Discover 6 peer-reviewed studies in Mental Health (2022–2025). Explore research findings powered by Prolific's diverse participant panel.
This page lists 6 peer-reviewed papers in the research area of Mental Health in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: Y Ai, A von Mühlenen
Year: 2025
Published in: Scientific Reports, 2025 - nature.com
Institution: University of Warwick
Research Area: Social media, Mental Health, Behavioral Science
Discipline: Behavioral Science
Negative social media comments significantly increase anxiety and decrease mood, with younger adults showing heightened sensitivity compared to older adults.
Methods: Participants shared blog posts on a simulated internet forum and were exposed to negative, neutral, or positive comments; mood and anxiety levels were measured using validated scales.
Key Findings: Impact of negative, neutral, and positive social media comments on anxiety and mood across adult participants.
Citations: 3
Sample Size: 128
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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: LD Griffin, B Kleinberg, M Mozes, KT Mai, M Vau
Year: 2023
Published in: arXiv preprint arXiv ..., 2023 - arxiv.org
Institution: University College London, Tilburg University
Research Area: LLM Influence, Psychology, Mental Health Research, LLM
Discipline: Artificial Intelligence, Psychology
Citations: 30
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Authors: E Scarpulla, MD Stosic, AE Weaver
Year: 2023
Published in: Frontiers in ..., 2023 - frontiersin.org
Institution: University of San Francisco, University of Southern California, University of Nevada, Las Vegas
Research Area: Social Media, Emotion Recognition, Mental Health
Discipline: Psychology
DOI: https://doi.org/10.3389/fpsyg.2023.1161300
Citations: 7
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Authors: M Whatnall, T Fozard, K Kolokotroni, J Marwood, T Evans, L Jane Ells, T Burrows
Year: 2022
Published in: British Medical Journal
Institution: Leeds Beckett University, The University of Newcastle Australia
Research Area: Eating behaviors, Mental Health, Weight Change in Young Adults, Public Health
Discipline: Public Health