ArticleJournal of medical Internet research2023
Digital Phenotyping Data to Predict Symptom Improvement and Mental Health App Personalization in College Students: Prospective Validation of a Predictive Model.
Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
17 citing papers in PubMed, 28 citations in OpenAlex.
- Engagement, Acceptability, and Effectiveness of the Self-Care and Coach-Supported Versions of the Vira Digital Behavior Change Platform Among Young Adults at Risk for Depression and Obesity: Pilot Randomized Controlled Trial.JMIR mental health · 2024Trial
- Impact of mobile health interventions for mental health literacy: A systematic review and meta-analysis of randomized controlled trials.PLOS digital health · 2026Article
- Toward hybrid mental health care: an early health technology assessment based on stakeholder analysis and economic simulations.International journal of technology assessment in health care · 2026Article
- A multilevel meta-analysis of passive smartphone sensing of adolescent mental health.NPJ digital medicine · 2026Article
- Distinguishing Common Digital Phenotyping and Self-Report Parameters for Monitoring and Predicting Depression: Scoping Review.JMIR mHealth and uHealth · 2026Article
- Using Smartphone-Based Digital Phenotyping to Predict Relapse in Serious Mental Disorders Among Slum Residents in Dhaka, Bangladesh: Protocol for a Machine Learning Study.JMIR research protocols · 2026Article
- A generative two-stage semantic intermediary framework for explainable mental health early warning in higher education.Frontiers in psychiatry · 2026Review
- Towards clinical subtypes in schizophrenia: integrating cognitive, functional, and digital phenotyping assessments.Molecular psychiatry · 2025Article
- What influences engagement with a bipolar disorder self-management app? A qualitative investigation of use of the PolarUs app.PLOS digital health · 2025Article
- Harnessing Digital Phenotyping for Early Self-Detection of Psychological Distress.Healthcare (Basel, Switzerland) · 2025Article
- Integrating Artificial Intelligence and Smartphone Technology to Enhance Personalized Assessment and Treatment for Eating Disorders.The International journal of eating disorders · 2025Review
- Transdiagnostic-focused apps for depression and anxiety: a meta-analysis.NPJ digital medicine · 2025Article
- A Mobile App Designed to Promote Shared Decision-Making in the Treatment of Psychotic Disorders: Feasibility and Acceptability Study.JMIR human factors · 2025Article
- The comprehensive clinical benefits of digital phenotyping: from broad adoption to full impact.NPJ digital medicine · 2025Review
- Assessing Digital Phenotyping for App Recommendations and Sustained Engagement: Cohort Study.JMIR formative research · 2024Article
- Digital Phenotyping: Data-Driven Psychiatry to Redefine Mental Health.Journal of medical Internet research · 2023Article
- Increasing the value of digital phenotyping through reducing missingness: a retrospective review and analysis of prior studies.BMJ mental health · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMental health apps offer a transformative means to increase access to scalable evidence-based care for college students. Yet low rates of engagement currently preclude the effectiveness of these apps. One promising solution is to make these apps more responsive and personalized through digital phenotyping methods able to predict symptoms and offer tailored interventions.
objectiveFollowing our protocol and using the exact model shared in that paper, our primary aim in this study is to assess the prospective validity of mental health symptom prediction using the mindLAMP app through a replication study. We also explored secondary aims around app intervention personalization and correlations of engagement with the Technology Acceptance Model (TAM) and Digital Working Alliance Inventory scale in the context of automating the study.
methodsThe study was 28 days in duration and followed the published protocol, with participants collecting digital phenotyping data and being offered optional scheduled and algorithm-recommended app interventions. Study compensation was tied to the completion of weekly surveys and was not otherwise tied to engagement or use of the app.
resultsThe data from 67 participants were used in this analysis. The area under the curve values for the symptom prediction model ranged from 0.58 for the UCLA Loneliness Scale to 0.71 for the Patient Health Questionnaire-9. Engagement with the scheduled app interventions was high, with a study mean of 73%, but few participants engaged with the optional recommended interventions. The perceived utility of the app in the TAM was higher (P=.01) among those completing at least one recommended intervention.
conclusionsOur results suggest how digital phenotyping methods can be used to create generalizable models that may help create more personalized and engaging mental health apps. Automating studies is feasible, and our results suggest targets to increase engagement in future studies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/37954.
Indexed as
Identifiers
What OpenQuestion holds
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.