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Longitudinal and Cohort Studies in Public Health (316) Depression and Mood Disorders; Suicide Prevention (1392) Prevention and Health Promotion (1015) Physical Activity for Older People (188)

Published on in Vol 9 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44682, first published .
The Effect of Activity Participation in Middle-Aged and Older People on the Trajectory of Depression in Later Life: National Cohort Study

The Effect of Activity Participation in Middle-Aged and Older People on the Trajectory of Depression in Later Life: National Cohort Study

The Effect of Activity Participation in Middle-Aged and Older People on the Trajectory of Depression in Later Life: National Cohort Study

Authors of this article:

Yan Gao1 Author Orcid Image ;   Zhihao Jia1 Author Orcid Image ;   Liangyu Zhao1 Author Orcid Image ;   Suyue Han1 Author Orcid Image
Article Authors Cited by (9) Tweetations (2) Metrics

    Original Paper

    School of Physical Education, Shandong University, Jinan, Shandong, China

    Corresponding Author:

    Yan Gao, PhD

    School of Physical Education

    Shandong University

    17922 Jingshi Road, Lixia District

    Jinan, Shandong, 250061

    China

    Phone: 86 18663709793

    Email: gaoyanluck@sdu.edu.cn


    Background: More activity participation is an important means of handling depression and promoting positive aging, but the impact of changes in activity participation on the developmental trajectory of depression has not been fully studied.

    Objective: The purpose of this study is to study the relationship between current activity participation and depression in middle-aged and older people (≥45 years old) and the relationship between activity participation and the developmental trajectory of depression in later life in China.

    Methods: This study used data from the China Health and Retirement Longitudinal Study (CHARLS) across 7 years and included a total of 4818 middle-aged and older people (≥45 years old). Controlling for relevant control variables, the latent growth curve model and the cross-lag model were used to assess the effect of changes in activity participation on the depression trajectory in later life and the main lag relationship between activity participation and depression. Activity participation as well as depression were measured using the self-reported activity and health status based on the CHARLS questionnaire.

    Results: Among the 4818 respondents, the mean values of physical activity participation, social leisure activity participation, and depression ranged from 76.98 (SD 15.16) to 83.95 (SD 5.72), from 7.43 (SD 8.67) to 9.25 (SD 10.16), and from 7.61 (SD 5.72) to 8.82 (SD 6.51), respectively. Our findings revealed that activity participation could be related to depression. Physical activity participation predicted initial depression (β=–0.631, P<.001) and its trajectory (β=0.461, P<.001). However, social leisure activity participation predicted initial depression (β=–0.223, P<.001) but did not predict its trajectory (β=0.067, P=.159). Finally, cross-lag regression analysis further demonstrated the predictive effect of activity participation on depression.

    Conclusions: This study demonstrates the prediction of activity participation for future depression in the Chinese middle-aged and older populations. The data showed that activity participation is significantly associated with changes in depression and future depression among middle-aged and older people in China. The Chinese government should encourage middle-aged and older people to participate in various activities, which can effectively prevent the aggravation of depression and also have a positive significance for positive aging.

    JMIR Public Health Surveill 2023;9:e44682

    doi:10.2196/44682

    Keywords

    activity participation; middle-aged and older people; depression; cohort study; developmental 


    Better activity participation not only has a positive impact on the physical and mental health of middle-aged and older people [1-3] but also improves the health-related quality of life (HRQoL) [4,5]. Specifically, active participation in various activities can provide middle-aged and older people with a series of positive feelings so that they can better cope with the pressure of daily life and maintain good social interaction. This will make them feel happier and more satisfied with their lives, which will further result in improved HRQoL and mental health [3].

    At present, activity participation has become a hot topic in depression research. If the elderly cannot adapt to changes brought about by physical function (PF) decline or the change in social roles (eg, changes in activity participation), anxiety and depression will be an inevitable consequence [6]. Depression is a common mental disorder. Worldwide, an estimated 5% of adults have depression. According to statistics, after the COVID-19 pandemic, there were about 374 million cases of anxiety (4802 cases per 100,000 people) and 246 million cases of major depression (3153 cases per 100,000 people), up by 26% and 28%, respectively, compared to the previous years [7]. Therefore, mental health problems have become a big problem worldwide. At the same time, the prevalence of depression in China is also a big issue. It is estimated that the prevalence of depression in the elderly in China is as high as 23.6%, and its prevalence will increase not only with age [8,9] but also with a trend of “rejuvenation” [10]. It is clear that depression in the middle-aged and the older population has become a major public health problem in China. Exploring the developmental trajectory of depression in the middle-aged and older populations and its relationship with related factors is of great significance for actively coping with healthy aging.

    Although some studies have shown that activity participation levels in middle-aged and older adults increase or stabilize over time [11,12], in general, the academic community generally believes that it becomes increasingly difficult for middle-aged and older people to maintain active participation as they grow older [13,14]. The concept of life cycle is widely used. In psychology, it mainly refers to the life cycle of people and the life cycle of the family and also refers to the process of birth, growth, aging, illness, and death. Life cycle theory holds that after middle-aged people enter old age, their physiological and psychological processes experience a series of changes [14], which leads to changes in activity participation [6]. In a nutshell, the activity participation of the elderly (eg, leisure activities) gradually becomes simpler and decreases over time [11,14]. Numerous studies have shown that older adults generally have lower levels of social activity participation, and this lower level may further decrease with age [15-17]. Approximately 39.0% of respondents showed low social activity engagement at baseline and decreased engagement within 10 years [15]. Especially among the elderly in China, they spend more time sedentary than the young [18]. In addition, life cycle theory [14] believes that when entering the final stage of the life cycle, both physical and mental functions decline sharply, suggesting that the trends in activity participation and depression should be nonlinear.

