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Abstract
Objectives Studies have shown that good cognitive function can moderate the relationship between non-exercise physical activity (NEPA) and activities of daily living (ADLs) disability to some extent, and this study mainly explores the relationship between ADL and NEPA and cognitive function in Chinese older adults.
Setting and participants Data came from a nationally representative sample of 2471 Chinese old adults (aged 65+) from the 2011, 2014 and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey.
Primary and secondary outcome measures A cross-lagged panel model combined with mediation analysis was used to determine the relationship between ADL and NEPA and the mediating effect of cognitive function on the ascertained ADL–NEPA relationship.
Results The more frequently people over the age of 65 in China participate in NEPA, the lower the risk of ADL disability. Cognitive function partially mediated this expected relationship, accounting for 9.09% of the total NEPA effect on ADL.
Conclusion Participating in more NEPA could reduce the risk of ADL disability, and participating in NEPA may reduce the risk of ADL disability through cognitive function to some extent.
- Aging
- EPIDEMIOLOGIC STUDIES
- GERIATRIC MEDICINE
- Health Services for the Aged
- PREVENTIVE MEDICINE
Data availability statement
Data are available on reasonable request. The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy concerns
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
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STRENGTHS AND LIMITATIONS OF THIS STUDY
This study used a cross-lagged panel model, which has the advantage of inferring causal relationships between variables compared with cross-sectional studies.
The study explored the connection between non-exercise physical activity and activities of daily living in the elderly, thereby increasing the evidence of the health benefits of non-exercise physical activity in the elderly.
The study used the Chinese Longitudinal Health and Longevity Study database and could not determine the lag time and the measurement method of leisure activities by itself, which might have introduced some bias.
Introduction
China has one of the fastest-growing ageing populations in the world. By 2040, China’s population over the age of 60 is expected to reach 28%, which will lead to an increase in disabled elderly people.1 ‘Disability’ refers to the state in which the elderly suffer from chronic diseases, physical injuries and psychological disorders that lead to impairment of physical functions, thereby limiting daily activities. Internationally, the activities of daily living (ADLs), proposed by Katz, are usually used as a criterion for judging whether the elderly are disabled and the degree of disability.2 It is predicted that the number of disabled elderly people in China will reach 61.68 million in 2030 and will further rise to 97.50 million in 2050, which will be accompanied by a heavy economic and care burden.3 ADL disability leads to the need for assistance in personal care functions, which is closely related to a series of physical and mental diseases such as cardiovascular disease, depression and dementia, and will greatly impair the quality of life of the elderly.4
WHO recommends that the elderly perform at least 150 min of moderate-to-vigorous physical activity (MVPA) while strengthening muscles at least twice a week.5 However, global surveillance data in 2016 showed that the prevalence of physical inactivity among adults remains high (27.5%), and the prevalence of physical inactivity in older adults is higher than in younger adults,6 suggesting that older adults are less likely to participate in MVPA. For older adults, leisure activities are an important part of their later life, which has important implications for active ageing, as leisure activities can improve the physical and mental health of individuals, such as improving well-being and promoting health.7 Based on the above considerations, studies have shown that non-exercise leisure activities are equally important for health, especially for inactive older adults.8 Non-exercise physical activity (NEPA) is not exercise for health and fitness, including housework and moderate-vigorous activities that are not typically classified as aerobic exercise (eg, cleaning, gardening and mowing).9 Although not as vigorous as physical activities, leisure activities such as housework and gardening are beneficial to health and delay the onset of disability.10 However, the potential mechanisms between NEPA and the risk of ADL disability in the elderly are not well understood.
Research on the relationship between NEPA and ADL disability in the elderly is limited. First of all, studies have shown that NEPA has a beneficial effect on cognitive ability.11 For example, gardening activities affect the level of brain nerve growth factors related to cognitive function (CF).12 In addition, ADL was negatively correlated with CF, while lower ADLs are associated with poorer CF.13 Not only does ADL disability serve as a marker of an underlying neurodegenerative condition but also has a direct and independent impact on CF in Chinese people over the age of 80.14 Thus, CF may play an important role in the relationship between ADL and NEPA.
