Linking Physical Activity to Personal Values: Feasibility and Acceptability Randomized Pilot of a Behavioral Intervention for Older Adults with Osteoarthritis Pain

Background Osteoarthritis (OA) pain is common and leads to functional impairment for many older adults. Physical activity can improve OA outcomes for older adults, but few are appropriately active. Behavioral interventions can reduce barriers to physical activity. We developed and tested a brief, novel behavioral intervention for older adults combining values to enhance motivation and strategic activity pacing to improve arthritis-related pain and functioning and increase physical activity. Methods A randomized feasibility and acceptability pilot trial compared Engage-PA to treatment as usual plus fitness tracker (TAU+) in N=40 adults age 65+ with OA pain in the knee or hip. Engage-PA involved two 60-minute telephone sessions. All participants wore a fitness tracker to collect daily steps throughout the study and completed baseline and post-treatment assessments of secondary outcomes (arthritis-related pain and physical functioning, physical activity, psychological distress, psychological flexibility, and value-guided action). The impact of COVID-19 on general wellbeing and physical activity was also assessed. Descriptive statistics were conducted for feasibility and acceptability outcomes. Indicators of improvement in secondary outcomes were examined via change scores from baseline to post-treatment and performing independent samples t-tests to assess for between-group differences. Results Feasibility was high; 100% accrual, low (5%) attrition, and 100% completion of study sessions. Acceptability was high, with 89% finding the intervention “mostly” or “very” helpful. Engage-PA participants demonstrated improvements in arthritis pain severity (Mdiff=1.68, p<.05), arthritis-related physical functioning (Mdiff=.875, p=.056), and self-reported activity (Mdiff=.875, p<.05) from baseline to post-treatment as compared to TAU+. Sixty-three percent of participants provided useable objective daily steps data. Other secondary outcome patterns were not interpretable in this small sample. COVID-19 added additional burden to participants, such that 50% were exercising less, 68% were more sedentary, and 72% lost access to spaces and social support to be active. Conclusions Engage-PA is a promising brief, novel behavioral intervention that has potential to support older adults in improving arthritis-related pain and functioning and increasing physical activity. The feasibility and acceptability of the intervention is particularly notable as most participants reported COVID-19 added more barriers to physical activity, and Engage-PA may be appealing in future studies. Trial Registration: clinicaltrials.gov, NCT04490395, registered 7/29/2020, https://clinicaltrials.gov/ct2/show/NCT04490395.

The Internet of Things From a Legal Perspective

The internet of things (IoT) is one of successive technological waves that could have great impact on different aspects of modern life. It is being used in transport, smart grids, healthcare, environmental monitoring, logistics, as well as for processing pure personal data through a fitness tracker, wearable medical device, smartwatch, smart clothing, wearable camera, and so forth. From a legal viewpoint, processing personal data has to be done in accordance with rules of data protection law. This law aims to protect data from collection to retention. It usually applies to the processing of personal data that identifies or can identify a specific natural person. Strict adherence to this law is necessary for protecting personal data from being misused and also for promoting the IoT industry. This chapter discusses the applicability of the data protection law to IoT and the consequences of non-compliance with this law. It also provides recommendations on how to effectively comply with the data protection law in the IoT environment.

Are Those Steps Worth Your Privacy?

Fitness trackers are increasingly popular. The data they collect provides substantial benefits to their users, but it also creates privacy risks. In this work, we investigate how fitness-tracker users perceive the utility of the features they provide and the associated privacy-inference risks. We conduct a longitudinal study composed of a four-month period of fitness-tracker use (N = 227), followed by an online survey (N = 227) and interviews (N = 19). We assess the users' knowledge of concrete privacy threats that fitness-tracker users are exposed to (as demonstrated by previous work), possible privacy-preserving actions users can take, and perceptions of utility of the features provided by the fitness trackers. We study the potential for data minimization and the users' mental models of how the fitness tracking ecosystem works. Our findings show that the participants are aware that some types of information might be inferred from the data collected by the fitness trackers. For instance, the participants correctly guessed that sexual activity could be inferred from heart-rate data. However, the participants did not realize that also the non-physiological information could be inferred from the data. Our findings demonstrate a high potential for data minimization, either by processing data locally or by decreasing the temporal granularity of the data sent to the service provider. Furthermore, we identify the participants' lack of understanding and common misconceptions about how the Fitbit ecosystem works.

