Participants and Procedures
The total of 170 community-dwelling older Taiwanese adults in this study at baseline using convenience sampling method. The all potential participants were recruited from four districts (Nangang, Wanhua, Daan, and Wenshan) of Taipei, Taiwan, from April (spring) to September (autumn) 2018. We used neighborhood broadcasts and local advertisements to recruit potential participants. Interested individuals contacted the study recruiters or neighborhood representatives. The sole inclusion criterion for the study was an age of 60 years or above. Furthermore, potential participants who were (i) unable to walk independently, (ii) not meet the minimum requirements of the accelerometer wear time (described later), or (iii) unable to understand the guidance for the questionnaires and physical function tests were excluded from the study. The 126 participants who remained after the application of these criteria were included in our study at baseline. The detailed procedure of the baseline recruitment used in this study has been previously reported [11]. At the baseline survey, each participant was administered a structured questionnaire by trained interviewers. After this interview, we gave each participant an accelerometer, which had to be worn for seven consecutive days. Every participant who completed the questionnaire, lower-extremity performance tests, and accelerometer portion of this study received a convenience store voucher worth 7 USD.
After one year, a follow-up lower-extremity performance test was conducted. A total of 98 of the older adults attended the on-site follow-up examination (follow-up rate: 77.8%). Finally, those with incomplete or missing study variable data in both the baseline and follow-up survey were subsequently excluded (n = 9). Ultimately, a total of 89 participants who provided complete data for the study variables were included in the analysis. Ethical approval was received from the Research Ethics Committee of the National Taiwan Normal University (REC number: 201711HM003). We obtained written informed consent from each participant, and the study was conducted in accordance with the ethical guidelines of the 1975 Declaration of Helsinki and all its revisions.
Accelerometer-assessed Daily Steps
Accelerometers (ActiGraph, Pensacola, FL, USA) were used to objectively assess each participants’ daily step counts. The validity and reliability of such triaxial accelerometer have been widely confirmed [12-17]. For each participant, the accelerometer was used to record movement for seven continuing days. By following the standard methods [17], we used 60-second epochs for all data analyses [18]. We asked each participant to wear the accelerometer on the right side of his/her waist at all times except for water-based activities. Non-wear time was defined as the periods of not less than 60 consecutive minutes of zero counts per minute (cpm), with an allowance of up to 2 minutes of between 0–99 cpm [14]. Participants with at least three valid days (a valid day was defined as at least 600 minutes of accelerometer wear time), including at least 1 weekend day, were included in this study. We utilized ActiLife software 6.0 (Pensacola, FL, USA) to analyze the accelerometer data. In accordance with the aforementioned step-based recommendation for older adults, we categorized the daily steps into “not taking at least 7,000 steps/day” and “taking at least 7,000 steps/day” [10].
Lower-extremity Performance Measures
The five-times-sit-to-stand test is used to evaluate lower-extremity performance [19]. In taking the test, the participants were instructed to rise from a chair (which was 46 centimeters high and armless) to a full standing position and then return to a seated position as quickly as possible for five repetitions. Each participant performed the test two times [20, 21]. The best performance in terms of the total time taken for all five repetitions (that is, the shortest time) was used for our analysis. For the cross-sectional analysis, we used a sex-specific median for dichotomizing the baseline lower-extremity performance of the participants into “better” and “worse” categories. For the prospective analyses, we categorized the lower-extremity performance of the participants into “maintained or improved” and “declined” by calculating the differences in the lower-extremity performance between the follow-up and baseline for each participant.
Covariates
Self-reported demographic characteristics, health-related behaviors, and the presence/absence of chronic diseases were assessed via interviewer-administered questionnaires. The covariates were sex, age group (60-74 or ≥ 75 years), educational level (university and higher or up to high school), marital status (married or not married), job status (with or without a full time job), living status (alone or with others), self-reported health (good or poor), current smoking status, alcohol consumption, balanced diet, hypertension status, blood lipid levels, diabetes status, depression status, and body mass index. Body mass index (BMI) was calculated using self-reported weight and height (categories: non-overweight and overweight) based on the cut-off points for the Asian population (24 kg/m2) [22]. Moreover, accelerometer-measured sedentary time (< 100 counts/minute) and accelerometer wear time were included as covariates as they could confound the relationship between physical activity and health outcomes [23, 24].
Statistical Analyses
Complete data for all the studied variables from 89 older adults were analyzed. Associations between taking at least 7,000 steps per day at the baseline and baseline lower-extremity performance (binary categories: “better” and “worse” based on a sex-specific median) and the difference in lower-extremity performance between the baseline and 1-year follow-up (binary categories: “maintained or improved” and “declined”) were examined using binary logistic regression models. Three different logistic regression models were conducted to investigate before and after adjusting for other covariates. The first model showed unadjusted analyses (Model 1). In the adjusted regression models, the analyses were first adjusted for sociodemographic characteristics (Model 2), and then further adjusted for health-related behaviors, chronic diseases, and accelerometer wear time (Model 3). Odds ratios (ORs) and their 95% confidence intervals (CIs) were estimated. Statistical analyses were conducted using SPSS 23.0 (IBM Inc., Armonk, NY, USA).