Article

Actigraphic assessment of a polysomnographic‐recorded nap: a validation study

University of California, San Diego - Department of Psychiatry Veterans Affairs, San Diego, CA, USA.
Journal of Sleep Research (Impact Factor: 2.95). 03/2011; 20(1 Pt 2):214-22. DOI: 10.1111/j.1365-2869.2010.00858.x
Source: PubMed

ABSTRACT This study aimed to determine if actigraphy could differentiate sleep and wake during a daytime nap and no-nap rest period. Fifty-seven subjects participated in the study; 30 subjects were in the nap group and the remaining 27 in the no-nap comparison group. All subjects wore actigraphs while simultaneously undergoing polysomnography (PSG). Three actigraphic sensitivity levels (high, medium, low) and two interval duration minimums (15 and 40 min) were used to score the nap and no-nap data. The variables examined included total sleep time (TST), sleep latency (SL), wake after sleep onset (WASO) and sleep efficiency (SE). The Bland-Altman technique was used to determine concordance. Epoch-by-epoch analysis examined actigraphic accuracy, sensitivity and specificity. For the naps, all actigraph settings except low-40 showed significant correlations with TST. The high and medium settings predicted SE significantly and the high settings predicted SL significantly. Bland-Altman analyses demonstrated high settings overestimated TST while high and medium settings overestimated SE. Overall, for the nap condition accuracy for the actigraph was 82-86%, sensitivity was 92-96% and specificity was 40-67%. In the no-nap condition, accuracy for the actigraph was 60-84%, sensitivity was 47-78% and specificity was 60-86%. Medium-40 and low-40 were the only settings that did not misidentify sleep in the no-nap condition. These results suggest that actigraphy can predict TST, SE and SL reliably, depending upon parameter settings, and actigraphy is a highly sensitive but not specific measure for daytime naps. Different actigraphy settings may be optimal depending upon the variables of interest. Discrimination of sleep and wake during periods of waking quiescence is not as robust as during periods of mainly daytime sleep.

Full-text

Available from: Sara Mednick, May 28, 2015
0 Followers
 · 
117 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: An automated wireless system (WS) for sleep monitoring was recently developed and validated for assessing nighttime sleep. Here, we aimed to evaluate the validity of the WS to correctly monitor daytime sleep during naps compared to polysomnography (PSG). We found that the WS underestimated wake, sleep onset latency, wake after sleep onset, and overestimated total sleep time, sleep efficiency and duration of REM sleep. Sensitivity was moderate for wake (58.51%) and light sleep (66.92%) and strong for deep sleep (83.46%) and REM sleep (82.12%). These results demonstrated that the WS had a low ability to detect wake and systematically over-scored REM sleep, implicating the WS as an inadequate substitute for PSG in diagnosing sleep disorders or for research in which sleep staging is essential.
    Behavioral Sleep Medicine 02/2015; 13(2):157-168. DOI:10.1080/15402002.2013.845782 · 1.56 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Sleep-wake disturbances and concomitant cognitive dysfunction in Parkinson's disease (PD) contribute significantly to morbidity in patients and their carers. Subjectively reported daytime sleep disturbance is observed in over half of all patients with PD and has been linked to executive cognitive dysfunction. The current study used daytime actigraphy, a novel objective measure of napping and related this to neuropsychological performance in a sample of PD patients and healthy, age and gender-matched controls. Furthermore this study aimed to identify patients with PD who may benefit from pharmacologic and behavioural intervention to improve these symptoms. Eighty-five PD patients and 21 healthy, age-matched controls completed 14 days of wrist actigraphy within two weeks of neuropsychological testing. Objective napping measures were derived from actigraphy using a standardised protocol and subjective daytime sleepiness was recorded by the previously validated Epworth Sleepiness Scale. Patients with PD had a 225% increase in the mean nap time per day (minutes) as recorded by actigraphy compared to age matched controls (39.2 ± 35.2 vs. 11.5 ± 11.0 minutes respectively, p < 0.001). Significantly, differences in napping duration between patients, as recorded by actigraphy were not distinguished by their ratings on the subjective measurement of excessive daytime sleepiness. Finally, those patients with excessive daytime napping showed greater cognitive deficits in the domains of attention, semantic verbal fluency and processing speed. This study confirms increased levels of napping in PD, a finding that is concordant with subjective reports. However, subjective self-report measures of excessive daytime sleepiness do not robustly identify excessive napping in PD. Fronto-subcortical cognitive dysfunction was observed in those patients who napped excessively. Furthermore, this study suggests that daytime actigraphy, a non-invasive and inexpensive objective measure of daytime sleep, can identify patients with PD who may benefit from pharmacologic and behavioural interventions to improve these symptoms.
    PLoS ONE 11/2013; 8(11):e81233. DOI:10.1371/journal.pone.0081233 · 3.53 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Abstract Objective: The rapid growth and evolution of health-related technology capabilities are driving an established presence in the marketplace and are opening up tremendous potential to minimize and/or mitigate barriers associated with achieving optimal health, performance, and readiness. This article summarizes technology-based strategies that promote healthy habits related to physical activity, nutrition, and sleep. Materials and Methods: The Telemedicine and Advanced Technology Research Center convened a workshop titled "Leveraging Technology: Creating & Sustaining Changes for Health" (May 29-30, 2013, Fort Detrick, MD). Participants included experts from academia (n=3), government (n=33), and industry (n=16). A modified Delphi method was used to establish expert consensus in six topic areas: (1) physical activity, (2) nutrition, (3) sleep, (4) incentives for behavior change, (5) usability/interoperability, and (6) mobile health/open platform. Results: Overall, 162 technology features, constructs, and best practices were reviewed and prioritized for physical activity monitors (n=29), nutrition monitors (n=35), sleep monitors (n=24), incentives for change (n=36), usability and interoperability (n=25), and open data (n=13). Conclusions: Leading practices, gaps, and research needs for technology-based strategies were identified and prioritized. This information can be used to provide a research and development road map for (1) leveraging technology to minimize barriers to enhancing health and (2) facilitating evidence-based techniques to create and sustain healthy behaviors.
    Telemedicine and e-Health 06/2014; 20(9). DOI:10.1089/tmj.2013.0328 · 1.54 Impact Factor