Determinants for Sustained Use of an Activity Tracker: Observational Study (Preprint)

BACKGROUND A lack of physical activity is considered to cause 6% of deaths globally. Feedback from wearables such as activity trackers has the potential to encourage daily physical activity. To date, little research is available on the natural development of adherence to activity trackers or on potential factors that predict which users manage to keep using their activity tracker during the first year (and thereby increasing the chance of healthy behavior change) and which users discontinue using their trackers after a short time. OBJECTIVE The aim of this study was to identify the determinants for sustained use in the first year after purchase. Specifically, we look at the relative importance of demographic and socioeconomic, psychological, health-related, goal-related, technological, user experience–related, and social predictors of feedback device use. Furthermore, this study tests the effect of these predictors on physical activity. METHODS A total of 711 participants from four urban areas in France received an activity tracker (Fitbit Zip) and gave permission to use their logged data. Participants filled out three Web-based questionnaires: at start, after 98 days, and after 232 days to measure the aforementioned determinants. Furthermore, for each participant, we collected activity data tracked by their Fitbit tracker for 320 days. We determined the relative importance of all included predictors by using Random Forest, a machine learning analysis technique. RESULTS The data showed a slow exponential decay in Fitbit use, with 73.9% (526/711) of participants still tracking after 100 days and 16.0% (114/711) of participants tracking after 320 days. On average, participants used the tracker for 129 days. Most important reasons to quit tracking were technical issues such as empty batteries and broken trackers or lost trackers (21.5% of all Q3 respondents, 130/601). Random Forest analysis of predictors revealed that the most influential determinants were age, user experience–related factors, mobile phone type, household type, perceived effect of the Fitbit tracker, and goal-related factors. We explore the role of those predictors that show meaningful differences in the number of days the tracker was worn. CONCLUSIONS This study offers an overview of the natural development of the use of an activity tracker, as well as the relative importance of a range of determinants from literature. Decay is exponential but slower than may be expected from existing literature. Many factors have a small contribution to sustained use. The most important determinants are technical condition, age, user experience, and goal-related factors. This finding suggests that activity tracking is potentially beneficial for a broad range of target groups, but more attention should be paid to technical and user experience–related aspects of activity trackers.

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Efficacy of an activity tracker-and app intervention to increase physical activity in the entire family: Pilot single-arm intervention study (Preprint)

10.2196/preprints.17410 ◽

2019 ◽

Author(s):

Stephanie Schoeppe ◽

Jo Salmon ◽

Susan L. Williams ◽

Deborah Power ◽

Stephanie Alley ◽

...

Keyword(s):

Physical Activity ◽

Intervention Study ◽

Post Intervention ◽

Entire Family ◽

Physical Activity Guidelines ◽

Mothers And Fathers ◽

Activity Tracker ◽

Activity Trackers ◽

Family Program ◽

Intervention Efficacy

BACKGROUND Interventions using activity trackers and smartphone apps have demonstrated their ability to increase physical activity in children and adults. However, they have not been tested in entire families. Further, few family-centred interventions have actively involved both parents, and assessed intervention efficacy separately for children, mothers and fathers. OBJECTIVE This study aimed to examine the short-term efficacy of an activity tracker and app intervention to increase physical activity in the entire family (children, mothers and fathers). METHODS This was a pilot single-arm intervention study with pre-post measures. Between 2017-2018, 40 families (58 children aged 6-10 years, 39 mothers, 33 fathers) participated in the 6-week Step it Up Family program in Queensland, Australia. Using commercial activity trackers combined with apps (Garmin Vivofit Jr for children, Vivofit 3 for adults), the intervention included individual and family-level goal-setting, self-monitoring, performance feedback, family step challenges, family social support and modelling, weekly motivational text messages, and an introductory session delivered face-to-face or via telephone. Parent surveys were used to assess intervention efficacy measured as pre-post intervention changes in moderate-to-vigorous physical activity (MVPA) in children, mothers and fathers. RESULTS Thirty-eight families completed the post intervention survey (95% retention). At post intervention, MVPA had increased in children by 58 min/day (boys: 54 min/day, girls: 62 min/day; all P < .001). In mothers, MVPA increased by 27 min/day (P < .001), and in fathers, it increased by 31 min/day (P < .001). Furthermore, the percentage of children meeting Australia’s physical activity guidelines for children (≥60 MVPA min/day) increased from 34% to 89% (P < .001). The percentage of mothers and fathers meeting Australia’s physical activity guidelines for adults (≥150 MVPA min/week) increased from 8% to 57% (P < .001) in mothers, and from 21% to 68% (P < .001) in fathers. CONCLUSIONS Findings suggest that an activity tracker and app intervention is an efficacious approach to increasing physical activity in entire families to meet national physical activity guidelines. The Step it Up Family program warrants further testing in a larger, randomised controlled trial to determine its long-term impact. CLINICALTRIAL No trial registration as this is not an RCT. It is a pilot single-arm intervention study

