Identification of possible depressive profile through mobile sensor data in university students
Resumo
The number of individuals with depression is gradually increasing worldwide. In the university population, the prevalence of depression is even higher than in the general population. Health and computing researchers have sought to create computational solutions that can support diagnosis and create interventions for people with a possible depressive profile, analyzing their behavior through the collection of digital phenotyping data, such as heart rate and sleep quality, coming from sensors present in mobile and wearable devices, such as smartphones and smartwatches. Research in this area is multidisciplinary, and Human-Computer Interaction (HCI) researchers have a major role in supporting the design of this technology. In this sense, the goal of this paper is mapping, among others, how final users and health professionals have been involved in this type of research. We performed a Systematic Mapping focusing on the university environment. We extracted data about the methodological approach adopted, sensor data collected, and human involvement. The results show which data is being collected and correlated with depression indicators, the absence of multidisciplinary teams, and the lack of adoption of protocols to manage mental health emergencies. We also discussed possibilities for human-in-the-loop approaches and some design implications for HCI teams.
Palavras-chave:
depression, mobile sensors, wearables, digital phenotyping, college students
Referências
A. T. Beck. 1961. An Inventory for Measuring Depression. Archives of General Psychiatry 4, 6 (June 1961), 561. DOI: 10.1001/archpsyc.1961.01710120031004
Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009. Anomaly detection. ACM Computing Surveys 41, 3 (July 2009), 1–58. DOI: 10.1145/1541880.1541882
Prerna Chikersal, Afsaneh Doryab, Michael Tumminia, Daniella K. Villalba, Janine M. Dutcher, Xinwen Liu, Sheldon Cohen, Kasey G. Creswell, Jennifer Mankoff, J. David Creswell, Mayank Goel, and Anind K. Dey. 2021. Detecting Depression and Predicting Its Onset Using Longitudinal Symptoms Captured by Passive Sensing: A Machine Learning Approach With Robust Feature Selection. ACM Transactions on Computer-Human Interaction 28, 1, Article 3 (January 2021), 41 pages. DOI: 10.1145/3422821
Jean Costa, François Guimbretière, Malte F. Jung, and Tanzeem Choudhury. 2019. BoostMeUp: Improving Cognitive Performance in the Moment by Unobtrusively Regulating Emotions with a Smartwatch. ACM Transactions on Computer-Human Interaction 3, 2, Article 40 (June 2019), 23 pages. DOI: 10.1145/3328911
Pim Cuijpers, Soledad Quero, Christopher Dowrick, and Bruce Arroll. 2019. Psychological Treatment of Depression in Primary Care: Recent Developments. Current Psychiatry Reports 21, 12 (November 2019). DOI: 10.1007/s11920-019-1117-x
Ruixuan Dai, Thomas Kannampallil, Jingwen Zhang, Nan Lv, Jun Ma, and Chenyang Lu. 2022. Multi-Task Learning for Randomized Controlled Trials: A Case Study on Predicting Depression with Wearable Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 2, Article 50 (July 2022), 23 pages. DOI: 10.1145/3534591
Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz. 2013. Predicting depression via social media. In Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media.
