This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Aging, is properly cited. The complete bibliographic information, a link to the original publication on http://aging.jmir.org, as well as this copyright and license information must be included.
Physical activity has been shown to positively affect many aspects of life, and the positive relationship between physical activity levels and health is well established. Recently, research on the interrelationship between physical activity levels and subjective experiences has gained attention. However, the underlying mechanisms that link physical activity levels with subjective experiences of physical fitness have not been sufficiently explained.
This study aimed to explore the role of physical activity tracking (PAT) in the relationship between physical activity levels and satisfaction with physical fitness in older adults. It is hypothesized that higher levels of physical activity are associated with a higher satisfaction with physical fitness in older adults and that this positive association is stronger for older people who use mobile devices for PAT.
As part of this study, 1013 participants aged 50 years or older and living in Switzerland were interviewed via computer-assisted telephone interviews. Bivariate and multivariate analyses were applied. The interaction effects between physical activity levels and PAT were evaluated using multiple linear regression analysis.
Descriptive analyses showed that 719 participants used at least 1 mobile device and that 136 out of 719 mobile device users (18.9%) used mobile devices for PAT. In the multivariate regression analysis, frequent physical activity was found to have a positive effect on satisfaction with physical fitness (beta=.24,
The results indicate the potential of PAT to enhance the physical fitness of older adults. However, the results also raise new issues in this context. Recommendations for further research and practice include the acquisition of longitudinal data, a more detailed observation of durations of use, and the development of devices for PAT considering health psychology and gerontology theories.
Physical inactivity is a major health risk facing people worldwide, especially older adults [
Against this background, it is important not only to look at absolute levels of physical activity but also to consider their subjective evaluation. This includes the question of how satisfied an individual is with his or her physical activity levels and how this satisfaction is achieved. Recommendations for physical activity levels only provide a general framework. A good measure of individual satisfaction is not a global goal of, for example, 10,000 steps or 150 min, rather, it is the subjective estimation of the positive effects of physical activity on health and well-being.
Research on the interrelationship between physical activity levels and subjective experiences has gained attention in recent years. Empirical research has shown the positive effects of physical activity on health-related quality of life [
There has been some work on the age-specific effects of physical activity on subjective well-being. Pawlowski et al [
Mobile tracking technologies such as activity trackers and other wristband sensors that track physical activity (eg, activity monitors, activity wristbands, and smartwatches), as well as apps on smartphones and tablets (eg, ActivityTracker, Runkeeper, and MyFitnessPal), are growing in popularity [
Despite a growing number of studies on PAT, the relevance of PAT in terms of the relationship between physical activity levels and subjective experiences of satisfaction with physical fitness has not been studied in detail yet. In addition, research on PAT has traditionally focused on young or middle-aged individuals [
The objective of this study was to contribute to the literature on the positive effects of physical activity levels by examining the importance of PAT in this context. More precisely, we are interested in whether the relationship between physical activity levels and older adults’ satisfaction with physical fitness is influenced by PAT with mobile devices (ie, activity trackers, smartwatches, smartphones, and tablets). Given the empirical evidence on the interrelation between physical activity levels and satisfaction with physical fitness in general [
Higher levels of physical activity are associated with a higher satisfaction with physical fitness.
Mobile technologies for PAT allow for the quantification of the levels of physical activity, enable goal setting, and make achievements more visible [
The positive association between physical activity levels and satisfaction with physical fitness is stronger for people who use mobile devices for PAT.
This secondary analysis is based on a survey performed in Switzerland [
A standardized questionnaire with 24 questions about users’ sociodemographic information and mobile device use for PAT was administered. A random sample of the permanent resident population of Switzerland aged 50 years and older was chosen from the commercial AZ-Direct database (based on the public phone book). No restrictions were imposed on upper age, current mobile device use, or type of housing. This study included a representative sample of all age groups examined across gender, education, and language region. Within the whole sample, 70.98% (719/1013) used at least 1 mobile device—such as a smartphone, tablet, and/or smartwatch—in their everyday life, whereas 29.02% (294/1013) reported that they did not use any of these devices. For the purpose of this paper, only the sample group of individuals who used any of the aforementioned mobile devices (n=719) was considered in the analyses. Participants’ age in this group ranged from 50 to 88 years, with a mean age of 62.7 years (SD 9.45); 50.5% (363/719) were female; and 49.5% (356/719) were male.
