This paper is in the following e-collection/theme issue:

New Methods (944) mHealth for Data Collection and Research (955) Sleep Monitoring, Sleep Quality, Sleep Disorders (183) Digital Biomarkers and Digital Phenotyping (191) Methods and New Tools in Mental Health Research (454) Wearable Devices and Sensors (888)

Published on in Vol 24, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25643, first published .
Tracking Subjective Sleep Quality and Mood With Mobile Sensing: Multiverse Study

Tracking Subjective Sleep Quality and Mood With Mobile Sensing: Multiverse Study

Tracking Subjective Sleep Quality and Mood With Mobile Sensing: Multiverse Study

Authors of this article:

Koen Niemeijer1 Author Orcid Image ;   Merijn Mestdagh1 Author Orcid Image ;   Peter Kuppens1 Author Orcid Image
Article Authors Cited by (9) Tweetations (3) Metrics

Journals

  1. Yang X, Knights J, Bangieva V, Kambhampati V. Association Between the Severity of Depressive Symptoms and Human-Smartphone Interactions: Longitudinal Study. JMIR Formative Research 2023;7:e42935 View
  2. Kulkarni P, Kirkham R, McNaney R. Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review. Sensors 2022;22(10):3893 View
  3. Vaghela M, Sasidhar K. Smartphone Mediated Tracking and Analysis of Sleep Patterns in Indian College Students. Human-Centric Intelligent Systems 2022;3(1):25 View
  4. Langener A, Bringmann L, Kas M, Stulp G. Predicting Mood Based on the Social Context Measured Through the Experience Sampling Method, Digital Phenotyping, and Social Networks. Administration and Policy in Mental Health and Mental Health Services Research 2024;51(4):455 View
  5. de Souza R, Brollo L, Carrerette F, Villela N, Oliveira M. Challenges in measuring sleep quality among women with endometriosis: A comparison of two questionnaires. Sleep Medicine 2024;114:250 View
  6. Moebus M, Holz C, da Costa J. Personalized interpretable prediction of perceived sleep quality: Models with meaningful cardiovascular and behavioral features. PLOS ONE 2024;19(7):e0305258 View
  7. Langener A, Siepe B, Elsherif M, Niemeijer K, Andresen P, Akre S, Bringmann L, Cohen Z, Choukas N, Drexl K, Fassi L, Green J, Hoffmann T, Jagesar R, Kas M, Kurten S, Schoedel R, Stulp G, Turner G, Jacobson N. A template and tutorial for preregistering studies using passive smartphone measures. Behavior Research Methods 2024;56(8):8289 View
  8. Hong M, Kang R, Yang J, Rhee S, Lee H, Kim Y, Lee K, Kim H, Lee Y, Youn T, Kim S, Ahn Y. Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study. Journal of Medical Internet Research 2024;26:e65994 View