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Last Posted: Dec 09, 2022
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Association of wearable device-measured vigorous intermittent lifestyle physical activity with mortality
E Stamatakis et al, Nature Medicine, December 8, 2022

Here, we examined the association of VILPA with all-cause, cardiovascular disease (CVD) and cancer mortality in 25,241 nonexercisers (mean age 61.8?years, 14,178 women/11,063 men) in the UK Biobank. Over an average follow-up of 6.9?years, during which 852 deaths occurred, VILPA was inversely associated with all three of these outcomes in a near-linear fashion. Compared with participants who engaged in no VILPA, participants who engaged in VILPA at the sample median VILPA frequency of 3?length-standardized bouts per day (lasting 1 or 2?min each) showed a 38%–40% reduction in all-cause and cancer mortality risk and a 48%–49% reduction in CVD mortality risk.

Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction
ZI Attoa et al, Nature Medicine, November 14, 2022

Although artificial intelligence (AI) algorithms have been shown to be capable of identifying cardiac dysfunction, defined as ejection fraction (EF) = 40%, from 12-lead electrocardiograms (ECGs), identification of cardiac dysfunction using the single-lead ECG of a smartwatch has yet to be tested. We digitally enrolled 2,454 unique patients from 46 US states and 11 countries, who sent 125,610 ECGs to the data platform between August 2021 and February 2022; 421 participants had at least one watch-classified sinus rhythm ECG within 30?d of an echocardiogram, of whom 16 (3.8%) had an EF?=?40%. The AI algorithm detected patients with low EF with an area under the curve of 0.885.

Multimodal machine learning in precision health: A scoping review
A Kline et al, NPJ Digital Medicine, November 7, 2022

Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research.

Unsettled Liability Issues for "Prediagnostic" Wearables and Health-Related Products.
Simon David A et al. JAMA 2022 9

Prediagnostic products and other health-related applications are bringing exciting technologies directly to consumers and mesh well with the goal of meeting patients “where they live,” sometimes literally. But these products also present a context that is rife with legal uncertainty

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Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.

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