Active Learning Through Video Conferencing to Maintain Physical Activity Among Older Adults: A Pilot Randomized Controlled Trial

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Kazuki Uemura Graduate School of Rehabilitation Science, Osaka Metropolitan University, Habikino-City, Japan

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Tsukasa Kamitani Section of Education for Clinical Research, Kyoto University Hospital, Kyoto, Japan

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Atsuya Watanabe Faculty of Engineering, Center for Liberal Arts and Sciences, Toyama Prefectural University, Imizu, Japan

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Hiroshi Okamoto Faculty of Engineering, Center for Liberal Arts and Sciences, Toyama Prefectural University, Imizu, Japan

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Kenshi Saho Department of Electronic and Computer Engineering, Ritsumeikan University, Kusatsu, Japan

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Minoru Yamada Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tokyo, Japan

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This randomized pilot trial investigated the feasibility of an active learning physical activity intervention through video conferencing and its preliminary effects. Participants comprised community-dwelling older adults who could use e-mail. The intervention group underwent a 12-week active learning intervention via video conferencing to promote a healthy lifestyle, particularly physical activity. The control group received information via e-mail once per week. The amount of physical activity and sedentary behavior was measured using an accelerometer at baseline, postintervention, and 24-week postintervention (36 weeks). Of the 31 participants, 29 were eligible and randomized into two groups (15 for the intervention and 14 for the control). Adherence to the intervention was 83%–100% (mean, 97%). Compared with the control group, the intervention group showed moderate maintenance effects on total physical activity and sedentary behavior at 36 weeks. Active learning physical activity intervention through video conferencing was found to be feasible and contributed to the prevention of physical activity decline in older adults.

Maintaining physical activity is critical for older adults because it supports longevity and prevents cognitive decline and frailty (Barha et al., 2020; Li, Ma, et al., 2022; Zhang et al., 2020). Group-based activities (Stevens & Cruwys, 2020) and peer interactions (Burton et al., 2018; Franco et al., 2015) contribute to better exercise adherence among older adults; however, few studies have examined whether the effects on physical activity are maintained after the intervention (Grande et al., 2020; Li, Szanton, et al., 2022; Oliveira et al., 2020). Hence, we proposed an active learning physical activity intervention that keeps older adults motivated through vicarious experiences drawn from others with similar characteristics. This intervention is a productive alternative to the passive reception of health information for sustainable behavioral changes (such as maintaining physical activity) (Uemura, Kamitani, et al., 2021; Uemura et al., 2018).

Electronic health (eHealth) utilizes information and communication technologies (ICTs) to support health. eHealth is gaining attention for its potential benefits in promoting physical activity, such as improved accessibility and enhanced compliance. Its temporal, geographical, and financial advantages over face-to-face interventions enable the remote delivery of health services, even in rural areas with poor healthcare access. Furthermore, internet use among older adults is increasing. In 2021, the proportion of internet users in Japan was approximately 80% in the 65–69 age group and 60% in the 70–79 age group (Ministry of Internal Affairs and Communications, Japan, 2020). We consider active learning physical activity intervention via video conferencing a novel eHealth intervention with broad accessibility in both urban and rural regions that may enable older adults to interact with peers because of the user-friendly interface. However, few studies have used online meeting platforms for live interparticipant communication to improve acceptance and motivation.

The feasibility of active learning through video conferencing for older adults (≥65 years) remains unclear. Although a recent systematic review reported that high participant retention is expected for eHealth interventions (Núñez de Arenas-Arroyo et al., 2021), most of the existing evidence on eHealth was obtained from studies with a lower age limit of 50–55 years (Kwan et al., 2020; Muellmann et al., 2018; Núñez de Arenas-Arroyo et al., 2021); this age range may mean that the participants had more experience and knowledge of ICT. Active learning through video conferencing encourages interactions and helps older adults overcome barriers to adopting ICT, which include a lack of social interaction and communication (Vaportzis et al., 2017). Therefore, we hypothesized that it could be a feasible and beneficial intervention for maintaining physical activity, similar to in-person programs that use active learning strategies.

We previously conducted a single-arm pilot trial to build an active learning physical activity intervention using video conferencing and confirmed its safety and minimal acceptability (satisfaction and perceived difficulty) among older adults (Uemura, Watanabe, et al., 2021). The primary aim of this pilot trial was to determine the feasibility of the study by measuring the proportion of adherence and retention during long-term follow-up using a randomized design.

The secondary aim was to investigate potential intervention effects on the prevention of physical activity decline, related behavioral skills, and self-efficacy, which would contribute to estimating the parameters required to design a definitive randomized controlled trial (RCT). Our intervention targeted healthy lifestyles, particularly physical activity, as it is considered one of the most effective interventions for preventing and managing frailty in older adults (Negm et al., 2019). Because physical activity gradually declines with aging, we focused on long-term maintenance with a postintervention follow-up rather than the short-term increase at a postintervention assessment. We also focused on the between-group difference (not the within-group difference) because there are seasonal variations in physical activity (Shephard & Aoyagi, 2009), which may mask the change resulting from the intervention. An RCT with a parallel group design allows us to extract the intervention effect under the same conditions of seasonal variation in physical activity between the intervention and control groups.

Methods

Trial Design

This study was a pilot RCT with two parallel groups (1:1 allocation) in a rural community in Toyama Prefecture (Imizu, Takaoka, Toyama, and Nanto). The intervention lasted 12 weeks. Outcome data were collected at baseline (August 2021) and postintervention (12 weeks, December 2021) and at a 24-week follow-up postintervention (36 weeks, July 2022). The CONSORT extension to randomized pilot and feasibility trials was used to guide trial design and execution (Eldridge et al., 2016). The study protocol was registered in the University Hospital Medical Information Network Clinical Trials Registry (UMIN000044835). The institutional review board of Toyama Prefectural University approved the trial protocol (number: R3-5). All participants signed an informed consent form before randomization, according to the 1975 Declaration of Helsinki.

