Abstract
Background
Physical function declines with ageing; however, the changes in the prevalence of impaired physical function during ageing and how the changes would be affected by physical activity are not well investigated. This study aimed to address the issues.
Methods
We included participants aged ≥ 65 years in the 2008–2010 wave, who were followed-up in the 2012–2014 and the 2016–2018 waves of the Health and Retirement Study. Types of impaired physical function included weak grip strength, slow walking speed, and poor standing balance. Their prevalence in each wave was calculated and their changes over time were estimated using the mixed-effects logistic regression model.
Results
A total of 6,537 older adults were included. The prevalence of slow walking speed ranked the highest in all the types of impaired physical function in all the 3 individual waves (all Pcomparison <0.001). During follow-up, participants were increasingly being older and the prevalences of all the types of impaired physical function were gradually increased after multivariate-adjustment (all Ptrend <0.001). These increases were unlikely to be significantly moderated by weight status, drinking history or the presence of diabetes (all Pinteraction ≥0.19). However, the increases in the prevalences of weak grip strength and slow walking speed were nonsignificant in participants with regular physical activity, but was significant in those without (Pinteraction = 0.009 and 0.01, respectively).
Conclusions
Despite gradual increases in the prevalence of impaired physical function during ageing, regular physical activity may help to preserve physical function in older adults.
Clinical trial registration
Not applicable.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-026-07156-9.
Keywords: Physical function, Ageing, Physical activity
Background
Physical function reflects the ability of an individual to complete the basic or instrumental physical tasks that are related to independent living [1]. Impairments in physical function, as reflected by weak grip strength, low walking speed, and/or poor standing balance, are predictive of poor future health outcomes, such as increased risks of diabetes, cardiovascular events, and all-cause mortality [2]. While it is well recognized that physical function declines with ageing and that impaired physical function is frequently reported in older adults [3–5], little is known about which physical function is most likely to be affected.
Insights into the changes in the impairments in physical function in older adults may assist in the decision-making for health care services, particularly from the public health perspective. Hitherto, a mixture of findings on the changes in the impairments of physical function in older adults have been reported in the literature [3–5]. For example, a Swedish study observed a trend towards fewer impairments in chair stands and walking speed among the older adults aged ≥ 60 years between 2001 and 04 and 2013–16 4, while a Danish study showed an increased trend in chair stand limitations in people older than 90 years from two cohorts born 10 years apart [3]. However, few studies have assessed the longitudinal changes in impaired physical function in older adults and how physical activity could affect the changes associated with ageing, in particular considering that physical activity, either aerobic physical activity or muscle-strengthening exercise, is increasingly advocated worldwide for health promotion [6–8].
Accordingly, we conducted this study to: (i) assess which physical function is most likely to be impaired, (ii) evaluate the changes in impaired physical function during ageing, and (iii) examine the influences of physical activity on the changes based on the Health and Retirement Study (HRS) [9], in which physical function was objectively measured by performance tests on grip strength, walking speed, and standing balance.
Methods
Study participants
Participants were from the HRS, which was a cross-nationally representative, biennial longitudinal survey of U.S. adults aged > 50 years and collected a wide range of data including demographics and health records through either phone or face-to-face interview [9]. Since 2006, one-half of the HRS participants were randomly invited for the measurement of physical function (including grip strength, walking speed, and standing balance), with the remaining (the other one-half) being completed in 2008. This rotation design was employed in all the subsequent biennial HRS surveys, generating a 4-year time-window for a periodic follow-up on the measures of physical function. In this study, we used data from the 2008–2018 waves of HRS, and stratified them as the 2008–2010 wave (combining data from 2008 to 2010 waves), the 2012–2014 wave (combining data from 2012 to 2014 waves), and the 2016–2018 wave (combining data from 2016 to 2018 waves), to increase the statistical power with a larger sample size (than any individual wave). Further details of the HRS design have been previously described [9], and its protocol had been approved by the Institutional Review Board at the University of Michigan (HUM00061128). Written informed consent was obtained from all participants.
