Smartphones in Mental Health: Detecting Depressive and Manic Episodes

Charting a course for genomic medicine from base pairs to bedside

Green, E. D., & Guyer, M. S. (2011) · Nature · doi:10.1038/nature09764 · Cited by 901 · see citing works

Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients

Grunerbl, A., Muaremi, A., Osmani, V., Bahle, G., Ohler, S., Troster, G., Mayora, O., Haring, C., & Lukowicz, P. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2014.2343154 · Cited by 384 · see citing works

Automatic Stress Detection in Working Environments From Smartphones’ Accelerometer Data: A First Step

Garcia-Ceja, E., Osmani, V., & Mayora, O. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2446195 · Cited by 267 · see citing works

Reflections of depression in acoustic measures of the patient’s speech

Alpert, M., Pouget, E. R., & Silva, R. R. (2001) · Journal of Affective Disorders · doi:10.1016/s0165-0327(00)00335-9 · Cited by 227 · see citing works

Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients

Gruenerbl, A., Osmani, V., Bahle, G., Carrasco, J. C., Oehler, S., Mayora, O., Haring, C., & Lukowicz, P. (2014) · Proceedings of the 5th Augmented Human International Conference · doi:10.1145/2582051.2582089 · Cited by 139 · see citing works

Monitoring activity of patients with bipolar disorder using smart phones

Osmani, V., Maxhuni, A., Grunerbl, A., Lukowicz, P., Haring, C., & Mayora, O. (2013) · Advances in Mobile Multimedia · doi:10.1145/2536853.2536882 · Cited by 89 · see citing works

Relapse prediction in schizophrenia through digital phenotyping: a pilot study

Barnett, I., Torous, J. B., Staples, P. C., Sandoval, L. R., Keshavan, M. S., & Onnela, J.-P. “. (2018) · Neuropsychopharmacology · doi:10.1038/s41386-018-0030-z · Cited by 435 · see citing works

Enabling Technologies for the Internet of Health Things

Rodrigues, J. J. P. C., Segundo, D. B. R., Junqueira, H. A., Sabino, M. H., Prince, R. M., Al-Muhtadi, J. F., & de Albuquerque, V. H. C. (2018) · IEEE Access · doi:10.1109/access.2017.2789329 · Cited by 415 · see citing works

Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing

Wang, R., Wang, W., daSilva, A., Huckins, J. F., Kelley, W. M., Heatherton, T. F., & Campbell, A. T. (2018) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3191775 · Cited by 340 · see citing works

Voice analysis as an objective state marker in bipolar disorder

Faurholt‐Jepsen, M., Busk, J., Frost, M. M., Vinberg, M., Christensen, E. M., Winther, O., Bardram, J. E., & Kessing, L. V. (2016) · Translational Psychiatry · doi:10.1038/tp.2016.123 · Cited by 236 · see citing works

Mobile phone and wearable sensor-based mHealth approaches for psychiatric disorders and symptoms: systematic review

Seppälä, J., De Vita, I., Jämsä, T., Miettunen, J., Isohanni, M., Rubinstein, K., Feldman, Y., Grasa, E., Corripio, I., Berdun, J., et al. (2019) · JMIR Mental Health · doi:10.2196/mental.9819 · Cited by 148 · see citing works

Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones

Jacobson, N. C., & Chung, Y. J. (2020) · Sensors · doi:10.3390/s20123572 · Cited by 139 · see citing works

Speech‐based markers for posttraumatic stress disorder in US veterans

Marmar, C. R., Brown, A. D., Qian, M., LASKA, E. M., Siegel, C., Li, M., Amara, D. A., Tsiartas, A., Richey, C., Smith, J. G., et al. (2019) · Depression and Anxiety · doi:10.1002/da.22890 · Cited by 129 · see citing works

The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good

Lepri, B., Staiano, J., Sangokoya, D., Letouzé, E., & Oliver, N. (2017) · Studies in Big Data · doi:10.1007/978-3-319-54024-5_1 · Cited by 111 · see citing works

Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing

Wang, R., Wang, W., Aung, M. H., Ben‐Zeev, D., Brian, R., Campbell, A. T., Choudhury, T., Hauser, M., Kane, J. M., Scherer, E. A., et al. (2017) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3130976 · Cited by 105 · see citing works

Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks

Adler, D. A., Ben-Zeev, D., Tseng, V. W. S., Kane, J., Brian, R., Campbell, A. T., Hauser, M., Scherer, E. A., & Choudhury, T. (2020) · JMIR mHealth and uHealth · doi:10.2196/19962 · Cited by 104 · see citing works

Smartphone-based objective monitoring in bipolar disorder: status and considerations

Faurholt‐Jepsen, M., Bauer, M., & Kessing, L. V. (2018) · International Journal of Bipolar Disorders · doi:10.1186/s40345-017-0110-8 · Cited by 100 · see citing works

The revolution of personalized psychiatry: will technology make it happen sooner?

