1. Mind over matter: are physical performance and subjective well-being linked in older adults? Results from the Study on global AGEing and adult health (SAGE). Theresa E. Gildner1, Melissa A. Liebert1, Paul Kowal2, and J. Josh Snodgrass1 1Department of Anthropology, University of Oregon, Eugene, OR; 2World Health Organization, Geneva, Switzerland
Abstract
Reduced physical function in older adults has been linked with decreased self-reported satisfaction with one’s life, likely due to detrimental changes in everyday activities and loss of independence. However, this association is not well studied in different cultural contexts. Logistic regressions tested the relationship between physical activity level (PAL), various measures of physical function, and several subjective well-being variables. Data were drawn from the six countries (China, Ghana, India, Mexico, Russian Federation, South Africa) in the World Health Organization’s SAGE Wave 1. Self-report data provided information on subjective well-being (depression, quality of life, happiness, and mood) and PAL. Three tests (grip strength, usual and rapid gait speed) measured physical function. Unexpectedly, higher normal and rapid gait speeds were associated with an increased risk of low self-rated well-being in all cases. Conversely, higher grip strength was consistently associated with decreased risk of reporting poor subjective well-being. These findings suggest that improving overall physical condition and grip strength may enhance perceptions of well-being in older individuals.
Methods
Physical Function Variables: Items from the Global Physical Activity Questionnaire measuring time spent in various daily activities were used to calculate participant PAL (Bull et al., 2009). Maximum grip strength achieved using a Smedley’s hand dynamometer was recorded to the nearest kilogram. The length of time required to travel 4 meters at both usual and rapid walking speed were recorded, calculated in m/s.
Well-Being Variables: Positive or negative diagnosis of depression was based on self-reported symptoms and an assignment algorithm (Kessler et al., 2010). Participants rated their overall quality of life (QOL) and happiness using a scale of 1 (very poor) to 5 (very good). These responses were recoded to create dichotomous variables (rating of very poor, poor, or moderate classified as poor, while a rating of good or very good was classified as good). Finally, participants rated their mood in relation to others. These responses were also recoded to create a dichotomous variable (rating of the same/worse mood classified as poor, while a rating of better mood was classified as good).
Statistics: Logistic regressions were used to examine the relationship between physical function and each of the well-being variables by country while controlling for sex, age, household setting (urban or rural), highest level of education, income quintile, marital status, and several health risks and conditions known to affect physical function (BMI, drinking and smoking, angina, stroke, chronic lung disease, arthritis, and diabetes).
Table 1. Logistic regression for prediction of subjective well-being from physical activity level (PALs) and measures of physical function (controlling for PALs), by country. Confounding variables included in the models not shown. Exp(B) values with 95% CIs. Comparisons are statistically significant at: *= p < 0.05, **= p < 0.01, ***= p <
Variable
China
Ghana
India
Mexico
Russian Federation
South Africa
Depression diagnosis
Physical activity level
1.0 ( )
1.1 ( )***
1.0 ( )**
1.0 ( )
1.0 ( )
1.0 ( )
Usual gait speed
1.1 ( )*
1.0 ( )
Rapid gait speed
Grip strength
0.9 ( )
1.0 ( )***
Quality of life rating
1.0 ( )
1.0 ( )
1.1 ( )**
1.0 ( )*
1.0 ( )*
Happiness rating
1.0 ( )**
Mood rating
0.9 ( )***
0.9 ( )**
1.1 ( )
0.9 ( )
Introduction
Societies worldwide are currently experiencing a demographic transition characterized by an increased population of older adults, a trend apparent at all levels of economic development (Kunkle et al., 2014). Older adults often suffer impaired physical function, negatively affecting ability to carry out everyday tasks and resulting in decreased self-reported satisfaction with daily life (Penninx, 1998). However, habitual physical activity in older individuals exerts a powerful influence on both somatic and mental health (Fox et al., 2007). Consistent exercise may also help older adults remain independent for an extended period of time (den Ouden et al., 2011). Yet, research examining how subjective well-being co-varies with physical performance and lifestyle factors has largely been restricted to high-income nations (Diener et al., 2003).