To compare the differences in the prevalences of different types of impaired physical function as well as to assess their changes during ageing, we included only participants who were aged ≥ 65 years and completed all the measures of physical function in the 2008–2010, the 2012–2014, and the 2016–2018 waves. The reason to include participants aged ≥ 65 years was mainly due to the evidence that walking speed was predominantly measured in this group of participants in HRS [9]. However, participants were excluded if they did not complete the measures of physical function.
Data collection
Demographic information (including age, sex, and race), lifestyle habits (including drinking history, smoking status, and self-reported physical activity), and disease history (including diabetes and heart disease) were collected using the standard questionnaires. Based on the frequency of physical activities performed at the intensity of vigorous, moderate, or mild, physical activity was stratified as the regular (performed every day or more than once a week), sometimes (performed once a week or one to three times a month), seldom (performed hardly ever or never) groups. However, in our analysis, we combined the group of sometimes and seldom groups into irregular physical activity group for simplicity. Body mass index (BMI) is calculated as weight (kg) divided by the square of height (m). We considered overweight/obesity as BMI ≥ 25 kg/m2, normal weight as BMI ≥ 18.5 kg/m2 and < 25 kg/m2, and underweight as BMI < 18.5 kg/m2 [10].
Physical function
Physical function was measured using standard protocols, as described previously [9, 11]. In brief, grip strength was determined by hand dynamometers, and calculated as the averages of the 2 measurements from both hands, with the larger one being used. Walking speed was measured with a timed walk of 2.5 m at a usual speed for 2 times, with the averages being analyzed. Standing balance was evaluated using the full tandem standing test. In this study, we considered participants with weak grip strength as grip strength < 27 kg for men or < 16 kg for women, with slow walking speed as walking speed < 0.8 m/sec, and with poor standing balance as an uncompleted full-tandem standing test [12]. To increase the sample size of the present study, if the participants tried but were unable to complete the test, they were deemed to be impaired in that physical function domain [4]. For comparative analyses of different forms of impairment in physical function, we defined weak grip strength, low walking speed, or poor standing balance as an individual type of impaired physical function [13, 14].
Statistical analyses
Characteristics of included participants are presented as means (standard deviations) for continuous variables or percentages [95% confidence intervals (CIs)] for categorical variables. Missing data on the demographic information or lifestyle habits were imputed using the multivariate imputation by chained equations procedure [15]. The point prevalences of different types of impaired physical function in each wave was compared using the chi-squared test. Changes in the prevalences of different types of impaired physical function over time were estimated by a mixed-effects logistic regression model [16], which included the survey wave as an independent variable, in addition to age (65–70, 70–80, and ≥ 80 years), sex (men and women), race/ethnicity (white, black, and others), weight status (underweight, normal weight, and overweight/obesity), drinking history and smoking status (yes and no), physical activity (regular and irregular), and disease history (with or without diabetes or heart disease). In these analyses the survey wave was analyzed as a continuous variable of 0, 4, and 8, corresponding to the time gap across different waves from the 2008–2010 wave (baseline wave). Moreover, the survey wave was also used to reflect the presence of ageing, given that participants would be growing older during the follow-up periods.
Subgroup analyses were performed to assess the changes in different types of impaired physical function during follow-up based on age, sex, race/ethnicity, weight status, drinking history, smoking status, physical activity, and disease history. Possible interactions were tested by incorporating the two-way product term (e.g., physical activity × survey wave) in the model. Sensitivity analyses were conducted by: (i) defining walking speed < 0.6 m/sec as slow walking speed [14]; (ii) defining grip strength < 32 kg for men and < 20 kg for women as weak grip strength [14]; and (iii) excluding participants with missing data. Since ageing was associated with increased risks of metabolic diseases such as diabetes or heart disease [17], we conducted additional sensitivity analysis to assess the influence of metabolic diseases on the changes in impaired physical function during follow-up.
All the statistical analyses were performed using Stata (Version 14.0, StataCorp LP, College Station, TX, USA), and a P-value < 0.05 was indicative of significance.
Results
Characteristics of included participants
We included 6,537 participants (mean age: 72.0 ± 4.8 years) in the 2008–2010 wave, who were followed-up for 8 years (Table 1). Of them, 88.3% were whites and 44.5% were females. About 75.8% of the participants were overweight/obese and about 21.3% were with regular physical activity in the 2008–2010 wave. During follow-up, participants were increasingly being older (Ptrend<0.001), along with an increase in the proportions of participants with diabetes or heart disease (both Ptrend<0.001). However, there was a decrease in the proportions of participants with overweight/obesity (Ptrend=0.004) or with regular physical activity (Ptrend<0.001).
