Diagnóstico de sintomas depressivos por meio de dispositivos móveis: uma revisão sistemática
DOI:
https://doi.org/10.37914/riis.v8i2.439Palavras-chave:
revisão sistemática, transtorno depressivo, sensores remotos, mHealthResumo
Enquadramento: a depressão é um problema generalizado de saúde mental, frequentemente subdiagnosticado e subtratado devido à dependência de autorrelatos subjetivos e acesso limitado a cuidados. As tecnologias de saúde móvel (mHealth), utilizando sensores de smartphones e wearables, oferecem soluções inovadoras para diagnósticos objetivos e escaláveis. Objetivos: esta revisão examina a eficácia dos sensores de dispositivos móveis no diagnóstico da depressão, identificando biossinais relevantes e explorando métodos de diagnóstico. Metodologia: uma busca sistemática seguindo as diretrizes PRISMA-DTA foi conduzida no MEDLINE/PubMed e Embase, com foco na precisão diagnóstica de sensores mHealth validados contra padrões ouro como DSM-5 ou PHQ-9. Resultados: onze estudos mostraram que acelerômetros e monitores de frequência cardíaca são essenciais para detectar movimento, atividade e padrões fisiológicos associados à depressão. Algoritmos de aprendizado de máquina, especialmente florestas aleatórias, alcançaram alta precisão diagnóstica. Conclusão: as tecnologias mHealth são promissoras para diagnósticos de depressão, mas melhorias na consistência metodológica, tamanho da amostra e validação externa são necessárias para uso clínico mais amplo.
Referências
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders: DSM-5™ (5th ed.). American Psychiatric Publishing Inc. https://psycnet.apa.org/doi/10.1176/appi.books.9780890425596
Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Beck depression inventory. Harcourt Brace Jovanovich. DOI: https://doi.org/10.1037/t00742-000
Chao, C. N. J. (2018). Traditional vs internet vs mobile: which is more effective way to reach potential customers. Journal of Business Administration Research, 7(2). https://doi.org/10.5430/jbar.v7n2p1 DOI: https://doi.org/10.5430/jbar.v7n2p1
Cho, C. H., Lee, T., Kim, M. G, In, H. P, Kim, L. & Lee, H. J. (2019). Mood prediction of patients with mood disorders by machine learning using passive digital phenotypes based on the circadian rhythm: prospective observational cohort study. Journal of Medical Internet Research, 21(4). https://doi.org/10.2196/11029 DOI: https://doi.org/10.2196/11029
Cornet, V. P. & Holden, R. J. J. (2018). Systematic review of smartphone-based passive sensing for health and wellbeing. Journal of Biomedical Informatics, 77, 120-132. https://doi.org/10.1016/j.jbi.2017.12.008 DOI: https://doi.org/10.1016/j.jbi.2017.12.008
De Angel, V., Lewis, S., White, K., Oetzmann, C., Leightley, D., Oprea, E., Lavelle, G., Matcham, F., Pace, A., & Mohr, D. C. J. N. (2022). Digital health tools for the passive monitoring of depression: a systematic review of methods. npj Digital Medicine, 5(3). https://doi.org/10.1038/s41746-021-00548-8 DOI: https://doi.org/10.1038/s41746-021-00548-8
Dijkers, M. (2013). Introducing GRADE: a systematic approach to rating evidence in systematic reviews and to guideline development. KT Update, 1(5). https://ktdrr.org/products/update/v1n5/dijkers_grade_ktupdatev1n5.html
Dogrucu, A., Perucic, A., Isaro, A., Ball, D., Toto, E., Rundensteiner, E. A., Agu, E., Davis-Martin, R., & Boudreaux, E. (2020). Moodable: on feasibility of instantaneous depression assessment using machine learning on voice samples with retrospectively harvested smartphone and social media data. Smart Health, 17, 100118. https://doi.org/10.1016/j.smhl.2020.100118 DOI: https://doi.org/10.1016/j.smhl.2020.100118
Faurholt-Jepsen, M., Busk, J., Þórarinsdóttir, H., Frost, M., Bardram, J. E., Vinberg, M., & Kessing, L. V. (2019). Objective smartphone data as a potential diagnostic marker of bipolar disorder. Australian and New Zealand Journal of Psychiatry, 53(2). https://doi.org/10.1177/0004867418808900 DOI: https://doi.org/10.1177/0004867418808900
Galvão, T. F., & Pereira, M. G. (2015). Avaliação da qualidade da evidência de revisões sistemáticas. Epidemiologia e Serviços de Saúde, 24(1), 173-175. https://doi.org/10.5123/S1679-49742015000100019 DOI: https://doi.org/10.5123/S1679-49742015000100019
Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery, and Psychiatry, 23(1), 56-62. https://doi.org/10.1136/jnnp.23.1.56 DOI: https://doi.org/10.1136/jnnp.23.1.56
Hess, S., Fischel, T., Dolfin, D., Hermesh, H., Horowitz, I., Sela, K., Liberman, A., Nevo, U., & Weizman, A. (2015). The use of smartphone app for early detection of manic or depressive episodes in affective patients. European Neuropsychopharmacology, 25(Suplemento 2), S392. https://doi.org/10.1016/S0924-977X(15)30514-9 DOI: https://doi.org/10.1016/S0924-977X(15)30514-9
Highland, D., & Zhou, G. (2022). A review of detection techniques for depression and bipolar disorder. Smart Health, 24, 100282. https://doi.org/10.1016/j.smhl.2022.100282 DOI: https://doi.org/10.1016/j.smhl.2022.100282
Institute for Health Metrics and Evaluation. (n.d.). Global health data exchange (GHDx). http://ghdx.healthdata.org/gbd-results-tool?params=gbd-api-2019-permalink/d780dffbe8a381b25e1416884959e88b
Jacobson, N. C., Weingarden, H., & Wilhelm, S. (2019). Using digital phenotyping to accurately detect depression severity. Journal of Nervous and Mental Disease, 207(10), 893–896. https://doi.org/10.1097/nmd.0000000000001042 DOI: https://doi.org/10.1097/NMD.0000000000001042
Kroenke, K., & Spitzer, R. L. (2002). The PHQ-9: a new depression diagnostic and severity measure. Psychiatric Annals, 32(9), 509-515. https://doi.org/10.3928/0048-5713-20020901-06 DOI: https://doi.org/10.3928/0048-5713-20020901-06
Lim, G. Y., Tam, W. W., Lu, Y., Ho, C. S., Zhang, M. W., & Ho, R. C. (2018). Prevalence of Depression in the Community from 30 Countries between 1994 and 2014. Scientific Reports, 8(1), 2861. https://doi.org/10.1038/s41598-018-21243-x DOI: https://doi.org/10.1038/s41598-018-21243-x
Macaskill, P., Gatsonis, C., Deeks, J., Harbord, R., & Takwoingi, Y. (2010). Chapter 10: Analysing and presenting results. In J. J. Deeks, P. M. Bossuyt, & C. Gatsonis (Eds.), Cochrane handbook for systematic reviews of diagnostic test accuracy version 1.0. The Cochrane Collaboration. https://methods.cochrane.org/sites/methods.cochrane.org.sdt/files/uploads/Chapter%2010%20-%20Version%201.0.pdf
Malgaroli, M., Calderon, A., & Bonanno, G. A. (2021). Networks of major depressive disorder: a systematic review. Clinical Psychology Review, 85, 102000. https://doi.org/10.1016/j.cpr.2021.102000 DOI: https://doi.org/10.1016/j.cpr.2021.102000
Mastoras, R. E., Iakovakis, D., Hadjidimitriou, S., Charisis, V., Kassie, S., Alsaadi, T., Khandoker, A., & Hadjileontiadis, L. J. (2019). Touchscreen typing pattern analysis for remote detection of the depressive tendency. Scientific Reports, 9, 13414. https://doi.org/10.1038/s41598-019-50002-9 DOI: https://doi.org/10.1038/s41598-019-50002-9
Masud, M. T., Mamun, M. A., Thapa, K., Lee, D. H., Griffiths, M. D., & Yang, S. H. (2020). Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone. Journal of Biomedical Informatics, 103, 103371. https://doi.org/10.1016/j.jbi.2019.103371 DOI: https://doi.org/10.1016/j.jbi.2019.103371
McInnes, M. D. F., Moher, D., Thombs, B. D., McGrath, T. A., Bossuyt, P. M., & The PRISMA-DTA Group. (2018). Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: the PRISMA-DTA statement. JAMA, 319(4), 388–396. https://doi.org/10.1001/jama.2017.19163 DOI: https://doi.org/10.1001/jama.2017.19163
Narziev, N., Goh, H., Toshnazarov, K., Lee, S. A., Chung, K. M., & Noh, Y. (2020). STDD: Short-Term Depression Detection with Passive Sensing. Sensors, 20(5), 1396. https://doi.org/10.3390/s20051396 DOI: https://doi.org/10.3390/s20051396
Netto, A. V. (2020). Application of blended care as a mechanism of action in the construction of digital therapeutics. einstein (São Paulo), 18. https://10.31744/einstein_journal/2020MD5640 DOI: https://doi.org/10.31744/einstein_journal/2020MD5640
Netto, A. V., & Petraroli, A. G. (2020). Modelagem de um sistema para o telemonitoramento de idosos com condição crônica baseado em biotelemetria, Journal of Health Informatics, 12(1). https://jhi.sbis.org.br/index.php/jhi-sbis/article/view/691/379