Mobile PAT comprises activity trackers (ie, wristbands with accelerometer technology for monitoring and tracking fitness-related behavior, mostly based on counting steps and time periods of physical activity), smartwatches (ie, computerized wristbands with various functionalities and apps similar to those of smartphones, which run on their own operating systems), and smartphone or tablet apps; all of these can be used for tracking physical activity. We generally asked whether the respondents used apps or devices for the purpose of tracking physical activity; however, we do not have information regarding whether the respondents used the devices or apps actively or passively (eg, active measurement or automatic recording of steps). The use of these devices and apps was measured by self-report (1=never, 2=seldom, 3=once a week, and 4=daily). Individuals who used any of these devices or apps for PAT at least once a week are referred to hereafter as the
To test the research hypotheses, a set of variables that allowed the exploration of the use of mobile devices for PAT in more detail was taken into account. One central construct in the analyses was satisfaction with physical fitness. Satisfaction is an individual’s mental state and a subjective measurement of his or her self-evaluated contentment. In the context of physical activity, this means that an individual is at ease with his or her physical activity levels (eg, he or she is satisfied with current fitness levels and the frequency and the intensity of exercise). Similar to previous research [
Research model of moderation analysis to show the effect of physical activity level on satisfaction with physical fitness moderated by physical activity tracking (PAT).
The level of physical activity was also measured using a self-reported question, as in previous studies [
SPSS version 24 (IBM Statistics) was used for statistical analyses. Missing data were excluded listwise. As a first step, we distinguished between the PAT and noPAT groups. To compare the characteristics of the 2 groups, we calculated
To test the research hypotheses, we performed multiple linear regression analyses (OLS). The dependent variable in these analyses was satisfaction with physical fitness (on a 5-point Likert scale). The independent variables included age (in years, mean centered), gender (reference female), income (reference more than CHF 9000), level of physical activity (on a 6-point scale), and PAT group (reference noPAT). In the first model (main-effect model), the main effects of the independent variables are reported. In a second model (interaction-effect model), we additionally included an interaction term between PAT and physical activity level. The interaction was calculated by multiplying the variables level of physical activity and PAT. Including the interaction term between physical activity level and PAT allowed us to model the effect of physical activity level on satisfaction with physical fitness depending on whether individuals tracked their physical activity with mobile devices or apps. This process is an example of moderation analysis (simple moderation analysis). As described by Hayes [
Altogether, 18.9% (136/719) participants used a device to track their physical activity and were therefore considered as members of the PAT group. Participants in the PAT group used an activity tracker (59.6%, 81/136), smartwatch (12.5%, 17/136), and/or smartphone or tablet app (57.4%, 78/136) to track their physical activity. Most members of the PAT group (74.3%, 101/136) used a single device to track their physical activity, whereas only a minority used 2 (22.0%, 30/136) or 3 (3.7%, 5/136) devices.
The remaining 81.1% (583/719) of participants used at least 1 mobile device but for purposes other than tracking their physical activity, and they were considered as members of the noPAT group.
On comparing participants in the PAT and noPAT groups (see
Characteristics of the physical activity tracking and no physical activity tracking groups (only respondents who own a mobile device, n=719).
Characteristics | User group | Significance | Cohen |
|||
PATa | noPAT | |||||
Age (years), mean (SD) | 61.95 (9.43) | 62.81 (9.43) | 0.34 | 0.01 | ||
Men | 80 (58.8) | 276 (47.3) | χ21=5.8 | 0.02 | —b | |
Women | 56 (41.2) | 307 (52.7) | χ21=5.8 | 0.02 | — | |
<CHF 4000 | 17 (14.3) | 65 (13.3) | 0.47 | — | ||
Between CHF 4000-9000 | 60 (50.4) | 277 (56.5) | 0.47 | — | ||
>CHF 9000 | 42 (35.3) | 148 (30.2) | 0.47 | — | ||
Satisfaction with physical fitnessc, mean (SD) | 2.81 (1.00) | 2.90 (0.92) | 0.32 | 0.187 | ||
Mean frequency of physical activityd, mean (SD) | 3.72 (1.13) | 3.58 (1.42) | 0.21 | 0.112 |
aPAT: physical activity tracking.
bNot applicable.
cMeasured on a 5-point Likert scale.
dMeasured on a 6-point scale.