Table 1.
Characteristics of included participants
| Characteristics of participantsa | 2008–2010 wave (n = 6,537) |
2012–2014 wave (n = 6,537) |
2016–2018 wave (n = 6,537) |
|---|---|---|---|
| Ageb, year | 72.0 ± 4.8 | 75.9 ± 4.8 | 80.0 ± 4.8 |
| Age category, % | |||
| 65–70 years | 40.3 (38.3–42.4) | 7.1 (6.1–08.3) | 0.05 (0.01–0.3) |
| 70–80 years | 52.7 (50.6–54.8) | 73.5 (71.6–75.3) | 56.4 (54.4–58.5) |
| ≥ 80 years | 7.0 (6.0-8.1) | 19.4 (17.8–21.1) | 43.5 (41.4–45.6) |
| BMIb, kg/m2 | 28.7 ± 5.2 | 28.5 ± 5.2 | 28.2 ± 5.1 |
|---|---|---|---|
| Weight status, % | |||
| Underweight | 0.8 (0.6–1.4) | 1.1 (0.7–1.6) | 1.9 (1.4–2.5) |
| Normal weight | 23.3 (21.6–25.1) | 24.6 (22.8–26.5) | 26.2 (24.4–28.0) |
| Overweight/Obesity | 75.8 (74.0-77.6) | 74.3 (72.5–76.1) | 72.0 (70.0-73.8) |
| Current smoking, % | |||
| Yes | 6.7 (5.8–7.9) | 5.5 (4.6–6.5) | 4.2 (3.5–5.2) |
| No | 93.3 (92.1–94.2) | 94.5 (93.5–95.4) | 95.8 (94.8–96.5) |
| Drinking history, % | |||
| Yes | 59.8 (57.7–61.8) | 58.1 (56.0-60.2) | 56.1 (54.0-58.2) |
| No | 40.2 (38.2–42.3) | 41.9 (39.8–44.0) | 43.9 (41.8–46.0) |
| Physical activity, % | |||
| Regular | 21.3 (19.6–23.1) | 19.9 (18.2–21.6) | 15.0 (13.5–16.5) |
| Irregular | 78.7 (76.9–80.4) | 80.1 (78.4–81.8) | 85.0 (83.5–86.5) |
| Diabetesc, % | |||
| Yes | 17.5 (15.9–19.1) | 21.8 (20.2–23.6) | 24.9 (23.1–26.7) |
| No | 82.5 (80.9–84.1) | 78.2 (76.4–79.8) | 75.1 (73.3–76.9) |
| CVDc, % | |||
| Yes | 24.1 (22.3–25.9) | 29.2 (27.4–31.2) | 35.8 (33.8–37.8) |
| No | 75.9 (74.1–77.7) | 70.8 (68.8–72.6) | 64.2 (62.2–66.2) |
BMI Body mass index, CVD Cardiovascular disease
a All data were presented as percentages (95% confidence intervals) unless otherwise indicated
b Data were presented as mean ± standard deviations
c It was ascertained by self-reported history
Comparisons of the prevalences of impaired physical function in each wave
Slow walking speed was the most frequent type of impaired physical function in older adults in the 2008–2010 wave (prevalence: 39.0%, 95% CI: 40.0% to 41.1%), followed by poor standing balance (prevalence: 18.6%, 95% CI: 17.0% to 20.3%), and weak grip strength (prevalence: 4.2%, 95% CI: 3.5% to 5.2%) (all Pcomparison <0.001, Figure S1A). This pattern remained similar in the following 2012–2014 and 2016–2018 waves. However, when using different cut-offs to define slow walking speed (that is, < 0.6 m/s) or weak grip strength (that is, < 32 kg for men and < 20 kg for women), the most frequent type of impaired physical function appeared to be varied (Figure S1B-D).
