This paper is in the following e-collection/theme issue:

Digital Biomarkers and Digital Phenotyping (198) Big Data (245) Machine Learning (1501) Fitness Trackers and Smart Pedometers/Accelerometers (571) mHealth for Wellness, Behavior Change and Prevention (2922) Wearable Devices and Sensors (903) Loneliness and Social Isolation (105)

Published on in Vol 7, No 7 (2019): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13209, first published .
Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data

Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data

Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning of Smartphone and Fitbit Data

Authors of this article:

Afsaneh Doryab1, 2 Author Orcid Image ;   Daniella K Villalba3 Author Orcid Image ;   Prerna Chikersal1 Author Orcid Image ;   Janine M Dutcher3 Author Orcid Image ;   Michael Tumminia4 Author Orcid Image ;   Xinwen Liu3 Author Orcid Image ;   Sheldon Cohen3 Author Orcid Image ;   Kasey Creswell3 Author Orcid Image ;   Jennifer Mankoff5 Author Orcid Image ;   John D Creswell3 Author Orcid Image ;   Anind K Dey6 Author Orcid Image
Article Authors Cited by (99) Tweetations (30) Metrics

Journals

  1. . Why Loneliness Interventions Are Unsuccessful: A Call for Precision Health. Advances in Geriatric Medicine and Research 2020 View
  2. Wang J, Deng H, Liu B, Hu A, Liang J, Fan L, Zheng X, Wang T, Lei J. Systematic Evaluation of Research Progress on Natural Language Processing in Medicine Over the Past 20 Years: Bibliometric Study on PubMed. Journal of Medical Internet Research 2020;22(1):e16816 View
  3. Potier R. The Digital Phenotyping Project: A Psychoanalytical and Network Theory Perspective. Frontiers in Psychology 2020;11 View
  4. Badal V, Graham S, Depp C, Shinkawa K, Yamada Y, Palinkas L, Kim H, Jeste D, Lee E. Prediction of Loneliness in Older Adults Using Natural Language Processing: Exploring Sex Differences in Speech. The American Journal of Geriatric Psychiatry 2021;29(8):853 View
  5. H. Birk R, Samuel G. Can digital data diagnose mental health problems? A sociological exploration of ‘digital phenotyping’. Sociology of Health & Illness 2020;42(8):1873 View
  6. Melcher J, Hays R, Torous J. Digital phenotyping for mental health of college students: a clinical review. Evidence Based Mental Health 2020;23(4):161 View
  7. Jayakumar P, Lin E, Galea V, Mathew A, Panda N, Vetter I, Haynes A. Digital Phenotyping and Patient-Generated Health Data for Outcome Measurement in Surgical Care: A Scoping Review. Journal of Personalized Medicine 2020;10(4):282 View
  8. He-Yueya J, Buck B, Campbell A, Choudhury T, Kane J, Ben-Zeev D, Althoff T. Assessing the relationship between routine and schizophrenia symptoms with passively sensed measures of behavioral stability. npj Schizophrenia 2020;6(1) View
  9. Manea V, Wac K. Co-Calibrating Physical and Psychological Outcomes and Consumer Wearable Activity Outcomes in Older Adults: An Evaluation of the coQoL Method. Journal of Personalized Medicine 2020;10(4):203 View
  10. Kelly J, Campbell K, Gong E, Scuffham P. The Internet of Things: Impact and Implications for Health Care Delivery. Journal of Medical Internet Research 2020;22(11):e20135 View
  11. Mendu S, Baglione A, Baee S, Wu C, Ng B, Shaked A, Clore G, Boukhechba M, Barnes L. A Framework for Understanding the Relationship between Social Media Discourse and Mental Health. Proceedings of the ACM on Human-Computer Interaction 2020;4(CSCW2):1 View