To analyze the effects of physical activity levels and PAT on satisfaction with physical fitness, a multiple linear regression (based on a simple moderation analysis) was performed. The level of physical activity, PAT, and interaction term between both variables were included as independent variables in the full model (model 2). The findings were compared with a model including only the main effects (model 1). In addition, we considered age, gender, and income as control variables in both regression models. All persons who used a mobile device in general were included in the analysis.
The main-effect model (model 1) has an adjusted
Overall, the interaction-effect model (model 2) demonstrates a significant amount of variance within people’s satisfaction with physical fitness (
In both models, higher levels of physical activity had a positive effect on satisfaction with physical fitness (main-effect model: beta=.27,
Multiple linear regression analysis of the predictors of satisfaction (measured using a 5-point Likert scale) with physical fitness (simple moderation analysis on only respondents who own a mobile device, n=719).
Predictor | Model 1: main-effect modela | Model 2: interaction-effect modelb | ||||
b (SE) | Beta | b (SE) | Beta | |||
Constant | 2.18 (0.14) | —c | <.001 | 2.26 (0.14) | — | <.001 |
Aged | 0.01 (0) | .06 | .12 | 0.01 (0) | .06 | .12 |
Gender: male (reference female) | 0.01 (0.07) | .01 | .84 | 0.01 (0.07) | .01 | .89 |
Income: <CHF 4000 (reference >CHF 9000) | −0.22 (0.12) | −.08 | .07 | −0.22 (0.12) | −.08 | .07 |
Income: between CHF 4000-9000 (reference >CHF 9000) | 0.08 (0.08) | .04 | .33 | 0.08 (0.08) | .04 | .34 |
Physical activitye | 0.19 (0.03) | .27 | <.001 | 0.17 (0.03) | .24 | <.001 |
PATf group (reference noPAT) | −0.07 (0.09) | −.03 | .46 | −0.73 (0.31) | −.31 | .02 |
Interaction: PAT * physical activity | — | — | — | 0.18 (0.08) | .30 | .03 |
aAdjusted
bAdjusted
cNot applicable.
dMean centered.
eMeasured on a 6-point scale (
fPAT: physical activity tracking.
Interaction diagram of the effects of physical activity level and physical activity tracking (PAT) on satisfaction with physical fitness. Graphic representation of unstandardized regression estimates for different groups depending on physical activity level and PAT. Values displayed for a female with mean age and medium income (between CHF 4000-9000). Frequency of physical activity was measured using a 6-point scale from never to daily and satisfaction with physical fitness was measured using a 5-point Likert scale.
This study was the first conducted in Switzerland—and to our best knowledge, among the first internationally—to examine the relevance of PAT in the relationship between physical activity level and satisfaction with physical fitness in a representative sample of individuals aged 50 years and older. Our analyses were based on 719 older individuals who use mobile devices in their everyday life.
Bivariate results showed that people who tracked their physical activity using a mobile device were more likely to be male. In the multivariate analysis, we addressed the relationship between physical activity level and satisfaction with physical fitness and mobile PAT in more detail. Examining the relevance of PAT for the positive effects of physical activity on satisfaction with physical fitness is an important research topic, as this might contribute to the well-being of older persons.
The multiple regression models revealed that physical activity level was a positive and significant predictor of satisfaction with physical fitness in both the main-effect model and the interaction-effect model. Individuals who were physically active more often were more satisfied with their physical fitness. These findings are in line with our research hypothesis (H1). Furthermore, we tested whether this relationship was influenced by the use of mobile devices for PAT. We found a significant positive interaction effect between physical activity level and PAT on satisfaction with physical activity; the positive effect of physical activity levels on satisfaction was stronger for people using PAT (H2). Results also showed that PAT had no positive effects on satisfaction with physical fitness in the main-effect model and for individuals who were not physically active on a regular basis (see
In general, people who track their physical activity with a mobile device are more aware of how active they actually are, as compared with individuals who do not track their physical activity [
This negative effect of PAT on satisfaction with physical fitness is an important finding in the evaluation of possible impacts of PAT on well-being and motivation for physical activity. The positive effects of PAT cannot be assumed for everyone. Our results show that in the case of less frequent (not daily) physical activity, PAT might have negative consequences for the users. On the other hand, the use of PAT supports satisfaction with physical fitness for older adults who are physically active on a daily basis. This is an interesting result because this relationship among physical activity, PAT, and satisfaction might also influence the long-term use of PAT and the motivation for physical activity. Further research is needed to investigate these findings in more detail.