Changes in the prevalences of impaired physical function during follow-up
There were significant increases in the prevalences of all the types of impaired physical function during the 8-year follow-up (all Ptrend <0.001, Tables 2, 3 and 4), after adjustment for age, sex, race/ethnicity, weight status, drinking history, current smoking, physical activity, and disease history. Sensitivity analysis showed similar outcomes when adopting different cut-offs for defining weak grip strength or slow walking speed (all Ptrend <0.001, Table S1), excluding participants with missing data (on the demographics or lifestyle habits) (all Ptrend <0.001, Table S1), or restricting participants to those without diabetes or heart disease (all Ptrend <0.001, Table S1).
Table 2.
Subgroup analysis on the changes in weak grip strengtha
| Subgroups | 2008–2010 wave | 2012–2014 wave | 2016–2018 wave | P trend b | P inteaction |
|---|---|---|---|---|---|
| Overall population | 4.2 (3.5–5.2) | 8.5 (7.4–9.8) | 18.4 (16.9–20.1) | < 0.001 | - |
| Gender | 0.26 | ||||
| Female | 4.6 (3.6-6.0) | 9.1 (7.6–10.9) | 18.5 (16.4–20.8) | < 0.001 | |
| Male | 3.7 (2.7–5.1) | 7.8 (6.3–9.7) | 18.3 (16.0-20.9) | < 0.001 | |
| Race/ethnicityc | 0.28 | ||||
| White | 4.4 (3.6–5.4) | 8.7 (7.5–10.1) | 19.3 (17.6–21.1) | < 0.001 | |
| Black | 0.5 (0.1–3.7) | 4.3 (2.2–8.4) | 8.1 (4.9–13.0) | 0.04 | |
| Age category | < 0.001 | ||||
| 65–70 years | 2.4 (1.6–3.6) | 4.5 (2.2–9.2) | - | 0.14 | |
| 70–80 years | 5.2 (4.1–6.7) | 6.7 (5.6–8.1) | 10.7 (09.0-12.5) | < 0.001 | |
| ≥ 80 years | 7.2 (4.0-12.6) | 16.8 (13.5–20.7) | 28.6 (25.8–31.5) | < 0.001 | |
| BMI category | 0.49 | ||||
| Underweight | 21.1 (7.9–45.3) | 39.1 (21.4–60.2) | 36.6 (23.3–52.3) | 0.40 | |
| Normal weight | 6.3 (4.5–8.8) | 11.2 (8.8–14.2) | 23.5 (20.2–27.2) | < 0.001 | |
| Overweight/Obesity | 3.4 (2.6–4.4) | 7.2 (6.1–8.6) | 16.1 (14.4–18.0) | < 0.001 | |
| Current smoking | 0.007 | ||||
| Yes | 0.7 (0.1–4.7) | 4.2 (1.7–9.7) | 19.6 (12.7–29.0) | 0.02 | |
| No | 4.5 (3.7–5.5) | 8.8 (7.6–10.1) | 18.4 (16.8–20.1) | < 0.001 | |
| Drinking history | 0.91 | ||||
| Yes | 3.8 (2.9-5.0) | 7.4 (6.1-9.0) | 17.1 (15.1–19.3) | < 0.001 | |
| No | 4.8 (3.6–6.4) | 10.1 (8.3–12.2) | 20.2 (17.7–22.8) | < 0.001 | |
| Physical activity | 0.009 | ||||
| Regular | 3.7 (2.3–5.8) | 6.5 (4.5–9.2) | 9.2 (6.5–12.9) | 0.37d | |
| Irregular | 4.4 (3.5–5.5) | 9.0 (7.8–10.5) | 20.1 (18.3–22.0) | < 0.001 | |
| Diabetes | 0.48 | ||||
| Yes | 6.0 (4.0-8.9) | 8.4 (6.2–11.3) | 22.5 (19.2–26.2) | < 0.001 | |
| No | 3.8 (3.0-4.8) | 8.6 (7.3–10.0) | 17.1 (15.4–19.0) | < 0.001 | |
| CVD | 0.001 | ||||
| Yes | 4.4 (2.9–6.5) | 7.5 (5.7–9.9) | 23.1 (20.3–26.2) | < 0.001 | |
| No | 4.2 (3.3–5.2) | 8.9 (7.6–10.5) | 15.9 (14.0-17.9) | < 0.001 |
a All data were presented as percentages, % (95% confidence intervals)
b It was analyzed by the mixed-effects logistic regression model, which included the survey wave (treated as a continuous variable) as an independent variable, in addition to age (65–70, 70–80, and ≥ 80 years), sex (men and women), race/ethnicity (white, black, and others), weight status (underweight, normal weight, and overweight/obesity), drinking history and smoking status (yes and no), physical activity (regular and irregular), and disease history (with or without diabetes or heart disease)
c There was a small proportion of participants with information on other races and was not included in the analysis
d It was assessed by fixed-effects model due to the non-convergence of mixed-effects model
Table 3.
