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. 2025 Jul 10;57:103165. doi: 10.1016/j.pmedr.2025.103165

The association of physical activity with perceived fatigability among community-dwelling older adults aged ≥60 years in regional China

Bin Yang a,1, Qing Ye b,c,1, Xiaojing Deng d,1, Zhiyong Wang b,c, Caihong Hu b, Yeping Bian d, Jian Xu d,⁎, Fei Xu b,c,⁎⁎
PMCID: PMC12280341  PMID: 40704105

Abstract

Objectives

This study aimed to investigate the association of physical activity with perceived fatigability among community-dwelling older adults in regional China.

Methods

Totally, 5484 community-dwelling residents aged 60+ years were randomly chosen from Nanjing municipality of China in this cross-sectional study in 2023. The outcome variable, perceived physical and mental fatigability, was assessed with the validated Chinese version of Pittsburgh Fatigability Scale. The independent variable, physical activity, was measured with the Chinese version of International Physical Activity Questionnaire. Mixed-effect logistic regression models were employed to compute odds ratios (ORs) and 95 % confidence intervals (95 %CIs) for examining associations of physical activity with both physical and mental fatigability.

Results

Among overall participants, the prevalence of physical and mental fatigability was 59.0 % (95CI = 57.7, 63.0) and 51.1 % (95 %CI = 49.8, 52.4), respectively. After adjustment for potential confounders, participants with sufficient physical activity were less likely to perceive either physical (OR = 0.66; 95 %CI = 0.55, 0.81) or mental (OR = 0.68; 95 %CI = 0.56, 0.83) fatigability compared to their counterparts with insufficient physical activity. Moreover, such negative associations of physical activity with physical and mental fatigability were observed for participants stratified by age/gender, with an exemption for the relationship between physical activity and physical fatigability in participants aged 80+ years.

Conclusions

Physical activity was negatively associated with either physical or mental fatigability for overall or age−/gender-specific community-dwelling residents aged 60+ years in regional China. This study has important implications for building healthy-aging societies, since it is possible to prevent or mitigate both physical and mental fatigability for older adults through population-level physical activity promotion.

Keywords: Aging, Fatigue, Fatigability, Physical activity, PFS, Older adult

Highlights

  • •

    In older adults, physical activity was negatively associated with physical/mental fatigability.

  • •

    Sex/age-specific association of physical activity with fatigability also held negative.

  • •

    Prevention of fatigue may be achieved through physical activity intervention.

1. Introduction

Fatigue is broadly defined as a perceived lack of physical and/or mental energy, typically including weariness, tiredness, and/or exhaustion (Guidelines Multiple Sclerosis Clinical Practice, 1998). Studies have shown that fatigue can significantly produce unfavorable health-related problems, including functional, physical, and/or mental conditions, and elevate the risk of death (Knoop et al., 2021; Avlund, 2010; Moreh et al., 2010; Zengarini et al., 2015; Yu et al., 2010). Fatigue is prevalent among older adults, with a prevalence of more than 50 % (Yu et al., 2010; Park et al., 2024; Eldadah, 2010; Jing et al., 2015; Vaes et al., 2022). Therefore, fatigue is a common public health concern among older individuals and warrants particular attention in a rapidly aging society.

Previously, when the term ‘fatigue’ was used to describe personal experience of lack of energy, it simply emphasized the overall subjective perception of tiredness, weariness, and/or exhaustion without considering daily activities (Avlund, 2010; Eldadah, 2010). Recently, a new concept, “fatigability”, has emerged to link traditionally self-perceived lack of energy to specific daily activities of standardized intensity and duration. This allows for a comprehensive evaluation of perceived lack of energy with a relatively objective assessment (Eldadah, 2010; Prochaska et al., 2020). Fatigability has a similar meaning to the traditional term “fatigue” regarding perceived lack of energy and is thus sometimes used interchangeably with “fatigue” in population studies. The prevalence of physical and mental fatigability ranged from 41.1 to 56.0 % and 21.8 to 28.9 %, respectively, as measured with the Pittsburgh Fatigability Scale (PFS) among community-dwelling older adults in the United States of America (USA) (Glynn and Qiao, 2023).