Perna, G., Grassi, M., Caldirola, D., & Nemeroff, C. B. (2017) · Psychological Medicine · doi:10.1017/s0033291717002859 · Cited by 99 · see citing works

Classification of bipolar disorder episodes based on analysis of voice and motor activity of patients

Maxhuni, A., Muñoz-Meléndez, A., Osmani, V., Perez, H., Mayora, O., & Morales, E. F. (2016) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2016.01.008 · Cited by 93 · see citing works

Online and mobile technologies for self-management in bipolar disorder: A systematic review.

Gliddon, E., Barnes, S. J., Murray, G., & Michalak, E. E. (2017) · Psychiatric Rehabilitation Journal · doi:10.1037/prj0000270 · Cited by 84 · see citing works

Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling

Antosik-Wójcińska, A. Z., Dominiak, M., Chojnacka, M., Kaczmarek‐Majer, K., Opara, K. R., Radziszewska, W., Olwert, A., & Święcicki, Ł. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104131 · Cited by 82 · see citing works

A Survey of Attention Management Systems in Ubiquitous Computing Environments

Anderson, C., Hübener, I., Seipp, A.-K., Ohly, S., David, K., & Pejović, V. (2018) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3214261 · Cited by 80 · see citing works

The Role of Data Type and Recipient in Individuals’ Perspectives on Sharing Passively Collected Smartphone Data for Mental Health: Cross-Sectional Questionnaire Study

Nicholas, J., Shilton, K., Schueller, S. M., Gray, E. L., Kwasny, M. J., & Mohr, D. C. (2019) · JMIR mHealth and uHealth · doi:10.2196/12578 · Cited by 77 · see citing works

Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone

Masud, M. T., Mamun, M. A., Thapa, K., Lee, D. H., Griffiths, M. D., & Yang, S. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103371 · Cited by 73 · see citing works

Stress modelling and prediction in presence of scarce data

Maxhuni, A., Hernandez-Leal, P., Sucar, L., Osmani, V., Morales, E., & Mayora, O. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.08.023 · Cited by 61 · see citing works

A Scoping Review of Sensors, Wearables, and Remote Monitoring For Behavioral Health: Uses, Outcomes, Clinical Competencies, and Research Directions

Hilty, D. M., Armstrong, C. M., Luxton, D. D., Gentry, M. T., & Krupinski, E. A. (2021) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-021-00199-2 · Cited by 68 · see citing works

Just-in-Time Adaptive Mechanisms of Popular Mobile Apps for Individuals With Depression: Systematic App Search and Literature Review

Teepe, G. W., Fonseca, A. D., Kleim, B., Jacobson, N. C., Salamanca-Sanabria, A., Car, L. T., Fleisch, E., & Kowatsch, T. (2021) · Journal of Medical Internet Research · doi:10.2196/29412 · Cited by 60 · see citing works

Using Passive Smartphone Sensing for Improved Risk Stratification of Patients With Depression and Diabetes: Cross-Sectional Observational Study.

Sarda, A., Munuswamy, S., Sarda, S., & Subramanian, V. (2019) · JMIR mHealth and uHealth · doi:10.2196/11041 · Cited by 56 · see citing works

Portable technologies for digital phenotyping of bipolar disorder: A systematic review

Saccaro, L. F., Amatori, G., Cappelli, A., Mazziotti, R. M., Dell’Osso, L., & Rutigliano, G. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.08.052 · Cited by 58 · see citing works

Predicting Negative Emotions Based on Mobile Phone Usage Patterns: An Exploratory Study

Hung, G. C., Yang, P.-C., Chang, C.-C., Chiang, J.-H., & Chen, Y. (2016) · JMIR Research Protocols · doi:10.2196/resprot.5551 · Cited by 49 · see citing works

Using Machine Learning and Smartphone and Smartwatch Data to Detect Emotional States and Transitions: Exploratory Study