Participants

Study participants were recruited from the community through advertisement inserts in three local newspapers, presentations at a community meeting, and word of mouth from June to July 2021. In addition, we worked with the city offices of Imizu, Takaoka, and Toyama and targeted our recruitment strategies in that region. We included adults aged 65 years or older who had a computer at home with access to the internet and could use e-mail, regardless of their experience with video conferencing. The participants were screened before applying the eligibility criteria, as they were required to apply for participation via e-mail. The exclusion criteria were as follows: (a) any disability in performing basic activities of daily living; (b) being diagnosed with dementia, Parkinson’s disease, or depression; or (c) having an unstable medical condition or severe musculoskeletal, pulmonary, neurological, or cardiac diseases that could restrict the ability to exercise.

Randomization and Masking

Researchers from Toyama Prefectural University enrolled eligible participants. A researcher who was not involved in recruitment, data collection, or data analysis conducted stratified block randomization with a block size of four stratified by sex (male/female) (Nagashima, 2017). Thus, allocation concealment was achieved. Participants were allocated to receive either active learning physical activity intervention by web-conferencing (intervention group) or delivery of information via e-mail (control group). Outcome assessors—research assistants who instructed participants on accelerometer measurements and usage—were blinded to the group allocation. Owing to the nature of the interventions, we could not mask the education staff or participants for group allocation after randomization.

Intervention

First, a briefing session via video conferencing (Zoom platform) was held with eight participants who had never used the medium (half of the intervention group) at the university. For all participants, accessibility to Zoom at their own homes was confirmed before the program to prevent network-related issues. They then underwent a 12-week active learning physical activity intervention (1 day/week, 90 min/day) via video conferencing. The 15 participants in the intervention group were divided into two sessions conducted on different days of the week. This program focused on the roles of physical activity, diet, and nutrition in promoting health and preventing geriatric syndrome (Table 1).

Table 1

Themes Underlying the Topics Covered in Each Course Among Older Adults

No.Active learning (intervention)Delivery of information material (*.pdf file) by e-mail (control)
ThemeTopicContents of material
1OrientationGuidance/icebreakerLetter about guidance and exercise menu
2Exercise 1Recommendations for walking habitsLetter recommending walking habits
3Exercise 2Procedure for resistance trainingLetter about procedures for resistance training
4Diet/nutrition 1Preventive measures against malnutritionLetter about malnutrition
5Diet/nutrition 2Muscle-building diet and nutritionLetter about muscle-building diet
6InformationAppraisal and utilization of health informationLetter about appraisal and utilization of health information
7Reflection 1Review of the first half of the programLetter about exercise menu
8Health problems that affect older adultsPreventive measures against mental illnessLetter about mental illness
9Workshop: Presentation on the health problems that affect older adultsLetter about physical frailty
10Letter about oral frailty
11Letter about falls and fractures
12Reflection 2Review of the entire program contentsLetter about the review of program contents

The following activities were conducted for each topic: (a) explorative learning for homework assignments, (b) group discussions, and (c) planning and application of health promotion techniques in everyday life. During the class, the research staff provided essential information about the themes. They then proposed learning tasks (e.g., “how to maintain a walking habit” or “what is a muscle-building diet and nutrition”) for the following week. The participants were required to research each learning task using any media they chose and report their thoughts and ways of leading a healthy lifestyle as part of their assignment. The following week, individual views were discussed in separate rooms (using the breakout room function in Zoom). Group work was conducted with groups of three to four participants each. After the group work, the participants introduced a summary of the information they shared with the other group members. Based on shared ideas and knowledge, the participants chose the behavior they wanted to focus on and executed the associated healthy behaviors in their daily lives under their plans and goals. Participants tracked their physical activity using an accelerometer and documented the results in a notebook. The research staff facilitated the classes and assisted the participants in summarizing the knowledge discussed throughout the group work. During the last 10–15 min of each class, the research staff provided instruction on the methods for basic exercises (e.g., calf raise, squat, hip abduction, and knee extension).

Control

The control group received a 90-min lecture on cognitive health during old age at the university, which did not include active learning strategies. Subsequently, they received information (*.pdf file) via e-mail once a week over the 12-week intervention period. Each week, the themes of the materials were similar between the intervention and control groups (Table 1). However, to prevent involving them in the active learning process, the content of the “guidance/ice-breaker,” “reflection,” and “workshops” (Session nos. 1, 7, and 9–11) was replaced by information supplements, such as an exercise menu and information about health problems.

Outcomes

Feasibility Measures

Feasibility was assessed based on participant retention and adherence to the intervention. Participant retention was calculated as the number (proportion) of participants who completed the intervention (or control period) and the final follow-up assessment. Adherence to the intervention was calculated as the proportion of attendance in the 12 sessions. Feasibility was assessed based on the proportion of retention and adherence. Proportions ≥ 80% were considered acceptable compared with previous systematic reviews investigating retention and adherence to exercise interventions in older adults (Hawley-Hague et al., 2016; Picorelli et al., 2014).

Accelerometry Measures

The intervention effects were assessed by comparing changes in the objectively measured physical activity from baseline to the 12- and 36-week follow-ups for the following variables. The participants were instructed to wear a GT3X-BT accelerometer (ActiGraph) on their right hip during waking hours, other than bathing, for 7 days. The obtained data were downloaded from the device in 60-s epochs using ActiLife software (version 6.13.4, ActiGraph). Nonwear time was excluded using the Choi algorithm (Choi et al., 2011). Data with ≥600 min wear time for ≥3 days were defined as valid. Each minute of activity was categorized using the intensity threshold values of counts per minute developed for older adults (Copeland & Esliger, 2009): <100 for sedentary behavior (<1.5 metabolic equivalent of task), 100–1040 for light-intensity physical activity (LPA) (1.5–3 metabolic equivalent of task), and >1,040 for moderate-to-vigorous physical activity (MVPA) (≥3 metabolic equivalent of task). Total physical activity (TPA) was the sum of all minutes registering 100 or more counts per minute. The daily average time spent (min/day) in each category was obtained for TPA, LPA, and MVPA. We also calculated the percentage of time spent sedentary as the proportion of sedentary behavior in the total valid wear time. At baseline and the 12-week (postintervention) assessment, the participants received the accelerometer and were instructed in person about the accelerometry measurement (i.e., how to wear, duration) by research assistants who were blinded to the group allocation. At the 36-week assessment, the participants received an accelerometer in the mail, with only the explanatory material enclosed, and used it in the same way as in the previous assessments.

