Integração de Questionários de Saúde e Dados Coletados de Dispositivos Vestíveis em Modelos de Aprendizado de Máquina: Uma Revisão Rápida da Literatura
Resumo
O avanço dos dispositivos vestíveis e do aprendizado de máquina tem ampliado a integração entre dados fisiológicos passivos e questionários de saúde. Esta revisão rápida analisou 13 estudos publicados entre 2020 e 2025 sobre o uso combinado dessas abordagens em aplicações de saúde digital. Os resultados identificaram três estratégias principais: uso de questionários como variável-alvo, integração multimodal entre autorrelatos e dados fisiológicos e predição contínua de escores clínicos. Observou-se potencial promissor, especialmente em modelos multimodais e temporais. Contudo, persistem desafios como heterogeneidade metodológica, amostras reduzidas e validação externa limitada. Conclui-se que essa convergência representa uma tendência consistente, mas ainda demanda maior padronização e validação para adoção em larga escala.Referências
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Moshe, I., Terhorst, Y., Opoku Asare, K., Sander, L. B., Ferreira, D., Baumeister, H., Mohr, D. C., and Pulkki-Råback, L. (2021). Predicting symptoms of depression and anxiety using smartphone and wearable data. Frontiers in Psychiatry, 12:625247. PMID: 33584388.
Mullick, T., Radovic, A., Shaaban, S., and Doryab, A. (2022). Predicting depression in adolescents using mobile and wearable sensors: Multimodal machine learning–based exploratory study. JMIR Formative Research, 6(6):e35807.
Price, G. D., Heinz, M. V., Collins, A. C., and Jacobson, N. C. (2024). Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample. Psychiatry Research, 332:115693.
Price, G. D., Heinz, M. V., Song, S. H., Nemesure, M. D., and Jacobson, N. C. (2023). Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data. Translational Psychiatry, 13(1):381.
Rao, K., Speier, W., Meng, Y., Wang, J., Ramesh, N., Xie, F., Su, Y., Nowell, W. B., Curtis, J. R., and Arnold, C. (2023). Machine learning approaches to classify self-reported rheumatoid arthritis health scores using activity tracker data: Longitudinal observational study. JMIR Formative Research, 7:e43107. PMID: 37017471.
Rykov, Y., Thach, T.-Q., Bojic, I., Christopoulos, G., and Car, J. (2021). Digital biomarkers for depression screening with wearable devices: cross-sectional study with machine learning modeling. JMIR mHealth and uHealth, 9(10):e24872.
Saylam, B. and Durmaz İncel, Ö. (2023). Quantifying digital biomarkers for well-being: Stress, anxiety, positive and negative affect via wearable devices and their time-based predictions. Sensors, 23(21):8987.
Saylam, B. and Durmaz İncel, Ö. (2024). Multitask learning for mental health: Depression, anxiety, stress (DAS) using wearables. Diagnostics, 14(5):501.
Smela, B., Toumi, M., Świerk, K., Francois, C., Biernikiewicz, M., Clay, E., and Boyer, L. (2023). Rapid literature review: definition and methodology. Journal of Market Access & Health Policy, 11(1):2241234.
Sun, S., Folarin, A. A., Zhang, Y., Cummins, N., Garcia-Dias, R., Stewart, C., Ranjan, Y., Rashid, Z., Conde, P., Laiou, P., et al. (2023). Challenges in using mhealth data from smartphones and wearable devices to predict depression symptom severity: retrospective analysis. Journal of medical Internet research, 25:e45233.
Tsai, C.-H., Chen, P.-C., Liu, D.-S., Kuo, Y.-Y., Hsieh, T.-T., Chiang, D.-L., Lai, F., and Wu, C.-T. (2022). Panic attack prediction using wearable devices and machine learning: Development and cohort study. JMIR Medical Informatics, 10(2):e33063. PMID: 35166679.
Zhang, Y., Stewart, C., Ranjan, Y., Conde, P., Sankesara, H., Rashid, Z., Sun, S., Dobson, R. J., and 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.
Machado-Jaimes, L., Bustamante-Bello, M. R., and Argüelles-Cruz, A. (2022). Development of an intelligent system for the monitoring and diagnosis of the well-being. Sensors, 22(24):9719.
Moshe, I., Terhorst, Y., Opoku Asare, K., Sander, L. B., Ferreira, D., Baumeister, H., Mohr, D. C., and Pulkki-Råback, L. (2021). Predicting symptoms of depression and anxiety using smartphone and wearable data. Frontiers in Psychiatry, 12:625247. PMID: 33584388.
Mullick, T., Radovic, A., Shaaban, S., and Doryab, A. (2022). Predicting depression in adolescents using mobile and wearable sensors: Multimodal machine learning–based exploratory study. JMIR Formative Research, 6(6):e35807.
Price, G. D., Heinz, M. V., Collins, A. C., and Jacobson, N. C. (2024). Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample. Psychiatry Research, 332:115693.
Price, G. D., Heinz, M. V., Song, S. H., Nemesure, M. D., and Jacobson, N. C. (2023). Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data. Translational Psychiatry, 13(1):381.
Rao, K., Speier, W., Meng, Y., Wang, J., Ramesh, N., Xie, F., Su, Y., Nowell, W. B., Curtis, J. R., and Arnold, C. (2023). Machine learning approaches to classify self-reported rheumatoid arthritis health scores using activity tracker data: Longitudinal observational study. JMIR Formative Research, 7:e43107. PMID: 37017471.
Rykov, Y., Thach, T.-Q., Bojic, I., Christopoulos, G., and Car, J. (2021). Digital biomarkers for depression screening with wearable devices: cross-sectional study with machine learning modeling. JMIR mHealth and uHealth, 9(10):e24872.
Saylam, B. and Durmaz İncel, Ö. (2023). Quantifying digital biomarkers for well-being: Stress, anxiety, positive and negative affect via wearable devices and their time-based predictions. Sensors, 23(21):8987.
Saylam, B. and Durmaz İncel, Ö. (2024). Multitask learning for mental health: Depression, anxiety, stress (DAS) using wearables. Diagnostics, 14(5):501.
Smela, B., Toumi, M., Świerk, K., Francois, C., Biernikiewicz, M., Clay, E., and Boyer, L. (2023). Rapid literature review: definition and methodology. Journal of Market Access & Health Policy, 11(1):2241234.
Sun, S., Folarin, A. A., Zhang, Y., Cummins, N., Garcia-Dias, R., Stewart, C., Ranjan, Y., Rashid, Z., Conde, P., Laiou, P., et al. (2023). Challenges in using mhealth data from smartphones and wearable devices to predict depression symptom severity: retrospective analysis. Journal of medical Internet research, 25:e45233.
Tsai, C.-H., Chen, P.-C., Liu, D.-S., Kuo, Y.-Y., Hsieh, T.-T., Chiang, D.-L., Lai, F., and Wu, C.-T. (2022). Panic attack prediction using wearable devices and machine learning: Development and cohort study. JMIR Medical Informatics, 10(2):e33063. PMID: 35166679.
Zhang, Y., Stewart, C., Ranjan, Y., Conde, P., Sankesara, H., Rashid, Z., Sun, S., Dobson, R. J., and 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.
Publicado
01/06/2026
Como Citar
MENDES, Nadiana K. N.; ANDRADE, Rossana M. C.; OLIVEIRA, Pedro A. M..
Integração de Questionários de Saúde e Dados Coletados de Dispositivos Vestíveis em Modelos de Aprendizado de Máquina: Uma Revisão Rápida da Literatura. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 26. , 2026, Ouro Preto/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 109-120.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2026.20388.