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

JMIR mhealth and uhealth ◽

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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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 ◽

10.1136/bjsports-2018-100324 ◽

2019 ◽

Vol 54 (20) ◽

pp. 1188-1194 ◽

Cited By ~ 5

Author(s):

Juliana S Oliveira ◽

Cathie Sherrington ◽

Elizabeth R Y Zheng ◽

Marcia Rodrigues Franco ◽

Anne Tiedemann

Keyword(s):

Quality Of Life ◽

Mental Health ◽

Physical Activity ◽

Systematic Review ◽

Older People ◽

Activity Levels ◽

Activity Tracker ◽

Activity Trackers ◽

The Impact

BackgroundOlder people are at high risk of physical inactivity. Activity trackers can facilitate physical activity. We aimed to investigate the effect of interventions using activity trackers on physical activity, mobility, quality of life and mental health among people aged 60+ years.MethodsFor this systematic review, we searched eight databases, including MEDLINE, Embase and CENTRAL from inception to April 2018. Randomised controlled trials of interventions that used activity trackers to promote physical activity among people aged 60+ years were included in the analyses. The study protocol was registered with PROSPERO, number CRD42017065250.ResultsWe identified 23 eligible trials. Interventions using activity trackers had a moderate effect on physical activity (23 studies; standardised mean difference (SMD)=0.55; 95% CI 0.40 to 0.70; I2=86%) and increased steps/day by 1558 (95% CI 1099 to 2018 steps/day; I2=92%) compared with usual care, no intervention and wait-list control. Longer duration activity tracker-based interventions were more effective than short duration interventions (18 studies, SMD=0.70; 95% CI 0.47 to 0.93 vs 5 studies, SMD=0.14; 95% CI −0.26 to 0.54, p for comparison=0.02). Interventions that used activity trackers improved mobility (three studies; SMD=0.61; 95% CI 0.31 to 0.90; I2=10%), but not quality of life (nine studies; SMD=0.09; 95% CI −0.07 to 0.25; I2=45%). Only one trial included mental health outcomes and it reported similar effects of the activity tracker intervention compared with control.ConclusionsInterventions using activity trackers improve physical activity levels and mobility among older people compared with control. However, the impact of activity tracker interventions on quality of life, and mental health is unknown.

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Wearable Activity Trackers in the Management of Rheumatic Diseases: Where Are We in 2020?

Sensors ◽

10.3390/s20174797 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4797

Author(s):

Thomas Davergne ◽

Antsa Rakotozafiarison ◽

Hervé Servy ◽

Laure Gossec

Keyword(s):

Physical Activity ◽

Time Management ◽

Automatic Monitoring ◽

Great Accuracy ◽

Self Monitoring ◽

Activity Tracker ◽

External Monitoring ◽

Activity Trackers ◽

Steps Per Day ◽

Wearable Activity Trackers

In healthcare, physical activity can be monitored in two ways: self-monitoring by the patient himself or external monitoring by health professionals. Regarding self-monitoring, wearable activity trackers allow automated passive data collection that educate and motivate patients. Wearing an activity tracker can improve walking time by around 1500 steps per day. However, there are concerns about measurement accuracy (e.g., lack of a common validation protocol or measurement discrepancies between different devices). For external monitoring, many innovative electronic tools are currently used in rheumatology to help support physician time management, to reduce the burden on clinic time, and to prioritize patients who may need further attention. In inflammatory arthritis, such as rheumatoid arthritis, regular monitoring of patients to detect disease flares improves outcomes. In a pilot study applying machine learning to activity tracker steps, we showed that physical activity was strongly linked to disease flares and that patterns of physical activity could be used to predict flares with great accuracy, with a sensitivity and specificity above 95%. Thus, automatic monitoring of steps may lead to improved disease control through potential early identification of disease flares. However, activity trackers have some limitations when applied to rheumatic patients, such as tracker adherence, lack of clarity on long-term effectiveness, or the potential multiplicity of trackers.