Although studies have shown that compared with vigorous activity, the elderly also gain health benefits from participating in regular NEPA,15 there is still a lack of research on the mechanism of interaction between NEPA and ADL. Therefore, this study first used a national longitudinal survey of the elderly in China to explore the interaction between NEPA and ADL. Then, given the potentially important role of CF in the mechanism, we further explored the mediating role of CF in this relationship.
Method
Patient and public involvement
This research was performed without patient involvement. Patients were not invited to comment on the study design or contribute to the writing or editing of the paper.
Data and sample
The data came from a tracking survey of the elderly organised by Peking University, the Chinese Longitudinal Health and Longevity Study (CLHLS), which randomly selected about half of the counties and cities in 23 provinces (cities and autonomous regions) in China for investigation. The baseline survey was conducted in 1998, and a follow-up was conducted every 3 years. As of 2008,16 8 surveys had been conducted, with a total of 113 000 household visits. The survey included the family structure and living arrangements, marital status, health status, socioeconomic characteristics and so on. More detailed information on CLHLS could be found elsewhere.17 This study used data from the latest three waves of CLHLS, and our study was based on respondents in 2011, excluding respondents who were added to replace those who died in the subsequent waves. At baseline, there were 9765 participants in the cohort. Those 65 and younger were excluded first, and then those reporting age 106 years or older were excluded due to a lack of reliable information to verify their advanced age.18 Respondents who lacked scores of ADL and NEPA at baseline were further excluded and finally, 8387 respondents were surveyed in 2011. Among the 8387 older adults surveyed in 2011, 5276 participated in the follow-up survey in 2014, and 2471 people participated in the follow-up survey in 2018. Therefore, a total of 2471 older adults obtained complete follow-up data for 2011, 2014 and 2018.
Measures
Activities of daily living
ADL was assessed using the Katz Index scale,2 which includes six tasks performed by participants in daily life that are essential to independent living: dressing, bathing, toileting, eating, indoor activities and continence.19 Each item had three response options: 0 (‘without assistance’), 1 (‘one part assistance) or 2 (‘more than one part assistance’). Total scores ranged from 0 to 18, with higher scores indicating worse ADL. ADL disability was defined as an inability to perform any task independently; participants were considered as having independent ADL if they were able to perform all tasks independently.
Non-exercise physical activity
The participants were interviewed about their participation in NEPA, including keeping domestic animals or pets, gardening and housework. The frequency of participation in each activity had three response options: 0 (never), 1 (at least once a week to once a quarter) or 2 (at least five times per week).11 Score on each activity ranged from 0 to 2, and non-exercise physical activities (ranging from 0 to 6).
Cognitive function
CF was assessed with a Chinese modified Mini-Mental State Examination (CMMSE),20 which consists of 30 items within 6 dimensions: orientation, registration, attention, language, memory and visual construction skills. Each item of the CMMSE is scored 1 if the answer is correct and 0 for an incorrect answer or ‘unable to answer’, and the total MMSE score ranges from 0 to 30 points, with higher scores reflecting better CF.
Control variables
To minimise the possibility that ADL–NEPA relationship is caused by a third variable, and to maximise the parsimony of our analytical model, only a limited number of covariates that were known to be associated with ADL and NEPA were controlled in our analysis. Those control variables were age (65+ or 80+), gender (men or women), education (no schooling (0 years of education) or some schooling (≥1 year of education)), marital status (married or unmarried), residence (city or township), the main source of financial support (retirement wages, families or others), exercise status (yes or no), physical performance (PP) limitation (yes or no), depressive symptoms,21 and sedentary leisure activities.
PP limitation22 is evaluated through three items, including the independent ability to stand from a chair without using hands, lifting a book from the floor from a standing position and turning 360°. If a participant cannot complete one of the projects independently, it is defined as having PP limitations.
Considering that people will participate in a variety of leisure activities at the same time, our research mainly analyses the relationship between NEPA and ADL, so the sedentary leisure activities (watching television or listening to the radio, reading books or newspapers, playing cards or mahjong) are coded at the same frequency and added (range from 0 to 6) as covariates are added to the final model.