DSM. 2022. Depressive Disorders. (March 2022). DOI: 10.1176/appi.books.9780890425787.x04_depressive_disorders
Daniel Eisenberg, Ezra Golberstein, and Sarah E. Gollust. 2007. Help-Seeking and Access to Mental Health Care in a University Student Population. Medical Care 45, 7 (July 2007), 594–601. DOI: 10.1097/mlr.0b013e31803bb4c1
Anya S. Evmenova, Heidi J. Graff, Vivian Genaro Motti, Kudirat Giwa-Lawal, and Hui Zheng. 2019. Designing a Wearable Technology Intervention to Support Young Adults With Intellectual and Developmental Disabilities in Inclusive Postsecondary Academic Environments. Journal of Special Education Technology 34, 2 (2019), 92–105. DOI: 10.1177/0162643418795833
Asma Ahmad Farhan, Chaoqun Yue, Reynaldo Morillo, Shweta Ware, Jin Lu, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, and Bing Wang. 2016. Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data. In Proceedings of the IEEE 11th International Conference on Wearable and Implantable Body Sensor Networks (BodyNet). 1–8. DOI: 10.1109/WH.2016.7764553
Shuichi Fukuda, Yuki Matsuda, Yuri Tani, Yutaka Arakawa, and Keiichi Yasumoto. 2020. Predicting Depression and Anxiety Mood by Wrist-Worn Sleep Sensor. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). 1–6. DOI: 10.1109/PerComWorkshops48775.2020.9156176
Enrique Garcia-Ceja, Michael Riegler, Petter Jakobsen, Jim Tørresen, Tine Nordgreen, Ketil J. Oedegaard, and Ole Bernt Fasmer. 2018. Depresjon: A Motor Activity Database of Depression Episodes in Unipolar and Bipolar Patients. In Proceedings of the 9th ACM Multimedia Systems Conference (MMSys ’18). Association for Computing Machinery, New York, NY, USA, 472–477. DOI: 10.1145/3204949.3208125
GBD. 2018. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 10159 (November 2018), 1789–1858.
Asma Ghandeharioun, Szymon Fedor, Lisa Sangermano, Dawn Ionescu, Jonathan Alpert, Chelsea Dale, David Sontag, and Rosalind Picard. 2017. Objective assessment of depressive symptoms with machine learning and wearable sensors data. In 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). 325–332. DOI: 10.1109/ACII.2017.8273620
Masood Habib, Zhelong Wang, Sen Qiu, Hongyu Zhao, and Aparna S. Murthy. 2022. Machine Learning Based Healthcare System for Investigating the Association Between Depression and Quality of Life. IEEE Journal of Biomedical and Health Informatics 26, 5 (2022), 2008–2019. DOI: 10.1109/JBHI.2022.3140433
Gabriella M. Harari, Nicholas D. Lane, Rui Wang, Benjamin S. Crosier, Andrew T. Campbell, and Samuel D. Gosling. 2016. Using Smartphones to Collect Behavioral Data in Psychological Science. Perspectives on Psychological Science 11, 6 (November 2016), 838–854. DOI: 10.1177/1745691616650285
Ralf Hartmann, Frank M. Schmidt, Christian Sander, and Ulrich Hegerl. 2019. Heart Rate Variability as Indicator of Clinical State in Depression. Frontiers in Psychiatry 9 (January 2019). DOI: 10.3389/fpsyt.2018.00735
Ahmed K. Ibrahim, Shona J. Kelly, Clive E. Adams, and Cris Glazebrook. 2013. A systematic review of studies of depression prevalence in university students. Journal of Psychiatric Research 47, 3 (March 2013), 391–400. DOI: 10.1016/j.jpsychires.2012.11.015
Nicholas C. Jacobson and Yeon Joo Chung. 2020. Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones. Sensors 20, 12 (June 2020), 3572. DOI: 10.3390/s20123572
Spencer L James et al. 2018. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 392, 10159 (November 2018), 1789–1858. DOI: 10.1016/s0140-6736(18)32279-7
Rachel N. Lipari, Jonaki Bose, Sarra L. Hedden, and Eunice Park-Lee. 2018. Key Substance Use and Mental Health Indicators in the United States: Results from the 2017 National Survey on Drug Use and Health. Technical Documents. 124 pages.
