Evaluating the impact of physical activity apps and wearables: interdisciplinary review

Background: Although many smartphone apps and wearables have been designed to improve physical activity, their rapidly evolving nature and complexity present challenges for evaluating their impact. Traditional methodologies, such as randomized controlled trials (RCTs), can be slow. To keep pace with rapid technological development, evaluations of mobile health technologies must be efficient. Rapid alternative research designs have been proposed, and efficient in-app data collection methods, including in-device sensors and device-generated logs, are available. Along with effectiveness, it is important to measure engagement (ie, users’ interaction and usage behavior) and acceptability (ie, users’ subjective perceptions and experiences) to help explain how and why apps and wearables work. Objectives: This study aimed to (1) explore the extent to which evaluations of physical activity apps and wearables: employ rapid research designs; assess engagement, acceptability, as well as effectiveness; use efficient data collection methods; and (2) describe which dimensions of engagement and acceptability are assessed. Method: An interdisciplinary scoping review using 8 databases from health and computing sciences. Included studies measured physical activity, and evaluated physical activity apps or wearables that provided sensor-based feedback. Results were analyzed using descriptive numerical summaries, chi-square testing, and qualitative thematic analysis. Results: A total of 1829 abstracts were screened, and 858 articles read in full. Of 111 included studies, 61 (55.0%) were published between 2015 and 2017. Most (55.0%, 61/111) were RCTs, and only 2 studies (1.8%) used rapid research designs: 1 single-case design and 1 multiphase optimization strategy. Other research designs included 23 (22.5%) repeated measures designs, 11 (9.9%) nonrandomized group designs, 10 (9.0%) case studies, and 4 (3.6%) observational studies. Less than one-third of the studies (32.0%, 35/111) investigated effectiveness, engagement, and acceptability together. To measure physical activity, most studies (90.1%, 101/111) employed sensors (either in-device [67.6%, 75/111] or external [23.4%, 26/111]). RCTs were more likely to employ external sensors (accelerometers: P=.005). Studies that assessed engagement (52.3%, 58/111) mostly used device-generated logs (91%, 53/58) to measure the frequency, depth, and length of engagement. Studies that assessed acceptability (57.7%, 64/111) most often used questionnaires (64%, 42/64) and/or qualitative methods (53%, 34/64) to explore appreciation, perceived effectiveness and usefulness, satisfaction, intention to continue use, and social acceptability. Some studies (14.4%, 16/111) assessed dimensions more closely related to usability (ie, burden of sensor wear and use, interface complexity, and perceived technical performance). Conclusions: The rapid increase of research into the impact of physical activity apps and wearables means that evaluation guidelines are urgently needed to promote efficiency through the use of rapid research designs, in-device sensors and user-logs to assess effectiveness, engagement, and acceptability. Screening articles was time-consuming because reporting across health and computing sciences lacked standardization. Reporting guidelines are therefore needed to facilitate the synthesis of evidence across disciplines

Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour

Health and fitness wearable technology has recently advanced, making it easier for an individual to monitor their behaviours. Previously self generated data interacts with the user to motivate positive behaviour change, but issues arise when relating this to long term mention of wearable devices. Previous studies within this area are discussed. We also consider a new approach where data is used to support instead of motivate, through monitoring and logging to encourage reflection. Based on issues highlighted, we then make recommendations on the direction in which future work could be most beneficial

CoachAI: A Conversational Agent Assisted Health Coaching Platform

Poor lifestyle represents a health risk factor and is the leading cause of morbidity and chronic conditions. The impact of poor lifestyle can be significantly altered by individual behavior change. Although the current shift in healthcare towards a long lasting modifiable behavior, however, with increasing caregiver workload and individuals' continuous needs of care, there is a need to ease caregiver's work while ensuring continuous interaction with users. This paper describes the design and validation of CoachAI, a conversational agent assisted health coaching system to support health intervention delivery to individuals and groups. CoachAI instantiates a text based healthcare chatbot system that bridges the remote human coach and the users. This research provides three main contributions to the preventive healthcare and healthy lifestyle promotion: (1) it presents the conversational agent to aid the caregiver; (2) it aims to decrease caregiver's workload and enhance care given to users, by handling (automating) repetitive caregiver tasks; and (3) it presents a domain independent mobile health conversational agent for health intervention delivery. We will discuss our approach and analyze the results of a one month validation study on physical activity, healthy diet and stress management

