Methodological Review: Healthcare in the pocket: Mapping the space of mobile-phone health interventions

Rapid developments in technology have encouraged the use of smartphones in physical activity research, although little is known regarding their effectiveness as measurement and intervention tools. This study systematically reviewed evidence on smartphones and their viability for measuring and influencing physical activity. Research articles were identified in September 2013 by literature searches in Web of Knowledge, PubMed, PsycINFO, EBSCO, and ScienceDirect. The search was restricted using the terms (physical activity OR exercise OR fitness) AND (smartphone* OR mobile phone* OR cell phone*) AND (measurement OR intervention). Reviewed articles were required to be published in international academic peer-reviewed journals, or in full text from international scientific conferences, and focused on measuring physical activity through smartphone processing data and influencing people to be more active through smartphone applications. Two reviewers independently performed the selection of articles and examined titles and abstracts to exclude those out of scope. Data on study characteristics, technologies used to objectively measure physical activity, strategies applied to influence activity; and the main study findings were extracted and reported. A total of 26 articles (with the first published in 2007) met inclusion criteria. All studies were conducted in highly economically advantaged countries; 12 articles focused on special populations (e.g. obese patients). Studies measured physical activity using native mobile features, and/or an external device linked to an application. Measurement accuracy ranged from 52 to 100 % (n = 10 studies). A total of 17 articles implemented and evaluated an intervention. Smartphone strategies to influence physical activity tended to be ad hoc, rather than theory-based approaches; physical activity profiles, goal setting, real-time feedback, social support networking, and online expert consultation were identified as the most useful strategies to encourage physical activity change. Only five studies assessed physical activity intervention effects; all used step counts as the outcome measure. Four studies (three pre–post and one comparative) reported physical activity increases (12–42 participants, 800–1,104 steps/day, 2 weeks–6 months), and one case-control study reported physical activity maintenance (n = 200 participants; >10,000 steps/day) over 3 months. Smartphone use is a relatively new field of study in physical activity research, and consequently the evidence base is emerging. Few studies identified in this review considered the validity of phone-based assessment of physical activity. Those that did report on measurement properties found average-to-excellent levels of accuracy for different behaviors. The range of novel and engaging intervention strategies used by smartphones, and user perceptions on their usefulness and viability, highlights the potential such technology has for physical activity promotion. However, intervention effects reported in the extant literature are modest at best, and future studies need to utilize randomized controlled trial research designs, larger sample sizes, and longer study periods to better explore the physical activity measurement and intervention capabilities of smartphones.

Measuring and Influencing Physical Activity with Smartphone Technology: A Systematic Review

A key challenge for mobile health is to develop new technology that can assist individuals in maintaining a healthy lifestyle by keeping track of their everyday behaviors. Smartphones embedded with a wide variety of sensors are enabling a new generation of personal health applications that can actively monitor, model and promote wellbeing. Automated wellbeing tracking systems available so far have focused on physical fitness and sleep and often require external non-phone based sensors. In this work, we take a step towards a more comprehensive smartphone based system that can track activities that impact physical, social, and mental wellbeing namely, sleep, physical activity, and social interactions and provides intelligent feedback to promote better health. We present the design, implementation and evaluation of BeWell, an automated wellbeing app for the Android smartphones and demonstrate its feasibility in monitoring multi-dimensional wellbeing. By providing a more complete picture of health, BeWell has the potential to empower individuals to improve their overall wellbeing and identify any early signs of decline.

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Bewell: A smartphone application to monitor, model and promote wellbeing

New technologies for encouraging physical activity, healthy diet, and other types of health behavior change now frequently appear in the HCI literature. Yet, how such technologies should be evaluated within the context of HCI research remains unclear. In this paper, we argue that the obvious answer to this question - that evaluations should assess whether a technology brought about the intended change in behavior - is too limited. We propose that demonstrating behavior change is often infeasible as well as unnecessary for a meaningful contribution to HCI research, especially when in the early stages of design or when evaluating novel technologies. As an alternative, we suggest that HCI contributions should focus on efficacy evaluations that are tailored to the specific behavior-change intervention strategies (e.g., self-monitoring, conditioning) embodied in the system and studies that help gain a deep understanding of people's experiences with the technology.

https://faculty.washington.edu/wpratt/Publications/Evaluting%20tech%20for%20behavior%20change-cr-FINAL.pdf

How to evaluate technologies for health behavior change in HCI research

Objective Cardiac rehabilitation (CR) is pivotal in preventing recurring events of myocardial infarction (MI). This study aims to investigate the effect of a smartphone-based home service delivery (Care Assessment Platform) of CR (CAP-CR) on CR use and health outcomes compared with a traditional, centre-based programme (TCR) in post-MI patients. Methods In this unblinded randomised controlled trial, post-MI patients were randomised to TCR (n=60; 55.7±10.4 years) and CAP-CR (n=60; 55.5±9.6 years) for a 6-week CR and 6-month self-maintenance period. CAP-CR, delivered in participants’ homes, included health and exercise monitoring, motivational and educational material delivery, and weekly mentoring consultations. CAP-CR uptake, adherence and completion rates were compared with TCR using intention-to-treat analyses. Changes in clinical outcomes (modifiable lifestyle factors, biomedical risk factors and health-related quality of life) across baseline, 6 weeks and 6 months were compared within, and between, groups using linear mixed model regression. Results CAP-CR had significantly higher uptake (80% vs 62%), adherence (94% vs 68%) and completion (80% vs 47%) rates than TCR (p Conclusions This smartphone-based home care CR programme improved post-MI CR uptake, adherence and completion. The home-based CR programme was as effective in improving physiological and psychological health outcomes as traditional CR. CAP-CR is a viable option towards optimising use of CR services. Trial registration number ANZCTR12609000251224.

https://heart.bmj.com/content/heartjnl/100/22/1770.full.pdf

Smartphone-based home care model improved use of cardiac rehabilitation in postmyocardial infarction patients: results from a randomised controlled trial

The prevalence of lifestyle-related health problems is increasing rapidly. Many of the diseases and health risks could be prevented or alleviated by making changes toward healthier lifestyles. We have developed the Wellness Diary (WD), a concept for personal and mobile wellness management based on Cognitive-Behavioral Therapy (CBT). Two implementations of the concept were made for the Symbian Series 60 (S60) mobile phone platform, and their usability, usage, and acceptance were studied in two 3-month user studies. Study I was related to weight management and study II to general wellness management. In both the studies, the concept and its implementations were well accepted and considered as easy to use and useful in wellness management. The usage rate of the WD was high and sustained at a high level throughout the study. The average number of entries made per day was 5.32 (SD = 2.59, range = 0-14) in study I, and 5.48 (SD = 2.60, range = 0-17) in study II. The results indicate that the WD is well suited for supporting CBT-based wellness management.

Jussi Kaasinen

Papers

Mobile Diary for Wellness Management—Results on Usage and Usability in Two User Studies

Wellness diary for mobile phones

User acceptance of a mobile diary for personal wellness management