Questionnaire Measures

We also measured self-reporting scale scores on exercise-related behavioral skills and self-efficacy using online forms. Exercise-related behavioral skills were assessed using five items about participants’ attitudes and behaviors regarding exercise. These include goal setting, exercise logs, gathering information, stimulus control, and reinforcement of exercise management (Takeda et al., 2009). Items were scored on a 5-point Likert-type scale (1 = never and 5 = often). Scores were summed to compute the scores for behavioral skills (range: 5–25). Higher scores indicate greater utilization of skills to carry out and maintain exercise. Self-efficacy for exercise was assessed using four items measuring participants’ confidence to exercise in situations that could hinder regular exercise, such as being tired, busy, in a bad mood, and during bad weather (Oka, 2003). Items were scored on a 5-point Likert-type scale (1 = strongly disagree and 5 = strongly agree). Scores were summed to compute the scores for self-efficacy (range: 4–20). Higher scores indicate greater confidence in exercising in a situation with a barrier. Both scales have high reliability and validity (Oka, 2003; Takeda et al., 2009).

Other Measurements at Baseline

In addition to age and sex, we collected information on sociodemographic characteristics, including education (<10, 10–12, and ≥12 years), and clinical characteristics including body mass index, chronic conditions (presence or absence), cognitive status (rapid dementia screening test [Kalbe et al., 2003; Sakai et al., 2006]), and physical function. In addition, the grip strength of the dominant hand and the usual gait speed were assessed using a physical function test.

Statistical Analysis

The mean and SDs, or frequencies and percentages, were used to summarize participants’ characteristics at baseline. Feasibility data were descriptively shown (raw count [number, %] for retention and mean and range [%] for adherence). Formal hypothesis testing was not performed per the CONSORT guidelines for pilot trials (Eldridge et al., 2016); as this was a pilot study with a small sample size, it might not have been possible to detect significant differences between groups, even if clinically relevant. Thus, we estimated the potential effects of inferring the size and direction of intervention effects. Between-group differences in temporal changes from baseline to 12 and 36 weeks (mean difference [MD] and 95% confidence interval [CI]) were calculated for all outcome measures. Hedges’ g effect sizes (standardized MD) were calculated and interpreted as small (g = 0.15), medium (g = 0.40), and large (g = 0.75) (Brydges, 2019). Outcome data were analyzed based on the intention-to-treat principle, using all available data at each follow-up assessment.

Although the complete case analysis was used as the primary analysis, for participants with at least one missing variable (13.8%), we also performed a multiple-imputation approach using the chained equation method with 20 imputed data sets based on the missing-at-random assumption as a sensitivity analysis. Missing values were imputed using all outcome measures, allocation, and other measurements at baseline. To adjust for differences in baseline covariates in the sensitivity analysis, we used linear mixed models to compute the regression coefficients of the product terms consisting of groups and time, indicating intervention effects. In addition, age was included as a covariate in the model because of the difference in the mean age between the groups. “Group” and “time” served as fixed factors, while individual participants served as random factors. All analyses were performed using Stata 17.0 (StataCorp LP).

Sample Size

To prepare for a future definitive RCT to detect a medium standardized effect size (0.5) with 90% power and two-sided 5% significance, a sample size of 15 per treatment arm is required, according to the recommended sample size for pilot studies (Whitehead et al., 2016). Assuming a 10% attrition rate, we targeted 34 older adults.

Results

Recruitment and Baseline Data

Recruitment for this pilot RCT started in June 2021, and the final assessment was completed in July 2022. Figure 1 shows the flow of participants through the study. Forty-one participants responded to the recruitment via e-mail, of whom 31 completed the consent process and were assessed for eligibility. Two participants were excluded because of musculoskeletal or cardiac diseases. Twenty-nine participants were identified as eligible and randomized (15 to the intervention group and 14 to the control group). Table 2 summarizes the participants’ characteristics at baseline. The mean age of the intervention group (73.9 years) was higher than that of the control group (69.4 years).

Figure 1
Figure 1

Flow of participants through the study.

Citation: Journal of Aging and Physical Activity 2024; 10.1123/japa.2023-0180

Table 2

Participants’ Characteristics at Baseline

Intervention group (n = 15)Control group (n = 14)
Age (years)73.9 (3.9)69.4 (3.2)
Female sex, n (%)5 (33)5 (36)
Education, n (%) (years)
 <101 (7)0 (0)
 10–124 (27)3 (21)
 ≥1310 (67)11 (79)
Rapid dementia screening test scores10.1 (1.9)10.4 (1.4)
Body mass index (kg/m2)23.6 (3.7)23.5 (3.9)
Chronic conditions, n (%)
 Hypertension11 (73)7 (50)
 Dyslipidemia6 (40)7 (50)
 Diabetes mellitus5 (33)2 (14)
 Heart disease2 (13)1 (7)
Physical function
 Grip strength (kg)29.0 (4.5)30.3 (6.9)
 Gait speed (m/s)1.3 (0.1)1.3 (0.1)

Note. Values are means (SDs) unless stated otherwise.

Feasibility

At 36 weeks, 26 (90%) of the 29 participants—14 (93%) intervention participants and 12 (86%) control participants—completed the final follow-up assessment. The proportion of adherence to the intervention program was 83%–100% (mean: 97%); one participant who dropped out after completing two sessions was excluded. No adverse events attributable to the intervention, such as falls during online sessions, were observed.