Mohini Kilaskar, Neha Saindane, Nabeel Ansari, Dhaval Doshi, and Mayuri Kulkarni. 2021. Machine Learning Algorithms for Analysis and Prediction of Depression. SN Computer Science 3, 2 (December 22, 2021), 103. DOI: 10.1007/s42979-021-00967-0
Kim, J., Hong, J., and Choi, Y. 2021. Automatic Depression Prediction Using Screen Lock/Unlock Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 2, Article 50 (July 2021), 23 pages. DOI: 10.1145/3460437
Beck, A. T. 1961. An Inventory for Measuring Depression. Archives of General Psychiatry 4, 6 (June 1961), 561. DOI: 10.1001/archpsyc.1961.01710120031004
Chandola, V., Banerjee, A., and Kumar, V. 2009. Anomaly detection. Computing Surveys 41, 3 (July 2009), 1–58. DOI: 10.1145/1541880.1541882
Chikersal, P., Doryab, A., Tumminia, M., Villalba, D. K., Dutcher, J. M., Liu, X., Cohen, S., Creswell, K. G., Mankoff, J., Creswell, J. D., Goel, M., and Dey, A. K. 2021. Detecting Depression and Predicting Its Onset Using Longitudinal Symptoms Captured by Passive Sensing: A Machine Learning Approach With Robust Feature Selection. ACM Transactions on Computer-Human Interaction 28, 1, Article 3 (Jan 2021), 41 pages. DOI: 10.1145/3422821
Costa, J., Guimbretière, F., Jung, M. F., and Choudhury, T. 2019. BoostMeUp: Improving Cognitive Performance in the Moment by Unobtrusively Regulating Emotions with a Smartwatch. ACM Transactions on Computer-Human Interaction 3, 2, Article 40 (June 2019), 23 pages. DOI: 10.1145/3328911
Cuijpers, P., Quero, S., Dowrick, C., and Arroll, B. 2019. Psychological Treatment of Depression in Primary Care: Recent Developments. Current Psychiatry Reports 21, 12 (Nov. 2019). DOI: 10.1007/s11920-019-1117-x
Dai, R., Kannampallil, T., Zhang, J., Lv, N., Ma, J., and Lu, C. 2022. Multi-Task Learning for Randomized Controlled Trials: A Case Study on Predicting Depression with Wearable Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 2, Article 50 (July 2022), 23 pages. DOI: 10.1145/3534591
De Choudhury, M., Gamon, M., Counts, S., and Horvitz, E. 2013. Predicting depression via social media. In Proceedings of the Seventh International Conference on Weblogs and Social Media.
DSM. 2022. Depressive Disorders. (March 2022). DOI: 10.1176/appi.books.9780890425787.x04_depressive_disorders
Eisenberg, D., Golberstein, E., and Gollust, S. E. 2007. Help-Seeking and Access to Mental Health Care in a University Student Population. Medical Care 45, 7 (July 2007), 594–601. DOI: 10.1097/mlr.0b013e31803bb4c1
Evmenova, A. S., Graff, H. J., Motti, V. G., Giwa-Lawal, K., and Zheng, H. 2019. Designing a Wearable Technology Intervention to Support Young Adults With Intellectual and Developmental Disabilities in Inclusive Postsecondary Academic Environments. Journal of Special Education Technology 34, 2 (2019), 92–105. DOI: 10.1177/0162643418795833
Farhan, A. A., Yue, C., Morillo, R., Ware, S., Lu, J., Bi, J., Kamath, J., Russell, A., Bamis, A., and Wang, B. 2016. Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data. In Proceedings of the 2016 IEEE Wireless Health (WH). 1–8. DOI: 10.1109/WH.2016.7764553
Fukuda, S., Matsuda, Y., Tani, Y., Arakawa, Y., and Yasumoto, K. 2020. Predicting Depression and Anxiety Mood by Wrist-Worn Sleep Sensor. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). 1–6. DOI: 10.1109/PerComWorkshops48775.2020.9156176
Garcia-Ceja, E., Riegler, M., Jakobsen, P., Tørresen, J., Nordgreen, T., Oedegaard, K. J., and Fasmer, O. B. 2018. Depresjon: A Motor Activity Database of Depression Episodes in Unipolar and Bipolar Patients. In Proceedings of the 9th ACM Multimedia Systems Conference (MMSys ’18). Association for Computing Machinery, New York, NY, USA, 472–477. DOI: 10.1145/3204949.3208125
GBD. 2018. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 392, 10159 (Nov. 2018), 1789–1858.