Sultana, M., Al-Jefri, M., & Lee, J. (2020) · JMIR mHealth and uHealth · doi:10.2196/17818 · Cited by 46 · see citing works

A framework for assessing neuropsychiatric phenotypes by using smartphone-based location data

Jongs, N., Jagesar, R. R., van Haren, N. E. M., Penninx, B. W. J. H., Reus, L. M., Visser, P. J., van der Wee, N. J. A., Koning, I. M., Arango, C., Sommer, I. E., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00893-4 · Cited by 44 · see citing works

Neuro-Nutrition and Exercise Synergy: Exploring the Bioengineering of Cognitive Enhancement and Mental Health Optimization

Clemente‐Suárez, V. J., Martín-Rodríguez, A., Curiel-Regueros, A., Rubio-Zarapuz, A., & Tornero-Aguilera, J. F. (2025) · Bioengineering · doi:10.3390/bioengineering12020208 · Cited by 47 · see citing works

Disease management apps and technical assistance systems for bipolar disorder: Investigating the patients´ point of view

Daus, H., Kislicyn, N., Heuer, S., & Backenstraß, M. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.12.059 · Cited by 44 · see citing works

Using Acoustic Speech Patterns From Smartphones to Investigate Mood Disorders: Scoping Review

Flanagan, O., Chan, A. H. Y., Roop, P. S., & Sundram, F. (2021) · JMIR mhealth and uhealth · doi:10.2196/24352 · Cited by 39 · see citing works

Pilot Quasi-Experimental Research on the Effectiveness of the Woebot AI Chatbot for Reducing Mild Depression Symptoms among Athletes

Qi, Y. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2301256 · Cited by 36 · see citing works

Human-centred artificial intelligence for mobile health sensing: challenges and opportunities

Dang, T., Spathis, D., Ghosh, A., & Mascolo, C. (2023) · Royal Society Open Science · doi:10.1098/rsos.230806 · Cited by 30 · see citing works

Utility of Consumer-Grade Wearable Devices for Inferring Physical and Mental Health Outcomes in Severe Mental Illness: Systematic Review

Hassan, L., Milton, A. C., Sawyer, C. S., Casson, A. J., Torous, J. B., Davies, A., Ruiz‐Yu, B., & Firth, J. (2025) · JMIR Mental Health · doi:10.2196/65143 · Cited by 30 · see citing works

High potential but limited evidence: Using voice data from smartphones to monitor and diagnose mood disorders.

Or, F., Torous, J. B., & Onnela, J.-P. “. (2017) · Psychiatric Rehabilitation Journal · doi:10.1037/prj0000279 · Cited by 30 · see citing works

Predicting Brain Functional Connectivity Using Mobile Sensing

Obuchi, M., Huckins, J. F., Wang, W., daSilva, A., Rogers, C., Murphy, E., Hedlund, E., Holtzheimer, P., Mirjafari, S., & Campbell, A. (2020) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3381001 · Cited by 29 · see citing works

Commonly available activity tracker apps and wearables as a mental health outcome indicator: A prospective observational cohort study among young adults with psychological distress

Knight, A., & Bidargaddi, N. P. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.04.099 · Cited by 26 · see citing works

The integration of geographic methods and ecological momentary assessment in public health research: A systematic review of methods and applications

Zhang, Y., Li, D., Li, X., Zhou, X., & Newman, G. (2024) · Social Science & Medicine · doi:10.1016/j.socscimed.2024.117075 · Cited by 24 · see citing works

Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods

Kim, S., & Lee, K. (2021) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s339412 · Cited by 25 · see citing works

Precision Psychiatry: Biomarker-Guided Tailored Therapy for Effective Treatment and Prevention in Major Depression

Jones, C., & Nemeroff, C. B. (2021) · Advances in experimental medicine and biology · doi:10.1007/978-981-33-6044-0_27 · Cited by 22 · see citing works

Mobile phone enabled mental health monitoring to enhance diagnosis for severity assessment of behaviours: a review

Gopalakrishnan, A., Venkataraman, R., Gururajan, R., Zhou, X., & Genrich, R. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.1042 · Cited by 19 · see citing works

The Extent of User Involvement in the Design of Self-tracking Technology for Bipolar Disorder: Literature Review

Majid, S., Reeves, S., Figueredo, G. P., Brown, S., Lang, A. R., Moore, M. R., & Morriss, R. K. (2021) · JMIR Mental Health · doi:10.2196/27991 · Cited by 18 · see citing works

