Behavior Change Techniques Incorporated in Fitness Trackers: Content Analysis (original) (raw)
Related papers
Behavior Change Techniques Present in Wearable Activity Trackers: A Critical Analysis
JMIR mHealth and uHealth, 2016
Background: Wearable activity trackers are promising as interventions that offer guidance and support for increasing physical activity and health-focused tracking. Most adults do not meet their recommended daily activity guidelines, and wearable fitness trackers are increasingly cited as having great potential to improve the physical activity levels of adults. Objective: The objective of this study was to use the Coventry, Aberdeen, and London-Refined (CALO-RE) taxonomy to examine if the design of wearable activity trackers incorporates behavior change techniques (BCTs). A secondary objective was to critically analyze whether the BCTs present relate to known drivers of behavior change, such as self-efficacy, with the intention of extending applicability to older adults in addition to the overall population. Methods: Wearing each device for a period of 1 week, two independent raters used CALO-RE taxonomy to code the BCTs of the seven wearable activity trackers available in Canada as of March 2014. These included Fitbit Flex, Misfit Shine, Withings Pulse, Jawbone UP24, Spark Activity Tracker by SparkPeople, Nike+ FuelBand SE, and Polar Loop. We calculated interrater reliability using Cohen's kappa. Results: The average number of BCTs identified was 16.3/40. Withings Pulse had the highest number of BCTs and Misfit Shine had the lowest. Most techniques centered around self-monitoring and self-regulation, all of which have been associated with improved physical activity in older adults. Techniques related to planning and providing instructions were scarce. Conclusions: Overall, wearable activity trackers contain several BCTs that have been shown to increase physical activity in older adults. Although more research and development must be done to fully understand the potential of wearables as health interventions, the current wearable trackers offer significant potential with regard to BCTs relevant to uptake by all populations, including older adults.
2022
Despite the indisputable personal and societal benefits of regular physical activity, a large portion of the population does not follow the recommended guidelines, harming their health and wellness. The World Health Organization has called upon governments, practitioners, and researchers to accelerate action to address the global prevalence of physical inactivity. To this end, an emerging wave of research in ubiquitous computing has been exploring the potential of interactive self-tracking technology in encouraging positive health behavior change. Numerous findings indicate the benefits of personalization and inclusive design regarding increasing the motivational appeal and overall effectiveness of behavior change systems, with the ultimate goal of empowering and facilitating people to achieve their goals. However, most interventions still adopt a "one-size-fits-all" approach to their design, assuming equal effectiveness for all system features in spite of individual and collective user differences. To this end, we analyze a corpus of 12 years of research in self-tracking technology for health behavior change, focusing on physical activity, to identify those design elements that have proven most effective in inciting desirable behavior across diverse population segments. We then provide actionable recommendations for designing and evaluating behavior change self-tracking technology based on age, gender, occupation, fitness, and health condition. Finally, we engage in a critical commentary on the diversity of the domain and discuss ethical concerns surrounding tailored interventions and directions for moving forward. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing systems and tools; HCI design and evaluation methods; HCI theory, concepts and models; • Applied computing → Consumer health.
Activity Trackers Influencing Motivation and Awareness: Study Among Fitness Centre Members
Digital Transformation – From Connecting Things to Transforming Our Lives, 2017
Consumer fitness technology products are becoming increasingly popular. This leads to interesting questions about the influence of activity trackers on a person's motivation to exercise. This study explored the role of activity trackers in motivating fitness centre members towards exercising and in increasing their awareness regarding their own health and physical activity. The study included 100 fitness centre members divided into a test group and a control group and three subgroups: OLD, NEW, and personal trainer (PT) members. The focus was on gym visit frequency during a 10-week test period and on tracking the consistency of activity levels. Participants also completed a pre and post study questionnaire assessing changes in their health and physical activity awareness. The results suggest that an activity tracker does not significantly influence fitness centre members' gym attendance or overall physical activity levels. Group comparisons reveal no statistically significant differences between groups, but observations of the descriptive statistics indicated that an activity tracker can bring some inspiration and other benefits, especially for PT clients and people who are just starting their new more physically active lifestyle. Using an activity tracker increased participants' perceived awareness of their own wellbeing, daily sitting time, and amount of sleep.
Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
Despite the indisputable personal and societal benefits of regular physical activity, a large portion of the population does not follow the recommended guidelines, harming their health and wellness. The World Health Organization has called upon governments, practitioners, and researchers to accelerate action to address the global prevalence of physical inactivity. To this end, an emerging wave of research in ubiquitous computing has been exploring the potential of interactive self-tracking technology in encouraging positive health behavior change. Numerous findings indicate the benefits of personalization and inclusive design regarding increasing the motivational appeal and overall effectiveness of behavior change systems, with the ultimate goal of empowering and facilitating people to achieve their goals. However, most interventions still adopt a "one-size-fits-all" approach to their design, assuming equal effectiveness for all system features in spite of individual and collective user differences. To this end, we analyze a corpus of 12 years of research in self-tracking technology for health behavior change, focusing on physical activity, to identify those design elements that have proven most effective in inciting desirable behavior across diverse population segments. We then provide actionable recommendations for designing and evaluating behavior change self-tracking technology based on age, gender, occupation, fitness, and health condition. Finally, we engage in a critical commentary on the diversity of the domain and discuss ethical concerns surrounding tailored interventions and directions for moving forward. CCS CONCEPTS • Human-centered computing → Ubiquitous and mobile computing systems and tools; HCI design and evaluation methods; HCI theory, concepts and models; • Applied computing → Consumer health.
