Human behaviour data analysis and noncommunicable diseases: a systematic mapping study

References

• Abreu, P. H., M. S. Santos, M. H. Abreu, B. Andrade, and D. C. Silva. 2017. “Predicting Breast Cancer Recurrence Using Machine Learning Techniques: A Systematic Review.” ACM Computing Surveys 49 (3): 1–40. Article no. 52. doi:10.1145/2988544.
   Web of Science ®Google Scholar
• Alberdi, A., A. Weakley, M. Schmitter-Edgecombe, D. J. Cook, A. Aztiria, A. Basarab, and M. Barrenechea. 2018. “Smart Home-Based Prediction of Multidomain Symptoms Related to Alzheimer's Disease.” IEEE Journal of Biomedical and Health Informatics 22: 1720–1731. doi:10.1109/JBHI.2018.2798062.
   PubMed Web of Science ®Google Scholar
• Alshurafa, N., H. Kalantarian, M. Pourhomayoun, J. J. Liu, S. Sarin, B. Shahbazi, and M. Sarrafzadeh. 2015. “Recognition of Nutrition Intake Using Time-frequency Decomposition in a Wearable Necklace Using a Piezoelectric Sensor.” IEEE Sensors Journal 15: 3909–3916. doi:10.1109/JSEN.2015.2402652.
   Web of Science ®Google Scholar
• Alshurafa, N., C. Sideris, M. Pourhomayoun, H. Kalantarian, M. Sarrafzadeh, and J. A. Eastwood. 2017. “Remote Health Monitoring Outcome Success Prediction Using Baseline and First Month Intervention Data.” IEEE Journal of Biomedical and Health Informatics 21: 507–514. doi:10.1109/JBHI.2016.2518673.
   PubMed Web of Science ®Google Scholar
• Bavaresco, R., H. Arruda, E. Rocha, J. Barbosa, and G.-P. Li. 2021. “Internet of Things and Occupational Well-being in Industry 4.0: A Systematic Mapping Study and Taxonomy.” Computers & Industrial Engineering 161: Article ID 107670. doi:10.1016/j.cie.2021.107670.
   Web of Science ®Google Scholar
• Bavaresco, R., J. Barbosa, H. Vianna, P. Büttenbender, and L. Dias. 2020. “Design and Evaluation of a Context-aware Model Based on Psychophysiology.” Computer Methods and Programs in Biomedicine189: Article ID 105299. doi:10.1016/j.cmpb.2019.105299.
   PubMed Web of Science ®Google Scholar
• Bentley, F., K. Tollmar, P. Stephenson, L. Levy, B. Jones, S. Robertson, E. Price, R. Catrambone, and J. Wilson. 2013. “Health Mashups: Presenting Statistical Patterns Between Wellbeing Data and Context in Natural Language to Promote Behavior Change.” ACM Transactions on Computer-Human Interaction 20 (5): 1–27. Article no. 30. doi:10.1145/2503823.
   Web of Science ®Google Scholar
• Birk, J. L., I. M. Kronish, N. Moise, L. Falzon, S. Yoon, and K. W. Davidson. 2019. “Depression and Multimorbidity: Considering Temporal Characteristics of the Associations Between Depression and Multiple Chronic Diseases.” Health Psychology 38: 802. doi:10.1037/hea0000737.
   PubMed Web of Science ®Google Scholar
• Bohanec, M., G. Tartarisco, F. Marino, G. Pioggia, P. E. Puddu, M. S. Schiariti, A. Baert, et al. 2021. “Heartman Dss: A Decision Support System for Self-management of Congestive Heart Failure.” Expert Systems with Applications 186: Article ID 115688. doi:10.1016/j.eswa.2021.115688.
   Web of Science ®Google Scholar
• Burns, M. N., M. Begale, J. Duffecy, D. Gergle, C. J. Karr, E. Giangrande, and D. C. Mohr. 2011. “Harnessing Context Sensing to Develop a Mobile Intervention for Depression.” Journal of Medical Internet Research 13: e55. doi:10.2196/jmir.1838.
   PubMed Web of Science ®Google Scholar
• Ceron, J. D., D. M. Lopez, and G. A. Ramirez. 2017. “A Mobile System for Sedentary Behaviors Classification Based on Accelerometer and Location Data.” Computers in Industry 92-93: 25–31. doi:10.1016/j.compind.2017.06.005.