From the perspective of population-level interventions for fatigue, it is particularly important to identify the modifiable factors of fatigue and then initiate practicable and effective intervention programs to prevent or mitigate fatigue. As a modifiable behavior, physical activity is a potentially optimal factor that is associated with fatigue, although the relationship between physical activity and fatigue is complex (Knoop et al., 2021). Some studies reported that physical activity might negatively impact fatigue, while a few investigations found that fatigue might reduce physical activity engagement [13−20]. However, almost all these studies focused on older participants with specific chronic diseases, not on general community-dwelling older adults (Eriksen and Bruusgaard, 2004; Nicklas et al., 2016; Park et al., 2018; Jacquet et al., 2021; Chan and Yu, 2022; Belloni et al., 2021; Cramp and Byron-Daniel, 2012; Wender et al., 2022). Recently, physical activity was also examined and found to be negatively associated with PFS-defined fatigability among community-dwelling older adults, primarily in the USA (Qiao et al., 2021; LaSorda et al., 2020; Wanigatunga et al., 2018; Schrack et al., 2019; Simonsick et al., 2014; Qiao et al., 2022a; Moored et al., 2022a; Moored et al., 2022b; Qiao et al., 2022b). Hence, it is important to further investigate the relationship between physical activity and fatigability among community-dwelling older adults worldwide.

Currently, only one study has reported fatigue among community-dwelling adults in China (Jing et al., 2015). This cross-sectional survey investigated the prevalence of fatigue using the Chalder Fatigue Scale among women aged ≥45 years (N = 1272) in Shunde municipality, Guangdong Province, China (Jing et al., 2015). Unfortunately, no information on physical activity was collected, and men were not included in the study (Jing et al., 2015). Moreover, in 2020, 18.7 % of residents (approximately 264 million) in China were older adults aged ≥60 years (China National Bureau of Statistics, 2024). To date, there has been no evidence regarding the association between physical activity and fatigue/fatigability among community-dwelling older adults in China. To address this gap, a community-based survey was conducted to examine the relationship between physical activity and fatigability among older men and women aged ≥60 years in Nanjing municipality, China.

2. Methods

2.1. Study design and participants

This cross-sectional survey, part of the Healthy Aging and Healthy Elderly study, was conducted in Nanjing municipality of China in 2023. There were 12 administrative districts and approximately 9.31 million registered residents in Nanjing municipality in 2020 (Nanjing Municipal Bureau of Statistics, 2024). Among these local inhabitants, 20.9% were older adults aged≥60 years (Nanjing Municipal Bureau of Statistics, 2024). The study participants were community-dwelling older residents aged ≥60 years from across the whole municipality (Zhang et al., 2023). The inclusion criteria were as follows: 1) a local registered resident in Nanjing, 2) ≥60 years old, 3) without literal or cognitive/mental problems, 4) not restricted or bedridden in daily activities, and 5) not suffering from an active infectious disease.

For sample size estimation, the following factors were considered: 1) population-based cross-sectional study design, 2) documented prevalence of fatigability (27.0 %) among adults aged ≥60 years (LaSorda et al., 2020; Cohen et al., 2021), 3) multistage sampling strategy, 4) expected statistical power (90 %), 5) assumed response rate of 90 %, and 6) age- and sex-stratified data analysis approach. Therefore, the sufficient sample size was approximately 6000 in this study.

Participants were randomly selected from all 12 administrative districts of Nanjing municipality using a multistage sampling approach. First, considering the five-level governance system in China (central government, province/municipality, urban district/rural county, administrative streets/townships, and administrative community), two administrative streets/townships were chosen randomly from each district. Second, one administrative community was selected from each chosen street/township, resulting in 24 communities involved in this study. Third, based on the sample size (N=6,000), approximately 250 participants were selected from each involved community. Finally, according to the household list of each community, 250 participants were chosen from each selected community with consideration of the age distribution of general older adults (60–69 vs. 70–79 vs. ≥80 years, 5 vs. 2.3 vs. 1) in Nanjing municipality (Nanjing Municipal Bureau of Statistics, 2024). Figure 1 shows the selection flowchart of the participants.

Fig. 1.

Fig. 1

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Flow chart of participant's selection in this study.

Each participant provided a written informed consent prior to the survey. The study was approved by the Ethics Committee of Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention. This study was conducted in accordance with the principles of the Declaration of Helsinki.

2.2. Data collection

The principles and approaches recommended in the “Scheme of the Chinese Chronic Non-communicable Diseases (NCDs) and Risk Factor Surveillance”, issued by the Chinese Center for Non-communicable Disease Prevention and Control (CCNCDC), were followed to gather questionnaire-based information, measure body height and weight, and collect blood samples (Wang et al., 2018). Data were collected via an interviewer-administered questionnaire about the participants' socio-demographic characteristics (including age, sex, residence area, educational attainment, marital status, and pre-retirement occupation), lifestyle and behaviors (including physical activity, smoking, drinking, meat intake, vegetable consumption, and sleep), histories of specific NCDs (including diabetes, hypertension, chronic obstructive pulmonary disease [COPD], coronary heart disease [CHD], stroke, chronic kidney disease [CKD], cancer, and abnormal lipid profile), as well as frailty and mental health state (referring to depressive symptoms).