Job characteristics, well-being and physical activity: A field study using a consumer fitness tracker

The relation between job characteristics and health is one of the most important fields of research within work and organizational psychology. Another prominent variable influencing health is physical activity. The physical activity mediated Demand-Control (pamDC) model (Häusser & Mojzisch, 2017, https://doi.org/10.1080/02678373.2017.1303759) combines these health indicators in a new theoretical framework. Based on the pamDC model the current study aims to clarify the role of leisure time physical activity (LTPA) in the interplay of job demands, job control and well-being. We expect physical activity to partially mediate the impact of job characteristics on health. To avoid self-report bias considering physical activity we used a consumer fitness tracker to collect additional data. In total, 104 white-collar workers participated in the study. The results show that job control and job demands could predict well-being in cross-sectional analyses. In longitudinal analyses, this was only the case for job demands. Regarding the proposed mediating effect of LTPA between job characteristics and health, we could not detect a significant mediation in our sample. This was true for both self-reported and objective data on physical activity. This study provides a first step in validating the pamDC model and has implications for future research.

What to do after a data breach? Examining apology and compensation as response strategies for health service providers

Innovative IT-enabled health services promise tremendous benefits for customers and service providers alike. Simultaneously, health services by nature process sensitive customer information, and data breaches have become an everyday phenomenon. The challenge that health service providers face is to find effective recovery strategies after data breaches to retain customer trust and loyalty. We theorize and investigate how two widely applied recovery actions (namely apology and compensation) affect customer reactions after a data breach in the specific context of fitness trackers. Drawing on expectation confirmation theory, we argue that the recovery actions derived from practice, apology, and compensation address the assimilation-contrast model’s tolerance range and, thus, always lead to satisfaction with the recovery strategy, which positively influences customers’ behavior. We employ an experimental investigation and collect data from fitness tracker users during a running event. In the end, we found substantial support for our research model. Health service providers should determine specific customer expectations and align their data breach recovery strategies accordingly.

Sleep and health: examining the relation of sleep to burnout and well-being using a consumer fitness tracker

Sleep is an essential requirement for both physiological and psychological functioning and has an impact on various health parameters. The present study aimed to examine how quantity and quality of sleep predicts burnout and well-being by using both self-reported and objectively collected sleep data. The participants were 104 white-collar workers who wore a fitness tracker for 14 consecutive days and filled out a questionnaire about sleep, burnout, and well-being. The results showed that self-reported sleep quality predicts burnout and well-being, but neither did self-reported nor objective sleep duration. We concluded that although measuring sleep duration with a consumer fitness tracker still needs to be improved, it is a useful addition to self-reported sleep measures. The study did solidify results from previous self-reported measures and point out the prominent role of sleep quality rather than hours of sleep.

mmFiT: Contactless Fitness Tracker Using mmWave Radar and Edge Computing Enabled Deep Learning

Internet of things (IoT) is transforming the way we imagine healthcare with ubiquitous connectivity, faster response and deeper personalized insights using large amounts of data. Fitness trackers provide useful insights to maintain balance of a healthy lifestyle. Nowadays, fitness trackers are available as wearable devices which creates a sense of unease in exercise and may cause skin irritation. In this paper, we present mmFiT, an edge computing enabled, contactless, real-time fitness tracker using a single mmwave radar point cloud data. It has the inherent advantage of user privacy preservation while tracking indoor fitness activities. Experimental results show that the system can classify various exercises with real-time accuracy of 95.53\% and is also capable of counting repetitions of exercises. This implementation is computationally inexpensive, and therefore, the system can be deployed in an IoT connected edge device for real-time operations. This system will be an ideal fit in a smart home or smart gymnasium setting.