Statistical analysis
Cross-lagged panel model (CLPM) is a longitudinal analysis model that examines the mutual predictive or quasi-causal relationship between variables. The model measures the two variables at different time points, and the estimated path coefficients of the premeasurement variables on the postmeasurement variables have a time sequence relationship, which conforms to the principle of epidemiological causal inference.23 24 In this study, the CLPM analysis was adopted to examine the direction and strength of the association between ADL and NEPA.
First, we constructed an unconstrained baseline model (model 1). Since CLPM usually assumes that prospective relationships between variables are stable over time,25 model 2 applied equality constraints to all the autoregressive paths, model 3 applied equality constraints to all the cross-lagged paths and model 4 applied equality constraints to all the autoregressive paths and the cross-lagged paths. Then we compared the model fit of model 1 with model 2, model 3 and model 4. Based on the comparison results of the above models, the paths that can be fixed to time-invariant were determined in model 5, and the covariates related to ADL and NEPA were added. In this analysis, gender, age, education, financial source, residence and PP limitation were treated as time-invariant variables, whereas all other covariates were time-varying variables. Assuming that CF mediates the causal temporal relationship of ADL-NEPA ascertained in model 5, model 6 was an autoregressive mediation model based on model 5 adding CF as a mediating variable. Based on model 6, we evaluated the mediating effect of CF, which was equal to the indirect effect of X predicting Y–M (the product of the two direct effects in the path).23
Due to our large sample size, the χ2 goodness-of-fit statistic will be more sensitive to this, which is not suitable as an evaluation index for model fitting. Thus, we used approximate root mean square error (RMSEA) and Comparative Fit Index (CFI) to evaluate the degree of model fit. RMSEA values below 0.05 indicate a good fit to the data, and values between 0.05 and 0.08 indicate a reasonable fit. For CFI, a value greater than 0.90 is considered an acceptable fit, and 0.95 or greater is considered a good fit.26 The invariance of the model is mainly determined by the difference in CFI (ΔCFI) and RMSEA (ΔRMSEA) between the base model and the constrained model: changes in CFI (ΔCFI) with values lower than −0.010 and changes in RMSEA (ΔRMSEA) with values higher than .015.27
Available data from all 2471 respondents were used in this study, and the missing data were dealt with full information maximum likelihood estimation.28 The highest absolute values of skewness and kurtosis for our observed variables were 2.991 and 8.828 for the ADL score (2018), therefore, the parameters were estimated with robust maximum likelihood. We reported standardised regression coefficients (βs) and p values throughout. All these analyses were conducted using the lavaan V.0.6-14 package in R V.4.2.2.
Results
Descriptive statistics
The distribution of the study variables measured in 2011, 2014 and 2018 is presented in table 1. The mean scores of the existing samples from 2011 to 2018 were 6.16, 6.27 and 6.96 for ADL, 2.63, 2.44 and 1.65 for NEPA and 26.83, 26.37 and 23.96 for CF, respectively. Among the 2471 survivors who were interviewed at each wave, the results of repeated-measures analysis of variance indicated that scores of ADL increased over time between 2011 and 2018 (F=144.676, p<0.001), whereas mean NEPA decreased (F=374.043, p<0.001).
Sample characteristics of ADL, NEPA, CF and covariates (n=2471)
Correlations between ADL, NEPA and CF are presented in online supplemental table. On any given occasion, NEPA and ADL were negatively and significantly correlated (p<0.01), the CF was positively correlated with NEPA and negatively correlated with ADL two both at a prior occasion and the same occasion (p<0.01).
Supplemental material
Reciprocal relationship between ADLs and leisure activities
We used CLPM to study the association between ADL and NEPA. As reported in table 2, the model was unstable after applying the equality constraints to all the autoregression paths (model 2), the model remained stable after applying the equality constraints to all the cross-lagged paths (model 3) and the model was unstable after applying the equality constraints to all autoregression paths and cross-lagged paths (model 4). Therefore, we fixed the cross-lagged paths to be time-invariant and added covariates for subsequent analysis.