Subgroup analysis on the changes in slow walking speeda
| Subgroups | 2008–2010 wave | 2012–2014 wave | 2016–2018 wave | P trend b | P inteaction |
|---|---|---|---|---|---|
| Overall population | 39.0 (37.0-41.1) | 45.2 (43.1–47.3) | 61.2 (59.1–63.2) | < 0.001 | - |
| Gender | 0.73 | ||||
| Female | 43.8 (41.1–46.7) | 49.9 (47.1–52.7) | 66.2 (63.5–68.8) | < 0.001 | |
| Male | 33.0 (30.1–36.0) | 39.3 (36.3–42.5) | 54.9 (51.7–58.0) | < 0.001 | |
| Race/ethnicityc | 0.50 | ||||
| White | 36.1 (33.9–38.2) | 41.8 (39.6–44.0) | 58.4 (56.2–60.6) | < 0.001 | |
| Black | 66.7 (59.6–73.1) | 76.3 (69.7–81.9) | 84.9 (79.0-89.4) | < 0.001 | |
| Age category | < 0.001 | ||||
| 65–70 years | 35.3 (32.2–38.5) | 39.4 (32.0-47.3) | - | 0.49 | |
| 70–80 years | 39.7 (36.9–42.5) | 42.3 (39.9–44.7) | 55.3 (52.5–58.0) | < 0.001 | |
| ≥ 80 years | 55.3 (47.3–63.0) | 58.4 (53.6–63.0) | 68.9 (65.9–71.8) | < 0.001 | |
| BMI category | 0.86 | ||||
| Underweight | 52.6 (30.6–73.7) | 26.1 (12.0-47.8) | 56.1 (40.6–70.5) | 0.52 | |
| Normal weight | 32.1 (28.2–36.3) | 38.2 (34.2–42.4) | 55.8 (51.7–59.8) | < 0.001 | |
| Overweight/Obesity | 41.0 (38.6–43.4) | 47.8 (45.4–50.2) | 63.3 (60.8–65.6) | < 0.001 | |
| Current smoking | 0.07 | ||||
| Yes | 44.9 (37.0–53.0) | 44.2 (35.5–53.2) | 60.9 (50.5–70.3) | 0.26 | |
| No | 38.6 (36.5–40.7) | 45.3 (43.1–47.4) | 61.2 (59.1–63.3) | < 0.001 | |
| Drinking history | 0.33 | ||||
| Yes | 34.9 (32.3–37.5) | 37.8 (35.2–40.5) | 55.4 (52.6–58.2) | < 0.001 | |
| No | 45.2 (41.9–48.5) | 55.4 (52.2–58.6) | 68.5 (65.5–71.4) | < 0.001 | |
| Physical activity | 0.01 | ||||
| Regular | 27.8 (23.9–32.1) | 31.9 (27.6–36.4) | 38.7 (33.5–44.1) | 0.23 | |
| Irregular | 42.0 (39.7–44.4) | 48.5 (46.2–50.9) | 65.1 (62.9–67.3) | < 0.001 | |
| Diabetes | 0.19 | ||||
| Yes | 45.9 (41.0–51.0) | 54.8 (50.3–59.3) | 70.3 (66.3–74.0) | < 0.001 | |
| No | 37.5 (35.3–39.8) | 42.5 (40.2–44.9) | 58.2 (55.7–60.5) | < 0.001 | |
| CVD | 0.63 | ||||
| Yes | 42.7 (38.5–46.9) | 49.5 (45.6–53.3) | 63.7 (60.2–67.0) | < 0.001 | |
| No | 37.8 (35.5–40.2) | 43.5 (41.0-45.9) | 59.8 (57.2–62.3) | < 0.001 |
a All data were presented as percentages, % (95% confidence intervals)
b It was analyzed by the mixed-effects logistic regression model, which included the survey wave (treated as a continuous variable) as an independent variable, in addition to age (65–70, 70–80, and ≥ 80 years), sex (men and women), race/ethnicity (white, black, and others), weight status (underweight, normal weight, and overweight/obesity), drinking history and smoking status (yes and no), physical activity (regular and irregular), and disease history (with or without diabetes or heart disease)
c There was a small proportion of participants with information on other races and was not included in the analysis
Table 4.