   Web of Science ®Google Scholar
• Chae, S. H., Y. Kim, K. S. Lee, and H. S. Park. 2020. “Development and Clinical Evaluation of a Web-Based Upper Limb Home Rehabilitation System Using a Smartwatch and Machine Learning Model for Chronic Stroke Survivors: Prospective Comparative Study.” JMIR mHealth and uHealth 8: Article ID e17216. doi:10.2196/17216.
   PubMed Web of Science ®Google Scholar
• Chen, P., J. Chai, J. Cheng, K. Li, S. Xie, H. Liang, X. Shen, R. Feng, and D. Wang. 2014. “A Smart Web Aid for Preventing Diabetes in Rural China: Preliminary Findings and Lessons.” Journal of Medical Internet Research 16: e98. doi:10.2196/jmir.3228.
   PubMed Web of Science ®Google Scholar
• Chikersal, P., A. Doryab, M. Tumminia, D. K. Villalba, J. M. Dutcher, X. Liu, S. Cohen, et al. 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): 1–41. Article no. 3. doi:10.1145/3422821.
   Web of Science ®Google Scholar
• Christakis, N. A., and J. H. Fowler. 2007. “The Spread of Obesity in a Large Social Network Over 32 Years.” New England Journal of Medicine 357: 370–379. doi:10.1056/NEJMsa066082.
   PubMed Web of Science ®Google Scholar
• Cooper, D. 2016. “What is a ‘mapping Study?’.” Journal of the Medical Library Association 104: 76–78. doi:10.3163/1536-5050.104.1.013.
   PubMed Web of Science ®Google Scholar
• Darvariu, V.-A., L. Convertino, A. Mehrotra, and M. Musolesi. 2020. “Quantifying the Relationships Between Everyday Objects and Emotional States Through Deep Learning Based Image Analysis Using Smartphones.” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies4: 1–21. doi:10.1145/3380997.
   PubMedGoogle Scholar
• Dias, L. P. S., J. L. V. Barbosa, L. P. Feijó, and H. D. Vianna. 2020. “Development and Testing of Iaware Model for Ubiquitous Care of Patients with Symptoms of Stress, Anxiety and Depression.” Computer Methods and Programs in Biomedicine 187: Article ID 105113. doi:10.1016/j.cmpb.2019.105113.
   PubMed Web of Science ®Google Scholar
• dos Santos Paula, L., J. L. V. Barbosa, and L. P. S. Dias. 2022. “A Model for Assisting in the Treatment of Anxiety Disorder.” Universal Access in the Information Society 21: 533–543. doi:10.1007/s10209-020-00786-9.
   Web of Science ®Google Scholar
• Eisenhardt, K. M., M. E. Graebner, and S. Sonenshein. 2016. “Grand Challenges and Inductive Methods: Rigor Without Rigor Mortis.” doi:10.5465/amj.2016.4004.
   Google Scholar
• El-Gayar, O. F., L. S. Ambati, and N. Nawa. 2020. “Wearables, Artificial intelligence, and the Future of Healthcare.” In AI and Big Data's Potential for Disruptive Innovation, 104–129. IGI Global. doi:10.4018/978-1-5225-9687-5.ch005.
   Google Scholar
• Farseev, A., and T. S. Chua. 2017. “Tweet Can Be Fit: Integrating Data From Wearable Sensors and Multiple Social Networks for Wellness Profile Learning.” ACM Transactions on Information Systems 35 (4): 1–34. Article no. 42. doi:10.1145/3086676.
   Web of Science ®Google Scholar
• Galvin, H. K., and P. R. DeMuro. 2020. “Developments in Privacy and Data Ownership in Mobile Health Technologies, 2016–2019.” Yearbook of Medical Informatics 29: 32–43. doi:10.1055/s-0040-1701987.
   PubMedGoogle Scholar
• Giuntini, F. T., K. L. de Moraes, M. T. Cazzolato, L. de Fátima Kirchner, M. de Jesus D. Dos Reis, A. J. M. Traina, A. T. Campbell, and J. Ueyama. 2021. “Modeling and Assessing the Temporal Behavior of Emotional and Depressive User Interactions on Social Networks.” IEEE Access 9: 93182–93194. doi:10.1109/ACCESS.2021.3091801.
   Web of Science ®Google Scholar
• Goetz, C., R. Bavaresco, R. Kunst, and J. Barbosa. 2022. “Industrial Intelligence in the Care of Workers' Mental Health: A Review of Status and Challenges.” International Journal of Industrial Ergonomics87: Article ID 103234. doi:10.1016/j.ergon.2021.103234.