    Currently, there are a large number of studies focusing on the relationship between activity participation and depression. Activity theory is commonly used to explain the relationship between social participation and mental health in older adults. Activity theory believes that middle-aged and older people can establish new roles to cope with the loss of their original roles so as to adapt to society and obtain psychological satisfaction [19]. In other words, older people can establish new roles through activity participation to alleviate the depressive symptoms caused by role changes [6]. This theory is also supported by the results of some clinical studies, which suggest that activity participation can prevent depression [20] and is becoming an effective treatment for depression [21]. This shows that leisure activities are actually a protective factor for depression in the elderly and that participation in activities may reduce the rate of depression [22]. Additionally, studies have shown that changes in PF and changes in depression are reciprocal [23-25]. Given the direct link between activity participation and PF, activity participation changes may also be associated with changes in depression. Therefore, changes in activity participation in middle-aged and older adults may predict changes in depression. Finally, behavioral activation (BA) theory suggests that engaging in activities that make individuals feel pleasure improves depression and promotes activity participation after depression improves [26]. This suggests that there may be a temporal sequence between activity participation and depression. Therefore, this study further determines the temporal order of the relationship between activity participation and depression.

    Previous studies have often included activity participation as a time-invariant dependent variable or a simple control variable and have simply verified the correlation between the initial levels of the 2 variables [22,27]. The effect of changes in activity participation on depression was rarely considered, and this led to a lack of discussion of the relationship between the initial levels of the 2 variables and the rate of change. Therefore, this study selected data from the China Health and Retirement Longitudinal Study (CHARLS) spanning 7 years using a latent growth curve model (LGCM) and a cross-lag model to explore the developmental trajectories of activity participation and depression and the patterns of interaction between their trajectories, further revealing the dynamic development relationship between variables. Based on the existing literature and theories, this study has a few overall hypotheses: (1) middle-aged and elderly activity participation shows a nonlinear downward trend with age, (2) middle-aged and elderly depression shows a nonlinear upward trend with age, (3) the activity participation of middle-aged and older adults predicts their depression during the same period, (4) the initial level and rate of change of activity participation in middle-aged and older adults could predict the development rate of the depression trajectory, and (5) the middle-aged and older adults’ early activity participation at 4 points in time predicts later depression.


    Sample and Data Collection

    Data were obtained from CHARLS. Our national baseline survey was launched in 2011, covering 150 county-level units, 450 village-level units, and about 17,000 people in 10,000 households, and these samples were tracked every 2-3 years thereafter. More details about CHARLS can be found in previous publications [28]. This database uses data from 4 waves in 2011 (wave I), 2013 (wave II), 2015 (wave III), and 2018 (wave IV), excluding respondents younger than 45 years old.

    The detailed sample-screening process is shown in Multimedia Appendix 1. This study first focused on all respondents in wave I. Since the purpose of the study was to study the relationship between activity participation and depression, plus with the control of relevant time-varying and invariant variables, 3908 respondents of the final wave I were excluded from our study. The specific reasons were as follows: they were younger than 45 years or their age was not recorded (n=367, 9.4%); they had self-reported memory-related disorders and brain damage, intellectual disability, missing data on brain damage, or intellectual disability at baseline (n= 528, 13.5%); they had no baseline data on depressive symptoms (n=2101, 53.7%); they had no activity participation data at baseline (n=1943, 49.7%); and they had missing values for relevant covariates (gender, marriage, education, self-rated health, chronic disease, smoking, drinking; n=1943, 49.7%). Next, this study performed the same first process on wave II, wave III, and wave IV data as wave I, and in the second step, the study excluded respondents who had not participated in all of them for 4 years. Finally, after these operations, individuals with missing values on any variable were excluded, and a total of 4818 valid individuals were included.

    Ethical Considerations

    This study was organized by the National Development Institute of Peking University, complied with the ethical guidelines of the 1975 Declaration of Helsinki, and was approved by the Ethics Committee of Peking University (approval number: IRB00001052-13074), hosted by the National Development Institute of Peking University. The studies involving human participants were reviewed and approved by the Research Ethics Committees of Peking University (IRB00001052-11015). The patients/participants provided their written informed consent to participate in this study.

    Activity Participation

    Combining the definitions of activity participation by previous scholars [29] and the characteristics of activity participation of Chinese seniors [30], this study believed that the activity participation of Chinese seniors can be divided into these categories: exercise activities, social activities, and interaction with friends. Based on the characteristics of CHARLS, this study further summarized the activity participation of middle-aged and older people as physical activity participation (participation in exercise-related activities) and social leisure activity participation (participation in activities related to socializing and friend-making).

    In addition, previous physical activity participation has mostly been measured only in terms of intensity, duration, and days of physical activity participation [31,32]. To measure physical activity participation more comprehensively, based on previous studies and the HRQoL scale of CHARLS [6,33], this study chose to comprehensively measure the physical activity participation of middle-aged and older people (as shown in Multimedia Appendix 2) from the aspects of PF, role-body (RP), body pain (BP), general health (GH).

    In this study, PF measured whether health conditions prevent normal physical activity participation, the physiological functioning dimension measures whether physical health problems lead to limited participation in physical activity, the somatic pain dimension measures BP and its impact on participation in daily physical activities, and the overall health dimension measures their own evaluation of health status and trends [33]. In terms of measuring social leisure activity participation, based on a previous study [30], 2 questions were asked (as shown in Multimedia Appendix 2): What social activities have been carried out in the past month? What is the frequency of participating in these activities?

    This study rescored these 2 types of activity parameters in reverse, ensuring that higher scores indicated more active activity participation. Then, based on the scoring rules of the Medical Outcomes Study (MOS) 36-item Short-Form (SF-36) Health Survey scale, the raw scores of the 2 activities’ participation in their respective components were calculated. The conversion scores of daily physical activity participation and social leisure activity participation were calculated using the range method between 0 and 100. Finally, the Cronbach α coefficient was used to measure the reliability of the above method. The results showed that the Cronbach α value of both physical activity participation and social leisure activity participation was higher than 0.6; hence, this scoring method is reliable [6,33].

    Depression

    Depression was screened using the Chinese version of the Center for Epidemiologic Study Depression Scale (CES-D), with 10 short-form scale items [33], and results illustrated the magnitude of depression. The CES-D in CHARLS is a simplified version of the depression scale developed by Radolff at the National Institute of Mental Health [34]. The CES-D consists of 10 questions asking respondents about depression experienced 1 week before, and existing research has demonstrated its applicability in middle-aged and older Chinese populations [35]. Of these 10 questions, this study reverse-coded questions 5 and 8, and the total score was the sum of all questions. Each question is scored on a 4-point scale, ranging from 0 to 3, and the total score ranges from 0 to 30; therefore, the higher the score, the more severe the depression [36].

