ArticleSleep medicine2021
Digital phenotyping of sleep patterns among heterogenous samples of Latinx adults using unsupervised learning.
Article in Sleep medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Digital Phenotyping in Health Research: Scoping Review of Methods, Gaps, and Opportunities.Journal of medical Internet research · 2026Article
- Unsupervised Learning Identifies Sleep Disturbance Subtypes Among Dementia Caregivers.Nursing research · 2026Article
- Exploring the predictive capacity of smartphone-based digital phenotyping to monitor pain and physical quality of life in advanced cancer patients, family caregivers, and dyads.Frontiers in pain research (Lausanne, Switzerland) · 2026Article
- Characterizing daily physical activity patterns with unsupervised learning via functional mixture models.Journal of behavioral medicine · 2025Article
- A scoping review of the clinical application of machine learning in data-driven population segmentation analysis.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review.Sensors (Basel, Switzerland) · 2023Review
- Monitoring sleep using smartphone data in a population of college students.Npj mental health research · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveThis study aimed to identify sleep disturbance subtypes ("phenotypes") among Latinx adults based on objective sleep data using a flexible unsupervised machine learning technique.
methodsThis study was an analysis of sleep data from three cross-sectional studies of the Precision in Symptom Self-Management Center at Columbia University. All studies focused on sleep health in Latinx adults at increased risk for sleep disturbance. Data on total sleep time (TST), time in bed (TIB), wake after sleep onset (WASO), sleep efficiency (SE), number of awakenings (NOA) and the mean length of nightly awakenings were collected using wrist-mounted accelerometers. Cluster analysis of the sleep data was conducted using an unsupervised machine learning approach that relies on mixtures of multivariate generalized linear mixed models.
resultsThe analytic sample included 494 days of data from 118 adults (Ages 19-77). A 3-cluster model provided the best fit based on deviance indices (ie, DΔ∼ -75 and -17 from 1- and 2- to 3-cluster models, respectively) and likelihood ratio (P
conclusionRobust digital data-driven modeling approaches can be useful for detecting sleep phenotypes from heterogenous patient populations, and have implications for designing precision sleep health strategies for management and early detection of sleep problems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.