Ghandeharioun, A., Fedor, S., Sangermano, L., Ionescu, D., Alpert, J., Dale, C., Sontag, D., and Picard, R. 2017. Objective assessment of depressive symptoms with machine learning and wearable sensors data. In 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). 325–332. DOI: 10.1109/ACII.2017.8273620
Habib, M., Wang, Z., Qiu, S., Zhao, H., and Murthy, A. S. 2022. Machine Learning Based Healthcare System for Investigating the Association Between Depression and Quality of Life. IEEE Journal of Biomedical and Health Informatics 26, 5 (2022), 2008–2019. DOI: 10.1109/JBHI.2022.3140433
Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., and Gosling, S. D. 2016. Using Smartphones to Collect Behavioral Data in Psychological Science. Perspectives on Psychological Science 11, 6 (Nov. 2016), 838–854. DOI: 10.1177/1745691616650285
Hartmann, R., Schmidt, F. M., Sander, C., and Hegerl, U. 2019. Heart Rate Variability as Indicator of Clinical State in Depression. Frontiers in Psychiatry 9 (Jan. 2019). DOI: 10.3389/fpsyt.2018.00735
Ibrahim, A. K., Kelly, S. J., Adams, C. E., and Glazebrook, C. 2013. A systematic review of studies of depression prevalence in university students. Journal of Psychiatric Research 47, 3 (March 2013), 391–400. DOI: 10.1016/j.jpsychires.2012.11.015
Jacobson, N. C., and Chung, Y. J. 2020. Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones. Sensors 20, 12 (June 2020), 3572. DOI: 10.3390/s20123572
James, S. L., et al. 2018. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 392, 10159 (Nov. 2018), 1789–1858. DOI: 10.1016/s0140-6736(18)32279-7
Lipari, R. N., Bose, J., Hedden, S. L., and Park-Lee, E. 2018. Key Substance Use and Mental Health Indicators in the United States: Results from the 2017 National Survey on Drug Use and Health. Technical documents. 124 pages.
Kilaskar, M., Saindane, N., Ansari, N., Doshi, D., and Kulkarni, M. 2021. Machine Learning Algorithms for Analysis and Prediction of Depression. SN Computer Science 3, 2 (Dec 22, 2021), 103. DOI: 10.1007/s42979-021-00967-0
Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009. Anomaly detection. ACM Computing Surveys 41, 3 (July 2009), 1–58. DOI: 10.1145/1541880.1541882
Prerna Chikersal, Afsaneh Doryab, Michael Tumminia, Daniella K. Villalba, Janine M. Dutcher, Xinwen Liu, Sheldon Cohen, Kasey G. Creswell, Jennifer Mankoff, J. David Creswell, Mayank Goel, and Anind K. Dey. 2021. Detecting Depression and Predicting Its Onset Using Longitudinal Symptoms Captured by Passive Sensing: A Machine Learning Approach With Robust Feature Selection. ACM Transactions on Computer-Human Interaction 28, 1, Article 3 (January 2021), 41 pages. DOI: 10.1145/3422821
Jean Costa, François Guimbretière, Malte F. Jung, and Tanzeem Choudhury. 2019. BoostMeUp: Improving Cognitive Performance in the Moment by Unobtrusively Regulating Emotions with a Smartwatch. ACM Transactions on Computer-Human Interaction 3, 2, Article 40 (June 2019), 23 pages. DOI: 10.1145/3328911
Pim Cuijpers, Soledad Quero, Christopher Dowrick, and Bruce Arroll. 2019. Psychological Treatment of Depression in Primary Care: Recent Developments. Current Psychiatry Reports 21, 12 (November 2019). DOI: 10.1007/s11920-019-1117-x
Ruixuan Dai, Thomas Kannampallil, Jingwen Zhang, Nan Lv, Jun Ma, and Chenyang Lu. 2022. Multi-Task Learning for Randomized Controlled Trials: A Case Study on Predicting Depression with Wearable Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 2, Article 50 (July 2022), 23 pages. DOI: 10.1145/3534591
Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz. 2013. Predicting depression via social media. In Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media.