    Control Variable

    Age, gender, education level, marital status, chronic disease, smoking, drinking, self-reported health, and number of chronic diseases were included as covariates in the analysis. The results of the 4 surveys of these variables were sorted out. This study took the baseline measurement results of gender and education level as time-invariant variables, leaving the remaining variables as time-varying variables. The education level was divided into “no formal education,” “primary school,” “junior high school,” and “high school education or above” [23,30]. Marital status included “married” and “other.” Self-perceived health status was reported as “very good,” “good,” “fair,” “poor,” or “bad.” The current smoking and drinking status was assessed by self-reporting based on the questions “Are you currently smoking?” and “Are you currently drinking?” Chronic diseases were all self-reported and marked as 0, 1, or at least 2 by the number of chronic diseases.

    Statistical Analysis

    Descriptive analysis was performed using IBM SPSS (version 24.0) to determine the basic characteristics of all respondents.

    The association of activity participation and depression was explored using Mplus (version 8.0). First, to study the respective trends in activity participation and depression in the middle-aged and older populations, an unconditional linear growth model and an unconditional quadratic growth model were constructed (see Multimedia Appendices 3 and 4). Second, a growth model with time-invariant and time-varying variables was constructed to study the interrelationship between depression and physical and leisure activities. Participation and depression were added to the model as time-varying variables, mainly looking at the relationship between activity participation and depression at the same time. Next, to avoid measurement errors and more accurately examine the relationship between depression and physical and social activities, a parallel latent growth model was developed (see Figure 1). Finally, after studying the dynamic properties of the variables using this model, the main lag relationship between depression and physical and social activities in the older population was further investigated using a cross-lag model (see Figure 2).

    The hypothetical model fit was tested using multiple fit indices, including the standardized root mean square residual (SRMR), the root mean square error of approximation (RMSEA), the comparative fit index (CFI), and the Tucker-Lewis index (TLI). Specifically, if SRMR≤0.08, the model is acceptable; if RMSEA≤0.08, the model is acceptable. For CFI and TLI, values >0.90 are considered acceptable [37].

    ‎
    Figure 1. (A) LGCM of activity participation and depression. (B) LGCM of social leisure activity participation and depression. The activity participation intercept, activity participation slope, depression intercept, and depression slope were regressed on the covariates simultaneously. Valid paths and nonsalient paths are plotted as solid and dashed lines, respectively. I: intercept; LGCM: latent growth curve model; S: slope.
    ‎
    Figure 2. (A) Cross-lag model of physical activity participation and depression. (B) Cross-lag model of social leisure activity participation and depression. Note: activity participation and depression in all 4 waves were regressed on the covariates simultaneously.

    Common Method Bias

    The Harman single-factor test is a diagnostic technique to evaluate the severity of common method bias. In this study, the Harman single-factor test was performed on all the data from the 4 surveys. The test results showed that in these 4 surveys, the variance explained by the first factor was 21.7%, which is significantly less than 40.0%. This suggests that there is no common methodological bias in this study [38].

    Descriptive Statistics

    Table 1 shows the descriptive statistics of all variables at baseline. The results showed that from 2011 to 2018, the average values of physical activity participation, social leisure activity participation, and depression ranged from 76.98 (SD 15.16) to 83.95 (SD 5.72), from 7.43 (SD 8.67) to 9.25 (SD 10.16), and from 7.61 (SD 5.72) to 8.82 (SD 6.51), respectively, respectively. Multimedia Appendices 5 and 6 are a clearer demonstration of the scores of and linear trends in depression, physical activity participation, and social leisure activity participation among middle-aged and older people during the 4 measurement periods. Multimedia Appendix 7 shows the binary correlations of key variables across 4 surveys. The results showed a negative correlation between physical activity participation, social leisure activity participation, and depression from 2011 to 2018. That is, the higher the scores of physical activity participation and social leisure activity participation, the lower the score of depression.

    Table 1. Descriptive analysis of participants (N=4818).
    VariableWave I, mean (SD)Wave II, mean (SD)Wave III, mean (SD)Wave IV, mean (SD)
    Age (years)57.13 (7.95)59.13 (7.94)61.13 (7.95)64.13 (7.95)
    Marital status1.36 (1.09)1.41 (1.17)1.45 (1.20)1.64 (1.38)
    Education level3.65 (1.90)2.06 (0.95)2.05 (0.96)1.95 (0.91)
    Self-reported health3.41 (0.10)2.60 (1.01)3.41 (1.06)2.99 (1.01)
    Chronic disease1.31 (1.36)1.58 (1.50)1.79 (1.80)2.37 (1.92)
    Smoking0.32 (0.47)0.2 (0.40)0.28 (0.45)0.27 (0.44)
    Drinking0.35 (0.48)0.29 (0.46)0.37 (0.48)0.34 (0.47)
    Depression7.93 (6.11)7.61 (5.72)7.75 (6.29)8.62 (6.51)
    Physical activity participation83.95 (5.72)79.25 (11.06)77.43 (13.93)76.98 (15.16)
    Social leisure activity participation7.43 (8.67)9.25 (10.16)8.93 (10.56)8.25 (10.20)

    Developmental Trajectories of Depression Levels Among Seniors

    To investigate the potential relationship between depressive symptoms and physical activity participation as well as social leisure activity participation, unconditional linear and unconditional quadratic latent growth curve models (LGCMs) were initially used to assess initial depression, physical activity participation, social leisure activity participation, and their trajectories over time in older adults. The results are shown in Multimedia Appendices 3 and 4, respectively. Multimedia Appendix 8 summarizes the fit indices for these models. Table 2 provides the within-subject mean for repeated measures (intercept/baseline), the between-subject variance in the intercept (intercept variance), the between-subject mean change (slope), the between-subject mean change (slope variance), and the quadratic slope variance and quadratic intercept variance.

    With regard to the depression degree, both the linear growth model (model 1) and the quadratic growth model (model 2) of the depression degree were suitable (model 1: CFI=0.981, TCL=0.977, RMSEA=0.057, SRMR=0.031; model 2: CFI=1.000, TCL=1.001, RMSEA=1.001, SRMR=0.013). Although the fitness was acceptable, the quadratic slope variance of depression in the quadratic growth model of depression was not statistically significant (B=0.011, P=.045), as shown in Table 2. Therefore, the study chose to analyze the data from model 1. It was found that the variance estimates of the intercept factor and slope factor of the depression degree were 18.705 (P<.001) and 0.163 P<.001), respectively, indicating that there were significant interindividual differences in initial levels and growth rates. In addition, the annual mean slope of the depression level was 0.110 (SD 0.013, P<.001), meaning the depression level increased linearly during the 4 survey periods.