Speech stress recognition using semi-eager learning

Yerigeri, V. V., & Ragha, L. K. (2020) · Cognitive Systems Research · doi:10.1016/j.cogsys.2020.10.001 · Cited by 16 · see citing works

Development and validation of the BD SX: a brief measure of mood and symptom variability for use with adults with bipolar disorder

BADAS Study Team, O’Rourke, N., Sixsmith, A. J., King, D. B., Shahir, H. Y., & Canham, S. L. (2016) · International Journal of Bipolar Disorders · doi:10.1186/s40345-016-0048-2 · Cited by 16 · see citing works

Digital Avatars: Promoting Independent Living for Older Adults

Bertoa, M. F., Moreno, N., Pérez-Vereda, A., Bandera, D., Álvarez-Palomo, J. M., & Canal, C. (2020) · Wireless Communications and Mobile Computing · doi:10.1155/2020/8891002 · Cited by 15 · see citing works

Ecological Momentary Assessment for Monitoring Risk of Suicide Behavior

Carretero, P., Campaña-Montes, J. J., & Artés-Rodrı́guez, A. (2020) · Current topics in behavioral neurosciences · doi:10.1007/7854_2020_170 · Cited by 15 · see citing works

Security and Privacy in Machine Learning for IoHT and IoMT: A ReviewPreprint

Priyadarshi, R., & Gheisari, M. (2024) · Preprints.org · doi:10.20944/preprints202403.0329.v1 · Cited by 12 · see citing works

Measurement equivalence of the BDSx scale with young and older adults with bipolar disorder

O’Rourke, N., Bachner, Y. G., Canham, S. L., Sixsmith, A. J., & BADAS Study Team (2017) · Psychiatry Research · doi:10.1016/j.psychres.2017.10.024 · Cited by 12 · see citing works

Use of voice features from smartphones for monitoring depressive disorders: Scoping review

Shin, J., & Bae, S. (2024) · Digital Health · doi:10.1177/20552076241261920 · Cited by 10 · see citing works

Acoustic and Natural Language Markers for Bipolar Disorder: A Pilot, mHealth Cross-Sectional Study

Crocamo, C., Cioni, R. M., Canestro, A., Nasti, C., Palpella, D., Piacenti, S., Bartoccetti, A., Re, M., Simonetti, V., Pietro, C. B. D. S., et al. (2025) · JMIR Formative Research · doi:10.2196/65555 · Cited by 10 · see citing works

Classification of Depressive Episodes Using Nighttime Data; a Multivariate and Univariate Analysis

Rodríguez-Ruiz, J. G., Galvan-Tejada, C. E., Vázquez-Reyes, S., Galván-Tejada, J. I., & Gamboa-Rosales, H. (2020) · Programming and Computer Software · doi:10.1134/s0361768820080198 · Cited by 10 · see citing works

Exploring self-tracking practices for those with lived experience of bipolar disorder

Majid, S., Morriss, R. K., Figueredo, G. P., & Reeves, S. (2022) · Designing Interactive Systems Conference · doi:10.1145/3532106.3533531 · Cited by 9 · see citing works

Nighttime Depression Episodes Classification using a Formal Method: Knowledge Discovery in Databases

Rodríguez-Ruiz, J. G., Galvan-Tejada, C. E., Vázquez-Reyes, S., Galván-Tejada, J. I., & Gamboa-Rosales, H. (2019) · International Conference in Software Engineering Research and Innovation (CONISOFT) · doi:10.1109/conisoft.2019.00037 · Cited by 9 · see citing works

Mobile Health (m-health) on Mental Health

Jang, J. S., & Cho, S. (2016) · Korean Journal of Stress Research · doi:10.17547/kjsr.2016.24.4.231 · Cited by 9 · see citing works

Processing of Electronic Health Records using Deep Learning: A reviewPreprint

Osmani, V., Li, L., Danieletto, M., Glicksberg, B. S., Dudley, J. T., & Mayora, O. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.01758 · Cited by 8 · see citing works

Passive detection of behavioral shifts for suicide attempt preventionPreprint

Moreno-Muñoz, P., Romero-Medrano, L., Moreno, Á., Herrera-López, J., Baca‐García, E., & Artés-Rodrı́guez, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.09848 · Cited by 8 · see citing works

Empathy and Empathic Disconnection in Difficult and Uneasy Situations: Facing the Suicidal Individual

