Succeeding with prolonged usage of consumer-based activity trackers in clinical studies: a mixed methods approach

Abstract

Background: Lack of physical activity (PA) is a risk factor for death and non-communicable disease. Despite this, more than one fourth of adults worldwide do not follow PA guidelines. As part of a feasibility study to test a complex intervention for increasing PA, we included a consumer-based activity tracker (AT) as a tool to measure PA outcomes and to track heart rate during exercise sessions. The aim of the present study was to identify factors that increase wear time when using a consumer-based AT for monitoring of participants in clinical research. Methods: Sixteen participants aged 55-74 years, with obesity, sedentary lifestyle, and elevated cardiovascular risk were recruited to a 12-month feasibility study. Participants wore a Polar M430 AT to collect continuous PA data during a six-month intervention followed by 6 months of follow-up. We performed quantitative wear time analysis, tested the validity of the AT, and completed two rounds of qualitative interviews to investigate how individual wear-time was linked to participant responses. Results: From 1 year of tracking, mean number of valid wear days were 292 (SD = 86), i.e. 80%. The Polar M430 provides acceptable measurements for total energy expenditure. Motivations for increased wear time were that participants were asked to wear it and the ability to track PA progress. Perceived usefulness included time keeping, heart rate-and sleep tracking, becoming more conscious about day-today activity, and improved understanding of which activity types were more effective for energy expenditure. Sources of AT annoyance were measurement inaccuracies and limited instruction for use. Suggestions for improvement were that the AT was big, unattractive, and complicated to use. Conclusions: Adherence to wearing a consumer-based AT was high. Results indicate that it is feasible to use a consumer-based AT to measure PA over a longer period. Potential success factors for increased wear time includes adequate instruction for AT use, allowing participants to choose different AT designs, and using trackers with accurate measurements. To identify accurate trackers, AT validation studies in the target cohort may be needed.

Consumer-Based Activity Trackers as a Tool for Physical Activity Monitoring in Epidemiological Studies During the COVID-19 Pandemic: Development and Usability Study

“…In this study, we used the proposed system to access data retrospectively from participants with privately owned activity trackers. Previously, we have successfully used the same technology for long-term prospective physical activity monitoring among participants in a lifestyle intervention study wearing a provided activity tracker for up to 1 year ([ 25 , 26 ] and Hopstock et al, unpublished data, 2020).…”

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

“…After an initial screening, which consisted of reviewing all article titles and abstracts, the full content of 102 of these articles was screened in more detail for eligibility. After removing the articles that did not meet the inclusion criteria detailed in Section 2.3 , 70 articles were deemed eligible for the review [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , …”

“…As detailed in Table 2 , the most frequently used type of wearable device is the Inertial Measurement Unit (IMU; N = 39) [ 34 , 37 , 44 , 46 , 52 , 54 , 55 , 56 , 57 , 58 , 60 , 61 , 62 , 63 , 66 , 71 , 72 , 73 , 75 , 78 , 79 , 81 , 82 , 83 , 84 , 87 , 88 , 90 , 91 , 92 , 93 , 94 , 95 , 97 , 98 , 99 , 100 , 101 , 102 ], and then, almost equally, the smartphone (N = 18) [ 38 , 39 , 40 , 41 , 42 , 43 , 45 , 47 , 51 , 64 , 68 , 69 , 70 , …”

“…Therefore, in Table 1, we only report the ranges of acquisition times that go from hours to years. Among the selected studies, as displayed in Figure 3 [40,43,45,48,49,[51][52][53]62,64,[67][68][69]87,91,95,99,102], 8% (N = 6) focused on aging and associated pathologies [38,56,66,88,91,100], and 4% (N = 3) focused on diseases associated with poor lifestyle [42,62,74]. Finally, five studies were classified as "others" [41,46,82,84,93] because they could not be grouped together in an existing group.…”

A Protocol Study to Establish Psychological Outcomes From the Use of Wearables for Health and Fitness Monitoring

“…People increasingly use technological tools for health and activity monitoring (1)(2)(3)(4). These activity tracking apps and wearables are considered self-empowering because they can help users make better-informed lifestyle decisions based on their data (5,6).…”