DSM. 2022. Depressive Disorders. (March 2022). DOI: 10.1176/appi.books.9780890425787.x04_depressive_disorders
Daniel Eisenberg, Ezra Golberstein, and Sarah E. Gollust. 2007. Help-Seeking and Access to Mental Health Care in a University Student Population. Medical Care 45, 7 (July 2007), 594–601. DOI: 10.1097/mlr.0b013e31803bb4c1
Anya S. Evmenova, Heidi J. Graff, Vivian Genaro Motti, Kudirat Giwa-Lawal, and Hui Zheng. 2019. Designing a Wearable Technology Intervention to Support Young Adults With Intellectual and Developmental Disabilities in Inclusive Postsecondary Academic Environments. Journal of Special Education Technology 34, 2 (2019), 92–105. DOI: 10.1177/0162643418795833
Asma Ahmad Farhan, Chaoqun Yue, Reynaldo Morillo, Shweta Ware, Jin Lu, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, and Bing Wang. 2016. Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data. In Proceedings of the IEEE 11th International Conference on Wearable and Implantable Body Sensor Networks (BodyNet). 1–8. DOI: 10.1109/WH.2016.7764553
Shuichi Fukuda, Yuki Matsuda, Yuri Tani, Yutaka Arakawa, and Keiichi Yasumoto. 2020. Predicting Depression and Anxiety Mood by Wrist-Worn Sleep Sensor. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). 1–6. DOI: 10.1109/PerComWorkshops48775.2020.9156176
Enrique Garcia-Ceja, Michael Riegler, Petter Jakobsen, Jim Tørresen, Tine Nordgreen, Ketil J. Oedegaard, and Ole Bernt Fasmer. 2018. Depresjon: A Motor Activity Database of Depression Episodes in Unipolar and Bipolar Patients. In Proceedings of the 9th ACM Multimedia Systems Conference (MMSys ’18). Association for Computing Machinery, New York, NY, USA, 472–477. DOI: 10.1145/3204949.3208125
GBD. 2018. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 10159 (November 2018), 1789–1858.
Asma Ghandeharioun, Szymon Fedor, Lisa Sangermano, Dawn Ionescu, Jonathan Alpert, Chelsea Dale, David Sontag, and Rosalind Picard. 2017. Objective assessment of depressive symptoms with machine learning and wearable sensors data. In 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). 325–332. DOI: 10.1109/ACII.2017.8273620
Masood Habib, Zhelong Wang, Sen Qiu, Hongyu Zhao, and Aparna S. Murthy. 2022. Machine Learning Based Healthcare System for Investigating the Association Between Depression and Quality of Life. IEEE Journal of Biomedical and Health Informatics 26, 5 (2022), 2008–2019. DOI: 10.1109/JBHI.2022.3140433
Gabriella M. Harari, Nicholas D. Lane, Rui Wang, Benjamin S. Crosier, Andrew T. Campbell, and Samuel D. Gosling. 2016. Using Smartphones to Collect Behavioral Data in Psychological Science. Perspectives on Psychological Science 11, 6 (November 2016), 838–854. DOI: 10.1177/1745691616650285
Ralf Hartmann, Frank M. Schmidt, Christian Sander, and Ulrich Hegerl. 2019. Heart Rate Variability as Indicator of Clinical State in Depression. Frontiers in Psychiatry 9 (January 2019). DOI: 10.3389/fpsyt.2018.00735
Ahmed K. Ibrahim, Shona J. Kelly, Clive E. Adams, and Cris Glazebrook. 2013. A systematic review of studies of depression prevalence in university students. Journal of Psychiatric Research 47, 3 (March 2013), 391–400. DOI: 10.1016/j.jpsychires.2012.11.015
Nicholas C. Jacobson and Yeon Joo Chung. 2020. Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones. Sensors 20, 12 (June 2020), 3572. DOI: 10.3390/s20123572
Spencer L James et al. 2018. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 392, 10159 (November 2018), 1789–1858. DOI: 10.1016/s0140-6736(18)32279-7
Rachel N. Lipari, Jonaki Bose, Sarra L. Hedden, and Eunice Park-Lee. 2018. Key Substance Use and Mental Health Indicators in the United States: Results from the 2017 National Survey on Drug Use and Health. Technical Documents. 124 pages.
