ArticleBMJ mental health2023
Increasing the value of digital phenotyping through reducing missingness: a retrospective review and analysis of prior studies.
Article in BMJ mental health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Geolocation-derived mobility indices and the association with clinical symptoms and functioning in severe mental illness: A multivariate meta-analysis.Journal of psychopathology and clinical science · 2025Pooled it
- Big data and psychiatry: advances, constraints and future directions.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026Article
- Eye-Tracking Phenotypes Across the Depressive Spectrum: Joint Modeling of Diagnostic Group and Symptom Severity.CNS neuroscience & therapeutics · 2026Article
- Big data research: the importance of the data-generating process and domain expertise.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026Article
- National implementation of a digital Parkinson's disease screening programme in Thailand: reach, adoption, and real-world performance of the CheckPD app.BMC public health · 2026Article
- Digital Phenotyping via Passive Network Traffic Monitoring: Prospective Observational Study in University Students.JMIR formative research · 2026Observational
- A review of the application of digital phenotyping in predicting peripartum depressive symptoms.NPJ digital medicine · 2026Article
- The Changing Concept in the History of Schizophrenia.Brain sciences · 2026Review
- Interindividual differences in digital phenotypes of major depressive disorder: A passive sensing study using smartphone and wearable sensor data.Behaviour research and therapy · 2026Article
- Distinguishing Common Digital Phenotyping and Self-Report Parameters for Monitoring and Predicting Depression: Scoping Review.JMIR mHealth and uHealth · 2026Article
- LINC: a framework for maintaining high-quality passive data in digital phenotyping studies.Scientific reports · 2026Article
- AI-enabled passive digital phenotyping of experiential negative symptoms in schizophrenia-spectrum disorders: a narrative review on the path from sensing to clinical endpoints.Frontiers in psychiatry · 2026Review
- Unobtrusive inference of diurnal rhythms from smartphone data.NPJ digital medicine · 2025Review
- The user experience of ambulatory assessment and mood monitoring in depression: a systematic review & meta-synthesis.NPJ digital medicine · 2025Article
- Social Media Detox and Youth Mental Health.JAMA network open · 2025Observational
- Article
- Digital measures of activity and motivation impact depression and anxiety in the real world.NPJ digital medicine · 2025Article
- Machine learning-based prediction of restless legs syndrome using digital phenotypes from wearables and smartphone data.Scientific reports · 2025Article
- Unobtrusive inference of circadian rhythms from smartphone data.medRxiv : the preprint server for health sciences · 2025Article
- A novel approach to depression detection using POV glasses and machine learning for multimodal analysis.Frontiers in psychiatry · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundDigital phenotyping methods present a scalable tool to realise the potential of personalised medicine. But underlying this potential is the need for digital phenotyping data to represent accurate and precise health measurements.
objectiveTo assess the impact of population, clinical, research and technological factors on the digital phenotyping data quality as measured by rates of missing digital phenotyping data.
methodsThis study analyses retrospective cohorts of mindLAMP smartphone application digital phenotyping studies run at Beth Israel Deaconess Medical Center between May 2019 and March 2022 involving 1178 participants (studies of college students, people with schizophrenia and people with depression/anxiety). With this large combined data set, we report on the impact of sampling frequency, active engagement with the application, phone type (Android vs Apple), gender and study protocol features on missingness/data quality.
findingsMissingness from sensors in digital phenotyping is related to active user engagement with the application. After 3 days of no engagement, there was a 19% decrease in average data coverage for both Global Positioning System and accelerometer. Data sets with high degrees of missingness can generate incorrect behavioural features that may lead to faulty clinical interpretations.
conclusionsDigital phenotyping data quality requires ongoing technical and protocol efforts to minimise missingness. Adding run-in periods, education with hands-on support and tools to easily monitor data coverage are all productive strategies studies can use today. CLINICAL IMPLICATIONS: While it is feasible to capture digital phenotyping data from diverse populations, clinicians should consider the degree of missingness in the data before using them for clinical decision-making.
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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.