scholarly journals Feedback From Activity Trackers Improves Daily Step Count After Knee and Hip Arthroplasty: A Randomized Controlled Trial

2018 ◽  
Vol 33 (11) ◽  
pp. 3422-3428 ◽  
Author(s):  
Neill Van der Walt ◽  
Lucy J. Salmon ◽  
Benjamin Gooden ◽  
Matthew C. Lyons ◽  
Michael O'Sullivan ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Hip Arthroplasty ◽  
Controlled Trial ◽  
Step Count ◽  
Daily Step ◽  
Randomized Controlled ◽  
Activity Trackers ◽  
Daily Step Count ◽  
Knee And Hip Arthroplasty

scholarly journals A Social Group-Based Information-Motivation-Behavior Skill Intervention to Promote Acceptability and Adoption of Wearable Activity Trackers Among Middle-Aged and Older Adults: Cluster Randomized Controlled Trial

10.2196/14969 ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. e14969
Author(s):  
Jing Liao ◽  
Hai-Yan Xiao ◽  
Xue-Qi Li ◽  
Shu-Hua Sun ◽  
Shi-Xing Liu ◽  
...  
Keyword(s):  
Older Adults ◽  
Randomized Controlled Trial ◽  
Social Group ◽  
Controlled Trial ◽  
Daily Step ◽  
Middle Aged ◽  
Randomized Controlled ◽  
Activity Trackers ◽  
Wearable Activity Trackers ◽  
Cluster Randomized

Background Wearable activity trackers offer potential to optimize behavior and support self-management. To assist older adults in benefiting from mobile technologies, theory-driven deployment strategies are needed to overcome personal, technological, and sociocontextual barriers in technology adoption. Objective To test the effectiveness of a social group–based strategy to improve the acceptability and adoption of activity trackers by middle-aged and older adults. Methods A cluster randomized controlled trial was conducted among 13 groups of middle-aged and older adults (≥45 years) performing group dancing (ie, square dancing) as a form of exercise in Guangzhou from November 2017 to October 2018. These dancing groups were randomized 1:1 into two arms, and both received wrist-worn activity trackers and instructions at the baseline face-to-face assessment. Based on the Information-Motivation-Behavior Skill framework, the intervention arm was also given a tutorial on the purpose of exercise monitoring (Information), encouraged to participate in exercise and share their exercise records with their dancing peers (Motivation), and were further assisted with the use of the activity tracker (Behavior Skill). We examined two process outcomes: acceptability evaluated by a 14-item questionnaire, and adoption assessed by the uploaded step count data. Intention-to-treat analysis was applied, with the treatment effects estimated by multilevel models. Results All dancing groups were followed up for the postintervention reassessment, with 61/69 (88%) participants of the intervention arm (7 groups) and 56/80 (70%) participants of the control arm (6 groups). Participants’ sociodemographic characteristics (mean age 62 years, retired) and health status were comparable between the two arms, except the intervention arm had fewer female participants and lower cognitive test scores. Our intervention significantly increased the participants’ overall acceptability by 6.8 points (95% CI 2.2-11.4), mainly driven by promoted motivation (adjusted group difference 2.0, 95% CI 0.5-3.6), increased usefulness (adjusted group difference 2.5, 95% CI 0.9-4.1), and better perceived ease of use (adjusted group difference 1.2, 95% CI 0.1-2.4), whereas enjoyment and comfort were not increased (adjusted group difference 0.9, 95% CI –0.4-2.3). Higher adoption was also observed among participants in the intervention arm, who were twice as likely to have valid daily step account data than their controlled counterparts (adjusted incidence relative risk [IRR]=2.0, 95% CI 1.2-3.3). The average daily step counts (7803 vs 5653 steps/day for the intervention and control, respectively) were similar between the two arms (adjusted IRR=1.4, 95% CI 0.7-2.5). Conclusions Our social group–based deployment strategy incorporating information, motivation, and behavior skill components effectively promoted acceptability and adoption of activity trackers among community-dwelling middle-aged and older adults. Future studies are needed to examine the long-term effectiveness and apply this social engagement strategy in other group settings or meeting places. Trial Registration Chinese Clinical Trial Registry ChiCTR-IOC-17013185; https://tinyurl.com/vedwc7h.