Intervention Effects

Table 3 describes the estimated MDs, 95% CIs, and effect sizes of the outcome measures from baseline to 12 and 36 weeks. The accelerometry data of one participant in the intervention group at the 12-week assessment were excluded from the analysis because there were fewer than 3 days of valid data, as shown in Figure 1. Moderate between-group differences in change from baseline were observed for TPA at 12 weeks (MD [95% CI] = 22.0 min/day [−16.8, 60.7], g = 0.44) and 36 weeks (MD [95% CI] = 25.4 min/day [−10.9, 61.8], g = 0.55), LPA at 12 weeks (MD [95% CI] = 19.5 min/day [−17.7, 56.6], g = 0.44) and 36 weeks (MD [95% CI] = 19.0 min/day [−11.5, 49.4], g = 0.49), and % time spent sedentary at 12 weeks (MD [95% CI] = −2.5% [−7.1, 2.1], g = 0.41) and 36 weeks (MD [95% CI] = −2.3% [−6.2, 1.8], g = 0.47), favoring the intervention arm. Small between-group differences in change from baseline were observed for MVPA at 12 weeks (MD [95% CI] = 2.5 min/day [−7.5, 12.5], g = 0.19) and 36 weeks (MD [95% CI] = 6.5 min/day [−10.7, 23.6], g = 0.30), favoring the intervention arm. In the questionnaire measures, medium to large between-group differences in change from baseline were observed for exercise-related behavioral skills scores at 12 weeks (MD [95% CI] = 3.1 [−0.2, 6.3], g = 0.71) and 36 weeks (MD [95% CI] = 2.3 [−0.8, 5.4], g = 0.59), favoring the intervention arm. The between-group difference in self-efficacy for exercise scores was small at 12 weeks (MD [95% CI] = 1.1 [−1.7, 4.0], g = 0.30) and diminished at 36 weeks (MD [95% CI] = 0.1 [−2.7, 3.0], g = 0.04).

Table 3

Within- and Between-Group Differences and Effect Sizes in Outcome Measure Changes From Baseline to 12 and 36 Weeks

OutcomeChanges from baseline to Weeks 12 and 36
Intervention groupControl groupBetween-group differences (95% confidence interval)Hedges’ g
Within-group differences (95% confidence interval)Within-group differences (95% confidence interval)
Accelerometry measures
 Total physical activity time (min/day)a
  12 weeks−0.5 [−18.9, 17.9]−22.5 [−57.8, 12.8]22.0 [−16.8, 60.7]0.44
  36 weeks8.5 [−17.3, 34.4]−16.9 [−45.4, 11.6]25.4 [−10.9, 61.8]0.55
 Light physical activity time (min/day)a
  12 weeks−3.5 [−20.9, 13.8]−23.0 [−57.0, 11.0]19.5 [−17.7, 56.6]0.40
  36 weeks−1.6 [−20.1, 17.0]−20.5 [−47.8, 6.8]19.0 [−11.5, 49.4]0.49
 Moderate-to-vigorous physical activity time (min/day)a
  12 weeks3.0 [−2.9, 9.0]0.5 [−8.0, 9.0]2.5 [−7.5, 12.5]0.19
  36 weeks10.1 [−4.6, 24.8]3.6 [−5.6, 12.9]6.5 [−10.7, 23.6]0.30
 % time spent sedentaryb
  12 weeks−0.8 [−2.9, 1.4]1.7 [−2.5, 6.0]−2.5 [−7.1, 2.1]0.41
  36 weeks−0.9 [−3.6, 1.7]1.4 [−1.8, 4.6]−2.3 [−6.2, 1.8]0.47
Questionnaire measures
 Exercise behavioral skill scoresa
  12 weeks4.4 [1.8, 7.1]1.4 [−0.9, 3.6]3.1 [−0.2, 6.3]0.71
  36 weeks3.2 [0.9, 5.5]0.9 [−1.4, 3.2]2.3 [−0.8, 5.4]0.59
 Self-efficacy for exercise scoresa
  12 weeks1.2 [−0.6, 3.0]0.07 [−2.3, 2.5]1.1 [−1.7, 4.0]0.30
  36 weeks0.1 [−1.6, 1.9]0 [−2.5, 2.5]0.1 [−2.7, 3.0]0.04

aA positive difference between the two groups indicated that the improvements were greater in the intervention group. bA negative difference between the two groups indicates that improvements were greater in the intervention group.

The results of the sensitivity analysis using linear mixed models with multiple imputation adjusted for age with the data on all participants were consistent with those of the complete-case analysis (Table 4).

Table 4

Estimated Mean and 95% Confidence Intervals From Linear Mixed Models With Multiple Imputation Adjusted for Age (Sensitivity Analysis)

OutcomeChanges from baseline to Weeks 12 and 36
Intervention groupControl groupBetween-group differences (95% confidence interval)
Within-group differences (95% confidence interval)Within-group differences (95% confidence interval)
Accelerometry measures
 Total physical activity time (min/day)a
  12 weeks−0.8 [−29.5, 27.9]−22.5 [−48.6, 3.7]21.7 [−17.1, 60.5]
  36 weeks5.5 [−22.0, 33.0]−16.7 [−46.0, 12.6]22.2 [−16.1, 60.6]
 Light physical activity time (min/day)a
  12 weeks−3.8 [−30.4, 22.8]−23.0 [−46.9, 0.9]19.2 [−16.5, 60.0]
  36 weeks−3.9 [−29.7, 21.8]−23.1 [−50.4, 4.3]19.1 [−16.7, 54.9]
 Moderate-to-vigorous physical activity time (min/day)a
  12 weeks3.0 [−7.3, 13.3]0.5 [−8.8, 9.9]2.5 [−11.4, 16.4]
  36 weeks9.5 [−0.2, 19.1]6.4 [−4.8, 17.5]3.1 [−12.2, 18.4]
 % time spent sedentaryb
  12 weeks−0.6 [−3.7, 2.5]1.7 [−1.1, 4.6]−2.3 [−6.6, 1.9]
  36 weeks−0.8 [−3.7, 2.1]1.4 [−1.7, 4.5]−2.2 [−6.3, 1.9]
Questionnaire measures
 Exercise behavioral skill scoresa
  12 weeks4.6 [2.2, 7.0]1.4 [−1.0, 3.7]3.3 [−0.08, 6.6]
  36 weeks3.6 [1.2, 5.9]0.5 [−1.9, 3.0]3.1 [−0.4, 6.5]
 Self-efficacy for exercise scoresa
  12 weeks1.5 [−0.4, 3.3]0.07 [−1.8, 1.9]1.4 [−1.2, 4.0]
  36 weeks0.6 [−1.3, 2.5]−0.6 [−2.5, 1.3]1.2 [−1.5, 3.8]

aA positive difference between the two groups indicated that the improvements were greater in the intervention group. bA negative difference between the two groups indicated that the improvements were greater in the intervention group.