   Web of Science ®Google Scholar
• Gold, S. M., O. Köhler-Forsberg, R. Moss-Morris, A. Mehnert, J. J. Miranda, M. Bullinger, A. Steptoe, M. A. Whooley, and C. Otte. 2020. “Comorbid Depression in Medical Diseases.” Nature Reviews Disease Primers 6: 69. doi:10.1038/s41572-020-0200-2.
   PubMed Web of Science ®Google Scholar
• Griffiths, J. C., J. De Vries, M. I. McBurney, S. Wopereis, S. Serttas, and D. S. Marsman. 2020. “Measuring Health Promotion: Translating Science Into Policy.” European Journal of Nutrition 59: 11–23. doi:10.1007/s00394-020-02359-1.
   PubMed Web of Science ®Google Scholar
• Grimaldi-Puyana, M., J. M. Fernández-Batanero, C. Fennell, and B. Sañudo. 2020. “Associations of Objectively-assessed Smartphone Use with Physical Activity, Sedentary Behavior, Mood, and Sleep Quality in Young Adults: A Cross-sectional Study.” International Journal of Environmental Research and Public Health 17: 1–12. doi:10.3390/ijerph17103499.
   Web of Science ®Google Scholar
• Grünerbl, A., A. Muaremi, V. Osmani, G. Bahle, S. Öhler, G. Tröster, O. Mayora, C. Haring, and P. Lukowicz. 2015. “Smartphone-based Recognition of States and State Changes in Bipolar Disorder Patients.” IEEE Journal of Biomedical and Health Informatics 19: 140–148. doi:10.1109/JBHI.2014.2343154.
   PubMed Web of Science ®Google Scholar
• Harriman, D. 2010. The Logical Leap: Induction in Physics. New York: Penguin.
   Google Scholar
• Heckler, W. F., J. V. de Carvalho, and J. L. V. Barbosa. 2022. “Machine Learning for Suicidal Ideation Identification: A Systematic Literature Review.” Computers in Human Behavior 128: Article ID 107095. doi:10.1016/j.chb.2021.107095.
   Web of Science ®Google Scholar
• Heckman, C. J., S. D. Darlow, L. M. Ritterband, E. A. Handorf, and S. L. Manne. 2016. “Efficacy of An Intervention to Alter Skin Cancer Risk Behaviors in Young Adults.” American Journal of Preventive Medicine 51: 1–11. doi:10.1016/j.amepre.2015.11.008.
   PubMed Web of Science ®Google Scholar
• Höchsmann, C., O. Müller, M. Ambühl, C. Klenk, K. Königstein, D. Infanger, S. P. Walz, and A. Schmidt-Trucksäss. 2019. “Novel Smartphone Game Improves Physical Activity Behavior in Type 2 Diabetes.” American Journal of Preventive Medicine 57: 41–50. doi:10.1016/j.amepre.2019.02.017.
   PubMed Web of Science ®Google Scholar
• Intarasirisawat, J., C. S. Ang, C. Efstratiou, L. Dickens, N. Sriburapar, D. Sharma, and B. Asawathaweeboon. 2020. “An Automated Mobile Game-based Screening Tool for Patients with Alcohol Dependence.” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies4: 1–23. doi:10.1145/3411837.
   PubMedGoogle Scholar
• Ismail, W. N., M. M. Hassan, H. A. Alsalamah, and G. Fortino. 2020. “CNN-based Health Model for Regular Health Factors Analysis in Internet-of-medical Things Environment.” IEEE Access 8: 52541–52549. doi:10.1109/ACCESS.2020.2980938.
   Web of Science ®Google Scholar
• Jin, J., B. Gao, S. Yang, B. Zhao, L. Luo, and W. L. Woo. 2020. “Attention-Block Deep Learning Based Features Fusion in Wearable Social Sensor for Mental Wellbeing Evaluations.” IEEE Access 8: 89258–89268. doi:10.1109/ACCESS.2020.2994124.
   Web of Science ®Google Scholar
• Jones, S. L., W. Hue, R. M. Kelly, R. Barnett, V. Henderson, and R. Sengupta. 2021. “Determinants of Longitudinal Adherence in Smartphone-Based Self-Tracking for Chronic Health Conditions: Evidence From Axial Spondyloarthritis.” Proceedings of the ACM on Interactive, Mobile, Wearable Ubiquitous Technologies 5 (1): 1–24. Article no. 16. doi:10.1145/3448093.
   Google Scholar
• Jung, H., H.-A. Park, and T.-M. Song. 2017. “Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals.” Journal of Medical Internet Research 19: Article ID e259. doi:10.2196/jmir.7452.
