Explore 8 peer-reviewed papers in Digital Health (2020–2025). Academic research using Prolific for high-quality human data collection.
This page lists 8 peer-reviewed papers in the discipline of Digital Health in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: T Mendel, N Singh, DM Mann, B Wiesenfeld
Year: 2025
Published in: Journal of medical ..., 2025 - jmir.org
Institution: The City University of New York, George Washington University, New York University
Research Area: LLMs in Digital Health, Health Queries, User Attitudes
Discipline: Digital Health
Laypeople primarily use search engines over large language models (LLMs) for health queries, perceiving LLMs as less useful but less biased and more human-like while exhibiting no significant difference in trust or ease of use.
Methods: A screening survey followed by logistic regression analysis and a follow-up survey; comparisons were performed using ANOVA, Tukey post hoc tests, and paired-sample Wilcoxon tests.
Key Findings: Demographics and behaviors of LLM and search engine users for health queries, perceived usefulness, ease of use, trustworthiness, bias, and anthropomorphism.
Citations: 21
Sample Size: 2002
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Authors: C Chen, Z Cui
Year: 2025
Published in: Journal of Medical Internet Research, 2025 - jmir.org
Institution: Medical College of Wisconsin
Research Area: Trust in AI, AI-assisted diagnosis, Health communication, Healthcare human-AI interaction
Discipline: Digital Health, Human-Computer Interaction (HCI), Behavioral Science
Patients trust and are more likely to seek help from doctors explicitly avoiding AI-assisted diagnosis rather than those using extensive or moderate AI, highlighting a strong aversion to AI in healthcare settings.
Methods: A randomized, web-based 4-group survey experiment was conducted with controls for sociodemographic factors and analysis using regression, mediation, and moderation techniques.
Key Findings: Trust in and intention to seek medical help from health care professionals using AI-assisted diagnosis versus those avoiding AI, and the influence of demographic, social, and experiential factors.
DOI: https://doi.org/10.2196/66083
Citations: 4
Sample Size: 1762
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Authors: C Heath, JM Williams, D Leightley
Year: 2025
Published in: JMIR mHealth and ..., 2025 - mhealth.jmir.org
Institution: Swansea University, King's College London, Reykjavík University
Research Area: mHealth Interventions, Crowdsourcing, Social Media Recruitment, Mental Health Research (PTSD, Harmful Gambling)
Discipline: Digital Health, Mental Health Research
Social media and online platforms like Facebook and Prolific were effective but faced challenges in recruiting and retaining military veterans with PTSD or harmful gambling for a digital mHealth intervention pilot study.
Methods: Multiple recruitment strategies were used, including paid and unpaid advertisements on Facebook, Prolific, direct mailing, event hosting with veterans' charities, snowball sampling, and incentives.
Key Findings: The effectiveness of different recruitment strategies for enrolling military veterans with PTSD or harmful gambling into a digital intervention study.
Sample Size: 79
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Authors: B Grimm, P Yilmam, B Talbot, L Larsen
Year: 2025
Published in: npj Digital Medicine, 2025 - nature.com
Institution: Videra Health
Research Area: Computational Mental Health Assessment, Multimodal Machine Learning
Discipline: Computational Health, Digital Medicine
A multimodal machine learning model using text (MPNet) and voice (HuBERT) analysis predicts depression, anxiety, and trauma from a single video-based question with strong performance and demographic consistency while significantly reducing assessment time.
Methods: Multimodal analysis combining MPNet for textual data and HuBERT for prosodic voice features trained on video-based responses.
Key Findings: Efficient prediction of self-reported scores for depression (PHQ-9), anxiety (GAD-7), and trauma (PCL-5) from brief video responses.
Sample Size: 2420
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Authors: CM Jones, D Diethei, J Schöning, R Shrestha
Year: 2022
Published in: Journal of Medical ..., 2023 - jmir.org
Research Area: Social Media, Misinformation, Social Influence
Discipline: Social Science, Digital Health
DOI: https://doi.org/10.2196/45583
Citations: 19
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Authors: SX Li, R Halabi, R Selvarajan, M Woerner
Year: 2022
Published in: JMIR Formative ..., 2022 - formative.jmir.org
Institution: Massachusetts General Hospital, Harvard Medical School, Boston University, University of Waterloo
Research Area: Digital Health, Remote Research Methods, Recruitment and Retention Studies
Discipline: Digital Health, Research Methodology
Citations: 19
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Authors: C Woodcock, B Mittelstadt, D Busbridge
Year: 2021
Published in: Journal of medical Internet ..., 2021 - jmir.org
Institution: Oxford University, Alan Turing Institute, University of Edinburgh
Research Area: Health Informatics, Explainable AI (XAI), Trust in AI, Digital Health
Discipline: Digital Health
DOI: https://doi.org/10.2196/29386
Citations: 52
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Authors: P Geldsetzer
Year: 2020
Published in: Journal of medical Internet research, 2020 - jmir.org
Institution: Stanford University
Research Area: Public Health Surveillance, Survey Methodology, Infectious Disease Outbreaks, COVID-19
Discipline: Public Health, Digital Health Research
Citations: 596