Conclusions
The present study found mixed support for the hypotheses:
PALs were not uniformly associated with risk of poor subjective well-being; the direction of significant findings varied by country and well-being measure. The few unpredicted findings may be the result of older adults in these populations engaging in physically-demanding work activities which negatively impact physical health and subjective well-being measures.
Unexpectedly, higher normal and rapid gait speeds were linked with poor reported well-being. However, individuals (over the age of 50) were analyzed together, but it is possible that different age groups display distinctive patterns.
As expected, higher grip strength was associated with better subjective well- being. Grip strength represents an excellent measure of general strength and muscle mass at older ages (den Ouden et al., 2011). Lower muscle mass and strength restricts daily activities and limits independence, thereby reducing reported well-being (Drenowski & Evans, 2001). Thus, stronger participants likely rated their well-being more favorably.
These results suggest the influence of PALs on measures of well-being varies by country, perhaps due to differences in the accuracy of self-reported PALs across the populations studied. Higher grip strength (a proxy of general physical condition) was significantly predictive of enhanced well-being measures, while higher gait speeds do not appear to be significantly associated with better well-being ratings. It therefore appears that higher overall physical strength is consistently associated with better subjective well-being across diverse economic and cultural contexts, likely due to the continued ability to engage in everyday activities and maintain independence.
Hypotheses
This study focused on examining these associations in low- and middle-income countries (LMICs) and tested the following hypotheses:
One: Physical activity levels (PALs) in older adults will be positively associated with measures of subjective well-being in all SAGE countries.
Two: Common measures of physical function (grip strength, usual and rapid gait speed) will exhibit a positive relationship with subjective well-being measures in older adults from each country.
Figure 2. SAGE participants engaged in various levels of physical activity
Key Findings: PALs and well-being
Higher PALs were associated with a decreased likelihood of depression diagnosis in India (p = 0.001), yet in Ghana high PALs were linked with an increased likelihood of depression diagnosis (p < 0.001). Higher PALs were associated with a low likelihood of reporting poor QOL in India (p = 0.008) and South Africa (p = 0.003). Elevated PALs were associated with a decreased likelihood of unhappiness in China (p < 0.001), and an increased likelihood of unhappiness in South Africa (p = 0.008). Higher PALs were linked with a decreased likelihood of poor mood in Ghana and Russia (p < 0.01), and an increased likelihood of poor mood in China (p = 0.004) (Table 1).
Key Findings: Physical function measures and well-being
Acknowledgments
We thank Nirmala Naidoo for her efforts in data analysis. Support for the research was provided by NIH NIA Interagency Agreement YA CN-0020; NIH R01-AG
Higher Grip Strength (Table 1)
Overall, positively associated with well-being:
Lower depression in Ghana (p < 0.001) and India (p < 0.001).
Improved QOL ratings in China, Ghana, Mexico, and South Africa (p < 0.05).
Increased happiness in South Africa (p < 0.001).
Improved mood ratings in both China and South Africa (p < 0.01).
Higher Usual Gait Speed (Table 1)
Poorer QOL ratings in China (p = 0.002).
Increased unhappiness in China (p < 0.001) and Ghana (p = 0.020).
Poorer mood in China (p = 0.007).
Higher Rapid Gait Speed (Table 1)
Poorer QOL ratings in India (p = 0.038), Mexico (p = 0.030), and South Africa (p = 0.030).
Increased unhappiness in India (p = 0.014).
Poorer mood in Ghana (p = 0.024).
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Figure 1. Map of six SAGE countries, showing study locations.
Study on global AGEing and adult health (SAGE) participants
Data were drawn from the WHO SAGE Wave 1. SAGE is a longitudinal study of nationally-representative samples of older adults (>50 years old) in six LMICs (China, Ghana, India, Mexico, Russian Federation, and South Africa) (Fig. 1). An individual questionnaire and performance tests were administered to participants to obtain measures of physical function and well-being (n = 30,653; Fig 2).