Self-tracking over time: The FITBIT® phenomenon
The evolution of pervasive sensor technologies has given rise to an increased availability of consumer wearables that track and monitor a variety of human activity. The use of these devices is closely linked to self-quantification; the practice of using the captured data and knowledge gained for self-improvement. In this study a research model based in human behaviour theories informs a qualitative approach to answer the research question, " How does the use of self-tracking devices influence individuals health and behaviours and goals over time? ". The findings show participants chose the FITBIT® to meet predetermined step counts, monitor activity and heath indicators, and to lose weight. As time progressed most users reported some change or adaptation to the way they used their devices. Participants used the knowledge gained to set new goals, reevaluate existing goals and over time reported an increased focus on participating in challenges and being part of a community to help stay motivated.
American Journal of Health Promotion, 2019
Abstract Objective: To examine whether a fitness tracker (FT) intervention changes physical activity (PA) behavior compared to a control condition or compared to an alternative intervention. Data Source: Searches between January 01, 2010, and January 01, 2019, were conducted in PubMed, CINAHL, Cochrane CENTRAL, EMBASE, and PsycINFO. Inclusion/Exclusion Criteria: Randomized clinical trials of adults using an FT to change PA behavior were included. Nonclinical trials, studies that included the delivery of structured exercise, and/or studies that only used the FT to assess PA were excluded. Data Extraction: Extracted features included characteristics of the study population, intervention components, PA outcomes, and results. Data Synthesis: Papers were pooled in a statistical meta-analysis using a fixed effects model. Where statistical pooling was not possible, standardized mean difference (SMD) and 95% confidence intervals (CI) were calculated. Findings were presented in a narrative form and tables. Results: Of 2076 articles found, 21 were included in the review. A small yet significant positive effect (SMD = 0.25, 95% CI = 0.17-0.32; P < .01; I2 = 56.9%; P = .03) was found in step count for interventions compared to control. A small yet significant negative effect (SMD = −0.11, 95% CI = −0.20 to −0.02; P = .02; I2 = 58.2%; P = 0.03) was found in moderate-to-vigorous PA for interventions compared to an alternative intervention. Conclusion: Trackers may enhance PA interventions, as a general positive effect is found in step count compared to a control. However, there is no evidence of a positive effect when interventions are compared to an alternative intervention. It is unknown whether results are due to other intervention components and/or clinical heterogeneity.
Factors Influencing Exercise Engagement When Using Activity Trackers (Preprint)
JMIR mHealth and uHealth, 2018
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, da...
Mobile and Wearable Device Features that Matter in Promoting Physical Activity
Journal of Mobile Technology in Medicine, 2016
Background: As wearable sensors/devices become increasingly popular to promote physical activity (PA), research is needed to examine how and which components of these devices people use to increase their PA levels. Aims: (1) To assess usability and level of engagement with the Fitbit One and daily SMS-based prompts in a 6-week PA intervention, and (2) to examine whether use/ level of engagement with specific intervention components were associated with PA change. Methods: Data were analyzed from a randomized controlled trial that compared (1) a wearable sensor/ device (Fitbit One) plus SMS-based PA prompts, and (2) Fitbit One only, among overweight/ obese adults (N067). We calculated average scores from Likert-type response items that assessed usability and level of engagement with device features (e.g., tracker, website, mobile app, and SMS-based prompts), and assessed whether such factors were associated with change in steps/day (using Actigraph GT3X'). Results: Participants reported the Fitbit One was easy to use and the tracker helped to be more active. Those who used the Fitbit mobile app (36%) vs. those who did not (64%) had an increase in steps at 6-week follow-up, even after adjusting for previous web/app use: '545 steps/ day (SE 0 265) vs. (28 steps/ day (SE 0242) (p 0.04). Conclusions: Level of engagement with the Fitbit One, particularly the mobile app, was associated with increased steps. Mobile apps can instantly display summaries of PA performance and could optimize self-regulation to activate change. More research is needed to determine whether such modalities might be cost-effective in future intervention research and practice.
Users’ experiences of wearable activity trackers: a cross-sectional study
BMC Public Health
Background: Wearable activity trackers offer considerable promise for helping users to adopt healthier lifestyles. This study aimed to explore users' experience of activity trackers, including usage patterns, sharing of data to social media, perceived behaviour change (physical activity, diet and sleep), and technical issues/barriers to use. Methods: A cross-sectional online survey was developed and administered to Australian adults who were current or former activity tracker users. Results were analysed descriptively, with differences between current and former users and wearable brands explored using independent samples t-tests, Mann-Whitney, and chi square tests. Results: Participants included 200 current and 37 former activity tracker users (total N = 237) with a mean age of 33.1 years (SD 12.4, range 18-74 years). Fitbit (67.5%) and Garmin devices (16.5%) were most commonly reported. Participants typically used their trackers for sustained periods (5-7 months) and most intended to continue usage. Participants reported they had improved their physical activity (51-81%) more commonly than they had their diet (14-40%) or sleep (11-24%), and slightly more participants reported to value the real time feedback (89%) compared to the long-term monitoring (78%). Most users (70%) reported they had experienced functionality issues with their devices, most commonly related to battery life and technical difficulties. Conclusions: Results suggest users find activity trackers appealing and useful tools for increasing perceived physical activity levels over a sustained period.
JMIR mHealth and uHealth
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.