Daily self-reported and automatically generated smartphone-based sleep measurements in patients with newly diagnosed bipolar disorder, unaffected first-degree relatives and healthy control individuals.
adult psychiatry
depression & mood disorders
Journal
Evidence-based mental health
ISSN: 1468-960X
Titre abrégé: Evid Based Ment Health
Pays: England
ID NLM: 100883413
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
09
03
2020
revised:
25
06
2020
accepted:
30
06
2020
pubmed:
26
8
2020
medline:
19
8
2021
entrez:
26
8
2020
Statut:
ppublish
Résumé
(1) To investigate daily smartphone-based self-reported and automatically generated sleep measurements, respectively, against validated rating scales; (2) to investigate if daily smartphone-based self-reported sleep measurements reflected automatically generated sleep measurements and (3) to investigate the differences in smartphone-based sleep measurements between patients with bipolar disorder (BD), unaffected first-degree relatives (UR) and healthy control individuals (HC). We included 203 patients with BD, 54 UR and 109 HC in this study. To investigate whether smartphone-based sleep calculated from self-reported bedtime, wake-up time and screen on/off time reflected validated rating scales, we used the Pittsburgh Sleep Quality Index (PSQI) and sleep items on the Hamilton Depression Rating Scale 17-item (HAMD-17) and the Young Mania Rating Scale (YMRS). (1) Self-reported smartphone-based sleep was associated with the PSQI and sleep items of the HAMD and the YMRS. (2) Automatically generated smartphone-based sleep measurements were associated with daily self-reports of hours slept between 12:00 midnight and 06:00. (3) According to smartphone-based sleep, patients with BD slept less between 12:00 midnight and 06:00, with more interruption and daily variability compared with HC. However, differences in automatically generated smartphone-based sleep were not statistically significant. Smartphone-based data may represent measurements of sleep patterns that discriminate between patients with BD and HC and potentially between UR and HC. Detecting sleep disturbances and daily variability in sleep duration using smartphones may be helpful for both patients and clinicians for monitoring illness activity. clinicaltrials.gov (NCT02888262).
Identifiants
pubmed: 32839276
pii: ebmental-2020-300148
doi: 10.1136/ebmental-2020-300148
pmc: PMC10231580
doi:
Banques de données
ClinicalTrials.gov
['NCT02888262']
Types de publication
Comparative Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
146-153Informations de copyright
© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: LVK and MV has been a consultant for Lundbeck within recent 3 years. JEB and MF are co-founders and shareholders of Monsenso ApS.
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