Mohini Kilaskar, Neha Saindane, Nabeel Ansari, Dhaval Doshi, and Mayuri Kulkarni. 2021. Machine Learning Algorithms for Analysis and Prediction of Depression. SN Computer Science 3, 2 (December 22, 2021), 103. DOI: 10.1007/s42979-021-00967-0
Kim, J., Hong, J., and Choi, Y. 2021. Automatic Depression Prediction Using Screen Lock/Unlock Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 2, Article 50 (July 2021), 23 pages. DOI: 10.1145/3460437
Beck, A. T. 1961. An Inventory for Measuring Depression. Archives of General Psychiatry 4, 6 (June 1961), 561. DOI: 10.1001/archpsyc.1961.01710120031004
Chandola, V., Banerjee, A., and Kumar, V. 2009. Anomaly detection. Computing Surveys 41, 3 (July 2009), 1–58. DOI: 10.1145/1541880.1541882
Chikersal, P., Doryab, A., Tumminia, M., Villalba, D. K., Dutcher, J. M., Liu, X., Cohen, S., Creswell, K. G., Mankoff, J., Creswell, J. D., Goel, M., and Dey, A. K. 2021. Detecting Depression and Predicting Its Onset Using Longitudinal Symptoms Captured by Passive Sensing: A Machine Learning Approach With Robust Feature Selection. ACM Transactions on Computer-Human Interaction 28, 1, Article 3 (Jan 2021), 41 pages. DOI: 10.1145/3422821
Costa, J., Guimbretière, F., Jung, M. F., and Choudhury, T. 2019. BoostMeUp: Improving Cognitive Performance in the Moment by Unobtrusively Regulating Emotions with a Smartwatch. ACM Transactions on Computer-Human Interaction 3, 2, Article 40 (June 2019), 23 pages. DOI: 10.1145/3328911
Cuijpers, P., Quero, S., Dowrick, C., and Arroll, B. 2019. Psychological Treatment of Depression in Primary Care: Recent Developments. Current Psychiatry Reports 21, 12 (Nov. 2019). DOI: 10.1007/s11920-019-1117-x
Dai, R., Kannampallil, T., Zhang, J., Lv, N., Ma, J., and Lu, C. 2022. Multi-Task Learning for Randomized Controlled Trials: A Case Study on Predicting Depression with Wearable Data. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 2, Article 50 (July 2022), 23 pages. DOI: 10.1145/3534591
De Choudhury, M., Gamon, M., Counts, S., and Horvitz, E. 2013. Predicting depression via social media. In Proceedings of the Seventh International Conference on Weblogs and Social Media.