Wearing light clothing and barefoot in a quiet room, each participant was assessed for body weight and height to the nearest 0.01 m and 0.1 kg, respectively. Both measurements were separately taken twice, and their mean values were used to calculate body mass index (BMI) (Wang et al., 2018). A 5-ml fasting venous blood sample was drawn from each participant for lipid profile measurement (Wang et al., 2018). A HITACHI7180 analyzer (Hitachi Co., Japan) was used to measure lipid profiles using detection kits from Shanghai Fosun Long March Medical Science Co., China.

2.3. Study variables

2.3.1. Outcome measure

The outcome variable, perceived fatigability, was measured using the validated simplified Chinese version of the Pittsburgh Fatigability Scale (PFS-SCHN) (Hu et al., 2021). The PFS assesses fatigability in older adults by measuring physical and mental fatigue with consideration of specific standardized daily activities [36, 37] (PFS in different languages is available at https://www.publichealth.pitt.edu/pittsburgh-fatigability-scale). In PFS, physical and mental fatigability are each assessed using an independent 10-item subscale (Glynn et al., 2015; Renner et al., 2021). The PFS-SCHN has demonstrated good validity and reliability in measuring fatigability among community-dwelling older adults in China (Hu et al., 2021). Each participant was asked to self-rate their perceived level of tiredness, weariness, and/or exhaustion on a scale ranging from 0 (no fatigue) to 5 (extreme fatigue) that the participant would imagine or expect after specific daily activities (Hu et al., 2021; Glynn et al., 2015; Renner et al., 2021). The corresponding values were assigned to each response option as 0, 1, 2, 3, 4, and 5. Consequently, the total score for physical or mental fatigability ranges from 0 to 50, with higher scores indicating greater fatigability (Hu et al., 2021; Glynn et al., 2015; Renner et al., 2021). Moreover, 15 was used as a cutoff value to categorize participants into “less physical fatigability (PFS<15)” or “greater physical fatigability (PFS≥15)”, while 13 was used to classify participants as “less mental fatigability (PFS<13)” or “greater mental fatigability (PFS≥13)” (Hu et al., 2021; Glynn et al., 2015; Renner et al., 2021; Simonsick et al., 2018).

2.3.2. Independent measure

The independent variable, physical activity, was measured using the validated Chinese version of the International Physical Activity Questionnaire (Qu and Li, 2004). Each participant was asked to self-report their physical activity time, intensity, and duration over the past seven days. Then, the total weekly moderate/vigorous physical activity (MVPA) time was calculated as the sum of moderate physical activity time and double the vigorous physical activity time in the last seven days (Chinese journal of epidemiology, 2022). For analysis, participants were classified as having “insufficient physical activity (<150 minutes/week)” or “sufficient physical activity (≥150 minutes/week)” based on their total MVPA time (Chinese journal of epidemiology, 2022).

2.3.3. Covariates

Participants' socio-demographic characteristics and several established influencing factors were considered in this study. Socio-demographic characteristics included age (60–69, 70–79, or ≥ 80 years old), sex (men or women), residence area (urban or suburban), educational attainment (≤6, 7–12, or ≥ 13 schooling years), marital status (having no partner/spouse or having a partner/spouse), and pre-retirement occupation (domestic worker, industrial worker, service/sales worker, office worker, academic/research staff, or self-employed) (Wang et al., 2018).

Lifestyle and behavioral factors included smoking, drinking, meat and vegetable consumption, sleep, and body weight status. Participants were categorized into “smokers” or “non-smokers”, and “drinkers” or “non-drinkers” according to definitions recommended by the CCNCDC (Wang et al., 2018). Consumption of meat (red and white meat) and vegetables was measured using a validated food frequency questionnaire (Liu et al., 2018). Based on recommendations for meat and vegetable consumption for older Chinese adults released by the China Nutrition Society in 2016 (Chinese Nutrition Society, 2016), participants were grouped as “recommendation reached” or “recommendation not reached” regarding meat and vegetable intake, respectively. Sleep quality was assessed using the Pittsburgh Sleep Quality Index, with a score of ≥5 indicating poor sleep quality (Buysse et al., 1989). Participants were also classified into three subgroups based on Chinese-specific BMI values: “<24 kg/m2”, “24–28 kg/m2”, or “≥28 kg/m2” (Division of Disease Control, Ministry of Health of the People's Republic of China. The Guideline for Prevention and Control of Overweight and Obesity in Chinese Adults, 2006).

Regarding NCDs adjusted for in the analysis, participants self-reported the histories of diabetes, hypertension, COPD, CHD, stroke, CKD, and cancers, while lipid profile, frailty, and depressive symptoms were assessed using specific approaches. Based on the objective measurement of fasting blood cholesterol, triglycerides, and high/low-density lipoproteins, participants were classified as having a normal lipid profile if the levels of cholesterol, triglycerides, and high/low-density lipoproteins were normal; otherwise, they were categorized as having an abnormal lipid profile. Frailty was assessed with the Groningen Frailty Indicator (GFI), and a GFI score of 3 was used to classify participants into the subgroup of “frail (GFI ≥ 3)” or “not frail (GFI<3)” (Huang et al., 2022). Using a score of 5 as the cutoff, the Patient Health Questionnaire scale was employed to screen participants with (≥5) or without (<5) depressive symptoms (Wang et al., 2014).