Donelli, D., & Rizzato, M. (2017) · Springer eBooks · doi:10.1007/978-3-319-47976-7_6 · Cited by 8 · see citing works

Smartphone-Based Wellness Assessment Using Mobile Environmental Sensors

McLeod, K., Spachos, P., & Plataniotis, K. N. (2020) · IEEE Systems Journal · doi:10.1109/jsyst.2020.3004558 · Cited by 7 · see citing works

HealthSense

Curtis, A., Pai, A., Cao, J. R., Moukaddam, N. J., & Sabharwal, A. (2019) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3300061.3345433 · Cited by 8 · see citing works

Detecting dressing failures using temporal–relational visual grammars

Ruiz, E., Osmani, V., Sucar, L. E., & Mayora, O. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0975-0 · Cited by 7 · see citing works

Automatic processing of Electronic Medical Records using Deep Learning

Osmani, V., Li, L., Danieletto, M., Glicksberg, B. S., Dudley, J. T., & Mayora, O. (2018) · International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3240925.3240961 · Cited by 7 · see citing works

Stress Modelling Using Transfer Learning in Presence of Scarce Data

Hernandez-Leal, P., Maxhuni, A., Sucar, L. E., Osmani, V., Morales, E., & Mayora, O. (2015) · International Conference on Ambient Intelligence for Health · doi:10.1007/978-3-319-26508-7_22 · Cited by 11 · see citing works

The behavioral and physiological correlates of affective mood switching in premenstrual dysphoric disorder

Brown, R. D., Bondy, E., Prim, J., Dichter, G. S., & Schiller, C. E. (2024) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2024.1448914 · Cited by 6 · see citing works

Non-Pervasive Monitoring of Daily-Life Behavior to Access Depressive Symptom Severity Via Smartphone Technology

Masud, M. T., Rahman, N., Alam, M. A., Griffiths, M. D., & Alamin, M. (2020) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp50017.2020.9230984 · Cited by 4 · see citing works

A review about Technology in mental health sensing and assessment

Jossou, T. R., Médénou, D., Ettahir, A., Ahouandjinou, M. H., Edoh, T. O., Houessouvo, R. C., & Pecchia, L. (2022) · ITM Web of Conferences · doi:10.1051/itmconf/20224601005 · Cited by 4 · see citing works

Internet of Medical Things ecosystem: regulations, challenges of standards, security mechanisms and future perspectives

Kumari, S., & Prabha, C. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-30300-5.00027-0 · Cited by 2 · see citing works

Verifying the validity of the whole person model of mental health education activities in colleges based on differential equation

Liu, S., & Alyoubi, K. H. (2021) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns.2021.2.00067 · Cited by 2 · see citing works

Using Mobile Sensing to Predict Episodes of Medical Conditions

Liaqat, D. (2016) · doi:10.1145/2930056.2933323 · Cited by 2 · see citing works

Extensible Environment for Monitoring and Detecting Symptoms of Depression

Moguel, E., Berrocal, J., García-Alonso, J. M., Flores-Martin, D., Galan, J., & Murillo, J. M. (2019) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00329 · Cited by 2 · see citing works

Detecting and Monitoring Depression Symptoms According to People’s Behaviour Through Mobile Devices

Flores-Martin, D., Laso, S., Berrocal, J., & Murillo, J. M. (2021) · Lecture notes in bioengineering · doi:10.1007/978-3-030-72567-9_1 · Cited by 1 · see citing works

A decision integration strategy algorithm to detect the depression severity level using wearable and profile data

Hossain, M. N., & Yang, S. (2024) · Iran Journal of Computer Science · doi:10.1007/s42044-024-00187-8 · Cited by 1 · see citing works

Using AI to Detect Psychosis Relapse: Scoping Review

Ghelfi, L., Healy, J., Piacenza, F., Ian, F., McNamara, N., Rabbi, K. A., Bond, B. W., O’Hora, E., Roddy, D. W., Kamali, M., et al. (2026) · JMIR Mental Health · doi:10.2196/92192 · Cited by 1 · see citing works

Speech and Language Markers of Bipolar Disorder: Challenges and Opportunities

Zaher, F., Ahrens, J., Raucher‐Chéné, D., Voppel, A., & Palaniyappan, L. K. (2026) · Bipolar Disorders · doi:10.1111/bdi.70119 · Cited by 1 · see citing works

Sistema de apoio a reablilitação neuromotora: modelo de acompanhamento remoto para a terapia do espelho

