Pilot Validation of Ambulatory Activity Monitors for Sleep Measurement in Huntington’s Disease Gene Carriers

Maskevich, Svetlana; Jumabhoy, R; Dao, Pierre D.M.; Stout, Julie C.; Drummond, Sean P.A.

doi:10.3233/jhd-170251

Cited by 23 publications

(28 citation statements)

References 31 publications

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“…Contradictory results indicating overestimation of total sleep time by activity monitors in seven HD patients was reported in the study of Maskevich et al (89), but the subjects in the analyzed sample were in the presymptomatic and early phase of the disease and had very little chorea. Those discrepancies indicate the need for caution in the interpretation of results obtained in studies of sleep and circadian rhythms in HD patients using activity monitors.…”

Section: Methods For Diagnosing Sleep Disorders In Hdsupporting

confidence: 55%

Sleep Disorders in Huntington’s Disease

Herzog-Krzywoszańska

Krzywoszański

2019

Front. Psychiatry

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Huntington’s chorea (Huntington’s disease, HD) is a genetic disorder caused by autosomal dominant mutation, leading to progressive neurodegenerative changes in the central nervous system. Involuntary movements such as chorea occur typically in HD patients, accompanied by progressive cognitive and psychiatric disturbances. Other common symptoms of HD are circadian and sleep abnormalities, which are observed from the earliest stages of the disease or even before the occurrence of clinical symptoms. The most common sleep problems reported by HD patients include insomnia, difficulties in falling asleep, frequent nocturnal awakenings, and excessive daytime sleepiness. Also, specific changes in sleep architecture have been identified in HD. In this paper, we review studies on sleep and circadian rhythm disorders in HD. We outline findings concerning sleep patterns and disturbances of circadian rhythms in HD patients, as well as the role of psychiatric disorders and motor disorders in HD patients’ sleep problems. We also discuss problems related to the different methods of diagnosing sleep disorders in HD. Furthermore, the adverse effects of medication used for the treatment of core HD symptoms as one of the sources of sleep disturbances in HD are emphasized. In conclusion, the diversity and complexity of the determinants of sleep and circadian rhythm disorders in HD are highlighted. Finally, the relevance of effective treatment to improve patients’ functioning and quality of life as well as the potential relief of their cognitive and emotional symptoms is addressed.

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Section: Methods For Diagnosing Sleep Disorders In Hdsupporting

confidence: 55%

Sleep Disorders in Huntington’s Disease

Herzog-Krzywoszańska

Krzywoszański

2019

Front. Psychiatry

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“…Tables 1 and 2 present the extracted details of each qualifying study involving nonsleep-staging and sleep-staging Fitbit models. Participants were diverse: normal sleepers as well as persons diagnosed with periodic limb movement in sleep (PLMS) [28], obstructive sleep apnea, sleep-disordered breathing [30], central disorders of hypersomnolence [26], insomnia [31], and depression [25], and Huntington disease gene carriers [38]. Sample size varied substantially between investigations, from 7 to 63 (median 30) participants, with approximately 77% of them involving more than 20 individuals.…”

Section: Resultsmentioning

confidence: 99%

“…A total of 7 studies (N=197) involved epoch-by-epoch (EBE) investigation of nonsleep-staging Fitbit models with reference to PSG in differentiating between sleep and wake state [25,27,31,38-41]. Out of these 7 studies, 1 consisted of two different samples, thereby increasing the total number of evaluations to 8.…”

Section: Resultsmentioning

confidence: 99%

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Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis

et al. 2019

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BackgroundWearable sleep monitors are of high interest to consumers and researchers because of their ability to provide estimation of sleep patterns in free-living conditions in a cost-efficient way.ObjectiveWe conducted a systematic review of publications reporting on the performance of wristband Fitbit models in assessing sleep parameters and stages.MethodsIn adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, we comprehensively searched the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane, Embase, MEDLINE, PubMed, PsycINFO, and Web of Science databases using the keyword Fitbit to identify relevant publications meeting predefined inclusion and exclusion criteria.ResultsThe search yielded 3085 candidate articles. After eliminating duplicates and in compliance with inclusion and exclusion criteria, 22 articles qualified for systematic review, with 8 providing quantitative data for meta-analysis. In reference to polysomnography (PSG), nonsleep-staging Fitbit models tended to overestimate total sleep time (TST; range from approximately 7 to 67 mins; effect size=-0.51, P<.001; heterogenicity: I2=8.8%, P=.36) and sleep efficiency (SE; range from approximately 2% to 15%; effect size=-0.74, P<.001; heterogenicity: I2=24.0%, P=.25), and underestimate wake after sleep onset (WASO; range from approximately 6 to 44 mins; effect size=0.60, P<.001; heterogenicity: I2=0%, P=.92) and there was no significant difference in sleep onset latency (SOL; P=.37; heterogenicity: I2=0%, P=.92). In reference to PSG, nonsleep-staging Fitbit models correctly identified sleep epochs with accuracy values between 0.81 and 0.91, sensitivity values between 0.87 and 0.99, and specificity values between 0.10 and 0.52. Recent-generation Fitbit models that collectively utilize heart rate variability and body movement to assess sleep stages performed better than early-generation nonsleep-staging ones that utilize only body movement. Sleep-staging Fitbit models, in comparison to PSG, showed no significant difference in measured values of WASO (P=.25; heterogenicity: I2=0%, P=.92), TST (P=.29; heterogenicity: I2=0%, P=.98), and SE (P=.19) but they underestimated SOL (P=.03; heterogenicity: I2=0%, P=.66). Sleep-staging Fitbit models showed higher sensitivity (0.95-0.96) and specificity (0.58-0.69) values in detecting sleep epochs than nonsleep-staging models and those reported in the literature for regular wrist actigraphy.ConclusionsSleep-staging Fitbit models showed promising performance, especially in differentiating wake from sleep. However, although these models are a convenient and economical means for consumers to obtain gross estimates of sleep parameters and time spent in sleep stages, they are of limited specificity and are not a substitute for PSG.

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“…Results of an online survey of juvenile Huntington's disease (HD) suggests that disrupted sleep is the most prevalent common, unrecognized symptom (87%), followed by periodic limb movements, tics, and pain ( 36 , 37 ). HD gene carriers complain about sleep problems, both in terms of sleep quality as well as excessive daytime sleepiness ( 38 , 39 ). Sleep complaints seem to be associated with neuropathology and neuropsychiatric symptoms in HD ( 40 ).…”

Section: Movement Disorders and Rating Scalesmentioning

confidence: 99%

Rating Scales for Movement Disorders With Sleep Disturbances: A Narrative Review

et al. 2018

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Introduction: In recent years, a wide variety of rating scales and questionnaires for movement disorders have been developed and published, making reviews on their contents, and attributes convenient for the potential users. Sleep disorders are frequently present in movement disorders, and some movement disorders are accompanied by specific sleep difficulties.Aim: The aim of this study is to perform a narrative review of the most frequently used rating scales for movement disorders with sleep problems, with special attention to those recommended by the International Parkinson and Movement Disorders Society.Methods: Online databases (PubMed, SCOPUS, Web of Science, Google Scholar), related references from papers and websites and personal files were searched for information on comprehensive or global rating scales which assessed sleep disturbances in the following movement disorders: akathisia, chorea, dystonia, essential tremor, myoclonus, multiple system atrophy, Parkinson's disease, progressive supranuclear palsy, and tics and Tourette syndrome. For each rating scale, its objective and characteristics, as well as a summary of its psychometric properties and recommendations of use are described.Results: From 22 rating scales identified for the selected movement disorders, only 5 included specific questions on sleep problems. Movement Disorders Society-Unified Parkinson's Disease Rating scale (MDS-UPDRS), Non-Motor Symptoms Scale and Questionnaire (NMSS and NMSQuest), Scales for Outcomes in Parkinson's Disease (SCOPA)-Autonomic and Progressive Supranuclear Palsy Rating Scale (PSPRS) were the only rating scales that included items for assessing sleep disturbances.Conclusions: Despite sleep problems are frequent in movement disorders, very few of the rating scales addresses these specific symptoms. This may contribute to an infra diagnosis and mistreatment of the sleep problems in patients with movement disorders.

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Pilot Validation of Ambulatory Activity Monitors for Sleep Measurement in Huntington’s Disease Gene Carriers

Cited by 23 publications

References 31 publications

Sleep Disorders in Huntington’s Disease

Sleep Disorders in Huntington’s Disease

Accuracy of Wristband Fitbit Models in Assessing Sleep: Systematic Review and Meta-Analysis

Rating Scales for Movement Disorders With Sleep Disturbances: A Narrative Review

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