Digital Psychophysiology: Integrasi AI dalam Data Perilaku, Kognitif, dan Biomedis untuk Pemantauan Kesehatan Mental
Keywords:
digital psychophysiology, artificial intelligence, kesehatan mental, digital phenotyping, multimodal fusionAbstract
Advances in digital sensor technology and artificial intelligence (AI) are expanding approaches to mental health monitoring through continuous data collection in everyday settings. Smartphones, wearable sensors, and digital monitoring systems enable the acquisition of behavioral, physiological, and cognitive data that can reflect an individual’s emotional dynamics, stress levels, mood changes, and psychological state. This article presents a narrative literature review that synthesizes developments in the integration of psychophysiological data and AI in digital mental health monitoring. The review covers various data sources, including neural signals, physiological indicators from wearable sensors, behavioral patterns obtained through smartphone sensing, and psychometric indicators. The analysis also covers AI approaches for multimodal data processing, ranging from machine learning and deep learning architectures to multimodal fusion strategies. The literature indicates that integrating various modalities through AI approaches has the potential to yield a more comprehensive representation of mental state dynamics by linking biological responses, daily behavioral patterns, and cognitive indicators. Despite this potential, the development of digital mental health systems still faces challenges related to dataset limitations, the heterogeneity and synchronization of multimodal data, model generalization, as well as issues of privacy and algorithmic transparency. This synthesis positions digital psychophysiology as an integrative framework that links psychophysiological measurements, digital phenotyping, and AI-based analytics to support the development of adaptive, multimodal, and sustainable mental health monitoring systems
References
Deeksha, & Firdose, Dr. S. (2025). Exploring Deep Learning Approaches to Stress Recognition Data, Models, and Applications in Review. International Journal for Research in Applied Science and Engineering Technology, 13(9), 1319–1336. https://doi.org/10.22214/ijraset.2025.74109
Aledavood, T., Luong, N., Baryshnikov, I., Darst, R., Heikkilä, R., Holmén, J., Ikäheimonen, A., Martikkala, A., Riihimäki, K., Saleva, O., Triana, A. M., & Isometsä, E. (2025). Multimodal Digital Phenotyping Study in Patients With Major Depressive Episodes and Healthy Controls (Mobile Monitoring of Mood): Observational Longitudinal Study. JMIR Mental Health, 12, e63622. https://doi.org/10.2196/63622
Barzilay, S., Fine, S., Akhavan, S., Haruvi-Catalan, L., Apter, A., Brunstein-Klomek, A., Carmi, L., Zohar, M., Kinarty, I., Friedman, T., & Fennig, S. (2023). Real-Time Real-World Digital Monitoring of Adolescent Suicide Risk During the Six Months Following Emergency Department Discharge: Protocol for an Intensive Longitudinal Study. JMIR Research Protocols, 12. https://doi.org/10.2196/46464
Baydili, İ., Tasci, B., & Tasci, G. (2025). Artificial Intelligence in Psychiatry: A Review of Biological and Behavioral Data Analyses. Diagnostics, 15(4), 434. https://doi.org/10.3390/diagnostics15040434
Busk, J., Faurholt-Jepsen, M., Frost, M., Bardram, J. E., Kessing, L. V., & Winther, O. (2020). Forecasting Mood in Bipolar Disorder from Smartphone Self-assessments: Hierarchical Bayesian approach. JMIR MHealth and UHealth, 8(4). https://doi.org/10.2196/15028
Chia, A. Z. R., & Zhang, M. W. B. (2022). Digital phenotyping in psychiatry: A scoping review. Technology and Health Care, 30(6), 1331–1342. https://doi.org/10.3233/THC-213648
Englhardt, Z., Ma, C., Morris, M. E., Xu, X. “Orson,” Chang, C.-C., Qin, L., McDuff, D., Liu, X., Patel, S., & Iyer, V. (2024). From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models. https://doi.org/10.1145/3659604
Esmi, N., Shahbahrami, A., Gaydadjiev, G., & de Jonge, P. (2025). TEREE: Transformer-based emotion recognition using EEG and Eye movement data. Intelligence-Based Medicine, 12. https://doi.org/10.1016/j.ibmed.2025.100305
