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

Beyond “#endpjparalysis”, tackling sedentary behaviour in health care

Reducing sedentary behaviour after hospitalization starts with reducing sedentary behaviour whilst in hospital. Although we have eradicated immobilisation as a therapeutic tool due to its potent detrimental effects, it is still in systemic use within health care systems and hospitals. Evidence shows that when in hospital, patients spend most of their time sedentary. In this editorial, we explore the determinants of, and a system-based approach to, reducing sedentary behaviour in health care

Effects of obesity on walking patterns and adaptability during obstacle crossing

Obesity is a worldwide public health epidemic with no sign of yet abating. Although previous studies have examined the impact of obesity on walking, little is known about the effects of practice on walking patterns in individuals with obesity. The purpose of this current study was to evaluate whether an obstacle-crossing task may detect walking deficits in a group of adults electing to undergo bariatric surgery. With a cross-sectional design, we collected walking parameters as 24 adults (M age= 46.19, SD= 12.90) with obese body mass index (BMI) scores (M BMI= 41.68, SD= 5.80) and 26 adults (M age= 21.88, SD= 3.48) with normal BMI scores (M BMI= 23.09, SD= 4.47) walked in 5 conditions for 5 trials each: on flat ground, crossing over low, medium, and high obstacles, and again on flat ground. The timing and distance of participants' steps were collected with a mechanized gait carpet (GAITRite, Inc.). We conducted 5 (condition) repeated measures (RM) ANOVAs on our main dependent variables, which measured how fast (velocity) and long (step length) participants' steps were and how much time they spent with one (single limb support time) versus two (double limb support time) feet on the ground. The results showed within session improvements in participants' walking patterns. Comparisons of the first and last trials on flat ground showed that participants took longer, faster steps by increasing step length and velocity (ps<.01). They also spent more time with one versus two feet on the ground via increased single limb support time and decreased double limb support time (ps<.001). Our findings suggest that an obstacle-crossing task may help spur improvements in walking patterns even before adults elect to undergo bariatric surgery

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution