ArticleJMIR mHealth and uHealth2021
Open-source Longitudinal Sleep Analysis From Accelerometer Data (DPSleep): Algorithm Development and Validation.
Article in JMIR mHealth and uHealth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Impact of objectively-measured sleep duration on cardiometabolic health: A systematic review of recent evidence.Frontiers in endocrinology · 2022Pooled it
- Clinical Implementation of Wearable-Derived Sleep and Activity Reporting for Inpatient Psychiatric Monitoring.JMIR formative research · 2026Article
- Individual causal effect estimation accounting for latent disease state modification among bipolar participants in mobile health studies.Biometrics · 2026Article
- A Chain mediation model linking experiential avoidance, cognitive flexibility, negative emotions, and sleep quality in university students.Scientific reports · 2026Article
- The correlations between napping and subjective sleep and daytime functioning in Chinese insomnia patients: a large-scale real-world study.BMC psychiatry · 2026Article
- A Dual In-Person and Remote Assessment Approach to Developing Digital End Points Relevant to Autism and Co-Occurring Conditions: Protocol for a Multisite Observational Study.JMIR research protocols · 2025Article
- Sleep Quality Assessment in Intensive Care Units: Comparing Actigraphy and the Richards Campbell Sleep Questionnaire-A Pilot Study in the Moroccan Context.Clocks & sleep · 2025Article
- Digital health technologies in the accelerating medicines Partnership® Schizophrenia Program.Schizophrenia (Heidelberg, Germany) · 2025Article
- Personalized prediction of negative affect in individuals with serious mental illness followed using long-term multimodal mobile phenotyping.Translational psychiatry · 2025Article
- A systematic review of passive data for remote monitoring in psychosis and schizophrenia.NPJ digital medicine · 2025Article
- Age- and sex-related differences in sleep patterns and their relations to self-reported sleep and mood.Sleep advances : a journal of the Sleep Research Society · 2025Article
- A narrative review on in-flight use of consumer sleep technologies for aviation research.Sleep advances : a journal of the Sleep Research Society · 2025Review
- Precision Assessment of Real-World Associations Between Stress and Sleep Duration Using Actigraphy Data Collected Continuously for an Academic Year: Individual-Level Modeling Study.JMIR formative research · 2024Article
- Intensive longitudinal assessment of mobility, social activity and loneliness in individuals with severe mental illness during COVID-19.Schizophrenia (Heidelberg, Germany) · 2023Article
- Linguistic and non-linguistic markers of disorganization in psychotic illness.Schizophrenia research · 2023Article
- Predicting states of elevated negative affect in adolescents from smartphone sensors: a novel personalized machine learning approach.Psychological medicine · 2023Article
- Defining the Digital Measurement of Scratching During Sleep or Nocturnal Scratching: Review of the Literature.Journal of medical Internet research · 2023Review
- Fluctuations in behavior and affect in college students measured using deep phenotyping.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundWearable devices are now widely available to collect continuous objective behavioral data from individuals and to measure sleep.
objectiveThis study aims to introduce a pipeline to infer sleep onset, duration, and quality from raw accelerometer data and then quantify the relationships between derived sleep metrics and other variables of interest.
methodsThe pipeline released here for the deep phenotyping of sleep, as the DPSleep software package, uses a stepwise algorithm to detect missing data; within-individual, minute-based, spectral power percentiles of activity; and iterative, forward-and-backward-sliding windows to estimate the major Sleep Episode onset and offset. Software modules allow for manual quality control adjustment of the derived sleep features and correction for time zone changes. In this paper, we have illustrated the pipeline with data from participants studied for more than 200 days each.
resultsActigraphy-based measures of sleep duration were associated with self-reported sleep quality ratings. Simultaneous measures of smartphone use and GPS location data support the validity of the sleep timing inferences and reveal how phone measures of sleep timing can differ from actigraphy data.
conclusionsWe discuss the use of DPSleep in relation to other available sleep estimation approaches and provide example use cases that include multi-dimensional, deep longitudinal phenotyping, extended measurement of dynamics associated with mental illness, and the possibility of combining wearable actigraphy and personal electronic device data (eg, smartphones and tablets) to measure individual differences across a wide range of behavioral variations in health and disease. A new open-source pipeline for deep phenotyping of sleep, DPSleep, analyzes raw accelerometer data from wearable devices and estimates sleep onset and offset while allowing for manual quality control adjustments.
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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.