Discussion

This pilot trial used a randomized design with a postintervention follow-up to investigate the feasibility and preliminary effects of active learning physical activity intervention by video conferencing among participants aged 65 years or older, to assess the value for a larger trial in the future. We confirmed high adherence to the intervention program (mean: 97%), in line with our previous study findings (Uemura, Watanabe, et al., 2021). In addition, the retention proportion, even at the 24-week follow-up postintervention, was high (90% overall). This finding indicates that our protocol was feasible for the participants and encourages further exploration of a definitive RCT. Adherence to the program and retention proportion in this study were better than those reported in an intervention trial using on-site exercise classes (adherence: 58%–77%; retention: 65%–85%) (Farrance et al., 2016; Picorelli et al., 2014). This adherence was similar to that reported in a recent video conferencing intervention study (Schwartz et al., 2021). Easy access to the program at home via video conferencing appeared to enhance adherence regardless of weather or transportation conditions.

This pilot education intervention also assisted in preventing adverse changes in the TPA, LPA, and sedentary behavior, showing moderate maintenance effects (Hedges’ g approximately 0.5) compared to the control group, which showed a trend of decreased physical activity and increased sedentary behavior. These results should be considered in the context of the present study’s preliminary nature. Evidence regarding the postintervention maintenance effects of eHealth interventions on physical activity is lacking; few studies conducted follow-up assessments after the completion of the intervention (Kwan et al., 2020; Muellmann et al., 2018; Núñez de Arenas-Arroyo et al., 2021). Concurrently, evidence regarding the beneficial impact of TPA, LPA, and reduced sedentary time on longevity (Dempsey et al., 2020; Ekelund et al., 2019) is accumulating; therefore, the maintenance effects of our intervention on participants could be beneficial. Although the standardized MD of MVPA at 36 weeks was small to medium (g = 0.30), our estimate of the MD (MD = 6.5 min) was not significantly different from that of a meta-analysis investigating the effect of eHealth interventions in healthy adults aged over 55 years (MD = 7.4 min) (Núñez de Arenas-Arroyo et al., 2021).

Existing literature on exercise interventions using video conferencing has focused on effective and safe supervision and the relationship between instructors and participants (Hong et al., 2018; Schwartz et al., 2021). In contrast, our eHealth intervention used video conferencing, which utilized live interparticipant interactions. Therefore, our intervention had additional strengths (e.g., peer support [Burton et al., 2018] or observational learning of behaviors from social-cognitive theory [Bandura, 1986]) that resulted in favorable impacts on behavioral change. These features of active online learning might encourage participants to adhere to the program and maintain habitual physical activity by providing social interaction and communication, which is a core concern for older adults who adopt ICT (Vaportzis et al., 2017).

Only older adults who could apply for the intervention via e-mail were included in this study. Hence, these findings should be interpreted in the context of older adults who are relatively familiar with ICT. Furthermore, although the participants might have been motivated at baseline, the mean number of steps per day was 5,597 in male participants and 4,195 in female participants, similar to those among Japanese older adults in the general population (male: 5,396; female: 4,656), according to the 2019 National Health and Nutrition Survey. In addition, physical activity declined in the control group despite receiving regular health information. Thus, our participants appeared to be appropriate targets for physical activity interventions. Furthermore, as the proportion of internet usage among adults aged 50–64 years is approximately 90% in Japan (Ministry of Internal Affairs and Communications, Japan, 2020), the number of older adults who can adopt our online program will increase in the coming years.

The strength of the present study was that it involved a postintervention follow-up period and demonstrated high retention. This high retention supports the feasibility of the long-term sustainability of intervention effects in definitive RCTs. A systematic review of the literature examining the effects of physical activity interventions, not limited to eHealth, at a minimum of 6 months follow-up also reported that most studies used self-report instruments (Sansano-Nadal et al., 2019), which are prone to recall and socially desirable bias. Our intervention study using objective measures (i.e., accelerometer) provides more accurate information about the sustained effects on the amounts of physical activity. However, our study had a few limitations. First, estimates of reported intervention effects are preliminary evidence that could guide a larger definitive RCT; however, these findings might not be replicable when the intervention is scaled up. Second, the results are not generalizable to the older population in the community and individuals with less ICT experience. Third, the sample size did not reach the target of 34 participants, as 10 were lost to attrition before the eligibility assessment. Preemergency measures were implemented in Toyama Prefecture during the severe local spread of coronavirus disease 2019 in August 2022 (before the start of the eligibility assessment), which could have affected the motivation of the participants. Rigid adherence to the sample size was not considered crucial, as the purpose of the study was to provide initial estimates of effect size. However, different strategies, including social media, are needed to achieve the recruitment goals for a larger trial.

Conclusions

The present pilot RCT suggests that active learning physical activity intervention through video conferencing could be an acceptable substitute for on-site health promotion programs in remote locations with limited access to healthcare. Our findings are preliminary, and a larger definitive RCT is needed to fully ascertain its effectiveness and long-term sustainability. However, active learning via video conferencing was found to be feasible and potentially effective in maintaining physical activity in community-dwelling older adults.

Acknowledgments

This work was supported by JSPS KAKENHI (grant number JP21K11576). The authors appreciate all participants for their engagement in this research. Furthermore, the authors thank all the research staff for their assistance with data collection and the city offices of Imizu, Takaoka, and Toyama for their assistance with participant recruitment. Trial Registration: The study protocol was registered with the UMIN Clinical Trials Registry. Trial ID: UMIN000044835.