scholarly journals A Social Group-Based Information-Motivation-Behavior Skill Intervention to Promote Acceptability and Adoption of Wearable Activity Trackers Among Middle-Aged and Older Adults: Cluster Randomized Controlled Trial (Preprint)

2019 ◽  
Author(s):  
Jing Liao ◽  
Hai-Yan Xiao ◽  
Xue-Qi Li ◽  
Shu-Hua Sun ◽  
Shi-Xing Liu ◽  
...  
Keyword(s):  
Older Adults ◽  
Randomized Controlled Trial ◽  
Social Group ◽  
Controlled Trial ◽  
Daily Step ◽  
Middle Aged ◽  
Randomized Controlled ◽  
Activity Trackers ◽  
Wearable Activity Trackers ◽  
Cluster Randomized

BACKGROUND Wearable activity trackers offer potential to optimize behavior and support self-management. To assist older adults in benefiting from mobile technologies, theory-driven deployment strategies are needed to overcome personal, technological, and sociocontextual barriers in technology adoption. OBJECTIVE To test the effectiveness of a social group–based strategy to improve the acceptability and adoption of activity trackers by middle-aged and older adults. METHODS A cluster randomized controlled trial was conducted among 13 groups of middle-aged and older adults (≥45 years) performing group dancing (ie, square dancing) as a form of exercise in Guangzhou from November 2017 to October 2018. These dancing groups were randomized 1:1 into two arms, and both received wrist-worn activity trackers and instructions at the baseline face-to-face assessment. Based on the Information-Motivation-Behavior Skill framework, the intervention arm was also given a tutorial on the purpose of exercise monitoring (Information), encouraged to participate in exercise and share their exercise records with their dancing peers (Motivation), and were further assisted with the use of the activity tracker (Behavior Skill). We examined two process outcomes: acceptability evaluated by a 14-item questionnaire, and adoption assessed by the uploaded step count data. Intention-to-treat analysis was applied, with the treatment effects estimated by multilevel models. RESULTS All dancing groups were followed up for the postintervention reassessment, with 61/69 (88%) participants of the intervention arm (7 groups) and 56/80 (70%) participants of the control arm (6 groups). Participants’ sociodemographic characteristics (mean age 62 years, retired) and health status were comparable between the two arms, except the intervention arm had fewer female participants and lower cognitive test scores. Our intervention significantly increased the participants’ overall acceptability by 6.8 points (95% CI 2.2-11.4), mainly driven by promoted motivation (adjusted group difference 2.0, 95% CI 0.5-3.6), increased usefulness (adjusted group difference 2.5, 95% CI 0.9-4.1), and better perceived ease of use (adjusted group difference 1.2, 95% CI 0.1-2.4), whereas enjoyment and comfort were not increased (adjusted group difference 0.9, 95% CI –0.4-2.3). Higher adoption was also observed among participants in the intervention arm, who were twice as likely to have valid daily step account data than their controlled counterparts (adjusted incidence relative risk [IRR]=2.0, 95% CI 1.2-3.3). The average daily step counts (7803 vs 5653 steps/day for the intervention and control, respectively) were similar between the two arms (adjusted IRR=1.4, 95% CI 0.7-2.5). CONCLUSIONS Our social group–based deployment strategy incorporating information, motivation, and behavior skill components effectively promoted acceptability and adoption of activity trackers among community-dwelling middle-aged and older adults. Future studies are needed to examine the long-term effectiveness and apply this social engagement strategy in other group settings or meeting places. CLINICALTRIAL Chinese Clinical Trial Registry ChiCTR-IOC-17013185; https://tinyurl.com/vedwc7h.

scholarly journals Work-related factors predict changes in physical activity among nurses participating in a web-based worksite intervention: A randomized controlled trial

