health and example, principles related to present-moment aware- Welfare. ness (Pagnini et al., 2016), engaging in meaningful Biddle, S. (1993). Psychological benefits of exercise and phy- activities (Reich & Zautra, 1990), and defusion and sical activity. Revista De Psicología Del Deporte, 2(2), cognitive flexibility (Hayes et al., 2011). Future work 99–107. Brown, W., Bauman, A., Chey, T., Trost, S., & Mummery, K. could evaluate the impact of a psychotherapeutic (2004). Comparison of surveys used to measure physical intervention promoting mastery to reduce psychologi- activity. Australian and New Zealand Journal of Public cal distress in mid-aged adults. Health, 28(2), 128–134. https://doi.org/10.1111/j.1467- In conclusion, this study of mid-aged Australian adults 842x.2004.tb00925.x suggests that mastery is inversely associated with psycho- Brown, W., Bauman, A., Timperio, A., Salmon, J., & Trost, S. logical distress. Future research is needed to explore the (2002). Measurement of adult physical activity: reliability, comparison and validity of self-report surveys for population longitudinal associations between mastery, dose of phy- surveillance. Unpublished report to the Department of sical activity, and distress, as well as underlying mechan- Health and Ageing. isms. Understanding the role of psychological and Burns, R. A., Anstey, K. J., & Windsor, T. D. (2011). Subjective physical resources, such as mastery and physical activity, well-being mediates the effects of resilience and mastery in mid-aged adults may provide a means to manage on depression and anxiety in a large community sample of young and middle-aged adults. The Australian and New distress, and reduce associated morbidity, premature Zealand Journal of Psychiatry, 45(3), 240–248. https://doi. death and economic burden in this population. org/10.3109/00048674.2010.529604 Burton, N. W., Haynes, M., Wilson, L. -A.M., Giles-Corti, B., Oldenburg, B. F., Brown, W. J., Giskes, K., & Turrell, G. Acknowledgments (2009). HABITAT: A longitudinal multilevel study of physi- cal activity change in mid-aged adults. BMC Public Health, We appreciate the responses from the HABITAT participants 9(1), 76. https://doi.org/10.1186/1471-2458-9-76 and acknowledge the work of the HABITAT investigators and Cairney, J., Faught, B. E., Hay, J., Wade, T. J., & Corna, L. M. staff in obtaining these data. (2005). Physical activity and depressive symptoms in older adults. Journal of Physical Activity & Health, 2(1), 98–114. Disclosure statement https://doi.org/10.1123/jpah.2.1.98 Enticott, J. C., Meadows, G. N., Shawyer, F., Inder, B., & Patten, S. No potential conflict of interest was reported by the authors. (2016). Mental disorders and distress: Associations with demographics, remoteness and socioeconomic deprivation of area of residence across Australia. Australian & New Funding Zealand Journal of Psychiatry, 50(12), 1169–1179. https:// doi.org/10.1177/0004867415615948 HABITAT was funded by three Australian National Health and Gucciardi, D. F., Law, K. H., Guerrero, M. D., Quested, E., Medical Research Council (NHMRC) Project Grants [#339718, Thøgersen-Ntoumani, C., Ntoumanis, N., & Jackson, B. #497236, #1047453]. (2020). 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