  12. Lee E, Torous J, De Choudhury M, Depp C, Graham S, Kim H, Paulus M, Krystal J, Jeste D. Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2021;6(9):856 View
  13. Wu C, Barczyk A, Craddock R, Harari G, Thomaz E, Shumake J, Beevers C, Gosling S, Schnyer D. Improving prediction of real-time loneliness and companionship type using geosocial features of personal smartphone data. Smart Health 2021;20:100180 View
  14. Hilty D, Armstrong C, Luxton D, Gentry M, Krupinski E. A Scoping Review of Sensors, Wearables, and Remote Monitoring For Behavioral Health: Uses, Outcomes, Clinical Competencies, and Research Directions. Journal of Technology in Behavioral Science 2021;6(2):278 View
  15. Xu X, Chikersal P, Dutcher J, Sefidgar Y, Seo W, Tumminia M, Villalba D, Cohen S, Creswell K, Creswell J, Doryab A, Nurius P, Riskin E, Dey A, Mankoff J. Leveraging Collaborative-Filtering for Personalized Behavior Modeling. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2021;5(1):1 View
  16. Lekkas D, Jacobson N. Using artificial intelligence and longitudinal location data to differentiate persons who develop posttraumatic stress disorder following childhood trauma. Scientific Reports 2021;11(1) View
  17. Zhang Y, Folarin A, Sun S, Cummins N, Ranjan Y, Rashid Z, Conde P, Stewart C, Laiou P, Matcham F, Oetzmann C, Lamers F, Siddi S, Simblett S, Rintala A, Mohr D, Myin-Germeys I, Wykes T, Haro J, Penninx B, Narayan V, Annas P, Hotopf M, Dobson R. Predicting Depressive Symptom Severity Through Individuals’ Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study. JMIR mHealth and uHealth 2021;9(7):e29840 View
  18. Wetzel B, Pryss R, Baumeister H, Edler J, Gonçalves A, Cohrdes C. “How Come You Don’t Call Me?” Smartphone Communication App Usage as an Indicator of Loneliness and Social Well-Being across the Adult Lifespan during the COVID-19 Pandemic. International Journal of Environmental Research and Public Health 2021;18(12):6212 View
  19. Escobar-Viera C, Cernuzzi L, Miller R, Rodríguez-Marín H, Vieta E, González Toñánez M, Marsch L, Hidalgo-Mazzei D. Feasibility of mHealth interventions for depressive symptoms in Latin America: a systematic review. International Review of Psychiatry 2021;33(3):300 View
  20. Alshdadi A. Cyber-physical system with IoT-based smart vehicles. Soft Computing 2021;25(18):12261 View
  21. Xu X, Mankoff J, Dey A. Understanding practices and needs of researchers in human state modeling by passive mobile sensing. CCF Transactions on Pervasive Computing and Interaction 2021;3(4):344 View
  22. Lee J, Solomonov N, Banerjee S, Alexopoulos G, Sirey J. Use of Passive Sensing in Psychotherapy Studies in Late Life: A Pilot Example, Opportunities and Challenges. Frontiers in Psychiatry 2021;12 View
  23. Baglione A, Cai L, Bahrini A, Posey I, Boukhechba M, Chow P. Understanding the Relationship Between Mood Symptoms and Mobile App Engagement Among Patients With Breast Cancer Using Machine Learning: Case Study. JMIR Medical Informatics 2022;10(6):e30712 View
  24. Yamada Y, Shinkawa K, Nemoto M, Arai T. Automatic Assessment of Loneliness in Older Adults Using Speech Analysis on Responses to Daily Life Questions. Frontiers in Psychiatry 2021;12 View
  25. Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F. Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. Journal of Biomedical Informatics 2023;138:104278 View
  26. Li X, Arif M. Research on the Application of Data Mining Technology in College Students’ Mental Health Education in the Network Age. Security and Communication Networks 2022;2022:1 View