BMC Nursing ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jennifer Brunet ◽  
Melissa Black ◽  
Heather E. Tulloch ◽  
Andrew L. Pipe ◽  
Robert D. Reid ◽  
...  
Keyword(s):  
Controlled Trial ◽  
Mood States ◽  
Step Count ◽  
Time Estimate ◽  
Daily Step ◽  
Related Factors ◽  
Web Based ◽  
Work Related ◽  
Randomized Controlled ◽  
Daily Step Count

Abstract Background Despite the numerous benefits associated with physical activity (PA), most nurses are not active enough and few interventions have been developed to promote PA among nurses. A secondary analysis of raw data from a single-centre, three-arm parallel-group randomized controlled trial was conducted to assess whether work-related characteristics and general mood states predict changes in total weekly moderate-to-vigorous intensity PA (MVPA) and average daily step-count among nurses participating in a 6-week web-based worksite intervention. Methods Seventy nurses (meanage: 46.1 ± 11.2 years) were randomized to an individual-, friend-, or team-based PA challenge. Participants completed questionnaires pre- and post-intervention assessing work-related characteristics (i.e., shift schedule and length, number of hours worked per week, work role) and general mood states (i.e., tension, depression, anger, confusion, fatigue, vigour). Participants received a PA monitor to wear before and during the 6-week PA challenge, which was used to assess total weekly MVPA minutes and average daily step-count. Data were analyzed descriptively and using multilevel modeling for repeated measures. Results Change in total weekly MVPA minutes, but not change in average daily step-count, was predicted by shift schedule (rotating vs. fixed) by time (estimate = − 17.43, SE = 6.18, p = .006), and work role (clinical-only vs. other) by time (estimate = 18.98, SE = 6.51, p = .005). General mood states did not predict change in MVPA or change in average daily step-count. Conclusions Given that nurses who work rotating shifts and perform clinical work showed smaller improvements in MVPA, it may be necessary to consider work-related factors/barriers (e.g., time constraints, fatigue) and collaborate with nurses when designing and implementing MVPA interventions in the workplace. Trial registration ClinicalTrials.gov: NCT04524572. August 24, 2020. This trial was registered retrospectively. This study adheres to the CONSORT 2010 statement guidelines.

scholarly journals Neural Network–Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study

10.2196/18142 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18142
Author(s):  
Ramin Mohammadi ◽  
Mursal Atif ◽  
Amanda Jayne Centi ◽  
Stephen Agboola ◽  
Kamal Jethwani ◽  
...  
Keyword(s):  
Neural Network ◽  
Physical Activity ◽  
Machine Learning ◽  
Learning Model ◽  
Step Count ◽  
Daily Step ◽  
Activity Tracker ◽  
Activity Trackers ◽  
Machine Learning Model ◽  
Daily Step Count

Background It is well established that lack of physical activity is detrimental to the overall health of an individual. Modern-day activity trackers enable individuals to monitor their daily activities to meet and maintain targets. This is expected to promote activity encouraging behavior, but the benefits of activity trackers attenuate over time due to waning adherence. One of the key approaches to improving adherence to goals is to motivate individuals to improve on their historic performance metrics. Objective The aim of this work was to build a machine learning model to predict an achievable weekly activity target by considering (1) patterns in the user’s activity tracker data in the previous week and (2) behavior and environment characteristics. By setting realistic goals, ones that are neither too easy nor too difficult to achieve, activity tracker users can be encouraged to continue to meet these goals, and at the same time, to find utility in their activity tracker. Methods We built a neural network model that prescribes a weekly activity target for an individual that can be realistically achieved. The inputs to the model were user-specific personal, social, and environmental factors, daily step count from the previous 7 days, and an entropy measure that characterized the pattern of daily step count. Data for training and evaluating the machine learning model were collected over a duration of 9 weeks. Results Of 30 individuals who were enrolled, data from 20 participants were used. The model predicted target daily count with a mean absolute error of 1545 (95% CI 1383-1706) steps for an 8-week period. Conclusions Artificial intelligence applied to physical activity data combined with behavioral data can be used to set personalized goals in accordance with the individual’s level of activity and thereby improve adherence to a fitness tracker; this could be used to increase engagement with activity trackers. A follow-up prospective study is ongoing to determine the performance of the engagement algorithm.

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