Subgroup analysis on the changes in poor standing balancea
| Subgroups | 2008–2010 wave | 2012–2014 wave | 2016–2018 wave | P trend b | P inteaction |
|---|---|---|---|---|---|
| Overall population | 18.6 (17.0-20.3) | 26.0 (23.9–27.6) | 39.8 (38.0-41.9) | < 0.001 | - |
| Gender | 0.006 | ||||
| Female | 22.9 (20.6–25.4) | 27.9 (25.4–30.5) | 42.5 (39.8–45.4) | < 0.001 | |
| Male | 13.2 (11.2–15.5) | 23.0 (20.4–25.7) | 36.5 (33.5–39.5) | < 0.001 | |
| Race/ethnicityc | 0.001 | ||||
| White | 18.1 (16.4–19.9) | 26.1 (24.2–28.1) | 40.7 (38.5–42.9) | < 0.001 | |
| Black | 23.1 (17.6–29.7) | 23.7 (18.1–30.3) | 34.9 (28.4–42.1) | 0.13 | |
| Age category | < 0.001 | ||||
| 65–70 years | 18.0 (15.6–20.7) | 18.7 (13.3–25.7) | - | 0.92 | |
| 70–80 years | 17.4 (15.3–19.7) | 23.2 (21.2–25.3) | 31.5 (29.0-34.2) | < 0.001 | |
| ≥ 80 years | 30.9 (24.1–38.7) | 37.8 (33.3–42.6) | 50.6 (47.4–53.8) | < 0.001 | |
| BMI category | 0.79 | ||||
| Underweight | 26.3 (11.1–50.5) | 39.1 (21.4–60.2) | 34.1 (21.2–49.9) | 0.74d | |
| Normal weight | 16.1 (13.2–19.6) | 24.8 (21.3–28.7) | 40.0 (36.0-44.1) | < 0.001 | |
| Overweight/Obesity | 19.2 (17.4–21.2) | 25.8 (23.7–28.0) | 39.9 (37.5–42.4) | < 0.001 | |
| Current smoking | 0.37 | ||||
| Yes | 23.8 (17.6–31.4) | 33.3 (25.5–42.3) | 43.5 (33.7–53.8) | 0.03 | |
| No | 18.2 (16.6–19.9) | 25.3 (23.4–27.2) | 39.7 (37.6–41.8) | < 0.001 | |
| Drinking history | 0.54 | ||||
| Yes | 17.2 (15.2–19.4) | 23.8 (21.5–26.2) | 36.4 (33.8–39.2) | < 0.001 | |
| No | 20.6 (18.1–23.4) | 28.4 (25.5–31.4) | 44.2 (41.1–47.4) | < 0.001 | |
| Physical activity | 0.39 | ||||
| Regular | 11.2 (8.6–14.4) | 18.2 (14.9–22.2) | 25.2 (20.7–30.2) | 0.004 | |
| Irregular | 20.6 (18.7–22.6) | 27.5 (25.5–29.7) | 42.4 (40.2–44.7) | < 0.001 | |
| Diabetes | 0.50 | ||||
| Yes | 24.9 (20.8–29.5) | 32.1 (28.1–36.5) | 45.8 (41.6–50.0) | < 0.001 | |
| No | 17.2 (15.6–19.1) | 23.9 (21.9–26.0) | 37.9 (35.6–40.3) | < 0.001 | |
| CVD | 0.02 | ||||
| Yes | 19.2 (16.1–22.8) | 26.4 (23.1–29.9) | 45.1 (41.6–48.6) | < 0.001 | |
| No | 18.4 (16.6–20.3) | 25.4 (23.3–27.7) | 36.9 (34.4–39.5) | < 0.001 |
a All data were presented as percentages (95% confidence intervals)
b It was analyzed by the mixed-effects logistic regression model, which included the survey wave (treated as a continuous variable) as an independent variable, in addition to age (65–70, 70–80, and ≥ 80 years), sex (men and women), race/ethnicity (white, black, and others), weight status (underweight, normal weight, and overweight/obesity), drinking history and smoking status (yes and no), physical activity (regular and irregular), and disease history (with or without diabetes or heart disease)
c There was a small proportion of participants with information on other races and was not included in the analysis
d It was assessed by fixed-effects model due to the non-convergence of mixed-effects model
Subgroup analysis showed that the increases in the prevalences of these types of impaired physical function during follow-up were unlikely to be moderated by weight status, drinking history or the presence of diabetes (all Pinteraction ≥0.19). In contrast, the presence of heart disease may augment the increases in the prevalences of weak grip strength and poor standing balance (Pinteraction = 0.001 and 0.02, respectively, Tables 2 and 4). However, the increases in the prevalences of weak grip strength and slow walking speed were nonsignificant in participants with regular physical activity, but significant in those without (Pinteraction = 0.009 and 0.01, respectively, Tables 2 and 3).