   Web of Science ®Google Scholar
• Kearns, M., and A. Roth. 2019. The Ethical Algorithm: The Science of Socially Aware Algorithm Design. Vol. 1. 1st ed. New York: Oxford University Press.
   Google Scholar
• Keshav, S. 2007. “How to Read a Paper.” ACM SIGCOMM Computer Communication Review 37: 83–84. doi:10.1145/1273445.1273458.
   Web of Science ®Google Scholar
• Krzyzanowski, M. C., P. N. Kizakevich, V. Duren-Winfield, R. Eckhoff, J. Hampton, L. T. Blackman Carr, G. McCauley, et al. 2020. “Rams Have Heart, a Mobile App Tracking Activity and Fruit and Vegetable Consumption to Support the Cardiovascular Health of College Students: Development and Usability Study.” JMIR Mhealth Uhealth 8: Article ID e15156. doi:10.2196/15156.
   PubMed Web of Science ®Google Scholar
• Kyada, D. S., and D. H. Baria. 2019. “Prevalence of Risk Factors of Non-communicable Diseases Among the Health Care Professionals of a Tertiary Care Hospital of South Gujarat, India.” World Journal of Advance Healthcare Research 3: 57–61. doi:10.3329/jhpn.v31i1.14752.
   Google Scholar
• Larentis, A. V., D. N. F. Barbosa, C. R. da Silva, and J. L. V. Barbosa. 2020. “Applied Computing to Education on Noncommunicable Chronic Diseases: A Systematic Mapping Study.” Telemedicine and E-Health 26: 147–163. doi:10.1089/tmj.2018.0282.
   PubMed Web of Science ®Google Scholar
• Leis, A., F. Ronzano, M. A. Mayer, L. I. Furlong, and F. Sanz. 2019. “Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis.” Journal of Medical Internet Research 21: 1–25. doi:10.2196/14199.
   Web of Science ®Google Scholar
• Li, H., S. Ge, B. Greene, and J. Dunbar-Jacob. 2019. “Depression in the Context of Chronic Diseases in the United States and China.” International Journal of Nursing Sciences 6: 117–122. doi:10.1016/j.ijnss.2018.11.007.
   PubMed Web of Science ®Google Scholar
• Li, D., J. Vaidya, M. Wang, B. Bush, C. Lu, M. Kollef, and T. Bailey. 2020. “Feasibility Study of Monitoring Deterioration of Outpatients Using Multimodal Data Collected by Wearables.” ACM Transactions on Computing for Healthcare 1: 1–22. doi:10.1145/3344256.
   Google Scholar
• Locke, E. A. 2007. “The Case for Inductive Theory Building.” Journal of Management 33: 867–890. doi:10.1177/0149206307307636.
   Web of Science ®Google Scholar
• Marsch, L. A. 2021. “Digital Health Data-driven Approaches to Understand Human Behavior.” Neuropsychopharmacology 46: 191–196. doi:10.1038/s41386-020-0761-5.
   PubMed Web of Science ®Google Scholar
• McGuire, A. M., C. Seib, J. Porter-Steele, and D. J. Anderson. 2019. “The Association Between Web-Based Or Face-to-Face Lifestyle Interventions on the Perceived Benefits and Barriers to Exercise in Midlife Women: Three-Arm Equivalency Study.” Journal of Medical Internet Research 21: Article ID e10963. doi:10.2196/10963.
   Web of Science ®Google Scholar
• Meegahapola, L., S. Ruiz-Correa, V. d. C. Robledo-Valero, E. E. Hernandez-Huerfano, L. Alvarez-Rivera, R. Chenu-Abente, and D. Gatica-Perez. 2021. “One More Bite? Inferring Food Consumption Level of College Students Using Smartphone Sensing and Self-Reports.” Proceedings of the ACM on Interactive, Mobile, Wearable Ubiquitous Technologies 5 (1): 1–28. Article no. 26. doi:10.1145/3448120.
   Google Scholar
• Milne-Ives, M., C. Lam, C. De Cock, M. H. Van Velthoven, and E. Meinert. 2020. “Mobile Apps for Health Behavior Change in Physical Activity, Diet, Drug and Alcohol Use, and Mental Health: Systematic Review.” JMIR Mhealth Uhealth 8: Article ID e17046. doi:10.2196/17046.