2.4. Statistical analysis

Descriptive analysis was conducted to present the distribution of participants (%) across selected personal characteristics and fatigability categories, and a chi-square test was used to examine the differences in fatigability by socio-demographic attributes. Two mixed-effects logistic regression models were then employed to compute the odds ratios (ORs) and 95 % confidence intervals (95 %CIs) to investigate the association between physical activity and fatigability among the overall study population and age−/sex-stratified participants. Model 1 was a univariable analysis with fatigability as the independent variable and community (our smallest sampling unit) as the random effect. Model 2 was a multivariable analysis with adjustment for age (where applicable), sex (where applicable), educational attainment, marital status, occupation, body weight status, smoking, drinking, consumption of meat and vegetables, self-reported histories of fatigue-inducing NCDs (diabetes, hypertension, COPD, CHD, stroke, CKD, cancer, and abnormal lipid profile), frailty, sleep quality, and depressive symptoms in addition to those included in Model 1. A two-sided p-value <0.05 was set as the significance level. Data were analyzed using SPSS version 20.0 for Windows (SPSS Inc., Chicago, IL, USA).

3. Results

Among the 6002 eligible participants selected, 5556 were successfully recruited (response rate = 92.6 %), and 5484 were included in the analysis with complete survey information. Of the 518 participants not included in the analysis, 144 refused to participate in the survey, 302 were not accessible at the appointed survey date, and 72 had incomplete survey data. No significant differences in age or sex were found between those analyzed and those not analyzed. Table 1 shows the selected characteristics of the participants categorized by sex. For the 5484 participants analyzed, their mean age (standard deviation) was 69.7 (6.8) years, with 57.6 %, 31.2 %, and 11.2 % of them aged 60–69 (younger-old), 70–79 (middle-old), and ≥ 80 years (oldest-old), respectively. Overall, 49.0 % were men and 39.1 % were urban residents.

Table 1.

Selected characteristics of participants aged 60+ years in study areas of Nanjing municipality, China, 2023 (N = 5484).
% (n) of participants
 p value ⁎
All Men Women
Overall 5484 49.0 (2687) 51.0 (2797)
Age (years)
60–69 57.6 (3157) 57.3 (1540) 57.8 (1617)
70–79 31.2 (1711) 31.5 (846) 30.9 (865) 0.90
80+ 11.2 (616) 11.2 (301) 5.7 (315)
Area
Urban 39.1 (2144) 1060 (39.4) 38.8 (1084) 0.60
Suburban 60.9 (3340) 1627 (60.6) 61.2 (1713)
Educational attainment (schooling years)
0–6 49.6 (2721) 38.8 (1044) 60.0 (1677)
7–12 44.9 (2465) 53.9 (1447) 36.4 (1018) <0.01
13+ 5.5 (299) 7.3 (196) 3.6 (102)
Occupation #
Domestic worker 19.6 (1075) 15.7 (422) 23.3 (653)
Industrial worker 42.4 (2327) 42.1 (1131) 42.8 (1196)
Service/sales worker 6.2 (338) 6.2 (165) 6.2 (173) <0.01
Office worker 8.1 (443) 9.8 (264) 6.4 (179)
Academic/research staff 7.0 (384) 8.6 (232) 5.4 (152)
Self-employed 16.7 (917) 17.6 (473) 15.9 (444)
Marital status
Single 13.8 (758) 10.7 (288) 16.8 (470) <0.01
Married/having a partner 86.2 (4726) 89.3 (2399) 83.2 (2327)
Smoking $
No 76.0 (4168) 53.6 (1441) 97.5 (2727) <0.01
Yes 24.0 (1316) 46.4 (1246) 2.5 (70)
Drinking †
No 87.0 (4773) 76.5 (2056) 97.1 (2717) <0.01
Yes 13.0 (711) 23.5 (631) 2.9 (80)
Body weight status ‡
BMI < 24 43.5 (2346) 43.5 (1169) 42.1 (1177)
24 ≤ BMI < 28 42.1 (1130) 42.1 (1130) 41.6 (1163) 0.14
BMI ≥ 28 14.4 (388) 14.4 (388) 16.3 (457)
* Chi-square test
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# Occupation referred to job before retirement, and was classified for each participant based on recommendations by China National Center for chronic non-communicable disease Prevention and Control.

$ Smoking status was defined as smokers (current- and ex-smokers) and non-smokers (who never smoked cigarettes).