References

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  • Brydges, C.R. (2019). Effect size guidelines, sample size calculations, and statistical power in gerontology. Innovation in Aging, 3(4), Article igz036.

    • Crossref
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    • Export Citation
  • Burton, E., Farrier, K., Hill, K.D., Codde, J., Airey, P., & Hill, A.M. (2018). Effectiveness of peers in delivering programs or motivating older people to increase their participation in physical activity: Systematic review and meta-analysis. Journal of Sports Sciences, 36(6), 666678.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choi, L., Liu, Z., Matthews, C.E., & Buchowski, M.S. (2011). Validation of accelerometer wear and nonwear time classification algorithm. Medicine & Science in Sports & Exercise, 43(2), 357364.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Copeland, J.L., & Esliger, D.W. (2009). Accelerometer assessment of physical activity in active, healthy older adults. Journal of Aging Physical Activity, 17(1), 1730.

    • Crossref
    • Search Google Scholar
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  • Dempsey, P.C., Strain, T., Khaw, K.T., Wareham, N.J., Brage, S., & Wijndaele, K. (2020). Prospective associations of accelerometer-measured physical activity and sedentary time with incident cardiovascular disease, cancer, and all-cause mortality. Circulation, 141(13), 11131115.

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  • Franco, M.R., Tong, A., Howard, K., Sherrington, C., Ferreira, P.H., Pinto, R.Z., & Ferreira, M.L. (2015). Older people’s perspectives on participation in physical activity: A systematic review and thematic synthesis of qualitative literature. British Journal of Sports Medicine, 49(19), 12681276.

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    • Search Google Scholar
    • Export Citation
  • Grande, G.D., Oliveira, C.B., Morelhão, P.K., Sherrington, C., Tiedemann, A., Pinto, R.Z., & Franco, M.R. (2020). Interventions promoting physical activity among older adults: A systematic review and meta-analysis. The Gerontologist, 60(8), 583599.

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    • Search Google Scholar
    • Export Citation
  • Hawley-Hague, H., Horne, M., Skelton, D.A., & Todd, C. (2016). Review of how we should define (and measure) adherence in studies examining older adults’ participation in exercise classes. BMJ Open, 6(6), Article e011560.

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    • Search Google Scholar
    • Export Citation
  • Hong, J., Kong, H.J., & Yoon, H.J. (2018). Web-based telepresence exercise program for community-dwelling elderly women with a high risk of falling: Randomized controlled trial. JMIR mHealth and uHealth, 6(5), Article e132.

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    • Search Google Scholar
    • Export Citation
  • Kalbe, E., Calabrese, P., Schwalen, S., & Kessler, J. (2003). The Rapid Dementia Screening Test (RDST): A new economical tool for detecting possible patients with dementia. Dementia and Geriatric Cognitive Disorders, 16(4), 193199.

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    • Search Google Scholar
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    • Search Google Scholar
    • Export Citation
  • Li, C., Ma, Y., Hua, R., Zheng, F., & Xie, W. (2022). Long-term physical activity participation trajectories were associated with subsequent cognitive decline, risk of dementia and all-cause mortality among adults aged ≥50 years: A population-based cohort study. Age and Ageing, 51(3), Article afac071.

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    • Search Google Scholar
    • Export Citation
  • Li, J., Szanton, S.L., McPhillips, M.V., Lukkahatai, N., Pien, G.W., Chen, K., Hladek, M.D., Hodgson, N., & Gooneratne, N.S. (2022). An mHealth-facilitated personalized intervention for physical activity and sleep in community-dwelling older adults. Journal of Aging Physical Activity, 30(2), 261270.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ministry of Internal Affairs and Communications, Japan. (2020). Results of FY2021 communication usage trend survey. https://www.soumu.go.jp/menu_news/s-news/01tsushin02_02000158.html

    • Search Google Scholar
    • Export Citation
  • Muellmann, S., Forberger, S., Möllers, T., Bröring, E., Zeeb, H., & Pischke, C.R. (2018). Effectiveness of eHealth interventions for the promotion of physical activity in older adults: A systematic review. Preventive Medicine, 108, 93110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nagashima, K. (2017, April 26). A computerized number generator through the stratified block randomization. Retrieved August 25, 2021. https://nshi.jp/contents/js/randblock/

    • Search Google Scholar
    • Export Citation
  • Negm, A.M., Kennedy, C.C., Thabane, L., Veroniki, A.A., Adachi, J.D., Richardson, J., Cameron, I.D., Giangregorio, A., Petropoulou, M., Alsaad, S.M., Alzahrani, J., Maaz, M., Ahmed, M.M., Kim, E., Tehfe, H., Dima, R., Sabanayagam, K., Hewston, P., Abu Alrob, H., & Papaioannou, A. (2019). Management of frailty: A systematic review and network meta-analysis of randomized controlled trials. Journal of American Medical Directors Association, 20(10), 11901198.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Núñez de Arenas-Arroyo, S., Cavero-Redondo, I., Alvarez-Bueno, C., Sequí-Domínguez, I., Reina-Gutiérrez, S., & Martínez-Vizcaíno, V. (2021). Effect of eHealth to increase physical activity in healthy adults over 55 years: A systematic review and meta-analysis. Scandinavian Journal of Medicine & Science in Sports, 31(4), 776789.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oka, K. (2003). Stages of change for exercise behavior and self-efficacy for exercise among middle-aged adults. Nihon Koshu Eisei Zasshi, 50(3), 208215.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oliveira, J.S., Sherrington, C., Zheng, R.Y.E., Franco, M.R., & Tiedemann, A. (2020). Effect of interventions using physical activity trackers on physical activity in people aged 60 years and over: A systematic review and meta-analysis. British Journal of Sports Medicine, 54(20), 11881194.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Picorelli, A.M., Pereira, L.S., Pereira, D.S., Felício, D., & Sherrington, C. (2014). Adherence to exercise programs for older people is influenced by program characteristics and personal factors: A systematic review. Journal of Physiotherapy, 60(3), 151156.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sakai, Y., Kotaka, A., Murayama, N., Takano, M., Hirose, K., Eto, K., & Arai, H. (2006). Japanese version of the rapid dementia screening test—effectiveness in detecting possible patients with dementia. Japanese Journal of Geriatric Psychiatry, 17(5), 539551. https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=200902234412242427