Model fits and comparisons for cross-lagged panel models
Figure 1 depicts model 5. After controlling for covariates, model 5 fitted the data adequately (RMSEA=0.031, CFI=0.962). There was no statistical significance in the 3-year cross-lagged effects of prior ADL on NEPA (β=−0.010, p>0.05), but the 3-year cross-lagged effects of NEPA on subsequent ADL (β=−0.072, p<0.001) was statistically significant. The results showed that the ADL and NEPA had a unidirectional causal temporal relationship, and the frequency of NEPA participation in the elderly negatively predicted the ADL scores of the elderly.
Cross-lagged panel model of the reciprocal relationship between NEPA and ADL. ADL, Aactivities of daily living; NEPA, Nnon-exercise physical activity. **p<0.01, ***p<0.001.
Mediating effect of CF
As figure 2 presents, after adding three indirect paths that shared CF as a potential mediator and adjustment for control variables, model 6 still had a good fit to the data (RMSEA=0.033, CFI=0.923). The 3-year cross-lagged effect of prior NEPA on subsequent ADL (β=−0.066, p<0.001) was significantly reduced in size compared with those in model 5. The indirect effect of prior NEPA on subsequent ADLs via CF (effect size=−0.003, p<0.05) was significant. The effect size of the longitudinal mediation was computed with MacKinnon’s formula for calculating the mediated percentage, which is the indirect effect divided by the total effect. However, the indirect effect size only accounted for 9.09% of the total effects demonstrated in model 6.
The longitudinal mediation relations among NEPA, CF and ADL. ADL, activities of daily living; CF, cognitive function; NEPA, non-exercise physical activity. **p<0.01,***p<0.001.
Discussion
Key results
In this large cohort study of the prospective relationship between NEPA, ADL and CF, during the follow-up, the percentages of missing values of ADL in 2014 and 2018 were 4.61% and 6.52%, respectively. CF had 2.31% deletion in 2012, 6.56% in 2014, 10.89% in 2018 and 10.89% in 2018, respectively. The percentages of missing values in 2014 and 2018 were 1.25% and 3.64%, respectively, our findings showed that more participation in NEPA is negatively correlated with the risk of ADL disability. In addition, CF partially mediates this prospective relationship, accounting for 9.09% of the total impact of NEPA on ADL. It showed that the frequency of NEPA participation can reduce the risk of ADL disability in the elderly by affecting CF.
Participating in physical activities can help reduce the risk of non-communicable diseases (such as diabetes, cardiovascular disease, hypertension) in the elderly29 and improve their physical and physiological functions.30 However, due to the increase in age and the decline in physical function, the elderly are more likely to participate in NEPA than physical activities. Previous studies on NEPA and the health of the elderly have focused on cardiovascular disease and survival.8 31 This study went further than previous studies and focused on the relationship between NEPA and ADL disability in the elderly, and found that there is a unidirectional causal temporal relationship between the frequency of NEPA and ADL disability, that is, the frequency of NEPA participation can affect the risk of ADL disability. This result is broadly consistent with the evidence from a few existing cohort studies.16 32 The results of this study also showed that ADL scores in the elderly did not affect the frequency of NEPA participation, which is inconsistent with the existing research results.33 This may be because of the different types of leisure activities in this study, and most of the elderly people who were eventually included in this study were under the age of 80, and their ADL scores were low during the baseline survey, resulting in ADL disability may not be fully evaluated.
Studies found that in all countries, time spent on housework was positively correlated with health, and reducing housework might accelerate the deterioration of ADL disability.34 35 Gardening activities require some form of physical exertion, which could enhance physiological stability and high-level functioning.36 A randomised controlled experiment showed that gardening could improve the ADLs of the elderly.37 Keeping pets is associated with a lower risk of ADL disability in the elderly population in China.32 For example, raising a dog was related to increased walking, and there was a direct relationship between walking a dog and better physical function or health.38 These findings are of great significance. Because the elderly with limited athletic ability can still benefit from NEPA.