   Web of Science ®Google Scholar
• Monteiro, L. Z., A. R. Varela, L. R. Alves, M. R. S. Santos, G. R. Lopes, M. A. Caetano Júnior, and S. S. Leandro. 2018. “Prevalence and Factors Associated with Alcohol and Tobacco Use Among Undergraduate Nursing Students.” Revista Eletrônica de Enfermagem 20: 1–10. doi:10.5216/ree.v20.45296.
   Google Scholar
• Nolte, E., and M. McKee. 2008. Caring for People with Chronic Conditions: A Health System Perspective. New York: Open University Press.
   Google Scholar
• Patrick, K., S. S. Intille, and M. F. Zabinski. 2005. “An Ecological Framework for Cancer Communication: Implications for Research.” Journal of Medical Internet Research 7: Article ID e23. doi:10.2196/jmir.7.3.e23.
   PubMed Web of Science ®Google Scholar
• Pérez-Rodríguez, G., M. Pérez-Pérez, F. Fdez-Riverola, and A. Lourenço. 2020. “Mining the Sociome for Health Informatics: Analysis of Therapeutic Lifestyle Adherence of Diabetic Patients in Twitter.” Future Generation Computer Systems 110: 214–232. doi:10.1016/j.future.2020.04.025.
   Web of Science ®Google Scholar
• Petersen, K., R. Feldt, S. Mujtaba, and M. Mattsson. 2008. “Systematic Mapping Studies in Software Engineering.” In Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, EASE '08, 68–77. Swinton, UK: British Computer Society.
   Google Scholar
• Pratap, A., D. C. Atkins, B. N. Renn, M. J. Tanana, S. D. Mooney, J. A. Anguera, and P. A. Areán. 2019. “The Accuracy of Passive Phone Sensors in Predicting Daily Mood.” Depression and Anxiety 36: 72–81. doi:10.1002/da.22822.
   PubMed Web of Science ®Google Scholar
• Price, C. P., and L. J. Kricka. 2007. “Improving Healthcare Accessibility Through Point-of-Care Technologies.” Clinical Chemistry 53: 1665–1675.
   PubMed Web of Science ®Google Scholar
• Ramsingh, J., and V. Bhuvaneswari. 2020. “An Efficient Map Reduce-Based Hybrid NBC-TFIDF Algorithm to Mine the Public Sentiment on Diabetes Mellitus – A Big Data Approach.” Journal of King Saud University – Computer and Information Sciences 32 (10): 1018–1029. doi:10.1016/j.jksuci.2018.06.011.
   Web of Science ®Google Scholar
• Rehackova, L., V. Araújo-Soares, S. Steven, A. J. Adamson, R. Taylor, and F. F. Sniehotta. 2020. “Behaviour Change During Dietary Type 2 Diabetes Remission: a Longitudinal Qualitative Evaluation of An Intervention Using a Very Low Energy Diet.” Diabetic Medicine 37: 953–962. doi:10.1111/dme.14066.
   PubMed Web of Science ®Google Scholar
• Sano, A., S. Taylor, A. W. McHill, A. J. Phillips, L. K. Barger, E. Klerman, and R. Picard. 2018. “Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study.” Journal of Medical Internet Research 20: Article ID e210. doi:10.2196/jmir.9410.
   PubMed Web of Science ®Google Scholar
• Santani, D., T. M. T. Do, F. Labhart, S. Landolt, E. Kuntsche, and D. Gatica-Perez. 2018. “DrinkSense: Characterizing Youth Drinking Behavior Using Smartphones.” IEEE Transactions on Mobile Computing17: 2279–2292. doi:10.1109/TMC.2018.2797901.
   Web of Science ®Google Scholar
• Stasak, B., J. Epps, and R. Goecke. 2019. “Automatic Depression Classification Based on Affective Read Sentences: Opportunities for Text-dependent Analysis.” Speech Communication 115: 1–14. doi:10.1016/j.specom.2019.10.003.
   Web of Science ®Google Scholar
• Sweeney-Magee, M., A. Moustaqim-Barrette, C. Gotay, and T. Dummer. 2020. “A Systematic Mixed Studies Review of Health Behaviour Change Interventions in Colorectal Cancer Survivors.” Journal of Advanced Nursing 76: 1936–1948. doi:10.1111/jan.14389.
   PubMed Web of Science ®Google Scholar
• Thorndike, F. P., L. M. Ritterband, L. A. Gonder-Frederick, H. R. Lord, K. S. Ingersoll, and C. M. Morin. 2013. “A Randomized Controlled Trial of An Internet Intervention for Adults with Insomnia: Effects on Comorbid Psychological and Fatigue Symptoms.” Journal of Clinical Psychology 69: 1078–1093. doi:10.1002/jclp.22032.