† Drinkers were defined as persons who drank alcohol, on average, at least two times a week for more than one year, while non-drinkers were those people who did not meet drinker's definition.

‡ BMI referred to body mass index, which was used to define participants' body weight status based on cutoffs recommended for Chinese adults.

Table 2 demonstrates the distribution of fatigability by selected personal characteristics among the participants in this study. For the overall participants, the prevalence of greater physical and mental fatigability was 59.0 % (95 %CI = 57.7–63.0) and 51.1 % (95 %CI = 49.8–52.4), respectively. There were significant differences in the prevalence of either greater physical or mental fatigability between participants stratified by age (both p < 0.01), showing an age-dependent pattern. Among participants aged 60–69, 70–79, and ≥ 80 years, the prevalence was 56.5 %, 60.1 %, and 68.0 %, respectively, for greater physical fatigability, whereas the prevalence was 49.1 %, 51.9 %, and 59.4 %, respectively, for greater mental fatigability. No difference was found in either physical or mental fatigability between the participants stratified by sex or residence.

Table 2.

The distribution of fatigability by selected personal characteristics among participants aged 60+ years in study areas of Nanjing municipality, China, 2023 (N = 5484).
% (n) of participants (N = 5484)
Higher physical fatigability ⁎ p value # Higher mental fatigability ⁎ p value #
Overall 59.0 (3233) 51.1 (2803)
Age (years)
60–69 56.5 (1785) 49.1 (1549)
70–79 60.1 (1029) <0.01 51.9 (888) <0.01
80+ 68.0 (419) 59.4 (366)
Gender
Men 57.9 (1556) 0.12 50.5 (1356) 0.35
Women 60.0 (1677) 51.7 (1447)
Area
Urban 58.8 (1260) 0.82 52.5 (1125) 0.11
Suburban 59.1 (1973) 50.2 (1678)
Educational attainment (schooling years)
0–6 61.6 (1675) 52.7 (1435)
7-,12 55.6 (1371) <0.01 48.8 (1202) 0.01
13+ 62.8 (187) 55.7 (166)
Occupation $
Domestic worker 62.3 (670) 54.1 (582)
Industrial worker 58.3 (1357) 51.0 (1187)
Service/sales worker 60.4 (204) 0.18 49.1 (166) 0.27
Office worker 58.2 (258) 51.5 (228)
Academic/research staff 57.8 (222) 49.2 (189)
Self-employed 56.9 (522) 49.2 (451)
Marital status
Single 63.6 (482) 0.01 55.3 (419) 0.01
Married/having a partner 58.2 (2751) 50.4 (2384)
Smoking †
No 59.2 (2468) 0.49 51.3 (2140) 0.54
Yes 58.1 (765) 50.4 (663)
Drinking ‡
No 59.6 (2847) 0.01 51.9 (2479) 0.00
Yes 54.3 (386) 45.6 (324)
Body weight status
BMI < 24 58.6 (1374) 51.1 (1199)
24 ≤ BMI < 28 58.9 (1350) 0.70 51.1 (1172) 1.00
BMI ≥ 28 60.2 (509) 51.1 (432)
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* Physical and mental fatigability was assessed using Pittsburgh Fatigability Scale, with the scores of ≥15 and ≥ 13 indicating higher physical and mental fatigability, respectively, in the study.

# Chi-square test

$ Occupation referred to job before retirement, and was classified for each participant based on recommendations by China National Center for chronic non-communicable disease Prevention and Control.

† Smoking status was defined as smokers (current- and ex-smokers) and non-smokers (who never smoked cigarettes).

‡ Drinkers were defined as persons who drank alcohol, on average, at least two times a week for more than one year, while non-drinkers were those people who did not meet drinker's definition.

¶ BMI referred to body mass index, which was used to define participants' body weight status based on cutoffs recommended for Chinese adults.

Table 3 presents the relationship between physical activity and fatigability among community-dwelling older adults aged ≥60 years in regional China. For the overall participant cohort, after adjustment for potential confounders, those with sufficient physical activity had significantly lower odds of experiencing both greater physical (OR = 0.66; 95 %CI = 0.55–0.81) and mental (OR = 0.68; 95 %CI = 0.56–0.83) fatigability compared to their counterparts with insufficient physical activity. Moreover, such negative associations of physical activity with physical and mental fatigability were also observed for participants stratified by sex or age, with the exception of the relationship between physical activity and physical fatigability among those aged ≥80 years.

Table 3.