Self-tracking modes: reflexive self-monitoring and data practices

The concept of ‘self-tracking’ (also referred to as life-logging, the quantified self, personal analytics and personal informatics) has recently begun to emerge in discussions of ways in which people can voluntarily monitor and record specific features of their lives, often using digital technologies. There is evidence that the personal data that are derived from individuals engaging in such reflexive self-monitoring are now beginning to be used by actors, agencies and organisations beyond the personal and privatised realm. Self-tracking rationales and sites are proliferating as part of a ‘function creep’ of the technology and ethos of self-tracking. The detail offered by these data on individuals and the growing commodification and commercial value of digital data have led government, managerial and commercial enterprises to explore ways of appropriating self-tracking for their own purposes. In some contexts people are encouraged, ‘nudged’, obliged or coerced into using digital devices to produce personal data which are then used by others. This paper examines these issues, outlining five modes of self-tracking that have emerged: private, communal, pushed, imposed and exploited. The analysis draws upon theoretical perspectives on concepts of selfhood, citizenship, biopolitics and data practices and assemblages in discussing the wider sociocultural implications of the emergence and development of these modes of self-tracking

Development of a mobile wellness application for back pain patients using persuasive technology

The objective of this report is to conduct preliminary investigation & gathering of user requirements, design the system in accordance to selected software development methodology and develop a mobile wellness application for back pain patients using persuasive technology. The system is aimed to address the issue of low back pain, a medical problem largely affecting the society through the use of technology, specifically mobile technology and persuasive technology in guiding patients on back care using their smartphones. In the literature reviews, the domain of low back pain was researched and the existing implementations of persuasive technology and mobile wellness were reviewed. This vital information was then collectively analyzed in designing the application using the methodology chosen. A general structure of the application is then formed based on the data collected

Self “Sensor”Ship: An Interdisciplinary Investigation of the Persuasiveness, Social Implications, and Ethical Design of Self-Sensoring Prescriptive Applications

This dissertation research investigates the social implications of computing artifacts that make use of sensor driven self-quantification to implicitly or explicitly direct user behaviors. These technologies are referred to here as self-sensoring prescriptive applications (SSPA’s). This genre of technological application has a strong presence in healthcare as a means to monitor health, modify behavior, improve health outcomes, and reduce medical costs. However, the commercial sector is quickly adopting SSPA’s as a means to monitor and/or modify consumer behaviors as well (Swan, 2013). These wearable devices typically monitor factors such as movement, heartrate, and respiration; ostensibly to guide the users to better or more informed choices about their physical fitness (Lee & Drake, 2013; Swan, 2012b). However, applications that claim to use biosensor data to assist in mood maintenance and control are entering the market (Bolluyt, 2015), and applications to aid in decision making about consumer products are on the horizon as well (Swan, 2012b). Interestingly, there is little existing research that investigates the direct impact biosensor data have on decision making, nor on the risks, benefits, or regulation of such technologies. The research presented here is inspired by a number of separate but related gaps in existing literature about the social implications of SSPA’s. First, how SSPA’s impact individual and group decision making and attitude formation within non-medicalcare domains (e.g. will a message about what product to buy be more persuasive if it claims to have based the recommendation on your biometric information?). Second, how the design and designers of SSPA’s shape social behaviors and third, how these factors are or are not being considered in future design and public policy decisions

Wearables and Wellness for the Young Elderly - Transforming Everyday Lives?

Mobile and wearable technologies have unique advantages within health and wellness. Current knowledge on wearables shows, however, that users easily abandon them after an initial use period. We wanted to investigate whether adding a social dimension to wearable use would be valuable in the young elderly age group, and investigate user experiences of wearables when introducing them to people not in the traditional target group of fitness wearables and without previous experience of wearables. In this exploratory study aimed at gaining a holistic picture to guide further research, we utilize multiple research methods to gain rich data. We found that the young elderly had a clear interest in the wearable. Introducing a social dimension to the wearable seemed beneficial as the young elderly quickly formed a both supportive and competitive relation. The social connection formed through the device appeared to be value adding for the participants and seems to at least partially address the previously identified barriers