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Assessing sleep in primary brain tumor patients using smart wearables and patient-reported data: Feasibility and interim analysis of an observational study. | LitMetric

AI Article Synopsis

  • Sleep-wake disturbances are prevalent among primary brain tumor patients, and this study explores the relationship between patient-reported outcomes (PROs) and data from smart wearables like Fitbits over a 4-week period.
  • Fifty-four patients participated, wearing Fitbits to track their sleep patterns and completing various PROs at the start and end of the study, showing high compliance and reliability in the data collected.
  • Results indicated that while some patients experienced significant improvements in sleep disturbance, a notable correlation was found between wake after sleep onset and sleep disturbance, highlighting a need for further research into sleep mechanics in this patient population.

Article Abstract

Background: Sleep-wake disturbances are common and disabling in primary brain tumor (PBT) patients but studies exploring longitudinal data are limited. This study investigates the feasibility and relationship between longitudinal patient-reported outcomes (PROs) and physiologic data collected via smart wearables.

Methods: Fifty-four PBT patients ≥ 18 years wore Fitbit smart-wearable devices for 4 weeks, which captured physiologic sleep measures (eg, total sleep time, wake after sleep onset [WASO]). They completed PROs (sleep hygiene index, PROMIS sleep-related impairment [SRI] and Sleep Disturbance [SD], Morningness-Eveningness Questionnaire [MEQ]) at baseline and 4 weeks. Smart wearable use feasibility (enrollment/attrition, data missingness), clinical characteristics, test consistency, PROs severity, and relationships between PROs and physiologic sleep measures were assessed.

Results: The majority (72%) wore their Fitbit for the entire study duration with 89% missing < 3 days, no participant withdrawals, and 100% PRO completion. PROMIS SRI/SD and MEQ were all consistent/reliable (Cronbach's alpha 0.74-0.92). Chronotype breakdown showed 39% morning, 56% intermediate, and only 6% evening types. Moderate-severe SD and SRI were reported in 13% and 17% at baseline, and with significant improvement in SD at 4 weeks ( = .014). Fitbit-recorded measures showed a correlation at week 4 between WASO and SD ( = 0.35,  = .009) but not with SRI ( = 0.24,  = .08).

Conclusions: Collecting sleep data with Fitbits is feasible, PROs are consistent/reliable, > 10% of participants had SD and SRI that improved with smart wearable use, and SD was associated with WASO. The skewed chronotype distribution, risk and impact of sleep fragmentation mechanisms warrant further investigation.

Trial Registration: NCT04 669 574.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11398942PMC
http://dx.doi.org/10.1093/nop/npae048DOI Listing

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