The relationship between physical activity and fatigability among participants aged 60+ years in study areas of Nanjing municipality, China, 2023 (N = 5484).
Odds ratio (95 % confidence interval) for experiencing higher fatigability
Physical activity ⁎ % (n/N) with higher physical fatigability # Model 1† Model 2‡ % (n/N) with higher mental fatigability # Model 1† Model 2‡
Overall
Insufficient 59.8 (2986/4992) 1 1 52.0(2597/4992) 1 1
Sufficient 50.2 (247/492) 0.68 (0.56, 0.82) 0.66 (0.55, 0.81) 41.9 (206/492) 0.66 (0.55, 0.80) 0.68 (0.56, 0.83)
Age (years)
60–69
Insufficient 57.2 (1617/2829) 1 1 49.8 (1409/2829) 1 1
Sufficient 51.2 (168/328) 0.79 (0.63, 0.99) 0.74 (0.58, 0.94) 42.7 (140/328) 0.75 (0.60, 0.95) 0.74 (0.58, 0.95)
70–79
Insufficient 61.3 (960/1565) 1 1 52.9 (828/1565) 1 1
Sufficient 47.3 (69/146) 0.56 (0.40, 0.79) 0.50 (0.34, 0.72) 41.1 (60/416) 0.62 (0.44, 0.88) 0.57 (0.39, 0.82)
80+
Insufficient 68.4 (409/598) 1 1 60.2 (360/598) 1 1
Sufficient 55.6 (10/18) 0.58 (0.22, 1.49) 0.72 (0.24, 2.10) 33.3 (6/18) 0.33 (0.12, 0.89) 0.30 (0.10, 0.94)
Gender
Men
Insufficient 58.7 (1414/2409) 1 1 51.3 (1237/2409) 1 1
Sufficient 51.1 (142/278) 0.74 (0.57, 0.94) 0.72 (0.55, 0.94) 42.8 (119/278) 0.71 (0.55, 0.91) 0.71 (0.54, 0.92)
Women
Insufficient 60.9 (1572/2583) 1 1 52.7 (1360/2583) 1 1
Sufficient 49.1 (105/214) 0.62 (0.47, 0.82) 0.61 (0.45, 0.82) 40.7 (87/214) 0.62 (0.46, 0.82) 0.64 (0.48, 0.87)
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* Physical activity was categorized into “insufficient physical activity (<150 min/week)” and “sufficient physical activity (≥150 min/week)” based on weekly moderate physical activity time.

# Physical and mental fatigability was assessed using Pittsburgh Fatigability Scale, with the scores of ≥15 and ≥ 13 indicating higher physical and mental fatigability, respectively, in the study.

† Model 1 was an unadjusted mixed-effect logistic regression model with physical activity as the single predictor and adjustment for neighborhood-level clustering effects.

‡ Model 2 was a multivariable mixed-effect logistics regression model with adjustment for age (where applicable), gender (where applicable), educational attainment, marital status, occupation, body weight status, smoking, drinking, consumption of meat and vegetable, self-reported histories of main chronic diseases (diabetes, hypertension, chronic pulmonary obstructive disease, stroke, coronary heart disease, kidney disease, cancer, abnormal lipid profile) frailty, sleeping quality, depression, and neighborhood-level clustering effects.

The association between physical activity and physical fatigability was not significant.

4. Discussion

This population-based study aimed to investigate the associations of physical activity with physical and mental fatigability, separately, among community-dwelling older adults who were representatively sampled from a typical mega-city in China. The prevalence of greater physical and mental fatigability was found to be as high as 59.0 % and 51.1 %, respectively, among adults aged ≥60 years in this region of China. Importantly, it was observed that physical activity was negatively associated with both physical and mental fatigability, not only among the overall older adults but also for the participants stratified by sex or age group, except for physical fatigability among those aged ≥80 years.

