ArticleJournal of medical Internet research2022
Tracking Subjective Sleep Quality and Mood With Mobile Sensing: Multiverse Study.
Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
- Smartphone-Based Grading and Rehabilitation in Patients With Facial Palsy Using Computer Vision: Prospective Validation Study.JMIR rehabilitation and assistive technologies · 2026Article
- Comparing personalized and population-based models for predicting momentary negative affect in internalizing disorders: A digital phenotyping study.Neuroscience applied · 2026Article
- Digital Monitoring of Micro- and Macro-Movement Regularity in Psychiatric Inpatients With Depression.Psychiatry investigation · 2026Article
- A psycho-ecological signal recognition framework for user behavior prediction on digital media platforms.PloS one · 2025Article
- A template and tutorial for preregistering studies using passive smartphone measures.Behavior research methods · 2024Article
- Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study.Journal of medical Internet research · 2024Article
- Predicting Mood Based on the Social Context Measured Through the Experience Sampling Method, Digital Phenotyping, and Social Networks.Administration and policy in mental health · 2024Article
- Personalized interpretable prediction of perceived sleep quality: Models with meaningful cardiovascular and behavioral features.PloS one · 2024Article
- Association Between the Severity of Depressive Symptoms and Human-Smartphone Interactions: Longitudinal Study.JMIR formative research · 2023Article
- Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review.Sensors (Basel, Switzerland) · 2022Review
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSleep influences moods and mood disorders. Existing methods for tracking the quality of people's sleep are laborious and obtrusive. If a method were available that would allow effortless and unobtrusive tracking of sleep quality, it would mark a significant step toward obtaining sleep data for research and clinical applications.
objectiveOur goal was to evaluate the potential of mobile sensing data to obtain information about a person's sleep quality. For this purpose, we investigated to what extent various automatically gathered mobile sensing features are capable of predicting (1) subjective sleep quality (SSQ), (2) negative affect (NA), and (3) depression; these variables are associated with objective sleep quality. Through a multiverse analysis, we examined how the predictive quality varied as a function of the selected sensor, the extracted feature, various preprocessing options, and the statistical prediction model.
methodsWe used data from a 2-week trial where we collected mobile sensing and experience sampling data from an initial sample of 60 participants. After data cleaning and removing participants with poor compliance, we retained 50 participants. Mobile sensing data involved the accelerometer, charging status, light sensor, physical activity, screen activity, and Wi-Fi status. Instructions were given to participants to keep their smartphone charged and connected to Wi-Fi at night. We constructed 1 model for every combination of multiverse parameters to evaluate their effects on each of the outcome variables. We evaluated the statistical models by applying them to training, validation, and test sets to prevent overfitting.
resultsMost models (on either of the outcome variables) were not informative on the validation set (ie, predicted R
conclusionsThe approach demonstrated in this paper has shown that different choices (eg, preprocessing choices, various statistical models, different features) lead to vastly different results that are bad and relatively good as well. Nevertheless, there were some promising results, particularly for SSQ, which warrant further research on this topic.
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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.