    Table 2. Intercept and slope estimates for unconditional linear and nonlinear latent growth models.
    Trajectory and modelsIntercept (SE)Intercept variance (SE)Slope (SE)Slope variance (SE)Quadratic (SE)Quadratic variance (SE)
    Trajectory of depression

    		Model 17.595 (0.079)a18.705 (0.653)a0.110 (0.013)a0.163 (0.026)aN/AbN/A

    		Model 27.910 (0.086)a16.305 (1.340)a–0.243 (0.042)a–0.074 (0.379)0.049 (0.006)a0.011 (0.005)
    Trajectory of physical activity participation

    		Model 382.620 (0.112)a17.351 (1.377)a–0.913 (0.032)a2.557 (0.132)aN/AN/A

    		Model 483.817 (0.113)a1.673 (3.334)a–2.594 (0.090)a9.874 (1.240)a0.234 (0.011)a0.046 (0.023)
    Trajectory of physical activity participation

    		Model 57.848 (0.121)a37.665 (1.575)a0.134 (0.022)a0.592 (0.077)aN/AN/A

    		Model 67.265 (0.124)a34.239 (3.580)a1.028 (0.073)a3.655 (1.058)a–0.128 (0.010)a0.049 (0.017)a

    aThe corresponding variable was statistically significant.

    bN/A: not applicable.

    Developmental Trajectories of Physical Activity Participation and Leisure Activity Participation Among Older Adults

    In terms of physical activity, the quadratic growth model (model 4) for their latent growth was better than the linear growth model (model 3); see Multimedia Appendix 8. With regard to the quadratic growth model for physical activity participation, the quadratic slope variance of physical activity participation was 0.046 (P=.048); see Table 2. Model 4 data were used as a result. Specifically, the initial level (intercept) of physical activity participation was 83.817 (P<.001). Physical activity participation decreased over the 4 test periods (slope=–2.594, P<.001), and the rate of decline increased year by year (curve slope=0.234, P<.001), indicating a nonlinear decline in physical activity participation over the 4 test periods. In addition, the estimated variances of the intercept factor and slope factor for physical activity participation were 1.673 and 9.874, respectively, and the variance of the slope factor was significant at the 0.001 level, indicating interindividual differences in growth rates.

    Based on the fitting degree of the latent growth model of social leisure activity participation, the fitting degree of the quadratic growth model (model 6) of social leisure activity participation was better than that of the linear growth model (model 5) of social leisure activity participation (see Multimedia Appendix 8, Table S3). In addition, the quadratic slope variance of social leisure activity participation was 0.049 (P<.001); see Table 2. Therefore, model 6 data were used for this study. Specifically, the initial level of social leisure activity participation (intercept) was 7.265 (P<.001). The slope of social leisure activity participation during the 4 survey periods was 1.028 (P<.001), indicating that social leisure activity participation showed a linear upward trend in the 4 test periods (slope=1.028, P<.001), but the rate of increase decreased year by year (curve slope=–0.128, P<.001). In addition, the estimated variances of the intercept factor and the slope factor of social leisure activity participation were 34.239 (P<.001) and 3.655 (P<.001), respectively, indicating that there were significant interindividual differences in the initial level and growth rate.

    Relationship Between Physical Activity Participation and Social Leisure Activity Ability and Depression

    To explore the potential relationship between the depression level and participation in physical and social leisure activities, a model was built with time-invariant and time-varying variables. This model examined whether taking physical activity participation and social leisure activity participation as time-varying variables has an effect on the depression level and whether the depression level is time-varying on physical activity participation when these endogenous variables are considered at the same time. The results of the model are shown in Table 3.

    The results showed that at any time point, the lower the physical activity participation and social leisure activity participation, the higher the degree of depression, which means that physical activity participation and social leisure activity participation did affect the depression level of middle-aged and older people at corresponding time points, and based on the results, physical activity participation has a greater effect on depression than social leisure activity participation.

    In addition, the effect of depression level on physical activity participation and social leisure activity participation was also considered. The results showed that at any time point, the higher the degree of depression, the lower the ability to participate in physical and social leisure activities, which means that the degree of depression does affect physical activity participation and social leisure activity participation for middle-aged and older people at corresponding time points.

    Table 3. Standardized coefficientsa in LGCMsb with depression, physical activity participation, and social leisure activity participation as time-varying variables.
    VariablesWave III depressionWave II depressionWave III depressionWave IV epression
    Physical activity participation (depression

    		Wave I physical activity participation (depression)–0.167 (–0.024)N/AcN/AN/A

    		Wave II physical activity participation (depression)N/A–0.284 (–0.05)N/AN/A

    		Wave III physical activity participation (depression)N/AN/A–0.368 (–0.06)N/A

    		Wave IV physical activity participation (depression)N/AN/AN/A–0.396 (–0.052)
    Social leisure activity participation (depression)

    		Wave I social leisure activity participation (depression)–0.064 (–0.092)N/AN/AN/A

    		Wave II social leisure activity participation (depression)N/A–0.058 (–0.058)N/AN/A

    		Wave III social leisure activity participation (depression)N/AN/A–0.056 (–0.068)N/A

    		Wave IV social leisure activity participation (depression)N/AN/AN/A–0.048 (–0.060)

    aAll P<.001.

    bLGCM: latent growth curve model.

    cN/A: not applicable.

    Parallel LGCM

    To avoid measurement errors and to more accurately examine whether depression is associated with physical activity participation and whether the trajectories change over time, a parallel LGCM was developed, as shown in Figure 1. This parallel LGCM examined the relationship between the intercepts and slopes of physical activity participation and social leisure activity participation and the intercepts and slopes of depression, and further examined the influence process between the 2 by setting regression equations between growth factors. This study, however, only focused on the interaction between the 2.

