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Factors Influencing Exercise Engagement When Using Activity Trackers: Nonrandomized Pilot Study.

ABSTRACT: BACKGROUND:It is well reported that tracking physical activity can lead to sustained exercise routines, which can decrease disease risk. However, most stop using trackers within a couple months of initial use. The reasons people stop using activity trackers can be varied and personal. Understanding the reasons for discontinued use could lead to greater acceptance of tracking and more regular exercise engagement. OBJECTIVE:The aim of this study was to determine the individualistic reasons for nonengagement with activity trackers. METHODS:Overweight and obese participants (n=30) were enrolled and allowed to choose an activity tracker of their choice to use for 9 weeks. Questionnaires were administered at the beginning and end of the study to collect data on their technology use, as well as social, physiological, and psychological attributes that may influence tracker use. Closeout interviews were also conducted to further identify individual influencers and attributes. In addition, daily steps were collected from the activity tracker. RESULTS:The results of the study indicate that participants typically valued the knowledge of their activity level the activity tracker provided, but it was not a sufficient motivator to overcome personal barriers to maintain or increase exercise engagement. Participants identified as extrinsically motivated were more influenced by wearing an activity tracker than those who were intrinsically motivated. During the study, participants who reported either owning multiple technology devices or knowing someone who used multiple devices were more likely to remain engaged with their activity tracker. CONCLUSIONS:This study lays the foundation for developing a smart app that could promote individual engagement with activity trackers.

SUBMITTER: Centi AJ

PROVIDER: S-EPMC7017648 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Factors Influencing Exercise Engagement When Using Activity Trackers: Nonrandomized Pilot Study.

Centi Amanda Jayne AJ Atif Mursal M Golas Sara Bersche SB Mohammadi Ramin R Kamarthi Sagar S Agboola Stephen S Kvedar Joseph C JC Jethwani Kamal K

JMIR mHealth and uHealth 20191024 10

<h4>Background</h4>It is well reported that tracking physical activity can lead to sustained exercise routines, which can decrease disease risk. However, most stop using trackers within a couple months of initial use. The reasons people stop using activity trackers can be varied and personal. Understanding the reasons for discontinued use could lead to greater acceptance of tracking and more regular exercise engagement.<h4>Objective</h4>The aim of this study was to determine the individualistic ...[more]

PMID: 31651405

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Project description: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.

Project description:BackgroundImproving physical activity (PA) is a core component of secondary prevention and cardiac (tele)rehabilitation. Commercially available activity trackers are frequently used to monitor and promote PA in cardiac patients. However, studies on the validity of these devices in cardiac patients are scarce. As cardiac patients are being advised and treated based on PA parameters measured by these devices, it is highly important to evaluate the accuracy of these parameters in this specific population.ObjectiveThe aim of this study was to determine the accuracy and responsiveness of 2 wrist-worn activity trackers, Fitbit Charge 2 (FC2) and Mio Slice (MS), for the assessment of energy expenditure (EE) in cardiac patients.MethodsEE assessed by the activity trackers was compared with indirect calorimetry (Oxycon Mobile [OM]) during a laboratory activity protocol. Two groups were assessed: patients with stable coronary artery disease (CAD) with preserved left ventricular ejection fraction (LVEF) and patients with heart failure with reduced ejection fraction (HFrEF).ResultsA total of 38 patients were included: 19 with CAD and 19 with HFrEF (LVEF 31.8%, SD 7.6%). The CAD group showed no significant difference in total EE between FC2 and OM (47.5 kcal, SD 112 kcal; P=.09), in contrast to a significant difference between MS and OM (88 kcal, SD 108 kcal; P=.003). The HFrEF group showed significant differences in EE between FC2 and OM (38 kcal, SD 57 kcal; P=.01), as well as between MS and OM (106 kcal, SD 167 kcal; P=.02). Agreement of the activity trackers was low in both groups (CAD: intraclass correlation coefficient [ICC] FC2=0.10, ICC MS=0.12; HFrEF: ICC FC2=0.42, ICC MS=0.11). The responsiveness of FC2 was poor, whereas MS was able to detect changes in cycling loads only.ConclusionsBoth activity trackers demonstrated low accuracy in estimating EE in cardiac patients and poor performance to detect within-patient changes in the low-to-moderate exercise intensity domain. Although the use of activity trackers in cardiac patients is promising and could enhance daily exercise behavior, these findings highlight the need for population-specific devices and algorithms.

Project description:Background: Exercise can ameliorate cancer- and treatment-related toxicities, but poor adherence to exercise regimens is a barrier. Exercise interventions using digital activity trackers (E-DATs) may improve exercise adherence, but data are limited for patients with cancer. We conducted a systematic review examining the feasibility of E-DATs in cancer survivors and effects on activity level, body composition, objective fitness outcomes, health-related quality of life (HRQoL), self-reported symptoms, and biomarkers. Methods: We identified randomized controlled trials (RCTs) of E-DATs in adult cancer survivors published in English between January 1, 2008, and July 27, 2017. Two authors independently reviewed article titles (n=160), removed duplicates (n=50), and reviewed the remaining 110 articles for eligibility. Results: A total of 12 RCTs met eligibility criteria, including 1,450 patients (mean age, 50-70 years) with the following cancers: breast (n=5), colon or breast (n=2), prostate (n=1), acute leukemia (n=1), or others (n=3). Duration of E-DATs ranged from 4 to 24 weeks, and the follow-up period ranged from 4 to 52 weeks, with retention rates of 54% to 95%. The technology component of E-DATs included pedometers (n=8); pedometers with smartphone application (n=1), Wii Fit (n=1), heart rate monitor (n=1); and a wireless sensor with accelerometer, gyroscope, and magnetometer (n=1). Adherence by at least one measure to E-DATs was >70% in 8 of 8 RCTs. Compared with controls, E-DATs significantly improved patients' step count in 3 of 5 RCTs, activity level in 6 of 9 RCTs, and HRQoL in 7 of 9 RCTs (all P≤05), with no significant changes in biomarkers (eg, interleukin 6, tumor necrosis factor α, C-reactive protein, c-peptide, lipid panel) in 3 RCTs. Duration of E-DAT was not significantly correlated with adherence or study retention. Conclusions: This systematic review shows that E-DATs are feasible to implement in cancer survivors. Future research should examine the optimal type, dose, and schedule of E-DATs for cancer survivors.

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Factors Influencing Exercise Engagement When Using Activity Trackers: Nonrandomized Pilot Study.

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Factors Influencing Exercise Engagement When Using Activity Trackers: Nonrandomized Pilot Study.

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