Discussion
Main findings
Our study, which was based on a nationally representative U.S. older population, showed that walking speed was most likely to be impaired compared with other measures of physical function; however, this pattern could be affected by the cut-off applied to define slow walking speed. Our study also showed that the prevalences of all the types of impaired physical function were gradually increased during the 8-year follow-up; however, the presence of heart disease may further augment the increases in the prevalences of weak grip strength and poor standing balance. In contrast, regular physical activity may prevent or delay the increases in prevalences of weak grip strength and slow walking speed during the ageing process.
Interpretations
Our study showed that the prevalence for weak grip strength was 4.2%, 39.0% for slow walking speed, and 18.6% for poor standing balance in the U.S. older adults aged ≥ 65 years, and may increase up to 18.4%, 61.2%, and 39.8%, respectively, following the 8-year follow-up. Our data are partly consistent with those reported in other studies. For example, a Spanish study found that slow walking speed (defined as < 0.8 m/s) was present in 42.6% of the older adults aged ≥ 65 years [18], and a Swedish study observed a prevalence of poor standing balance of about 28.2% in the older adults aged ≥ 66 years [4]. However, no prior studies have made direct comparisons on the prevalence of individual type of impaired physical function in the older population. In this study, we showed for the first time that, relative to weak grip strength or poor standing balance, slow walking speed was the most common type of impaired physical function in the U.S. older adults. This finding attests to the impact of aging on lower limb muscle strength, and suggests that interventions (e.g., physical exercise [19]) designed to improve walking speed should be particularly considered in clinical practice for healthy ageing. However, it is noteworthy that the comparative outcomes were somewhat influenced by the criteria applied to define impaired physical function. In our sensitivity analysis, where slow walking speed was defined as walking speed < 0.6 m/s 14, poor standing balance was shown to be the most frequently occurred type of impaired physical function in older adults. Accordingly, caution should be exercised in interpreting the findings.
Unlike previous studies that employed several independent cross-sectional surveys to show the trends in the prevalences of different types of impaired physical function over time in older adults [3–5], our study was conducted based on a longitudinal survey over an 8-year follow-up. As expected, our study found that there were significant and substantial increases in the prevalences of all the types of impaired physical function in older adults during the ageing process. However, as evidenced by our subgroup analyses with interaction tests, neither weight status, nor drinking history, nor the presence of diabetes would significantly affect these increases over time, despite higher prevalences of impaired physical function shown in participants with overweight/obesity or diabetes compared with their counterparts. In contrast, the presence of heart disease was associated with augmented increases in the prevalences of weak grip strength and poor standing balance, indicating that the presence of heart disease may accelerate the impairments in physical function during ageing, at least partly.
