Toward Precision Psychiatry: A Scoping Review of Self-Monitoring and Artificial Intelligence for Bipolar Symptom Monitoring

Anastia Nursyafaat; Aat Sriati

doi:10.31004/riggs.v4i4.3365

Authors

Anastia Nursyafaat Universitas Padjadjaran
Aat Sriati Universitas Padjadjaran

DOI:

https://doi.org/10.31004/riggs.v4i4.3365

Keywords:

Bipolar Disorder, Digital Health, Self-Monitoring, Wearable Devices, Hybrid Interventions

Abstract

This scoping review aims to map and analyze the utilization of digital technologies, including self-monitoring applications, wearable devices, and hybrid interventions, for monitoring symptoms of bipolar disorder, as well as to explore their potential implementation in Indonesia. A systematic literature search was conducted across EBSCO Host, ScienceDirect, Oxford Academic, and Emerald Insight, focusing on studies published between 2020 and 2025 that addressed digital monitoring in individuals with bipolar disorder. The review identified ten studies that met the inclusion criteria, consisting of randomized controlled trials, mixed-methods studies, exploratory qualitative studies, pilot studies, and trial protocols. Key findings were categorized into four themes: (1) active self-monitoring via manual input or Ecological Momentary Assessment, which enhances self-awareness, promotes consistent daily routines, and supports symptom management; (2) passive monitoring and digital phenotyping using sensors and wearable devices, providing continuous objective data for early detection of relapse; (3) hybrid approaches integrating monitoring with therapeutic modules such as psychoeducation, mindfulness, and relapse prevention, which improve social functioning and treatment adherence; and (4) psychosocial factors and user engagement, highlighting the importance of personalization, peer support, inclusive language, and interactive features. Overall, digital interventions demonstrate considerable potential as a complement to conventional care by empowering patients, facilitating early detection of mood episodes, and enabling more personalized management. In Indonesia, the adoption of inclusive, evidence-based, and contextually adaptive digital strategies tailored to local digital literacy and infrastructure is expected to strengthen mental health services and sustainably enhance patient empowerment.

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References

Asosiasi Penyelenggara Jasa Internet Indonesia. (2024, January 18). APJII: Jumlah pengguna internet Indonesia tembus 221 juta orang. https://apjii.or.id/berita/d/apjii-jumlah-pengguna-internet-indonesia-tembus-221-juta-orang?utm

Berry, N., Lobban, F., & Bucci, S. (2019). A qualitative exploration of service user views about using digital health interventions for self-management in severe mental health problems. BMC Psychiatry, 19, 35. https://doi.org/10.1186/s12888-018-1979-1

Breitinger, S., Gardea-Resendez, M., Langholm, C., Xiong, A., Laivell, J., Stoppel, C., Harper, L., Volety, R., Walker, A., D'Mello, R., Byun, A. J. S., Zandi, P., Goes, F. S., Frye, M., & Torous, J. (2023). Digital phenotyping for mood disorders: Methodology-oriented pilot feasibility study. JMIR Mental Health, 10, e47006. https://doi.org/10.2196/47006

Cormack, F., McCue, M., Skirrow, C., Taptiklis, N., van Schaik, T., Fehnert, B., King, J., & Barnett, J. H. (2025). Characterizing longitudinal patterns in cognition, mood, and activity in depression with 6-week high-frequency wearable assessment: An observational study. JMIR Mental Health, 11(1), e46895. https://doi.org/10.2196/46895

Faurholt-Jepsen, M., Blauenfeldt Kyster, N., Dyreholt, M. S., Christensen, E. M., Bondo-Kozuch, P., Lerche, A. S., Smidt, B., Knorr, U., Brøndmark, K., Cardoso, A.-M. B., Mathiesen, A., Sjælland, R., Sponsor, L. L., Mardosas, D., Sarauw-Nielsen, I. P., Bukh, J. D., Heller, T. V., Frost, M., Iversen, N., Bardram, J. E., Busk, J., Vinberg, M., & Kessing, L. V. (2023). The effect of smartphone-based monitoring and treatment including clinical feedback versus smartphone-based monitoring without clinical feedback in bipolar disorder: The SmartBipolar trial—a study protocol for a randomized controlled parallel-group trial. Trials, 24, 583. https://doi.org/10.1186/s13063-023-07625-1