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DETERMINING RELATIVE IMPORTANCE OF HUMAN GUT MICROBIOTA, AGE, GENDER AND LIFESTYLE PATTERN AS A PREDICTOR FOR BMI USING LOGMPIE DATA

PLANT ARCHIVES ◽

10.51470/plantarchives.2021.v21.no2.054 ◽

2021 ◽

Vol 21 (2) ◽

Author(s):

Komal Jani ◽

Shelly Gupta

Keyword(s):

Physical Activity ◽

Body Mass Index ◽

Random Forest ◽

Gut Microbiota ◽

Relative Abundance ◽

Gut Microbiome ◽

Relative Importance ◽

Human Gut ◽

Human Gut Microbiota ◽

Lifestyle Pattern

We use the ‘Relative Abundance Table’ and ‘LogMPIE Study Metadata’ from the “Landscape of Gut Microbiome - Pan-India Exploration”, or LogMPIE dataset to find out the relative importance of human gut microbiota abundance (specifically genus), age, gender, and lifestyle pattern as a predictor for BMI (Body Mass Index). The LogMPIE data is taken from 1004 subjects and 993 unique microorganisms are reported along with BMI, age, and physical activity. We use Random Forest Regressor to find out the relative importance of the above-mentioned features (microorganism genus abundance, age, gender, and lifestyle pattern) in predicting the BMI of a subject. The objective here is not the prediction of BMI using the features but to find out the relative importance of these features as much as these affect the BMI.

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Physical Activity Assessment Using an Activity Tracker in Patients with Rheumatoid Arthritis and Axial Spondyloarthritis: Prospective Observational Study (Preprint)

10.2196/preprints.7948 ◽

2017 ◽

Author(s):

Charlotte Jacquemin ◽

Hervé Servy ◽

Anna Molto ◽

Jérémie Sellam ◽

Violaine Foltz ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Physical Activity ◽

Observational Study ◽

Mobile Devices ◽

Prospective Observational Study ◽

Axial Spondyloarthritis ◽

World Health ◽

Activity Tracker ◽

Activity Trackers ◽

Number Of Patients

BACKGROUND Physical activity can be tracked using mobile devices and is recommended in rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) management. The World Health Organization (WHO) recommends at least 150 min per week of moderate to vigorous physical activity (MVPA). OBJECTIVE The objectives of this study were to assess and compare physical activity and its patterns in patients with RA and axSpA using an activity tracker and to assess the feasibility of mobile devices in this population. METHODS This multicentric prospective observational study (ActConnect) included patients who had definite RA or axSpA, and a smartphone. Physical activity was assessed over 3 months using a mobile activity tracker, recording the number of steps per minute. The number of patients reaching the WHO recommendations was calculated. RA and axSpA were compared, using linear mixed models, for number of steps, proportion of morning steps, duration of total activity, and MVPA. Physical activity trajectories were identified using the K-means method, and factors related to the low activity trajectory were explored by logistic regression. Acceptability was assessed by the mean number of days the tracker was worn over the 3 months (ie, adherence), the percentage of wearing time, and by an acceptability questionnaire. RESULTS A total of 157 patients (83 RA and 74 axSpA) were analyzed; 36.3% (57/157) patients were males, and their mean age was 46 (standard deviation [SD] 12) years and mean disease duration was 11 (SD 9) years. RA and axSpA patients had similar physical activity levels of 16 (SD 11) and 15 (SD 12) min per day of MVPA (P=.80), respectively. Only 27.4% (43/157) patients reached the recommendations with a mean MVPA of 106 (SD 77) min per week. The following three trajectories were identified with constant activity: low (54.1% [85/157] of patients), moderate (42.7% [67/157] of patients), and high (3.2% [5/157] of patients) levels of MVPA. A higher body mass index was significantly related to less physical activity (odds ratio 1.12, 95% CI 1.11-1.14). The activity trackers were worn during a mean of 79 (SD 17) days over the 90 days follow-up. Overall, patients considered the use of the tracker very acceptable, with a mean score of 8 out 10. CONCLUSIONS Patients with RA and axSpA performed insufficient physical activity with similar levels in both groups, despite the differences between the 2 diseases. Activity trackers allow longitudinal assessment of physical activity in these patients. The good adherence to this study and the good acceptability of wearing activity trackers confirmed the feasibility of the use of a mobile activity tracker in patients with rheumatic diseases.

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The Use of Wearable Activity Trackers Among Older Adults: Focus Group Study of Tracker Perceptions, Motivators, and Barriers in the Maintenance Stage of Behavior Change (Preprint)

10.2196/preprints.9832 ◽

2018 ◽

Author(s):

Anastasia Kononova ◽

Lin Li ◽

Kendra Kamp ◽

Marie Bowen ◽

R.V. Rikard ◽

...