    • Search Google Scholar
    • Export Citation
  • Sansano-Nadal, O., Giné-Garriga, M., Brach, J.S., Wert, D.M., Jerez-Roig, J., Guerra-Balic, M., Oviedo, G., Fortuño, J., Gómara-Toldrà, N., Soto-Bagaria, L., Pérez, L.M., Inzitari, M., Solà, I., Martin-Borràs, C., & Roqué, M. (2019). Exercise-based interventions to enhance long-term sustainability of physical activity in older adults: A systematic review and meta-analysis of randomized clinical trials. International Journal of Environmental Research and Public Health, 16(14), Article 2527.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, H., Har-Nir, I., Wenhoda, T., & Halperin, I. (2021). Staying physically active during the COVID-19 quarantine: Exploring the feasibility of live, online, group training sessions among older adults. Translational Behavioral Medicine, 11(2), 314322.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shephard, R.J., & Aoyagi, Y. (2009). Seasonal variations in physical activity and implications for human health. European Journal of Applied Physiology, 107(3), 251271.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevens, M., & Cruwys, T. (2020). Membership in sport or exercise groups predicts sustained physical activity and longevity in older adults compared to physically active matched controls. Annals of Behavioral Medicine, 54(8), 557566.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takeda, N., Oka, K., Sakai, K., & Nakamura, Y. (2009). The relationship between exercise behavioral skills and the stages of change for exercise behavior among Japanese adults. Japanese Journal of Behavioral Medicine, 14(1), 814.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uemura, K., Kamitani, T., Yamada, M., & Okamoto, H. (2021). Longitudinal effects of active learning education on lifestyle behavior and physical function in older adults. Journal of the American Medical Directors Association, 22(2), 459463.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uemura, K., Watanabe, A., Kamitani, T., Yamada, M., Saho, K., & Okamoto, H. (2021). Feasibility of active learning health education by video conferencing among older adults. Geriatrics & Gerontology International, 21(11), 10641066.

    • Crossref
    • Search Google Scholar
    • Export Citation
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The present randomized pilot trial suggests that an active learning physical activity intervention through video conferencing is feasible and potentially effective in maintaining physical activity in community-dwelling older adults.

Our online active learning program can be an acceptable substitute for an on-site health promotion program in remote locations with limited access to healthcare.

Our findings are preliminary, and a larger, definitive randomized controlled trial is needed to fully ascertain its effectiveness and long-term sustainability.

  • Collapse
  • Expand
  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.

  • Barha, C.K., Best, J.R., Rosano, C., Yaffe, K., Catov, J.M., & Liu-Ambrose, T. (2020). Sex-specific relationship between long-term maintenance of physical activity and cognition in the health ABC study: Potential role of hippocampal and dorsolateral prefrontal cortex volume. The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 75(4), 764770.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brydges, C.R. (2019). Effect size guidelines, sample size calculations, and statistical power in gerontology. Innovation in Aging, 3(4), Article igz036.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Burton, E., Farrier, K., Hill, K.D., Codde, J., Airey, P., & Hill, A.M. (2018). Effectiveness of peers in delivering programs or motivating older people to increase their participation in physical activity: Systematic review and meta-analysis. Journal of Sports Sciences, 36(6), 666678.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choi, L., Liu, Z., Matthews, C.E., & Buchowski, M.S. (2011). Validation of accelerometer wear and nonwear time classification algorithm. Medicine & Science in Sports & Exercise, 43(2), 357364.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Copeland, J.L., & Esliger, D.W. (2009). Accelerometer assessment of physical activity in active, healthy older adults. Journal of Aging Physical Activity, 17(1), 1730.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dempsey, P.C., Strain, T., Khaw, K.T., Wareham, N.J., Brage, S., & Wijndaele, K. (2020). Prospective associations of accelerometer-measured physical activity and sedentary time with incident cardiovascular disease, cancer, and all-cause mortality. Circulation, 141(13), 11131115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ekelund, U., Tarp, J., Steene-Johannessen, J., Hansen, B.H., Jefferis, B., Fagerland, M.W., Whincup, P., Diaz, K.M., Hooker, S.P., Chernofsky, A., Larson, M.G., Spartano, N., Vasan, R.S., Dohrn, I.M., Hagströmer, M., Edwardson, C., Yates, T., Shiroma, E., Anderssen, S.A., & Lee, I.M. (2019). Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: Systematic review and harmonised meta-analysis. BMJ, 366, Article l4570.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Eldridge, S.M., Chan, C.L., Campbell, M.J., Bond, C.M., Hopewell, S., Thabane, L., & Lancaster, G.A. (2016). CONSORT 2010 statement: Extension to randomised pilot and feasibility trials. BMJ, 355, Article i5239.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Farrance, C., Tsofliou, F., & Clark, C. (2016). Adherence to community based group exercise interventions for older people: A mixed-methods systematic review. Preventive Medicine, 87, 155166.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Franco, M.R., Tong, A., Howard, K., Sherrington, C., Ferreira, P.H., Pinto, R.Z., & Ferreira, M.L. (2015). Older people’s perspectives on participation in physical activity: A systematic review and thematic synthesis of qualitative literature. British Journal of Sports Medicine, 49(19), 12681276.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grande, G.D., Oliveira, C.B., Morelhão, P.K., Sherrington, C., Tiedemann, A., Pinto, R.Z., & Franco, M.R. (2020). Interventions promoting physical activity among older adults: A systematic review and meta-analysis. The Gerontologist, 60(8), 583599.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hawley-Hague, H., Horne, M., Skelton, D.A., & Todd, C. (2016). Review of how we should define (and measure) adherence in studies examining older adults’ participation in exercise classes. BMJ Open, 6(6), Article e011560.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, J., Kong, H.J., & Yoon, H.J. (2018). Web-based telepresence exercise program for community-dwelling elderly women with a high risk of falling: Randomized controlled trial. JMIR mHealth and uHealth, 6(5), Article e132.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalbe, E., Calabrese, P., Schwalen, S., & Kessler, J. (2003). The Rapid Dementia Screening Test (RDST): A new economical tool for detecting possible patients with dementia. Dementia and Geriatric Cognitive Disorders, 16(4), 193199.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kwan, R.Y.C., Salihu, D., Lee, P.H., Tse, M., Cheung, D.S.K., Roopsawang, I., & Choi, K.S. (2020). The effect of e-health interventions promoting physical activity in older people: A systematic review and meta-analysis. European Review of Aging and Physical Activity, 17, Article 7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, C., Ma, Y., Hua, R., Zheng, F., & Xie, W. (2022). Long-term physical activity participation trajectories were associated with subsequent cognitive decline, risk of dementia and all-cause mortality among adults aged ≥50 years: A population-based cohort study. Age and Ageing, 51(3), Article afac071.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, J., Szanton, S.L., McPhillips, M.V., Lukkahatai, N., Pien, G.W., Chen, K., Hladek, M.D., Hodgson, N., & Gooneratne, N.S. (2022). An mHealth-facilitated personalized intervention for physical activity and sleep in community-dwelling older adults. Journal of Aging Physical Activity, 30(2), 261270.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ministry of Internal Affairs and Communications, Japan. (2020). Results of FY2021 communication usage trend survey. https://www.soumu.go.jp/menu_news/s-news/01tsushin02_02000158.html

