Bridging the Gap Between Research and Practice: A Systematic Literature Review of IoT and Machine Learning Applications in Mental Healthcare Systems
4 affiliations
- 1Faculty of Business and Communications, INTI International University, Persiaran Perdana BBN Putra Nilai, 71800 Nilai, Negeri Sembilan, Malaysia
- 2Department of Radiodiagnosis, Nootan Medical College & Research Centre, Sankalchand Patel University (SPU), Visnagar, Gujarat, India
- 3Rushford Business School, Switzerland
- 4Xingtai Vocational College of Applied Technology, Malaysia
Abstract
Objective: This study conducts a systematic review of Internet of Things (IoT) and Machine Learning (ML) applications to identify existing gaps and scope for improvisation.
Design: Databases such as Scopus, Web of Science, PubMed, IEEE Xplore, and ScienceDirect were searched to find relevant studies focusing on IoT and ML in mental healthcare. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, 18 studies were selected for the final review. Cyber-Physical Systems (CPS) theory and thematic analysis were employed to answer research questions.
Result/outcomes: Four dimensions were identified as: continuous behavioural monitoring, ML-driven predictive analytics, real-time adaptive mental healthcare interventions, and responsible clinical deployment. IoT and ML technologies were found to have significantly improved patients’ monitoring, predicting risks, and enhancing interventions using digital phenotyping, wearable data sensing, digital phenotyping and AI-driven analytics. However, some challenges in effective implementation of IoT and ML applications included explainability, ethical governance, interoperability, and clinical integration.
Conclusion: An Operational Framework for Real-Time IoT-ML Mental Healthcare Systems is proposed to bring together sensing, analytics, and interventions. This framework recommends taking adequate actions to support either scalability or clinically sustainable mental healthcare delivery.
Related
- Perceptions, attitudes, and concerns of underrepresented groups regarding the use of big data in children and young people’s healthcare research – A systematic literature review
- Bridging Pedagogy and Technology: A Systematic Literature Review on Inquiry-Collaborative Models Assisted with Virtual Laboratory in Physics Education.
- Factors affecting women's disclosure of domestic abuse and healthcare professionals' preparedness to identify and respond: a systematic literature review.
- Participatory action research in rural and vulnerable settings for community health: a systematic literature review.
- Factors Associated with Research Literacy in University Students: A Systematic Literature Review 2015-2025.
BibTeX
@article{vasudevan2026bridging,
title = {{Bridging the Gap Between Research and Practice: A Systematic Literature Review of IoT and Machine Learning Applications in Mental Healthcare Systems}},
author = {Asokan Vasudevan and Rajendra N. Solanki and Marcus Teunissen and Wu Wenya},
journal = {Asia Pacific Journal of Health Management},
year = {2026},
month = oct,
doi = {10.24083/apjhm.v21i2.5925},
url = {https://doi.org/10.24083/apjhm.v21i2.5925},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - Bridging the Gap Between Research and Practice: A Systematic Literature Review of IoT and Machine Learning Applications in Mental Healthcare Systems
AU - Asokan Vasudevan
AU - Rajendra N. Solanki
AU - Marcus Teunissen
AU - Wu Wenya
PY - 2026
DA - 2026/10/28
JO - Asia Pacific Journal of Health Management
DO - 10.24083/apjhm.v21i2.5925
UR - https://doi.org/10.24083/apjhm.v21i2.5925
AB - Objective: This study conducts a systematic review of Internet of Things (IoT) and Machine Learning (ML) applications to identify existing gaps and scope for improvisation. Design: Databases such as Scopus, Web of Science, PubMed, IEEE Xplore, and ScienceDirect were searched to find relevant studies focusing on IoT and ML in mental healthcare. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, 18 studies were selected for the final review. Cyber-Physical Systems (CPS) theory and thematic analysis were employed to answer research questions. Result/outcomes: Four dimensions were identified as: continuous behavioural monitoring, ML-driven predictive analytics, real-time adaptive mental healthcare interventions, and responsible clinical deployment. IoT and ML technologies were found to have significantly improved patients’ monitoring, predicting risks, and enhancing interventions using digital phenotyping, wearable data sensing, digital phenotyping and AI-driven analytics. However, some challenges in effective implementation of IoT and ML applications included explainability, ethical governance, interoperability, and clinical integration. Conclusion: An Operational Framework for Real-Time IoT-ML Mental Healthcare Systems is proposed to bring together sensing, analytics, and interventions. This framework recommends taking adequate actions to support either scalability or clinically sustainable mental healthcare delivery.
SN - 1833-3818
SN - 2204-3136
ER -