Netto, A. V. (2024). Proposal for the application of a blended care platform for patients with depressive symptoms. Revista Saúde Multidisciplinar, 16(1). https://doi.org/10.53740/rsm.v16i1.742 DOI: https://doi.org/10.53740/rsm.v16i1.742
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., & Brennan, S. E. J. S. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Systematic Reviews, 10(89). https://doi.org/10.1186/s13643-021-01626-4 DOI: https://doi.org/10.1186/s13643-021-01626-4
Sarda, A., Munuswamy, S., Sarda, S., & Subramanian, V. (2019). Using passive smartphone sensing for improved risk stratification of patients with depression and diabetes: cross-sectional observational study. JMIR Mhealth Uhealth, 7(1), e11041. https://doi.org/10.2196/11041 DOI: https://doi.org/10.2196/11041
Schueler, S., Schuetz, G. M., & Dewey, M. (2012). The revised QUADAS-2 tool [letter]. Annals of Internal Medicine, 156(4). https://doi.org/10.7326/0003-4819-156-4-201202210-00018 DOI: https://doi.org/10.7326/0003-4819-156-4-201202210-00018
Seppälä, J., De Vita, I., Jämsä, T., Miettunen, J., Isohanni, M., Rubinstein, K., Feldman, Y., Grasa, E., Corripio, I., & Berdun, J. J. J. (2019). Mobile phone and wearable sensor-based mHealth approaches for psychiatric disorders and symptoms: systematic review. JMIR Mental Health, 6(2), e9819. https://doi.org/10.2196/mental.9819 DOI: https://doi.org/10.2196/mental.9819
Snaith, R., & Taylor, C. (1985). Rating scales for depression and anxiety: a current perspective. British Journal of Clinical Pharmacology, 19(S1), 17S-20S. https://doi.org/10.1111/j.1365-2125.1985.tb02737.x DOI: https://doi.org/10.1111/j.1365-2125.1985.tb02737.x
Ware, S., Yue, C., Morillo, R., Lu, J., Shang, C., Bi, J., Kamath, J., Russel, A., Bamis, A., & Wang, B. (2020). Predicting depressive symptoms using smartphone data. Smart Health, 15, 100093. https://doi.org/10.1016/j.smhl.2019.100093 DOI: https://doi.org/10.1016/j.smhl.2019.100093
Whiting, P. F., Rutjes, A. W., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., Leeflang, M. M., Sterne, J. A., Bossuyt, P. M., & Medicine, Q. G. J. A. (2011). QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Annals of Internal Medicine, 155(8). https://doi.org/10.7326/0003-4819-155-8-201110180-00009 DOI: https://doi.org/10.7326/0003-4819-155-8-201110180-00009
Wolff, R. F., Moons, K. G., Riley, R. D., Whiting, P. F., Westwood, M., Collins, G. S., Reitsma, J. B., Kleijnen, J., & Mallett, S. on behalf of the PROBAST group†. (2019). PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Annals of Internal Medicine, 170(1). https://doi.org/10.7326/M18-1376 DOI: https://doi.org/10.7326/M18-1376
Zanella-Calzada, L. A., Galván-Tejada, C. E., Chávez-Lamas, N. M., del Carmen Gracia-Cortés, M., Magallanes-Quintanar, R., Celaya-Padilla, J. M., Galván-Tejada, J. I., & Gamboa-Rosales, H. (2019). Feature extraction in motor activity signal: towards depression episodes detection in unipolar and bipolar patients. Diagnostics, 9(1), 8. https://doi.org/10.3390/diagnostics9010008 DOI: https://doi.org/10.3390/diagnostics9010008
Zarate, D., Stravopoulos, V., Ball, M., Collier, G., & Jacobson, N. (2022). Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry, 22, 421. https://doi.org/10.1186/s12888-022-04013-y DOI: https://doi.org/10.1186/s12888-022-04013-y
Zebin, T., Peek, N., & Casson, A. J. (2019). Physical activity-based classification of serious mental illness group participants in the UK Biobank using ensemble dense neural networks [Abstract]. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, pp. 1251–1254. https://doi.org/10.1109/EMBC.2019.8857532 DOI: https://doi.org/10.1109/EMBC.2019.8857532
Zhou, Q., Lu, S., Wu, Y., & Wang, J. (2020). Property-oriented material design based on a data-driven machine learning technique. The Journal of Physical Chemistry Letters, 11(10). https://doi.org/10.1021/acs.jpclett.0c00951ttps://doi.org/10.1038/s41598-018-21243-x DOI: https://doi.org/10.1021/acs.jpclett.0c00665
Downloads
Publicado
Como Citar
Edição
Secção
Licença
Direitos de Autor (c) 2025 Matheus Damasceno, Paulo Abrantes, André Takahata, Antonio Valerio Netto
Este trabalho encontra-se publicado com a Licença Internacional Creative Commons Atribuição 4.0.