Since “fatigue” and “fatigability” are sometimes used interchangeably, it is interesting to compare our findings with previously documented associations of physical activity with fatigue and fatigability. Earlier studies on the relationship between physical activity and fatigue had a similar study design, participant characteristics, and sample size, whereas definitions of fatigue and measurement instruments were different (Eriksen and Bruusgaard, 2004; Nicklas et al., 2016; Park et al., 2018; Jacquet et al., 2021; Chan and Yu, 2022; Belloni et al., 2021; Cramp and Byron-Daniel, 2012; Wender et al., 2022). These existing investigations were primarily interventions or follow-up studies (Eriksen and Bruusgaard, 2004; Nicklas et al., 2016; Park et al., 2018; Jacquet et al., 2021; Chan and Yu, 2022; Belloni et al., 2021; Cramp and Byron-Daniel, 2012; Wender et al., 2022). The participants in almost all these studies were individuals with one or more specific chronic conditions, such as cancer, regardless of whether they were recruited from hospitals or communities (Eriksen and Bruusgaard, 2004; Nicklas et al., 2016; Park et al., 2018; Jacquet et al., 2021; Chan and Yu, 2022; Belloni et al., 2021; Cramp and Byron-Daniel, 2012; Wender et al., 2022). The sample sizes in these studies ranged from dozens to hundreds (Eriksen and Bruusgaard, 2004; Nicklas et al., 2016; Park et al., 2018; Jacquet et al., 2021; Chan and Yu, 2022; Belloni et al., 2021; Cramp and Byron-Daniel, 2012; Wender et al., 2022). However, the definition of “fatigue” was different across these existing studies, with terms such as “fatigue”, “chronic fatigue syndrome”, “disease-related fatigue”, or “fatigability” used to describe perceived weariness, tiredness, or exhaustion. Moreover, different instruments have been used to measure the concept of fatigue, including the Multidimensional Fatigue Inventory (Nicklas et al., 2016; Jacquet et al., 2021), the Brief Fatigue Inventory scale (Cramp and Byron-Daniel, 2012), and the 1994 criteria of the Centers for Disease Control and Prevention (Wender et al., 2022). Among the available studies, one was a 15-month follow-up observation conducted among Norwegian nurses in 1999 (Eriksen and Bruusgaard, 2004). In this cohort study, “fatigue” was assessed not with a validated instrument but just a simple question: “During the previous 14 days, how did you feel during the day?” with optional answers of “always fit”, “usually fit”, “varied between fit and fatigued”, “usually fatigued”, or “always fatigued” (Eriksen and Bruusgaard, 2004). In contrast, among previous studies reporting the association between physical activity and fatigability (Qiao et al., 2021; LaSorda et al., 2020; Wanigatunga et al., 2018; Schrack et al., 2019; Simonsick et al., 2014; Qiao et al., 2022a; Moored et al., 2022a; Moored et al., 2022b; Qiao et al., 2022b), participant characteristics, sample sizes, and study designs were also different (Qiao et al., 2021; LaSorda et al., 2020; Wanigatunga et al., 2018; Schrack et al., 2019; Simonsick et al., 2014; Qiao et al., 2022a; Moored et al., 2022a; Moored et al., 2022b; Qiao et al., 2022b). However, most of these studies used PFS to measure fatigability (Qiao et al., 2021; LaSorda et al., 2020; Qiao et al., 2022a; Moored et al., 2022a; Moored et al., 2022b; Qiao et al., 2022b), which greatly facilitated comparisons across findings.

Consistently, physical activity was negatively associated with either fatigue or fatigability, irrespective of the numerous differences in the aforementioned studies (Eriksen and Bruusgaard, 2004; Nicklas et al., 2016; Park et al., 2018; Jacquet et al., 2021; Chan and Yu, 2022; Belloni et al., 2021; Cramp and Byron-Daniel, 2012; Wender et al., 2022; Qiao et al., 2021; LaSorda et al., 2020; Wanigatunga et al., 2018; Schrack et al., 2019; Simonsick et al., 2014; Qiao et al., 2022a; Moored et al., 2022a; Moored et al., 2022b; Qiao et al., 2022b). Interestingly, a bidirectional association between physical activity and either fatigue or fatigability has also been reported in some studies (Qiao et al., 2021; Egerton et al., 2016). For instance, using baseline data from an intervention program, a case-control study reported that older adults with fatigue tended to be physically inactive in Norway (Egerton et al., 2016). In this Norwegian study, participants were 172 female volunteers, not randomly recruited community-dwellers, and fatigue was assessed with the Fatigue Severity Scale (Egerton et al., 2016). Additionally, a family-based cohort study reported a bidirectional relationship between physical activity and PFS-defined physical fatigability among 2079 older adults in the USA (Qiao et al., 2021). It appears that elevated physical activity might alleviate fatigue/fatigability, and, conversely, fatigue/fatigability might reduce physical activity engagement for older adults (Qiao et al., 2021; Egerton et al., 2016). Therefore, the association between physical activity and fatigue/fatigability is complex and warrants further investigation using different study designs and among diverse populations worldwide.

Findings on the relationship between physical activity and fatigability in this study align with those reported in most previous investigations regarding the association of physical activity with either fatigue or fatigability, although this study differed from previous surveys in study design and participant characteristics (Eriksen and Bruusgaard, 2004; Nicklas et al., 2016; Park et al., 2018; Jacquet et al., 2021; Chan and Yu, 2022; Belloni et al., 2021; Cramp and Byron-Daniel, 2012; Wender et al., 2022; Qiao et al., 2021; LaSorda et al., 2020; Wanigatunga et al., 2018; Schrack et al., 2019; Simonsick et al., 2014; Qiao et al., 2022a; Moored et al., 2022a; Moored et al., 2022b; Qiao et al., 2022b). Our study was a cross-sectional investigation and included community-dwelling residents who were representative of the general population of older adults in the entire municipality of Nanjing. An inverse association of physical activity was observed with both physical and mental fatigability for overall as well as age- and sex-stratified participants in this study. This finding suggests that the negative association between physical activity and physical or mental fatigability may hold true for the general community-dwelling older population in China. Therefore, our study not only reinforces previous findings on the association between physical activity and physical fatigability, but also adds evidence to the existing literature on the relationship between physical activity and mental fatigability in older adults.

