Is It Possible to Predict Heart Failure Risk Through a Wearable Sensor?

Half of community-based elderly adults who have heart failure, which is common among this age group, typically do not live past the 5-year mark if their condition required hospitalization at some point. Is there a way to better predict who is at risk for heart failure among them? Study results published in Nature and Science of Sleep indicate this may be possible.

Previous research has linked sleep disturbance—also common among elderly individuals—as measured by human rest/activity patterns to heart failure. The authors of the current study investigated the effect that sustained rest/activity could have on risk of incident heart failure in community-based elderly adults, citing a “paucity of studies on the longitudinal role of objectively measured sleep function.” They used actigraphic recordings to collect these data.

Participants included 1099 community-based elderly adults, all participating in the Rush Memory and Aging Project. Their mean (SD) age at baseline was 80.7 (7.4) years (range, 56-100), and 76% were female.

Eighty study participants developed heart failure, and their results suggest a link between increased rest fragmentation and higher risk for incident heart failure. Frequent arousals from sleep, sympathetic surges, higher blood pressure, and repetitive low oxygen levels all contribute to this risk.

This connection was especially apparent among elderly patients whose rest fragmentation fell into the 90th percentile, which upped their risk of developing incident heart failure by 57% compared with those at the 10th percentile. “This effect was equivalent to that of being over a decade older,” the authors noted.

All study enrollees wore a professional-grade watch-like activity monitor for up to 11 days on their nondominant wrist, from which rest/activity data were collected for 9.3 (0.9) days. There was a yearly follow-up for 6.1 (3.6) years (range, 1-14). Their degree of heart failure was assessed at each of their follow-up visits, and 3 Cox proportional hazards models were used “to examine the relationship between rest fragmentation index and incident heart failure”:

• Model A adjusted for total daily physical activity
• Model B adjusted for lifestyle factors and comorbidities (eg, insomnia, alcohol use, anxiety, etc)
• Model C adjusted for baseline cardiovascular risk and disease variables

Additional study results from Model A show that for every 1 SD increase in rest fragmentation, the equivalent HR was 1.26 (95% CI, 1.06-1.14; P = .01), or being 5.2 years older. Model B was not associated with a similar risk relationship. However, with Model C, rest fragmentation and risk for incident heart failure retained their relationship of a higher HR for every 1 SD increase (HR, 1.21; 95% CI, 1.01-1.45; P = .037).

“This study presents evidence that rest fragmentation is independently associated with the development of incident heart failure during 14 years of follow-up,” the study authors noted.

They point to the importance of ongoing investigations into how several factors affect heart function in elderly individuals, namely daytime naps vs nightly sleeping, underlying autonomic conditions, and the cardio-dynamic pathway. Updated wearable sleep monitors are also necessary to “better distinguish the relationship between sleep, rest period, and risk for heart failure.”

Reference

Gao L, Lim ASP, Wong PM, et al. Fragmentation of rest/activity patterns in community-based elderly individuals predicts incident heart failure. Nat Sci Sleep. 2020;12:299-307. doi:10.2147/NSS.S253757