The social grounds of self-tracking in insurance: A mixed-method approach to adoption and use.

Scholars have explored the role of self-tracking in mediating people's values, perceptions, and practices. But little is known about its institutionalised forms, although it is becoming a routine component of health policies and insurance programs. Furthermore, the role of structural elements such as sociodemographic variables, socialisations, and trajectories has been neglected. Using both quantitative (n = 818) and qualitative (n = 44) data gathered from users and non-users of an insurance program's self-tracking intervention, and drawing from Bourdieu's theoretical framework, we highlight the impact of users' social background on the adoption and use of the technology. We show that older, poorer, and less educated individual are less likely to adopt the technology, and describe four prototypical categories of users, the meritocrats, the litigants, the scrutinisers and the good-intentioned. Each category displays different reasons and ways to use the technology that are grounded in users' socialisations and life trajectories. Results suggest that too much emphasis may have been put on self-tracking's transformative powers and not enough on its reproductive inertia, with important consequences for both scholars, designers, and public health stakeholders.

Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Protocol of a Microrandomized Trial.

BACKGROUND: Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user- and time-varying context characteristics. Ideally, JITAIs include effective intervention components, and delivery tailoring is based on effective moderators of intervention effects. Using machine learning techniques to infer each user's context from smartphone sensor data is a promising approach to further enhance tailoring. OBJECTIVE: The primary objective of this study is to quantify main effects, interactions, and moderators of 3 intervention components of a smartphone-based intervention for physical activity. The secondary objective is the exploration of participants' states of receptivity, that is, situations in which participants are more likely to react to intervention notifications through collection of smartphone sensor data. METHODS: In 2017, we developed the Assistant to Lift your Level of activitY (Ally), a chatbot-based mobile health intervention for increasing physical activity that utilizes incentives, planning, and self-monitoring prompts to help participants meet personalized step goals. We used a microrandomized trial design to meet the study objectives. Insurees of a large Swiss insurance company were invited to use the Ally app over a 12-day baseline and a 6-week intervention period. Upon enrollment, participants were randomly allocated to either a financial incentive, a charity incentive, or a no incentive condition. Over the course of the intervention period, participants were repeatedly randomized on a daily basis to either receive prompts that support self-monitoring or not and on a weekly basis to receive 1 of 2 planning interventions or no planning. Participants completed a Web-based questionnaire at baseline and postintervention follow-up. RESULTS: Data collection was completed in January 2018. In total, 274 insurees (mean age 41.73 years; 57.7% [158/274] female) enrolled in the study and installed the Ally app on their smartphones. Main reasons for declining participation were having an incompatible smartphone (37/191, 19.4%) and collection of sensor data (35/191, 18.3%). Step data are available for 227 (82.8%, 227/274) participants, and smartphone sensor data are available for 247 (90.1%, 247/274) participants. CONCLUSIONS: This study describes the evidence-based development of a JITAI for increasing physical activity. If components prove to be efficacious, they will be included in a revised version of the app that offers scalable promotion of physical activity at low cost. TRIAL REGISTRATION: ClinicalTrials.gov NCT03384550; https://clinicaltrials.gov/ct2/show/NCT03384550 (Archived by WebCite at http://www.webcitation.org/74IgCiK3d). INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/11540.

Accuracy of a smartphone pedometer application according to different speeds and mobile phone locations in a laboratory context.

BACKGROUND: The purpose of this study was to compare the accuracy of a smartphone application and a mechanical pedometer for step counting at different walking speeds and mobile phone locations in a laboratory context. METHODS: Seventeen adults wore an iPphone6© with Runtastic Pedometer© application (RUN), at 3 different locations (belt, arm, jacket) and a pedometer (YAM) at the waist. They were asked to walk on an instrumented treadmill (reference) at various speeds (2, 4 and 6 km/h). RESULTS: RUN was more accurate than YAM at 2 km/h (p < 0.05) and at 4 km/h (p = 0.03). At 6 km/h the two devices were equally accurate. The precision of YAM increased with speed (p < 0.05), while for RUN, the results were not significant but showed a trend (p = 0.051). Surprisingly, YAM underestimates the number of step by 60.5% at 2 km/h. The best accurate step counting (0.7% mean error) was observed when RUN is attached to the arm and at the highest speed. CONCLUSIONS: RUN pedometer application could be recommended mainly for walking sessions even for low walking speed. Moreover, our results confirm that the smartphone should be strapped close to the body to discriminate steps from noise by the accelerometers (particularly at low speed).