Keyword(s):

Physical Activity ◽

Social Support ◽

Older Adults ◽

Focus Groups ◽

Short Term ◽

Activity Tracker ◽

Activity Trackers ◽

Maintenance Stage ◽

Wearable Activity Trackers

BACKGROUND Wearable activity trackers offer the opportunity to increase physical activity through continuous monitoring. Viewing tracker use as a beneficial health behavior, we explored the factors that facilitate and hinder long-term activity tracker use, applying the transtheoretical model of behavior change with the focus on the maintenance stage and relapse. OBJECTIVE The aim of this study was to investigate older adults’ perceptions and uses of activity trackers at different points of use: from nonuse and short-term use to long-term use and abandoned use to determine the factors to maintain tracker use and prevent users from discontinuing tracker usage. METHODS Data for the research come from 10 focus groups. Of them, 4 focus groups included participants who had never used activity trackers (n=17). These focus groups included an activity tracker trial. The other 6 focus groups (without the activity tracker trial) were conducted with short-term (n=9), long-term (n=11), and former tracker users (n=11; 2 focus groups per user type). RESULTS The results revealed that older adults in different tracker use stages liked and wished for different tracker features, with long-term users (users in the maintenance stage) being the most diverse and sophisticated users of the technology. Long-term users had developed a habit of tracker use whereas other participants made an effort to employ various encouragement strategies to ensure behavior maintenance. Social support through collaboration was the primary motivator for long-term users to maintain activity tracker use. Short-term and former users focused on competition, and nonusers engaged in vicarious tracker use experiences. Former users, or those who relapsed by abandoning their trackers, indicated that activity tracker use was fueled by curiosity in quantifying daily physical activity rather than the desire to increase physical activity. Long-term users saw a greater range of pros in activity tracker use whereas others focused on the cons of this behavior. CONCLUSIONS The results suggest that activity trackers may be an effective technology to encourage physical activity among older adults, especially those who have never tried it. However, initial positive response to tracker use does not guarantee tracker use maintenance. Maintenance depends on recognizing the long-term benefits of tracker use, social support, and internal motivation. Nonadoption and relapse may occur because of technology’s limitations and gaining awareness of one’s physical activity without changing the physical activity level itself.

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Daily Physical Activity and Sleep Measured by Wearable Activity Trackers during the Coronavirus Disease 2019 Pandemic: A Lesson for Preventing Physical Inactivity during Future Pandemics

Applied Sciences ◽

10.3390/app11219956 ◽

2021 ◽

Vol 11 (21) ◽

pp. 9956

Author(s):

Hidetaka Hamasaki

Keyword(s):

Physical Activity ◽

Physical Inactivity ◽

The Body ◽

Daily Physical Activity ◽

Healthcare Organizations ◽

Activity Tracker ◽

Activity Trackers ◽

Wearable Activity Trackers ◽

Wearable Activity Tracker ◽

The Impact

Wearable activity trackers are devices that are comfortably worn on the body and are designed to be effective in monitoring daily physical activity and improving physical fitness of the wearer. This review aimed to investigate the impact of the coronavirus disease 2019 (COVID-19) pandemic on physical activity measured using wearable activity trackers and discuss future perspectives on wearable activity trackers during pandemics. Daily physical activity was significantly decreased during the COVID-19 pandemic. The implementation of strict public health measures, such as total lockdown, can decrease people’s physical activity by 50% or more of that prior to the lockdown. Physical inactivity is significantly associated with several health problems, including obesity, diabetes, cardiovascular disease, and cancers; therefore, an effective healthcare system to prevent physical inactivity during pandemics should be established. It is essential to create a network between healthcare organizations and wearable activity tracker users to monitor real-time health status and prepare for the future pandemic.

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Parental Perspectives of a Wearable Activity Tracker for Children Younger Than 13 Years: Acceptability and Usability Study

JMIR mhealth and uhealth ◽

10.2196/13858 ◽

2019 ◽

Vol 7 (11) ◽

pp. e13858 ◽

Cited By ~ 14

Author(s):

Kelly A Mackintosh ◽

Stephanie E Chappel ◽

Jo Salmon ◽

Anna Timperio ◽

Kylie Ball ◽

...