DSM. 2022. Depressive Disorders. (March 2022). DOI: 10.1176/appi.books.9780890425787.x04_depressive_disorders
Eisenberg, D., Golberstein, E., and Gollust, S. E. 2007. Help-Seeking and Access to Mental Health Care in a University Student Population. Medical Care 45, 7 (July 2007), 594–601. DOI: 10.1097/mlr.0b013e31803bb4c1
Evmenova, A. S., Graff, H. J., Motti, V. G., Giwa-Lawal, K., and Zheng, H. 2019. Designing a Wearable Technology Intervention to Support Young Adults With Intellectual and Developmental Disabilities in Inclusive Postsecondary Academic Environments. Journal of Special Education Technology 34, 2 (2019), 92–105. DOI: 10.1177/0162643418795833
Farhan, A. A., Yue, C., Morillo, R., Ware, S., Lu, J., Bi, J., Kamath, J., Russell, A., Bamis, A., and Wang, B. 2016. Behavior vs. introspection: refining prediction of clinical depression via smartphone sensing data. In Proceedings of the 2016 IEEE Wireless Health (WH). 1–8. DOI: 10.1109/WH.2016.7764553
Fukuda, S., Matsuda, Y., Tani, Y., Arakawa, Y., and Yasumoto, K. 2020. Predicting Depression and Anxiety Mood by Wrist-Worn Sleep Sensor. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). 1–6. DOI: 10.1109/PerComWorkshops48775.2020.9156176
Garcia-Ceja, E., Riegler, M., Jakobsen, P., Tørresen, J., Nordgreen, T., Oedegaard, K. J., and Fasmer, O. B. 2018. Depresjon: A Motor Activity Database of Depression Episodes in Unipolar and Bipolar Patients. In Proceedings of the 9th ACM Multimedia Systems Conference (MMSys ’18). Association for Computing Machinery, New York, NY, USA, 472–477. DOI: 10.1145/3204949.3208125
GBD. 2018. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 392, 10159 (Nov. 2018), 1789–1858.
Ghandeharioun, A., Fedor, S., Sangermano, L., Ionescu, D., Alpert, J., Dale, C., Sontag, D., and Picard, R. 2017. Objective assessment of depressive symptoms with machine learning and wearable sensors data. In 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII). 325–332. DOI: 10.1109/ACII.2017.8273620
Habib, M., Wang, Z., Qiu, S., Zhao, H., and Murthy, A. S. 2022. Machine Learning Based Healthcare System for Investigating the Association Between Depression and Quality of Life. IEEE Journal of Biomedical and Health Informatics 26, 5 (2022), 2008–2019. DOI: 10.1109/JBHI.2022.3140433
Harari, G. M., Lane, N. D., Wang, R., Crosier, B. S., Campbell, A. T., and Gosling, S. D. 2016. Using Smartphones to Collect Behavioral Data in Psychological Science. Perspectives on Psychological Science 11, 6 (Nov. 2016), 838–854. DOI: 10.1177/1745691616650285
Hartmann, R., Schmidt, F. M., Sander, C., and Hegerl, U. 2019. Heart Rate Variability as Indicator of Clinical State in Depression. Frontiers in Psychiatry 9 (Jan. 2019). DOI: 10.3389/fpsyt.2018.00735
Ibrahim, A. K., Kelly, S. J., Adams, C. E., and Glazebrook, C. 2013. A systematic review of studies of depression prevalence in university students. Journal of Psychiatric Research 47, 3 (March 2013), 391–400. DOI: 10.1016/j.jpsychires.2012.11.015
Jacobson, N. C., and Chung, Y. J. 2020. Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones. Sensors 20, 12 (June 2020), 3572. DOI: 10.3390/s20123572
James, S. L., et al. 2018. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet 392, 10159 (Nov. 2018), 1789–1858. DOI: 10.1016/s0140-6736(18)32279-7
Lipari, R. N., Bose, J., Hedden, S. L., and Park-Lee, E. 2018. Key Substance Use and Mental Health Indicators in the United States: Results from the 2017 National Survey on Drug Use and Health. Technical documents. 124 pages.
Kilaskar, M., Saindane, N., Ansari, N., Doshi, D., and Kulkarni, M. 2021. Machine Learning Algorithms for Analysis and Prediction of Depression. SN Computer Science 3, 2 (Dec 22, 2021), 103. DOI: 10.1007/s42979-021-00967-0
Publicado
07/11/2024
Como Citar
SAUD, Conrado Santos; MOTTI, Vivian; NERIS, Luciano; BLEICHER, Tais; NERIS, Vania Paula de Almeida.
Identification of possible depressive profile through mobile sensor data in university students. In: SIMPÓSIO BRASILEIRO SOBRE FATORES HUMANOS EM SISTEMAS COMPUTACIONAIS (IHC), 23. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 660-672.