Correia, R. D. C. (2016) · doi:10.11606/d.55.2016.tde-29032016-105948

Article Open Access

Lawford‐Smith, H. (2015)

Hopes and Challenges Moving Forward

Fallin, M. D., Holingue, C., Ostrow, L., Leaf, P. J., Manderscheid, R. W., Shern, D. L., Thrul, J., Zandi, P. P., & Eaton, W. W. (2019) · Oxford University Press eBooks · doi:10.1093/oso/9780190916602.003.0020

Digital Shift: Assessment of Mental States Through Passive Mobile Sensing

Krajchevska, E., Petreska, N., Handjiski, O., Andovska, S., Ilijoski, B., Lameski, P., Ribarski, P., & Tojtovska, B. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-04206-5_15

Digital Assessments of Psychiatric Disorders

Quist, R. E., Bhattacharya, S., Chacko, S. M., & Jacobson, N. C. (2025) · doi:10.1093/med/9780197640654.003.0072

Using Artificial Intelligence to Detect Relapse of Psychosis: A Scoping Review (Preprint)Preprint

Ghelfi, L. (2026) · doi:10.2196/preprints.90919

Using Artificial Intelligence to Detect Relapse of Psychosis: A Scoping Review (Preprint)Preprint

Ghelfi, L., Healy, J., Piacenza, F., Ian, F., McNamara, N., Rabbi, K. A., Bond, B. W., O’Hora, E., Roddy, D. W., Kamali, M., et al. (2026) · doi:10.2196/preprints.92192

Mobile Sensing Applications for Mental Health: A Scoping Review

Suruliraj, B., Alslaity, A., Meier, S. M., & Orji, R. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2647542

EmoSay: Artificial Intelligence-Driven Text-to-Emotional-Speech System for Affective Communication in Extended Reality

Adewale, S., Ubur, S. D., Chandrashekar, N. D., Emebo, O., & Gračanin, D. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31421-5_1

Machine learning for early detection and mood shift prediction in bipolar disorder: a systematic review

Fang, S., & Chen, S.-H. (2026) · International Journal of Bipolar Disorders · doi:10.1186/s40345-026-00436-8

Exploring the nature, development and effects of complex emotions in Sheffield and Plymouth, UK: a longitudinal mixed methods study protocol

Ohlsen, S., Anderson, D., Bastounis, A., Bhui, K. S., Booth, A., Burton, C. D., Byng, R., Chico, T. J., Couperthwaite, L. M. Z., Crimlisk, H., et al. (2026) · BMJ Open · doi:10.1136/bmjopen-2026-117088

Towards smart phone based monitoring of bipolar disorder

Grünerbl, A., Oleksy, P., Bahle, G., Häring, C., Weppner, J., & Lukowicz, P. (2012) · ACM Workshop on Mobile Systems, Applications, and Services for Healthcare (mHealthSys) · doi:10.1145/2396276.2396280 · Cited by 53 · see citing works

Utilizing Smartphones as an Effective Way to Support Patients with Bipolar Disorder: Results of the Monarca Study

Haring, C., Banzer, R., Gruenerbl, A., Oehler, S., Bahle, G., Lukowicz, P., & Mayora, O. (2015) · European Psychiatry · doi:10.1016/s0924-9338(15)30442-9 · Cited by 9 · see citing works

Using smart phone mobility traces for the diagnosis of depressive and manic episodes in bipolar patients

Gruenerbl, A., Osmani, V., Bahle, G., Carrasco, J. C., Oehler, S., Mayora, O., Haring, C., & Lukowicz, P. (2014) · Proceedings of the 5th Augmented Human International Conference · doi:10.1145/2582051.2582089 · Cited by 139 · see citing works

Objective smartphone data as a potential diagnostic marker of bipolar disorder

Faurholt‐Jepsen, M., Busk, J., Þórarinsdóttir, H., Frost, M. M., Bardram, J. E., Vinberg, M., & Kessing, L. V. (2018) · Australian & New Zealand Journal of Psychiatry · doi:10.1177/0004867418808900 · Cited by 75 · see citing works

Ecological Momentary Assessment of Bipolar Disorder Episodes with a Smartphone Application: Study Protocol

Blom, M., Karilampi, U., Carlsen, H. K., Ioannou, M., Sörhag, M., & Steingrímsson, S. (2017) · European Psychiatry · doi:10.1016/j.eurpsy.2017.01.1999