Faurholt-Jepsen, M., Busk, J., Rohani, D. A., Frost, M., Tønning, M. L., Bardram, J. E., & Kessing, L. V. (2022). Differences in mobility patterns according to machine learning models in patients with bipolar disorder and patients with unipolar disorder. Journal of Affective Disorders, 306, 246–253. https://doi.org/10.1016/j.jad.2022.03.054
Galatzer-Levy, I., Abbas, A., Ries, A., Homan, S., Sels, L., Koesmahargyo, V., Yadav, V., Colla, M., Scheerer, H., Vetter, S., Seifritz, E., Scholz, U., & Kleim, B. (2021). Validation of visual and auditory digital markers of suicidality in acutely suicidal psychiatric inpatients: Proof-of-concept study. Journal of Medical Internet Research, 23(6). https://doi.org/10.2196/25199
Huang, D., Emedom-Nnamdi, P., Onnela, J.-P., & Van Meter, A. (2025). Design and feasibility of smartphone-based digital phenotyping for long-term mental health monitoring in adolescents. PLOS Digital Health, 4(7), e0000883. https://doi.org/10.1371/journal.pdig.0000883
Kadirvelu, B., Bellido Bel, T., Freccero, A., Di Simplico, M., Nicholls, D., & Faisal, A. A. (2026). Digital Phenotyping for Adolescent Mental Health: Feasibility Study Using Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data. Journal of Medical Internet Research, 28, e72501–e72501. https://doi.org/10.2196/72501
Kargarandehkordi, A., Li, S., Lin, K., Phillips, K. T., Benzo, R. M., & Washington, P. (2025). Fusing Wearable Biosensors with Artificial Intelligence for Mental Health Monitoring: A Systematic Review. Biosensors, 15(4), 202. https://doi.org/10.3390/bios15040202
Khoo, L. S., Lim, M. K., Chong, C. Y., & McNaney, R. (2024). Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches. Sensors, 24(2), 348. https://doi.org/10.3390/s24020348
Kumar, A., & Kumar, A. (2025). EEG-based emotion recognition: A deep learning approach to brain region analysis. Biomedical Signal Processing and Control, 110. https://doi.org/10.1016/j.bspc.2025.108111
Linardon, J., Chen, K., Gajjar, S., Eadara, A., Wang, S., Flathers, M., Burns, J., & Torous, J. (2025). Smartphone digital phenotyping in mental health disorders: A review of raw sensors utilized, machine learning processing pipelines, and derived behavioral features. Psychiatry Research, 348, 116483. https://doi.org/10.1016/j.psychres.2025.116483
Martin, J., Rueda, A., Lee, G. H., Tassone, V. K., Park, H., Ivanov, M., Darnell, B. C., Beavers, L., Campbell, D. M., Nguyen, B., Torres, A., Jung, H., Lou, W., Nazarov, A., Ashbaugh, A., Kapralos, B., Litz, B., Jetly, R., Dubrowski, A., … Bhat, V. (2024). Digital Interventions to Understand and Mitigate Stress Response: Protocol for Process and Content Evaluation of a Cohort Study. JMIR Research Protocols, 13. https://doi.org/10.2196/54180
Moura, I., Teles, A., Viana, D., Marques, J., Coutinho, L., & Silva, F. (2023). Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. Journal of Biomedical Informatics, 138, 104278. https://doi.org/10.1016/j.jbi.2022.104278
NI, Y. (2025). Advancing Emotion Recognition in Wearable Devices: Core Technologies, Challenges and Practical Solutions. Theoretical and Natural Science, 79(1), 59–66. https://doi.org/10.54254/2753-8818/2025.19943
Nickels, S., Edwards, M. D., Poole, S. F., Winter, D., Gronsbell, J., Rozenkrants, B., Miller, D. P., Fleck, M., McLean, A., Peterson, B., Chen, Y., Hwang, A., Rust-Smith, D., Brant, A., Campbell, A., Chen, C., Walter, C., Arean, P. A., Hsin, H., … Fromer, M. (2021). Toward a mobile platform for real-world digital measurement of depression: User-centered design, data quality, and behavioral and clinical modeling. JMIR Mental Health, 8(8). https://doi.org/10.2196/27589
Qian, K., Zhang, H., Jing, X., Hu, B., Yamamoto, Y., & Schuller, B. W. (2025). Foundation models for digital mental health: igniting the dawn. Medicine Plus, 2(2), 100085. https://doi.org/10.1016/j.medp.2025.100085
R, R., & Pushpalatha, M. P. (2025). Advancements in AI-Driven Detection and Monitoring of Depression and Anxiety using Multimodal Digital Biomarkers and Behavioural Data. 2025 International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3), 1–5. https://doi.org/10.1109/ISAC364032.2025.11156233