Our research shows that good CF partially regulates the association between NEPA and ADL disability. Studies have found that high levels of NEPA are associated with a lower risk of cognitive impairment, and this effect is not related to regular physical exercise.39 Studies have found that housework is associated with higher CF, especially in terms of attention and memory.40 This may be because, in the elderly, the time spent doing housework is positively correlated with brain volume, especially grey matter volume.41 Participating in gardening activities can significantly increase the levels of brain-derived neurotrophic factor and platelet-derived growth factor, indicating the potential benefits of gardening activities for CF in the elderly.12 A cross-sectional study showed that people who have frequent contact with pets have a better cognitive state than people who do not have pets or do not have regular contact with pets and that having cats is related to better CF (verbal learning/memory).38 While CF and ADL disability were negatively correlated with each other. Previous studies have also proved it: cognitive improvements may lead to behavioural changes that delay the onset of ADL disability.42
Limitations
In this study, the power of the 3-year lag effect of ADL on NEPA in model 5 was less than 10%, and the authenticity of this pathway needs to be verified by a larger sample size.However, because at least three follow-up records were required, this was the largest sample size that could be screened from the CLHLS database.
Notably, we tested only one possible mechanism for the relationship between ADL and NEPA, even though the mediating role of CF was statistically significant, only a small portion of the direct effect could be explained. Therefore, we cannot determine whether this interaction over time is mainly caused by NEPA and ADL itself, or by the influence of other mechanisms. Previous studies have also noticed that the process of the relationship between NEPA and ADL may involve emotional health, healthy behaviour and CF.43 The mediating role of CF found in this study may be just one of many steps between NEPA and ADL. Further research is needed to solve this problem.
Longitudinal mediation designs provide more reliable evidence than cross-sectional studies, but they also have some limitations that cannot be ignored. First, ADL, NEPA and CF were derived from self-reports that were prone to recall bias, and the measures of leisure activities are somewhat limited and do not measure the exact scale or quality of these activities. Second, CLHLS is followed up every 3 years, which makes it impossible for this study to determine the lag time in the CLPM on its own. In addition, when comparing the stability of the models, the model fitting indexes rejected the stability hypothesis in autoregressive paths, which may be because the decline rate of ADL and the frequency of participation in NEPA in the elderly gradually accelerated over time. Finally, because the behavioural characteristics of the elderly are difficult to generalise, according to previous research, NEPA and ADL are closely related to education, socioeconomic status and lifestyle.32 Although we controlled for a limited number of important covariates, such as sociodemographic information and lifestyle, confounding by other unknown variables was still possible.
Conclusion
In conclusion, cross-lagged longitudinal data from CLHLS suggest that participating in more NEPA could reduce the risk of ADL disability, and participating in NEPA may reduce the risk of ADL disability through CF to some extent. Elucidating the impact of NEPA participation on ADL disability may provide an important basis for developing policies and intervention programmes to promote active NEPA participation in older adults.
Data availability statement
Data are available on reasonable request. The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy concerns
Ethics statements
Patient consent for publication
Ethics approval
This study involves human participants and was approved by the Research Ethics Committee of Peking University (IRB00001052-13074). Participants gave informed consent to participate in the study before taking part.
Acknowledgments
The authors would like to thank the Center for Healthy Aging and Development Studies, Peking University for supporting this database. Data used for this research were provided by the study entitled 'Chinese Longitudinal Healthy Longevity Survey' (CLHLS) managed by the Center for Healthy Aging and Development Studies, Peking University.
References
Supplementary materials
Supplementary Data
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
Footnotes
Contributors ZZ: conceptualisation (lead), writing—original draft (lead), formal analysis (lead), writing—review and editing (equal). XC: writing—original draft (lead), writing—review and editing (equal). YQ: conceptualisation (supporting), formal analysis (supporting), writing—review and editing (equal). JY: methodology (lead), formal analysis (supporting), writing—review and editing (equal). JF: conceptualisation (supporting), project administration (equal), revision and polishing. ZS: data curation (equal), project administration (equal), revision and polishing. XC: conceptualisation (supporting), supervision (equal). HZ: conceptualisation (supporting), supervision (equal). YL: conceptualisation (supporting), supervision (equal). ZZ is the lead study investigator. HZ, YL and XC are the guarantors.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.