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O2‐07‐04: Efficacy of internet‐based training of clinicians to implement an evidence‐based intervention for dementia caregivers
Author(s) -
Mittelman Mary S.,
Epstein Cynthia,
Hobday John V.
Publication year - 2015
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2015.07.168
Subject(s) - psychological intervention , dementia , intervention (counseling) , test (biology) , certification , randomized controlled trial , medicine , nursing , psychology , disease , paleontology , surgery , pathology , political science , law , biology
quality in SNF patients with dementia is frequently accomplished by validated subjective assessment tools (Pittsburg Sleep Quality Index [PSQI]) or wearable technology (wrist actigraphy). Subjective assessment is time consuming, and wearable technology on OAsD could lead to logistical and safety issues due to well-known neuropsychiatric symptomology including disinhibition, hallucinations, and aggression. The purpose of this study was to investigate whether a non-wearable sensor system could detect sleep efficiency without the use of wearable technology. Methods: We installed sleep sensor systems in the bedrooms of 10 older adults with moderately severe dementia residing in a dementia special care unit (Age M 1⁄4 84.30, SD 1⁄4 8.58; FAST M 1⁄4 6.00; SD 1⁄4.47). Sixty-three days of continuous sleep efficiency data were obtained from the system and converted to nine data bins (63 days / 7 days1⁄4 9), or 9 repeated measures. Bins represented each older adult’s average weekly sleep efficiency score. Lower efficiency scores (range 0-100) indicate higher levels of restlessness while in bed. Individual growth curve (IGC) and bootstrap resampling (10,000 simulations) techniques were applied. Monthly subjective measures of sleep quality were also collected for comparison using the PSQI. Results: Sleep efficiency showed a significant linear effect over time, F(1, 80) 1⁄4 4.94, p 1⁄4 .023, indicating efficiency temporally decreased. Variance of the random intercepts was also significant, Var(u0j) 1⁄4 .002, p 1⁄4 .001, showing efficiency scores varied significantly across OAsD at baseline. A large association between efficiency and PSQI data was also observed (r 1⁄4 -.53, 95% CI 1⁄4 -.85, -.12). As sleep efficiency decreased, PSQI scores increased, further suggesting poorer sleep quality. Conclusions: The empirical measurement of sleep quality is a vital area of study so that care providers can efficiently and accurately identify and treat sleep problems and disorders in OAsD residing in SNFs. The use of non-wearable sleep sensor technology with this population, or other populations unable to tolerate wearable sleep technology, appears warranted and worthy of future investigation.