   PubMed Web of Science ®Google Scholar
• Uban, A.-S., B. Chulvi, and P. Rosso. 2021. “An Emotion and Cognitive Based Analysis of Mental Health Disorders From Social Media Data.” Future Generation Computer Systems 124: 480–494. doi:10.1016/j.future.2021.05.032.
   Web of Science ®Google Scholar
• Vianna, H. D., and J. L. V. Barbosa. 2020. “Pompilos, a Model for Augmenting Health Assistant Applications with Social Media Content.” Journal of Universal Computer Science 26: 4–32.
   Web of Science ®Google Scholar
• Ware, S., C. Yue, R. Morillo, J. Lu, C. Shang, J. Bi, J. Kamath, et al. 2020. “Predicting Depressive Symptoms Using Smartphone Data.” Smart Health 15: Article ID 100093. doi:10.1016/j.smhl.2019.100093.
   Google Scholar
• Ware, S., C. Yue, R. Morillo, J. Lu, C. Shang, J. Kamath, A. Bamis, et al. 2018. “Large-scale Automatic Depression Screening Using Meta-data From WiFi Infrastructure.” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2: 1–27. doi:10.1145/3287073.
   Google Scholar
• WHO. 2017. “Depression – Fact Sheet.” Accessed 20 October 2021. http://www.who.int/mediacentre/factsheets/fs369/en/.
   Google Scholar
• WHO. 2018. “Noncommunicable Diseases.” Accessed 20 October 2021. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases.
   Google Scholar
• WHO. 2020. “Global Action Plan for the Prevention and Control of Noncommunicable Diseases.” https://apps.who.int/iris/bitstream/handle/10665/94384/9789241506236_eng.pd.
   Google Scholar
• Woiceshyn, J., and U. Daellenbach. 2018. “Evaluating Inductive Vs Deductive Research in Management Studies.” Qualitative Research in Organizations and Management: An International Journal 13: 183–195. doi:10.1108/QROM-06-2017-1538.
   Web of Science ®Google Scholar
• Xu, X., P. Chikersal, A. Doryab, D. K. Villalba, J. M. Dutcher, M. J. Tumminia, T. Althoff, et al. 2019. “Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection Among College Students.” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3: 1–33. doi:10.1145/3351274.
   PubMedGoogle Scholar
• Xu, X., P. Chikersal, J. M. Dutcher, Y. S. Sefidgar, W. Seo, M. J. Tumminia, D. K. Villalba, et al. 2021. “Leveraging Collaborative-Filtering for Personalized Behavior Modeling: A Case Study of Depression Detection Among College Students.” Proceedings of the ACM on Interactive, Mobile, Wearable Ubiquitous Technologies 5 (1): 1–27. doi:10.1145/3448107.
   Google Scholar
• Yang, X., R. McEwen, L. R. Ong, and M. Zihayat. 2020. “A Big Data Analytics Framework for Detecting User-level Depression From Social Networks.” International Journal of Information Management 54: Article ID 102141. doi:10.1016/j.ijinfomgt.2020.102141.
   Web of Science ®Google Scholar
• You, C.-W., Y. Chuang, H.-Y. Lin, J.-T. Tsai, Y.-C. Huang, C.-H. Kuo, M.-C. Huang, et al. 2019. “SoberComm.” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies3: 1–31. doi:10.1145/3351277.
   PubMedGoogle Scholar
• Yue, C., S. Ware, R. Morillo, J. Lu, C. Shang, J. Bi, J. Kamath, et al. 2020. “Automatic Depression Prediction Using Internet Traffic Characteristics on Smartphones.” Smart Health 18: Article ID 100137. doi:10.1016/j.smhl.2020.100137.
   PubMedGoogle Scholar
• Zakaria, C., R. Balan, and Y. Lee. 2019. “StressMon: Scalable Detection of Perceived Stress and Depression Using Passive Sensing of Changes in Work Routines and Group Interactions.” Proceedings of the ACM on Human-Computer Interaction 3: 1–29. doi:10.1145/3359139.
   PubMedGoogle Scholar
• Zhang, Y., Y. Chen, and L. Ma. 2018. “Depression and Cardiovascular Disease in Elderly: Current Understanding.” Journal of Clinical Neuroscience 47: 1–5. doi:10.1016/j.jocn.2017.09.022.
   PubMed Web of Science ®Google Scholar