Institute for Health Metrics and Evaluation. (2021). GBD results tool. https://vizhub.healthdata.org/gbd-results//

Jagfeld, G., Lobban, F., Humphreys, C., Rayson, P., & Huntley Jones, S. (2023). How people with a bipolar disorder diagnosis talk about personal recovery in peer online support forums: Corpus framework analysis using the POETIC framework. JMIR Medical Informatics, 11(1), e46544. https://medinform.jmir.org/2023/1/e46544

Kementerian Kesehatan Republik Indonesia. (2018). Riset kesehatan dasar (Riskesdas) 2018. https://kesprimkom.kemkes.go.id/assets/uploads/contents/others/RAK_keswa_2020-2024_(1)1.pdf

Kementerian Kesehatan Republik Indonesia. (2020). Rencana aksi kesehatan jiwa 2020–2024. https://kesprimkom.kemkes.go.id/assets/uploads/contents/others/RAK_keswa_2020-2024_(1)1.pdf

Liu, X., Zhang, Y., & Chen, L. (2025). Machine learning for classification of pediatric bipolar disorder with and without psychotic symptoms based on thalamic subregional structural volume. BMC Psychiatry, 25, 18. https://doi.org/10.1186/s12888-025-07018-5

Moore, S., Jay, C., & Johnson, L. (2016). Self-monitoring practices, attitudes, and needs of individuals with bipolar disorder: Implications for the design of technologies to manage mental health. ResearchGate. https://www.researchgate.net/publication/291690551

Murnane, E. L., Cosley, D., Chang, P., Guha, S., Frank, E., Gay, G., & Matthews, M. (2016). Self-monitoring practices, attitudes, and needs of individuals with bipolar disorder: Implications for the design of technologies to manage mental health. Journal of the American Medical Informatics Association, 23(1), 71–82. https://doi.org/10.1093/jamia/ocv165

Pahwa, M., McElroy, S. L., Priesmeyer, R., Siegel, G., Siegel, P., Nuss, S., Bowden, C. L., & El-Mallakh, R. S. (2023). KIOS: A smartphone app for self-monitoring for patients with bipolar disorder. Bipolar Disorders, 25(5), 550–563. https://doi.org/10.1111/bdi.13362

Papaioannou, A., Smith, J., & Lee, K. (2025). Digital health interventions for mental health disorders: An umbrella review of meta-analyses of randomised controlled trials. The Lancet Digital Health. https://doi.org/10.1016/j.landig.2025.100878

Polhemus, A., Simblett, S., Dawe-Lane, E., Gilpin, G., Elliott, B., Jilka, S., Novak, J., Nica, R. I., Temesi, G., & Wykes, T. (2022). Health tracking via mobile apps for depression self-management: Qualitative content analysis of user reviews. JMIR Human Factors, 9(4), e40133. https://humanfactors.jmir.org/2022/4/e40133

Sánchez-Gutiérrez, T., Barbeito, S., Mayoral, M., Moreno, M., Rios-Aguilar, S., Arango, C., & Calvo, A. (2025). THINK APP: A mobile app–based intervention for adolescents with first-episode psychosis. Schizophrenia Bulletin, 51(1), 123–134. https://doi.org/10.1093/schbul/sbz102

Sigurðardóttir, S. G., Islind, A. S., & Óskarsdóttir, M. (2022). Collecting data from a mobile app and a smartwatch supports treatment of schizophrenia and bipolar disorder. Studies in Health Technology and Informatics, 290, 445–450. https://doi.org/10.3233/SHTI220445

World Health Organization. (2025, September 12). Bipolar disorder. https://www.who.int/news-room/fact-sheets/detail/bipolar-disorder