Keyword(s):

Physical Activity ◽

Activity Levels ◽

Distance Information ◽

Activity Tracker ◽

Activity Trackers ◽

Activity Promotion ◽

Parental Perspectives ◽

Physical Activity Levels ◽

Wearable Activity Trackers ◽

Wearable Activity Tracker

Background There is increasing availability of, and interest in, wearable activity trackers for children younger than 13 years. However, little is known about how children and parents use these activity trackers or perceive their acceptability. Objective This study primarily aimed to ascertain parental perspectives on the acceptability and usability of wearables designed to monitor children’s physical activity levels. Secondary aims were to (1) identify practical considerations for future use in physical activity interventions and promotion initiatives; (2) determine use of different features and functions incorporated into the accompanying app; and (3) identify parents’ awareness of their child’s current physical activity levels. Methods In total, 36 children (18 boys and 18 girls) aged 7-12 years were asked to wear a wrist-worn activity tracker (KidFit) for 4 consecutive weeks and to use the accompanying app with parental assistance and guidance. Each week, one parent from each family (n=25; 21 mothers and 4 fathers) completed a Web-based survey to record their child’s activity tracker use, app interaction, and overall experiences. At the end of the 4-week period, a subsample of 10 parents (all mothers) participated in face-to-face interviews exploring perceptions of the acceptability and usability of wearable activity trackers and accompanying apps. Quantitative and qualitative data were analyzed descriptively and thematically, respectively. Thematic data are presented using pen profiles, which were constructed from verbatim transcripts. Results Parents reported that they and their children typically found the associated app easy to use for activity tracking, though only step or distance information was generally accessed and some difficulties interpreting the data were reported. Children were frustrated with not being able to access real-time feedback, as the features and functions were only available through the app, which was typically accessed by, or in the presence of, parents. Parents identified that children wanted additional functions including a visual display to track and self-monitor activity, access to the app for goal setting, and the option of undertaking challenges against schools or significant others. Other barriers to the use of wearable activity trackers included discomfort of wearing the monitor because of the design and the inability to wear for water- or contact-based sports. Conclusions Most parents reported that the wearable activity tracker was easy for their child or children to use and a useful tool for tracking their children’s daily activity. However, several barriers were identified, which may impact sustained use over time; both the functionality and wearability of the activity tracker should therefore be considered. Overall, wearable activity trackers for children have the potential to be integrated into targeted physical activity promotion initiatives.

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Parental Perspectives of a Wearable Activity Tracker for Children Younger Than 13 Years: Acceptability and Usability Study (Preprint)

10.2196/preprints.13858 ◽

2019 ◽

Author(s):

Kelly A Mackintosh ◽

Stephanie E Chappel ◽

Jo Salmon ◽

Anna Timperio ◽

Kylie Ball ◽

...

Keyword(s):

Physical Activity ◽

Activity Levels ◽

Distance Information ◽

Activity Tracker ◽

Activity Trackers ◽

Activity Promotion ◽

Parental Perspectives ◽

Physical Activity Levels ◽

Wearable Activity Trackers ◽

Wearable Activity Tracker

BACKGROUND There is increasing availability of, and interest in, wearable activity trackers for children younger than 13 years. However, little is known about how children and parents use these activity trackers or perceive their acceptability. OBJECTIVE This study primarily aimed to ascertain parental perspectives on the acceptability and usability of wearables designed to monitor children’s physical activity levels. Secondary aims were to (1) identify practical considerations for future use in physical activity interventions and promotion initiatives; (2) determine use of different features and functions incorporated into the accompanying app; and (3) identify parents’ awareness of their child’s current physical activity levels. METHODS In total, 36 children (18 boys and 18 girls) aged 7-12 years were asked to wear a wrist-worn activity tracker (KidFit) for 4 consecutive weeks and to use the accompanying app with parental assistance and guidance. Each week, one parent from each family (n=25; 21 mothers and 4 fathers) completed a Web-based survey to record their child’s activity tracker use, app interaction, and overall experiences. At the end of the 4-week period, a subsample of 10 parents (all mothers) participated in face-to-face interviews exploring perceptions of the acceptability and usability of wearable activity trackers and accompanying apps. Quantitative and qualitative data were analyzed descriptively and thematically, respectively. Thematic data are presented using pen profiles, which were constructed from verbatim transcripts. RESULTS Parents reported that they and their children typically found the associated app easy to use for activity tracking, though only step or distance information was generally accessed and some difficulties interpreting the data were reported. Children were frustrated with not being able to access real-time feedback, as the features and functions were only available through the app, which was typically accessed by, or in the presence of, parents. Parents identified that children wanted additional functions including a visual display to track and self-monitor activity, access to the app for goal setting, and the option of undertaking challenges against schools or significant others. Other barriers to the use of wearable activity trackers included discomfort of wearing the monitor because of the design and the inability to wear for water- or contact-based sports. CONCLUSIONS Most parents reported that the wearable activity tracker was easy for their child or children to use and a useful tool for tracking their children’s daily activity. However, several barriers were identified, which may impact sustained use over time; both the functionality and wearability of the activity tracker should therefore be considered. Overall, wearable activity trackers for children have the potential to be integrated into targeted physical activity promotion initiatives.

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