    The parallel LGCM of physical activity participation for depression was well fitted (CFI=0.908, TLI=0.873, RMSEA=0.056). In Figure 1, the effective paths and nonsalient paths are drawn as solid and dashed lines, respectively. Detailed results are shown in Table 4. At the initial level, physical activity participation was negatively correlated with depression (r=–0.631, P<.001), that is, the worse the individual's initial physical activity participation, the higher the depression. Initial physical activity participation positively predicted the rate of change in depression (β=0.461, P<.001), indicating that individuals with higher initial physical activity participation scores have a higher rate of depression growth. Similarly, the slope of physical activity participation in the middle-aged and older populations affected not only the initial depression level (β=–0.261, P<.001) but also the rate of change in depression (β=–0.69, P<.001), indicating that the growth rate of physical activity participation suppresses the increase in the initial level of depression and its growth rate.

    In addition, another parallel LGCM was built for social leisure activity participation and depression (see Table 4). The parallel LGCM fit well (CFI=0.951, TLI=0.933, RMSEA=0.033, RSMR=0.036). In Figure 1, the effective and nonsalient paths are plotted as solid and dashed lines, respectively. At the initial level, the degree of depression was negatively correlated with social leisure activity participation (r=–0.223, P<.001), indicating that the higher the individual's initial social leisure activity participation, the lower the depression. Initial social leisure activity participation did not predict the rate of change in depression (β=0.067, P=.159), indicating that initial social leisure activity participation is not associated with the rate of increase in depression. Similarly, although the slope of social leisure activity participation in the middle-aged and older populations did not affect the initial level of depression (β=0.08, P=.087), it did affect the rate of change in depression (β=–0.303, P<.001), indicating that the growth of social leisure activity participation suppresses the growth rate of depression.

    Table 4. Results of LGCMsa for activity participation and depression.
    PathEstimate (SE)P value
    Physical activity participation → depression

    		Ib physical activity participation → I depression–0.631 (0.031)<.001

    		Sc physical activity participation → I depression–0.261 (0.025)<.001

    		I physical activity participation → S depression0.461 (0.065)<.001

    		S physical activity participation → S depression–0.69 (0.061)<.001
    Social leisure activity participation → depression

    		I social leisure activity participation → I depression–0.23 (0.027)<.001

    		S social leisure activity participation → I depression0.08 (0.047).087

    		I social leisure activity participation → S depression0.067 (0.047).159

    		S social leisure activity participation → S depression–0.303 (0.084)<.001

    aLGCM: latent growth curve model.

    bI: intercept.

    cS: slope.

    Cross-Lag Model

    The dynamic characteristics of the variables were studied using the LGCM. To further investigate the main lag relationship between activity participation and depression in the middle-aged and older populations, cross-lag regression analysis was performed across 4 measures. Cross-lag regression analysis can reveal complex relationships between 2 variables. The autoregressive effect of each variable is controlled by setting a coefficient of stability, which is the best way to test for “pure” effects between variables and to understand to what extent one variable predicts the other. In addition, to achieve a more compact model, the crossing paths across the waves were restricted to achieve equality, as shown in Figure 2. This procedure sets autoregressive effects as fixed effects, which avoids convergence problems. The relationship between variables was not considered stable across time, because only the lag effect was set as a random effect.

    The results are shown in Table 5. Both activity participation and depression maintained high stability across the 4 measurements. Physical activity participation in the older population in wave I significantly and negatively predicted depression in wave II (β=–0.125, P<.001). Physical activity participation in the middle-aged and older populations in wave II significantly and negatively predicted depression in wave III (β=–0.216, P<.001). Physical activity participation in the older population in wave III significantly and positively predicted depression in wave IV (β=–0.233, P<.001). Likewise, prior depression also had a significant negative impact on later physical activity participation.

    Table 5. Cross-lag regression path coefficients.a
    Regression pathWave I to wave IIWave II to wave IIIWave III to wave IV
    Physical activity participation and depression

    		Physical activity participation → depression–0.125–0.216–0.233

    		Depression → physical activity participation–0.874–0.737–0.685

    		Depression → depression0.4560.4140.414

    		Physical activity participation → physical activity participation0.09–0.049–0.049
    Social leisure activity participation and depression

    		Social leisure activity participation → depression–0.04–0.045–0.044

    		Depression → social leisure activity participation–0.058–0.054–0.058

    		Depression → depression0.5150.4750.481

    		Social leisure activity participation → social leisure activity participation0.3990.4300.446

    aAll P<.001.

    Similar results were shown in the cross-lag model of social leisure activity participation and depression as in the cross-lag model of physical activity participation and depression. The social leisure activity participation of the older population in wave I significantly and negatively predicted depression in wave II (β=–0.04, P<.001). Social leisure activity participation in the middle-aged and older populations in wave II significantly and negatively predicted depression in wave III (β=–0.045, P<.001). The social leisure activity participation of the older population in wave III significantly and predicted depression in wave IV (β=–0.044, P<.001). Similarly, previous depression also had a significant negative impact on later social leisure activity participation.


    Principal Results

    Based on a CHARLS survey spanning 7 years, this study found that activity participation has a long-term effect on depression. First, physical activity participation decreased significantly between 2011 and 2018 among middle-aged and older Chinese populations, suggesting that the physical activity participation of middle-aged and older adults decreases with age [14,18]. Studies have shown that chronic diseases related to aging can lead to the risk of arthritis and disability and that impairment of activities of daily living gradually increase with age [39,40], which in turn leads to a lifestyle change in middle-aged and older people. A series of chain reactions end up with a reduction in physical activity participation [41]. Life cycle theory can explain this change well. This study selected respondents over the age of 45 years. As the survey continued, the life cycle of these respondents gradually entered a decline period, so their PFs also began to decline. We can even speculate that this phenomenon might be more pronounced if the initial age restriction is controlled to over 60 years [14]. However, it is worth mentioning that it is precisely because of our selection of the age of ≥45 years (the average age in wave I was 57 years) that as these people get older, they start to withdraw from work (the Chinese government stipulates that 60 years is the retirement age for citizens) and have more time for social leisure activity. Therefore , there was a significant increase in leisure activity participation from wave I to wave II. However, after wave II, most of them (the average age in wave II was 60 years) also entered old age, and social leisure activity began to decline continuously, which is also consistent with previous studies [14]. Finally, based on the analysis of the trajectory of depression, the level of depression in middle-aged and older people in China increased significantly between 2011 and 2018, and updated evidence was provided again to prove that the trajectory of depression is affected by age [8,23,42].

