ArticleSleep2021
A multitrait, multimethod matrix approach for a consumer-grade wrist-worn watch measuring sleep duration and continuity.
Article in Sleep, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Measuring Sleep Stages and Screening for Obstructive Sleep Apnea with a Wearable Multi-Sensor System in Comparison to Polysomnography.Nature and science of sleep · 2023Article
- Predictive Modeling of Mental Illness Onset Using Wearable Devices and Medical Examination Data: Machine Learning Approach.Frontiers in digital health · 2022Article
- Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data.NPJ digital medicine · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesWe examined associations between self-reports about typical sleep patterns and sleep data derived from a wearable device worn on a nightly basis for a prolonged period (mean = 214 nights). We hypothesized that sleep characteristics would correlate better across different methods of assessment (self-report versus wearable) than they would correlate within the same method, a classic psychometric approach (multitrait, multimethod matrix).
methodsA cross-national sample of 6,230 adult wearable users completed a brief sleep questionnaire collecting data on sleep duration and number of awakenings (NAW) and provided informed consent to link their responses to data from their wearable watches. The data collection for the wearable occurred over 12 months and the sleep questionnaire was completed subsequent to that.
resultsResults indicated a large (r = .615) correlation between sleep duration as assessed with the wearable and by self-report. A medium-to-large correlation (r = .406) was also seen for NAW. The multitrait, multimethod matrix suggested minimal method variance, i.e. similar "traits" (sleep duration and NAW) correlated across methods but within a given method, and such "traits" were generally unrelated.
conclusionsThe results suggest that the longer period of data collection with the wearable generates more stable estimates of sleep than have been reported in most studies of actigraphy. Alternatively, the data might imply that individuals modify their self-reports about sleep via daily feedback to align their perceptions to the output of the wearable.
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