ArticleJournal of the American Medical Informatics Association : JAMIA2017
Movement patterns in women at risk for perinatal depression: use of a mood-monitoring mobile application in pregnancy.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 4 of them syntheses that pooled it.
What it found
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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.
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
37 citing papers in PubMed, 4 syntheses or guidelines pooled it, 71 citations in OpenAlex.
- Perinatal Mental Health Detection and Prediction Using Mobile Sensing Data: Systematic Review.JMIR mental health · 2026Pooled it
- Digital screening for mental health in pregnancy and postpartum: A systematic review.Archives of women's mental health · 2024Pooled it
- Technologies Applied to the Mental Health Care of Pregnant Women: A Systematic Literature Review.Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia · 2023Pooled it
- Digital biomarkers from geolocation data in bipolar disorder and schizophrenia: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019Pooled it
- Measuring emotion dysregulation in pregnancy using smartphone activity: a prospective research study in the United States.BMC pregnancy and childbirth · 2026Article
- HappyMums mobile application study protocol: use of a smartphone application to gather data predictive of antenatal depression.BMJ open · 2026Observational
- Passive sensing of anhedonia and amotivation in a transdiagnostic sample.Journal of psychopathology and clinical science · 2025Article
- Exploring the Requirements for an mHealth App to Prevent and Manage Perinatal Anxiety and Depression.Health science reports · 2025Article
- Exploring the Relationship Between Smartphone GPS Patterns and Quality of Life in Patients With Advanced Cancer and Their Family Caregivers: Longitudinal Study.JMIR formative research · 2025Article
- Exploring User Experiences of the Mom2B mHealth Research App During the Perinatal Period: Qualitative Study.JMIR formative research · 2024Article
- Harnessing Consumer Wearable Digital Biomarkers for Individualized Recognition of Postpartum Depression Using the All of Us Research Program Data Set: Cross-Sectional Study.JMIR mHealth and uHealth · 2024Article
- Physical activity trajectory during pregnancy and associations with maternal fatigue using a growth mixture modeling approach.Scientific reports · 2024Article
- Navigating the Intersection of Technology and Depression Precision Medicine.Advances in experimental medicine and biology · 2024Review
- Adaptation and integration of the thinking healthy programme into pregnancy schools in Istanbul, Turkey.BMC pregnancy and childbirth · 2023Article
- Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools.JMIR aging · 2022Article
- Technology-Based Approaches for Supporting Perinatal Mental Health.Current psychiatry reports · 2022Review
- Online Anomaly Detection for Smartphone-Based Multivariate Behavioral Time Series Data.Sensors (Basel, Switzerland) · 2022Article
- Article
- Smartphone sensing of social interactions in people with and without schizophrenia.Journal of psychiatric research · 2021Article
- Passive sensing on mobile devices to improve mental health services with adolescent and young mothers in low-resource settings: the role of families in feasibility and acceptability.BMC medical informatics and decision making · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesTo examine, using a smartphone application, whether mood is related to daily movement patterns in pregnant women at risk for perinatal depression. MATERIALS AND
methodsThirty-six women with elevated depression symptoms (PHQ-9 ≥ 5) in pregnancy used the application for 8 weeks. Mood was reported using application-administered surveys daily (2 questions) and weekly (PHQ-9 and GAD-7). The application measured daily mobility (distance travelled on foot) and travel radius. Generalized linear mixed-effects regression models estimated the association between mood and movement.
resultsWomen with milder depression symptoms had a larger daily radius of travel (2.7 miles) than women with more severe symptoms (1.9 miles), P = .04. There was no difference in mobility. A worsening of mood from the prior day was associated with a contracted radius of travel, as was being in the group with more severe symptoms. No significant relationships were found between anxiety and either mobility or radius. DISCUSSION: We found that the association of mood with radius of travel was more pronounced than its association with mobility. Our study also demonstrated that a change in mood from the prior day was significantly associated with radius but not mood on the same day that mobility and radius were measured.
conclusionThis study lays the groundwork for future research on how smartphone mood-monitoring applications can combine actively and passively collected data to better understand the relationship between the symptoms of perinatal depression and physical activity that could lead to improved monitoring and novel interventions.
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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.