Rashid, Z., Folarin, A. A., Zhang, Y., Ranjan, Y., Conde, P., Sankesara, H., Sun, S., Stewart, C., Laiou, P., & Dobson, R. J. B. (2024). Digital Phenotyping of Mental and Physical Conditions: Remote Monitoring of Patients Through RADAR-Base Platform. JMIR Mental Health, 11. https://doi.org/10.2196/51259
Ringwald, W. R., King, G., Vize, C. E., & Wright, A. G. C. (2025). Passive Smartphone Sensors for Detecting Psychopathology. JAMA Network Open, 8(7), e2519047. https://doi.org/10.1001/jamanetworkopen.2025.19047
Sanfelici, R., Dwyer, D. B., Antonucci, L. A., & Koutsouleris, N. (2020). Individualized Diagnostic and Prognostic Models for Patients With Psychosis Risk Syndromes: A Meta-analytic View on the State of the Art. Biological Psychiatry, 88(4), 349–360. https://doi.org/10.1016/j.biopsych.2020.02.009
Song, M., Yang, Z., Triantafyllopoulos, A., Zhang, Z., Nan, Z., Tang, M., Takeuchi, H., Nakamura, T., Kishi, A., Ishizawa, T., Yoshiuchi, K., Schuller, B., & Yamamoto, Y. (2024). Empowering Mental Health Monitoring Using a Macro-Micro Personalization Framework for Multimodal-Multitask Learning: Descriptive Study. JMIR Mental Health, 11. https://doi.org/10.2196/59512
Song, S., Seo, Y. Bin, Hwang, S. Y., Kim, H. Y., & Kim, J. (2024). Digital Phenotyping of Geriatric Depression Using a Community-Based Digital Mental Health Monitoring Platform for Socially Vulnerable Older Adults and Their Community Caregivers: 6-Week Living Lab Single-Arm Pilot Study. JMIR MHealth and UHealth, 12. https://doi.org/10.2196/55842
Srivastava, S., Kansal, K., Sai, S., & Chamola, V. (2025). Secure cognitive health monitoring using a directed acyclic graph-based and AI-enhanced IoMT framework. Digital Communications and Networks, 11(3), 594–602. https://doi.org/10.1016/j.dcan.2024.08.014
Wang, J., Ouyang, H., Jiao, R., Cheng, S., Zhang, H., Shang, Z., Jia, Y., Yan, W., Wu, L., & Liu, W. (2024). The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis. Npj Digital Medicine, 7(1), 121. https://doi.org/10.1038/s41746-024-01117-5
Wang, L., Wang, C., Li, C., Murai, T., Bai, Y., Song, Z., Zhang, S., Zhang, Q., Huang, Y., Bi, X., & Jiang, J. (2025). AI-assisted multi-modal information for the screening of depression: a systematic review and meta-analysis. Npj Digital Medicine, 8(1), 523. https://doi.org/10.1038/s41746-025-01933-3
Wang, Z., Wu, W., & Zeng, C. (2024). PhysioFormer: Integrating Multimodal Physiological Signals and Symbolic Regression for Explainable Affective State Prediction. PLoS One, 20(10). https://doi.org/https://doi.org/10.1371/journal.pone.0335221
Xia, Z., Chikersal, P., Venkatesh, S., Walker, E., Dey, A. K., & Goel, M. (2025). Longitudinal Digital Phenotyping of Multiple Sclerosis Severity Using Passively Sensed Behaviors and Ecological Momentary Assessments: Real-World Evaluation. Journal of Medical Internet Research, 27. https://doi.org/10.2196/70871
Yang, Z., Heaukulani, C., Sim, A., Buddhika, T., Abdul Rashid, N. A., Wang, X., Zheng, S., Quek, Y. F., Basu, S., Lee, K. W., Tang, C., Verma, S., Morris, R. J. T., & Lee, J. (2025). Utility of Digital Phenotyping Based on Wrist Wearables and Smartphones in Psychosis: Observational Study. JMIR MHealth and UHealth, 13, e56185–e56185. https://doi.org/10.2196/56185
Yücel, K., Özcanhan, M. H., & Ünlütürk, M. S. (2026). MTWIN: A mental health monitoring framework toward a Digital Twin. Smart Health, 40, 100646. https://doi.org/10.1016/j.smhl.2026.100646
Zhan, Y., Liu, H., & Wang, Y. (2026). Digital phenotyping of depression: A multi-modal passive sensing approach to identifying novel behavioral and physiological markers of treatment response. Journal of Psychiatric Research, 194, 40–50. https://doi.org/10.1016/j.jpsychires.2025.12.036
Zhang, Y., Stewart, C., Ranjan, Y., Conde, P., Sankesara, H., Rashid, Z., Sun, S., Dobson, R. J. B., & Folarin, A. A. (2025). Large-scale digital phenotyping: Identifying depression and anxiety indicators in a general UK population with over 10,000 participants. Journal of Affective Disorders, 375, 412–422. https://doi.org/10.1016/j.jad.2025.01.124
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Copyright (c) 2026 Yuli Dwi Hartanto, Annastasya Nabila Elsa Wulandari (Author)
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