To analyze these mechanisms in greater detail, future studies should try to objectively measure the frequency and level of physical activity with tracking technology and electronic momentary assessments to get a more reliable measurement. A further point of interest might be whether the results would differ if activity had been measured using time instead of frequency. Some older people may be active frequently but have a short overall duration, and others may be active infrequently with a long weekly duration.
Furthermore, it can be assumed that activity trackers, or other devices and apps for PAT, are often purchased because individuals are unsatisfied with their current situation, such as their fitness level. In this case, there might be a reverse causality: negative satisfaction has an effect on using PAT and not vice versa. A recent study [
On the basis of the empirical data in this study, it is not possible to reach a concrete conclusion, and the above assumptions need to be examined in greater detail. Future work could extend this research by using longitudinal data. This would allow for the examination of intraindividual change processes and objective tracking data, as it would be possible to study the relevant motivational mechanisms and relationships over time.
With regard to the devices used for PAT, it should be noted that most current devices and apps have been developed without considering health psychology or gerontology theories. It is therefore unlikely that the devices used for PAT have been customized for long-term use or sustainable success among older users. A qualitative study with activity trackers showed that older adults might feel overstrained by predetermined goals that are not individualized [
As this study had a specific regional focus, the generalization of our findings is limited. The data provide only a cross-sectional view of the phenomena, but it is likely that there will be a further increase in mobile activity and health tracking among older individuals in general. Further research, possibly including longitudinal data, is required to examine the potential increase and to make inferences related to mobile activity tracking and subjective well-being and health over time and across individuals. Furthermore, it is possible that participants understand questions on subjective measurements (eg, the frequency of physical activity and their satisfaction with physical fitness) differently, which can affect the results. Furthermore, we did not have any information on the intensity of using PAT. We could only distinguish the PAT and noPAT groups. However, a more active use of PAT in everyday life might have greater effects. A qualitative study showed that older people often passively use devices for PAT in their everyday lives [
We only studied satisfaction with physical fitness. This key outcome of the study was only measured using a single item, which might be subject to bias. It should be noted that an overall evaluation of physical activity and its effects on subjective experiences should ideally be realized in a more differentiated matter. A 1-item measure is problematic, as the different dimensions of the phenomenon cannot be distinguished. However, the available variables used in our secondary data enabled us to analyze the relevance of mobile PAT in terms of the relationship between physical activity and subjective fitness in an exploratory way. Future studies should therefore operationalize this concept through a multidimensional approach and extend this view by both including other measures of satisfaction and quality of life and by using validated questionnaires to confirm the findings.
In addition, in this first study, data on important background factors (eg, technology knowledge, attitudes toward technology, and objective health status), fitness status (eg, objective measures of exercise, fitness status, and activity levels), everyday life factors (eg, coping with activities of daily life and social contact), and psychological factors (eg, attitudes toward health prevention and one’s own life and aging, personality, and well-being) were unavailable. Further studies with a wider range of variables and a longitudinal design are therefore required to examine the study topic in greater detail.
This study provides some evidence that PAT can enhance the positive effect of physical activity levels on satisfaction with physical fitness. The results indicate the potential of mobile PAT to improve the well-being of older adults. Especially for older individuals, mobile devices can allow for the easy longitudinal monitoring and documentation of their health status. However, the results also raise new issues concerning the relationship between PAT and satisfaction with physical fitness. PAT showed a negative effect on satisfaction with physical fitness for individuals who were not physically active on a regular basis. We discussed this finding in the context of self-optimization through PAT, long-term use of the devices, and older adults’ specific requirements in terms of usability and usefulness. Further research is required in this fast-moving field to understand relevant processes and causalities in greater detail.
no physical activity tracking group
ordinary least squares
physical activity tracking
physical activity tracking group
The authors would like to thank the older adults who participated in the study. They also thank Urte Scholz and Takeshi Nakagawa for fruitful discussions on the first draft of the manuscript.
None declared.