    • Search Google Scholar
    • Export Citation
  • Muellmann, S., Forberger, S., Möllers, T., Bröring, E., Zeeb, H., & Pischke, C.R. (2018). Effectiveness of eHealth interventions for the promotion of physical activity in older adults: A systematic review. Preventive Medicine, 108, 93110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nagashima, K. (2017, April 26). A computerized number generator through the stratified block randomization. Retrieved August 25, 2021. https://nshi.jp/contents/js/randblock/

    • Search Google Scholar
    • Export Citation
  • Negm, A.M., Kennedy, C.C., Thabane, L., Veroniki, A.A., Adachi, J.D., Richardson, J., Cameron, I.D., Giangregorio, A., Petropoulou, M., Alsaad, S.M., Alzahrani, J., Maaz, M., Ahmed, M.M., Kim, E., Tehfe, H., Dima, R., Sabanayagam, K., Hewston, P., Abu Alrob, H., & Papaioannou, A. (2019). Management of frailty: A systematic review and network meta-analysis of randomized controlled trials. Journal of American Medical Directors Association, 20(10), 11901198.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Núñez de Arenas-Arroyo, S., Cavero-Redondo, I., Alvarez-Bueno, C., Sequí-Domínguez, I., Reina-Gutiérrez, S., & Martínez-Vizcaíno, V. (2021). Effect of eHealth to increase physical activity in healthy adults over 55 years: A systematic review and meta-analysis. Scandinavian Journal of Medicine & Science in Sports, 31(4), 776789.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oka, K. (2003). Stages of change for exercise behavior and self-efficacy for exercise among middle-aged adults. Nihon Koshu Eisei Zasshi, 50(3), 208215.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oliveira, J.S., Sherrington, C., Zheng, R.Y.E., Franco, M.R., & Tiedemann, A. (2020). Effect of interventions using physical activity trackers on physical activity in people aged 60 years and over: A systematic review and meta-analysis. British Journal of Sports Medicine, 54(20), 11881194.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Picorelli, A.M., Pereira, L.S., Pereira, D.S., Felício, D., & Sherrington, C. (2014). Adherence to exercise programs for older people is influenced by program characteristics and personal factors: A systematic review. Journal of Physiotherapy, 60(3), 151156.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sakai, Y., Kotaka, A., Murayama, N., Takano, M., Hirose, K., Eto, K., & Arai, H. (2006). Japanese version of the rapid dementia screening test—effectiveness in detecting possible patients with dementia. Japanese Journal of Geriatric Psychiatry, 17(5), 539551. https://jglobal.jst.go.jp/en/detail?JGLOBAL_ID=200902234412242427

    • Search Google Scholar
    • Export Citation
  • Sansano-Nadal, O., Giné-Garriga, M., Brach, J.S., Wert, D.M., Jerez-Roig, J., Guerra-Balic, M., Oviedo, G., Fortuño, J., Gómara-Toldrà, N., Soto-Bagaria, L., Pérez, L.M., Inzitari, M., Solà, I., Martin-Borràs, C., & Roqué, M. (2019). Exercise-based interventions to enhance long-term sustainability of physical activity in older adults: A systematic review and meta-analysis of randomized clinical trials. International Journal of Environmental Research and Public Health, 16(14), Article 2527.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schwartz, H., Har-Nir, I., Wenhoda, T., & Halperin, I. (2021). Staying physically active during the COVID-19 quarantine: Exploring the feasibility of live, online, group training sessions among older adults. Translational Behavioral Medicine, 11(2), 314322.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shephard, R.J., & Aoyagi, Y. (2009). Seasonal variations in physical activity and implications for human health. European Journal of Applied Physiology, 107(3), 251271.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stevens, M., & Cruwys, T. (2020). Membership in sport or exercise groups predicts sustained physical activity and longevity in older adults compared to physically active matched controls. Annals of Behavioral Medicine, 54(8), 557566.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Takeda, N., Oka, K., Sakai, K., & Nakamura, Y. (2009). The relationship between exercise behavioral skills and the stages of change for exercise behavior among Japanese adults. Japanese Journal of Behavioral Medicine, 14(1), 814.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uemura, K., Kamitani, T., Yamada, M., & Okamoto, H. (2021). Longitudinal effects of active learning education on lifestyle behavior and physical function in older adults. Journal of the American Medical Directors Association, 22(2), 459463.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Uemura, K., Watanabe, A., Kamitani, T., Yamada, M., Saho, K., & Okamoto, H. (2021). Feasibility of active learning health education by video conferencing among older adults. Geriatrics & Gerontology International, 21(11), 10641066.

    • Crossref
    • Search Google Scholar
    • Export Citation
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