    Second, the study found a horizontal correlation between activity participation and depression within the same period and further demonstrated that this relationship persisted during different periods. Specifically, models incorporating time-varying and invariant variables proved that between 2011 and 2018, activity participation in middle-aged and older adults significantly predicted depression in the same period and that depression in middle-aged and older adults also significantly predicted participation in activities in the same period. This suggests that the trajectory of depression is affected by activity participation, and the developmental trajectory of activity participation is similarly affected by depression [42-44]. The behavioral theory of depression and BA theory can successfully explain the mutual influence of activity participation on depression. The behavioral theory of depression states that low-response-conditioned positive reinforcement (RCPR) rates can directly trigger depression, so depression can be improved by re-establishing RCPR rates through altering activity participation and other activities that make individuals feel pleasant [45] and that improvements in depression in turn will lead to greater activity participation [46]. Previous studies have also found that the activity participation of middle-aged and older people may be plagued by poor physical and mental health (eg, depression, anxiety) [47] and that poor mental health and related symptoms (eg, irritability) result in reducing activity participation through perceptions of stress, perceptions of health, and some physical symptoms [27].

    Additionally, a parallel LGCM showed that the higher the initial level of activity participation, the slower the rise in depression, which is consistent with previous analyses that showed that activity participation is a protective factor for depression among older adults [48,49]. Active participation in various activities as an active lifestyle attitude in middle-aged and older people can inhibit the increase in depression [50]. Moreover, the parallel LGCM also found that the slope of activity participation was significantly correlated with the slope of depression, suggesting that the rate of decline in activity participation among older adults could predict the rate of decline in depression, meaning that the faster the decline in activity participation, the sooner the depression rate increase. In addition, our study suggests that the development of depression partly influences the development of activity participation. One possible explanation is that depression seriously affects the physical health of patients [51], and physical health is directly related to the activity participation of middle-aged and elderly people [52].

    Finally, the study performed a cross-lag regression analysis on activity participation and depression in middle-aged and elderly populations to observe the time series between activity participation and depression. Results showed that activity participation in middle-aged and older people can positively predict subsequent depression and that depression can also predict subsequent activity participation; however, no 1-way time series of activity participation and depression was found. This result further proves the bidirectional correlation between activity participation and depression in middle-aged and older people [53].

    Limitations

    This study has some limitations. First, the participation data of this research activity came from the subjective answers of middle-aged and older people. As much as the study team tried to control some biases caused by subjective answers, such as excluding respondents with cognitive impairment, intellectual disability, and memory problems from the study, there would still be problems posed by subjective answers. Second, physical activity participation and social leisure activity participation were categorized to represent the activity participation of middle-aged and older people. The frequency of participation and the quality of activities were included in the selection of indicators; however, with regard to the different types of activity participation, the classification is still not detailed. For future studies, a detailed division of different types of activity participation should be made to compare the effects of different types of activity participation on the physical and mental health of middle-aged and older people. Finally, the focus of the study was the effect of activity participation on depression. Although the study found that the development of depression also affects the development of activity participation, it did not focus on explaining this relationship, and future research should pay more attention to depression’s impact on activity participation.

    Conclusion

    Study results support a bidirectional association between activity participation and depression. Activity participation affects not only the initial level of depression but also the trend in changes in depression. In the future, more attention should be paid to depression in middle-aged and older people with less active participation. The interconnectedness of early activity participation and subsequent depression is noteworthy and may have implications for public health. Overall, this study highlights the relationship between activity participation and depression level changes, bringing positive implications for preventing depression aggravation in later life.

    Acknowledgments

    This work was supported by the National Social Science Foundation of China (grant number: 21BTY054), the China Postdoctoral Science Foundation (grant number: 2017M622169), and the Future Project for Youth Scholar of Shandong University (grant number: 2017WLJH17). The content of the paper is solely the responsibility of the authors and does not necessarily represent the official views of the funders.

    We thank all the interviewees for their voluntary participation in the China Health and Retirement Longitudinal Study (CHARLS).

    Data Availability

    The data sets generated and analyzed during the study are available from the corresponding author upon reasonable request.

    Authors' Contributions

    YG was responsible for the study design, data analysis, interpretation of the data, and writing of the manuscript. ZJ was responsible for the study design, data analysis, and writing of the manuscript. LZ and SH were responsible for the study design and writing of the manuscript.

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    Flowchart of study respondents.

    DOCX File , 351 KB
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    Multimedia Appendix 2

    HRQoL variable selection. HRQoL: health-related quality of life.

    DOCX File , 13 KB
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    Multimedia Appendix 3

    Unconditional linear LGCM. i: intercept; LGCM: latent growth curve model; s: slope.

    DOCX File , 91 KB
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    Multimedia Appendix 4

    Unconditional nonlinear LGCM. i: intercept; LGCM: latent growth curve model; s: slope.

    DOCX File , 137 KB
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    Multimedia Appendix 5

    Change trajectory of depression.

    DOCX File , 3542 KB
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    Multimedia Appendix 6

    Change trajectory of physical activity participation and social leisure activity participation.

    DOCX File , 3542 KB
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    Multimedia Appendix 7

    Correlation coefficient matrix.

    DOCX File , 15 KB
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    Multimedia Appendix 8

    Fit statistics for LGCMs. LGCM: latent growth curve model.