There may be some potential mechanisms underlying the negative relationship between physical activity and fatigue/fatigability. First, regular physical activity can improve sleep quality, depression, psychosocial stress, and physical capacity (US Department of Health and Human Service, 1996), and thereby helping people feel more energetic. Second, sufficient physical activity can positively impact the prevention and control of fatigue-inducing NCDs, including hypertension, type 2 diabetes, metabolic syndrome, cardiovascular diseases, and osteoarthritis (Haseler et al., 2019). Third, physical activity may increase the secretion of dopamine, which may help alleviate perceived fatigue/fatigability (Hattori et al., 1994).

It is impossible to address all aspects of the relationship between physical activity and fatigue/fatigability with one study. Usually, each single study may just add one or more incremental values to the existing literature. This study added new points to the available evidence on the association of physical activity with fatigability, as some innovations and strengths were evident. First, our participants were community-dwelling, non-illness-specific older individuals. Moreover, these participants were randomly selected from across the whole municipality and are representative of the overall 1.95 million older adults aged ≥60 years in Nanjing (Nanjing Municipal Bureau of Statistics, 2024). Second, the associations between physical activity and both physical and mental fatigability were examined in a single study. Third, fatigability, rather than the traditional concept of “fatigue”, was employed to assess the perception of tiredness, weariness, or exhaustion for participants, which links perceived lack of energy to daily activities. Fourth, the validated Chinese version of the PFS was used to measure fatigability. Fifth, in addition to adjusting for socio-demographic characteristics and lifestyle/behaviors in the analysis, fatigue-inducing NCDs, frailty, sleep quality, and depressive symptoms, as well as community-level clustering effects, were also controlled for. Finally, our findings showed that physical activity was negatively associated with both physical and mental fatigability for the overall as well as age- and sex-specific participants. This suggests that such a relationship between physical activity and fatigability might hold true for community-dwelling older adults in China.

However, this study had some limitations. First, owing to the nature of a cross-sectional design, the direction of association between physical activity and fatigability could not be determined in the study. Second, the information on physical activity, lifestyle/behaviors, and particularly histories of NCDs was self-reported, which implies a potential recall bias. Third, because not all individuals with fatigue-inducing NCDs could be identified in a timely manner, statistical efficiency might be underestimated in multivariable analysis when only self-reported NCDs were controlled for. Fourth, only 18 participants aged ≥80 years achieved a sufficient level of physical activity, which might explain the lack of a significant association between physical activity and physical fatigability among the oldest participants. Therefore, the findings of this study should be interpreted with caution. In the future, well-designed cohort or intervention studies are encouraged to further investigate the association between physical activity and fatigability and to determine the direction of the association among general community-dwelling older residents.

In conclusion, physical activity was negatively associated with both physical and mental fatigability among overall or age- and sex-specific community-dwelling residents aged ≥60 years in regional China. Considering the vast number and increasing rate of older adults worldwide, this study has important implications for building healthy-aging societies, since it is possible to prevent or mitigate both physical and mental fatigability for older adults through population-level physical activity promotion.

CRediT authorship contribution statement

Bin Yang: Writing – review & editing, Writing – original draft, Investigation. Qing Ye: Writing – review & editing, Writing – original draft, Investigation, Formal analysis. Xiaojing Deng: Writing – review & editing, Writing – original draft. Zhiyong Wang: Writing – review & editing, Writing – original draft, Investigation. Caihong Hu: Writing – review & editing, Writing – original draft, Investigation. Yeping Bian: Writing – review & editing, Writing – original draft. Jian Xu: Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology, Conceptualization. Fei Xu: Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization.

Ethics approval

Written informed consent was obtained from each participant before the study, which was approved by the Ethics Committee of Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention. All data analyzed in this study were de-identified. The methods employed in this study were in line with recommendations by the Declaration of Helsinki.

Role of the funder/sponsor

All funders did not have any role in the whole study.

Funding

This study was supported by Nanjing Medical Science and Technology Development Fund (ZKX24059; Recipient: FX).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

Our special thanks go to Drs. Tianrui Deng, Huiqing Xu and Guofeng Ao (Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China; School of Public Health, Nanjing Medical University, Nanjing, China) for their contributions to this work. We also highly thank the healthcare workers involved in data collection in the study.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.pmedr.2025.103165.

Contributor Information

Jian Xu, Email: yinfengchris@163.com.

Fei Xu, Email: frankxufei@163.com.

Appendix A. Supplementary data

Supplementary material 1

mmc1.pdf (488.2KB, pdf)

Data availability

Data will be made available on request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary material 1

mmc1.pdf (488.2KB, pdf)

Data Availability Statement

Data will be made available on request.

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