The Rise of Consumer Health Wearables: Promises and Barriers

By L. Piwek, D. Ellis, Sally Andrews, Adam N. Joinson. PLoS Medicine, 2016

1,110 citations recorded by the source corpus.

Lukasz Piwek and colleagues consider whether wearable technology can become a valuable asset for health care.

Subfields: General Health Professions, Marketing, Physiology

View Paper: https://www.semanticscholar.org/paper/f4b4ea6ec74f436ce38aeb832fb1f3df687d30e0

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Citations in the corpus

Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning (2017, background): “Their use is higher among those already motivated to keep a watchful eye over their health, and many people abandon using them soon after purchase (Piwek et al. 2016).”
Statistical physics of vaccination (2016, background): “Furthermore, the miniaturization of devices created a wide range of wearable able to measure our interactions, movements, as well as vital signs [639].”
A Critical Review of Consumer Wearables, Mobile Applications, and Equipment for Providing Biofeedback, Monitoring Stress, and Sleep in Physically Active Populations (2018, background): “Various academic reviews have summarized existing technologies (Duking et al., 2016; Halson et al., 2016; Piwek et al., 2016; Baron et al., 2017).”
Seven HCI Grand Challenges (2019, background): “…of patients who will bring wearable data to their medical consultation appointments, and (ii) the high potential for errors, when patients without medical training attempt to interpret symptoms based on data stemming from devices that may be unreliable (Piwek, Ellis, Andrews, & Joinson, 2016).”
Effect of Wearable Technology Combined With a Lifestyle Intervention on Long-term Weight Loss: The IDEA Randomized Clinical Trial. (2016, neutral): “Median (25th-75th percentile) 5 (3-8) 5 (3-8) 5 (3-8)”
Enabling Technologies for the Internet of Health Things (2018, background): “It is predicted that 110 million of wearable devices will be sold by 2018 [116].”
The generalizability crisis (2019, background): “…routines that we carry with us for large swathes of the day—that psychologists are utilizing to predict a variety of outcomes, from social interaction, personality, mood, to general health (Davidson, 2020; Ellis, 2020; Miller, 2012; Piwek et al., 2016; Harari et al., 2019; Stachl et al., 2020).”
Deep learning for healthcare: review, opportunities and challenges (2018, positive): “Potentially, these devices could give patients direct access to personal analytics that can contribute to their health, facilitate preventive care and aid in the management of ongoing illness [100].”