    DOCX File , 14 KB
    1. Yu W, Chen R, Zhang M, Li Z, Gao F, Yu S, et al. Cognitive decline trajectories and influencing factors in China: a non-normal growth mixture model analysis. Arch Gerontol Geriatr 2021 Jul;95:104381. [CrossRef] [Medline]
    2. Bath PA, Deeg D. Social engagement and health outcomes among older people: introduction to a special section. Eur J Ageing 2005 Mar 9;2(1):24-30 [FREE Full text] [CrossRef] [Medline]
    3. Martinez HN, Santaella RE, Rodriguez-Garcia AM. Benefits of physical activity for the promotion of active aging in elderly. Bibliographic review. Retos-Nuevas Tendencias En Educacion Fisica Deporte Y Recreacion 2021;2021(39):829-834. [Medline]
    4. Meyer S, Rosenblum S. Children with celiac disease: health-related quality of life and leisure participation. Am J Occup Ther 2016;70(6):7006220010p1-7006220010p8. [CrossRef] [Medline]
    5. Lemon BW, Bengtson VL, Peterson JA. An exploration of the activity theory of aging: activity types and life satisfaction among in-movers to a retirement community. J Gerontol 1972 Oct 01;27(4):511-523. [CrossRef] [Medline]
    6. Wang L, Liu W. Effects of family doctor contract services on the health-related quality of life among individuals with diabetes in China: evidence from the CHARLS. Front Public Health 2022;10:865653 [FREE Full text] [CrossRef] [Medline]
    7. Herrman H, Patel V, Kieling C, Berk M, Buchweitz C, Cuijpers P, et al. Time for united action on depression: a Lancet–World Psychiatric Association Commission. Lancet 2022 Mar;399(10328):957-1022. [CrossRef]
    8. Yang Y. Is old age depressing? Growth trajectories and cohort variations in late-life depression. J Health Soc Behav 2007 Mar 24;48(1):16-32. [CrossRef] [Medline]
    9. Li D, Zhang D, Shao J, Qi X, Tian L. A meta-analysis of the prevalence of depressive symptoms in Chinese older adults. Arch Gerontol Geriatr 2014;58(1):1-9. [CrossRef] [Medline]
    10. Zhang Y, Zhao M. Gender disparities and depressive symptoms over the life course and across cohorts in China. J Affect Disord 2021 Dec 01;295:620-627. [CrossRef] [Medline]
    11. Agahi N, Lennartsson C, Kåreholt I, Shaw BA. Trajectories of social activities from middle age to old age and late-life disability: a 36-year follow-up. Age Ageing 2013 Nov 24;42(6):790-793. [CrossRef] [Medline]
    12. Park S, Kwon E, Lee H. Life course trajectories of later-life cognitive functions: does social engagement in old age matter? Int J Environ Res Public Health 2017 Apr 07;14(4):393 [FREE Full text] [CrossRef] [Medline]
    13. Sluzki CE. Personal social networks and health: conceptual and clinical implications of their reciprocal impact. Fam Syst Health 2010 Mar;28(1):1-18. [CrossRef] [Medline]
    14. Zhu C, Zhou L, Zhang X. Effects of leisure activities on the cognitive ability of older adults: a latent variable growth model analysis. Front Psychol 2022 Apr 13;13:838878 [FREE Full text] [CrossRef] [Medline]
    15. Dong X, Li Y, Simon MA. Social engagement among U.S. Chinese older adults--findings from the PINE Study. J Gerontol A Biol Sci Med Sci 2014 Nov 05;69 Suppl 2(Suppl 2):S82-S89 [FREE Full text] [CrossRef] [Medline]
    16. Kim DE, Yoon JY. Trajectory classes of social activity and their effects on longitudinal changes in cognitive function among older adults. Arch Gerontol Geriatr 2022 Jan;98:104532. [CrossRef] [Medline]
    17. Kelly ME, Duff H, Kelly S, McHugh Power JE, Brennan S, Lawlor BA, et al. The impact of social activities, social networks, social support and social relationships on the cognitive functioning of healthy older adults: a systematic review. Syst Rev 2017 Dec 19;6(1):259 [FREE Full text] [CrossRef] [Medline]
    18. Feng Q, Fong JH, Zhang W, Liu C, Chen H. Leisure activity engagement among the oldest old in China, 1998–2018. Am J Public Health 2020 Oct;110(10):1535-1537. [CrossRef]
    19. Wu CP, Jiang XQ. An Introduction to Gerontology. Beijing: Renmin University of China Press; 2006.
    20. Glass TA, De Leon CFM, Bassuk SS, Berkman LF. Social engagement and depressive symptoms in late life: longitudinal findings. J Aging Health 2006 Aug 30;18(4):604-628. [CrossRef] [Medline]
    21. Greaves CJ, Farbus L. Effects of creative and social activity on the health and well-being of socially isolated older people: outcomes from a multi-method observational study. J R Soc Promot Health 2006 May 07;126(3):134-142. [CrossRef] [Medline]
    22. Hong S, Hasche L, Bowland S. Structural relationships between social activities and longitudinal trajectories of depression among older adults. Gerontologist 2009 Feb 18;49(1):1-11 [FREE Full text] [CrossRef] [Medline]
    23. Huang J, Wong R, Chen C, Mao I, Huang C, Chang W, et al. Trajectory of depression symptoms and related factors in later life--a population based study. J Affect Disord 2011 Oct;133(3):499-508. [CrossRef] [Medline]
    24. Niti M, Ng T, Kua EH, Ho RCM, Tan CH. Depression and chronic medical illnesses in Asian older adults: the role of subjective health and functional status. Int J Geriatr Psychiatry 2007 Nov;22(11):1087-1094. [CrossRef] [Medline]
    25. Wang L, van Belle G, Kukull WB, Larson EB. Predictors of functional change: a longitudinal study of nondemented people aged 65 and older. J Am Geriatr Soc 2002 Sep;50(9):1525-1534. [CrossRef] [Medline]
    26. Carvalho JP, Hopko DR. Behavioral theory of depression: reinforcement as a mediating variable between avoidance and depression. J Behav Ther Exp Psychiatry 2011 Jun;42(2):154-162. [CrossRef] [Medline]
    27. Dragioti E, Gerdle B, Levin L, Bernfort L, Dong H. Association between participation activities, pain severity, and psychological distress in old age: a population-based study of Swedish older adults. Int J Environ Res Public Health 2021 Mar 10;18(6):2795 [FREE Full text] [CrossRef] [Medline]
    28. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol 2014 Feb 12;43(1):61-68 [FREE Full text] [CrossRef] [Medline]
    29. Deeg DJH, Bath PA. Self-rated health, gender, and mortality in older persons: introduction to a special section. Gerontologist 2003 Jun 01;43(3):369-371. [CrossRef] [Medline]
    30. Wang R, Chen Z, Zhou Y, Shen L, Zhang Z, Wu X. Melancholy or mahjong? Diversity, frequency, type, and rural-urban divide of social participation and depression in middle- and old-aged Chinese: a fixed-effects analysis. Soc Sci Med 2019 Oct;238:112518. [CrossRef] [Medline]
    31. Rahman M, Liang C, Gu D, Ding Y, Akter M. Understanding levels and motivation of physical activity for health promotion among Chinese middle-aged and older adults: a cross-sectional investigation. J Healthc Eng 2019;2019:9828241 [FREE Full text] [CrossRef] [Medline]
    32. Lau PWC, Wang JJ, Ransdell LL, Shi L. The effectiveness of Facebook as a social network intervention to increase physical activity in Chinese young adults. Front Public Health 2022 Jul 22;10:912327 [FREE Full text] [CrossRef] [Medline]
    33. Hao X, Yang Y, Gao X, Dai T. Evaluating the effectiveness of the health management program for the elderly on health-related quality of life among elderly people in China: findings from the China Health and Retirement Longitudinal Study. Int J Environ Res Public Health 2019 Jan 03;16(1):113 [FREE Full text] [CrossRef] [Medline]
    34. Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Measur 2016 Jul 26;1(3):385-401. [CrossRef]
    35. Zhou L, Ma X, Wang W. Relationship between cognitive performance and depressive symptoms in Chinese older adults: the China Health and Retirement Longitudinal Study (CHARLS). J Affect Disord 2021 Feb 15;281:454-458. [CrossRef] [Medline]
    36. Amtmann D, Kim J, Chung H, Bamer AM, Askew RL, Wu S, et al. Comparing CESD-10, PHQ-9, and PROMIS depression instruments in individuals with multiple sclerosis. Rehabil Psychol 2014 May;59(2):220-229 [FREE Full text] [CrossRef] [Medline]
    37. Wang X, Lu J, Liu Q, Yu Q, Fan J, Gao F, et al. Childhood experiences of threat and deprivation predict distinct depressive symptoms: a parallel latent growth curve model. J Affect Disord 2022 Dec 15;319:244-251. [CrossRef] [Medline]
    38. Podsakoff PM, MacKenzie SB, Lee J, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol 2003 Oct;88(5):879-903. [CrossRef] [Medline]
    39. Rosso AL, Eaton CB, Wallace R, Gold R, Curb JD, Stefanick ML, et al. Combined impact of geriatric syndromes and cardiometabolic diseases on measures of functional impairment. J Gerontol A Biol Sci Med Sci 2011 Mar 11;66(3):349-354 [FREE Full text] [CrossRef] [Medline]
    40. Chen C, Mullan J, Griffiths D, Kreis IA, Lan T, Chiu H. Trajectories of depression and their relationship with health status and social service use. Arch Gerontol Geriatr 2011 Sep;53(2):e118-e124. [CrossRef] [Medline]
    41. Zimmer Z, Hickey T, Searle MS. The pattern of change in leisure activity behavior among older adults with arthritis. Gerontologist 1997 Jun 01;37(3):384-392. [CrossRef] [Medline]
    42. Tan KL, Yadav H. Depression among the urban poor in Peninsular Malaysia: a community based cross-sectional study. J Health Psychol 2013 Jan 09;18(1):121-127. [CrossRef] [Medline]
    43. Wassink-Vossen S, Collard RM, Oude Voshaar RC, Comijs HC, de Vocht HM, Naarding P. Physical (in)activity and depression in older people. J Affect Disord 2014 Jun;161:65-72. [CrossRef] [Medline]
    44. Lindwall M, Larsman P, Hagger MS. The reciprocal relationship between physical activity and depression in older European adults: a prospective cross-lagged panel design using SHARE data. Health Psychol 2011 Jul;30(4):453-462. [CrossRef] [Medline]
    45. Abreu PR, Santos CE. Behavioral models of depression: a critique of the emphasis on positive reinforcement. Int J Behav Consult Ther 2008;4(2):130-145. [CrossRef]
    46. van Genugten CR, Schuurmans J, Hoogendoorn AW, Araya R, Andersson G, Baños R, et al. Examining the theoretical framework of behavioral activation for major depressive disorder: smartphone-based ecological momentary assessment study. JMIR Ment Health 2021 Dec 06;8(12):e32007 [FREE Full text] [CrossRef] [Medline]
    47. Lordan G, Pakrashi D. Make time for physical activity or you may spend more time sick!. Soc Indic Res 2014 Jan 17;119(3):1379-1391. [CrossRef]
    48. Schuch FB, Vancampfort D, Firth J, Rosenbaum S, Ward PB, Silva ES, et al. Physical activity and incident depression: a meta-analysis of prospective cohort studies. Am J Psychiatry 2018 Jul 01;175(7):631-648. [CrossRef] [Medline]
    49. Wang X, Guo J, Liu H, Zhao T, Li H, Wang T. Impact of social participation types on depression in the elderly in China: an analysis based on counterfactual causal inference. Front Public Health 2022 Apr 1;10:792765 [FREE Full text] [CrossRef] [Medline]
    50. Siv G, Ove H, Eystein S. Active social participation and depression among older persons. J Clin Nurs 2010 Oct;19(s1):151.
    51. Luo Y, Wang Z, Zheng X. [Association between the change of daily living activities and symptoms of depression in Chinese middle-aged and elderly people]. Zhonghua Liu Xing Bing Xue Za Zhi 2017 Aug 10;38(8):1055-1059. [CrossRef] [Medline]
    52. Yi Y, Park Y. Structural equation model of the relationship between functional ability, mental health, and quality of life in older adults living alone. PLoS One 2022 Aug 3;17(8):e0269003 [FREE Full text] [CrossRef] [Medline]
    53. Bae S. Autoregressive cross-lagged modelling of the relationship between social activity, depressive symptoms, and cognitive function in Korean elderly. Psychogeriatrics 2021 May 09;21(3):350-358. [CrossRef] [Medline]

    ‎
    BA: behavioral activation
    BP: body pain
    CES-D: Center for Epidemiologic Study Depression Scale
    CFI: comparative fit index
    CHARLS: China Health and Retirement Longitudinal Study
    HRQoL: health-related quality of life
    LGCM: latent growth curve model
    PF: physical function
    RCPR: response-conditioned positive reinforcement
    RMSEA: root mean square error of approximation
    SRMR: standardized root mean square residual
    TLI: Tucker-Lewis index

    Edited by A Mavragani, T Sanchez; submitted 29.11.22; peer-reviewed by X Hou, T Martino; comments to author 11.01.23; revised version received 29.01.23; accepted 02.02.23; published 23.03.23

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    ©Yan Gao, Zhihao Jia, Liangyu Zhao, Suyue Han. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 23.03.2023.

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