Increased physical activity has been linked to a decreased risk of impaired physical function in older adults and is generally regarded beneficial for the preservation of physical function during ageing [20]. In line with this concept, we found that there were no significant increases in the prevalences of weak grip strength and slow walking speed among participants who engaged in regular physical activity over the 8-year follow-up. In contrast, there were notable increases among those with irregular physical activity. Together with the evidence that increased physical activity is also associated with reduced risks of cardiovascular events and cancer mortality [21, 22], our study supports the recommendation to promote physical activity for healthy ageing.
Strengths and limitations
The strengths of our study included the objective-measurements of physical function, a longitudinal cohort with 3 consecutive survey waves, and a series of subgroup analyses with robust outcomes. However, out study have some limitations. First, the lack of data on other potential confounders, such as nutritional status, might affect the robustness of our findings. Second, physical activity was self-reported. This may lead to an increased risk of recall bias. Moreover, the unavailability of information on the types of physical activity (e.g., aerobic versus resistance physical activity) made it impossible to assess which specific type of physical activity was most powerful to slow or prevent the increases in the prevalences of impaired physical function during the ageing process. Third, it is reported that there was an increase in the time spent on sedentary behavior in the older adults aged ≥ 65 years from 2007 to 2016 based on the National Health and Nutrition Examination Surveys [23], which is shown to be related to the increased risk of impaired physical function [24]. However, we were not able to assess its influence on the changes in impaired physical function due to the lack of the data on sedentary behavior in HRS. Fourth, due to the relatively small sample size, we were not able to assess the age- and sex-specific changes in the objectively-measured physical function. Finally,
Conclusions
In conclusion, our study showed that walking speed appeared to be the most frequent type of impaired physical function in the U.S. older adults, but this may be subject to the cut-off for the definition of slow walking speed. Moreover, despite an increase in the prevalence of impaired physical function during the 8-year follow-up, regular physical activity may help to delay or prevent this increase, particularly for that of weak grip strength and slow walking speed in older adults aged ≥ 65 years. However, the presence of heart disease may augment the increase. These findings highlight the need to implement effective health services to promote physical activity and prevent heart disease in the older adults at the population level.
Supplementary Information
Supplementary Material 1. Figure S1. Proportions of impaired physical function by different cut-offs. Table S1. Sensitivity analyses
Acknowledgements
None.
Abbreviations
- BMI
Body mass index
- HRS
Health and Retirement Study
Authors’ contributions
Drs Shanhu Qiu and Xue Cai had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.Study concept and design: Shanhu Qiu, Bo Xie, Zilin Sun, Xue Cai, Tongzhi WuAcquisition, analysis, or interpretation of data: Shanhu Qiu, Bo Xie, Xue CaiDrafting of the manuscript: Shanhu Qiu, Tongzhi WuCritical revision of the manuscript for important intellectual content: All authors.Statistical analysis: Shanhu Qiu, Xue CaiObtained funding: Shanhu Qiu, Xue CaiStudy supervision: Zilin Sun, Tongzhi Wu.
Funding
The Health and Retirement Study is sponsored by the National Institute on Aging (grant No. NIA U01AG009740) and is conducted by the University of Michigan. Moreover, the present work was partly supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project (grant No. 2024ZD0523300), the National Natural Science Foundation of China (grant No. 72374043) and the Nanjing Special Fund for Health Science and Technology Development (grant No. YKK23279). Xue Cai was supported by the Research Personnel Cultivation Programme of Zhongda Hospital, Southeast University (No. CZXM-GSP-RC08). The funders had no roles in the design of the study and collection, analysis, and interpretation of data or in writing the manuscript.
Data availability
All the data are available from the official website of the Health and Retirement Study (https://hrs.isr.umich.edu/).
Declarations
Ethics approval and consent to participate
The details of the HRS design have been previously described, and its protocol had been approved by the Institutional Review Board at the University of Michigan (HUM00061128). Written informed consent was obtained from all participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Shanhu Qiu and Xue Cai contributed equally to this work.
Contributor Information
Zilin Sun, Email: sunzilin1963@126.com.
Xue Cai, Email: kameyuki@126.com.
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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. Figure S1. Proportions of impaired physical function by different cut-offs. Table S1. Sensitivity analyses
Data Availability Statement
All the data are available from the official website of the Health and Retirement Study (https://hrs.isr.umich.edu/).