  27. NATHANI N, Hasan Z. IMPACT OF AI IN INTERNET OF MEDICAL THINGS FOR HEALTH CARE DELIVERY. International Journal of Engineering Technologies and Management Research 2021;8(8):18 View
  28. Yan R, Ringwald W, Vega J, Kehl M, Bae S, Dey A, Low C, Wright A, Doryab A. Exploratory machine learning modeling of adaptive and maladaptive personality traits from passively sensed behavior. Future Generation Computer Systems 2022;132:266 View
  29. Chen Z, Kulkarni P, Galatzer-Levy I, Bigio B, Nasca C, Zhang Y. Modern views of machine learning for precision psychiatry. Patterns 2022;3(11):100602 View
  30. Yan R, Liu X, Dutcher J, Tumminia M, Villalba D, Cohen S, Creswell D, Creswell K, Mankoff J, Dey A, Doryab A. A Computational Framework for Modeling Biobehavioral Rhythms from Mobile and Wearable Data Streams. ACM Transactions on Intelligent Systems and Technology 2022;13(3):1 View
  31. Vega J, Li M, Aguillera K, Goel N, Joshi E, Khandekar K, Durica K, Kunta A, Low C. Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices. Frontiers in Digital Health 2021;3 View
  32. Stuart A, Katz D, Stevenson C, Gooch D, Harkin L, Bennasar M, Sanderson L, Liddle J, Bennaceur A, Levine M, Mehta V, Wijesundara A, Talbot C, Bandara A, Price B, Nuseibeh B. Loneliness in older people and COVID-19: Applying the social identity approach to digital intervention design. Computers in Human Behavior Reports 2022;6:100179 View
  33. Zhang Z, Amegbor P, Sabel C. Assessing the Current Integration of Multiple Personalised Wearable Sensors for Environment and Health Monitoring. Sensors 2021;21(22):7693 View
  34. Gopalakrishnan A, Venkataraman R, Gururajan R, Zhou X, Genrich R. Mobile phone enabled mental health monitoring to enhance diagnosis for severity assessment of behaviours: a review. PeerJ Computer Science 2022;8:e1042 View
  35. Qirtas M, Zafeiridi E, Pesch D, White E. Loneliness and Social Isolation Detection Using Passive Sensing Techniques: Scoping Review. JMIR mHealth and uHealth 2022;10(4):e34638 View
  36. Zhang J, Li D, Dai R, Cos H, Williams G, Raper L, Hammill C, Lu C. Predicting Post-Operative Complications with Wearables. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2022;6(2):1 View
  37. Davidson E, Taylor C, Ayers C, Quach N, Tu X, Lee E. The Relationship Between Loneliness and Positive Affect in Older Adults. The American Journal of Geriatric Psychiatry 2022;30(6):678 View
  38. Van Assche E, Antoni Ramos-Quiroga J, Pariante C, Sforzini L, Young A, Flossbach Y, Gold S, Hoogendijk W, Baune B, Maron E. Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art. European Neuropsychopharmacology 2022;60:100 View
  39. Badal V, Nebeker C, Shinkawa K, Yamada Y, Rentscher K, Kim H, Lee E. Do Words Matter? Detecting Social Isolation and Loneliness in Older Adults Using Natural Language Processing. Frontiers in Psychiatry 2021;12 View
  40. Doryab A. Critical thinking of mobile sensing for health. XRDS: Crossroads, The ACM Magazine for Students 2021;28(1):34 View
  41. Panlilio L, Lee A, Smith K, Epstein D. Were self-described introverts "immune" to increased drug use and entrapment during the pandemic?. Drug and Alcohol Dependence Reports 2022;2:100024 View
  42. Adler D, Wang F, Mohr D, Choudhury T, Chen C. Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies. PLOS ONE 2022;17(4):e0266516 View
  43. Smith E, Storch E, Vahia I, Wong S, Lavretsky H, Cummings J, Eyre H. Affective Computing for Late-Life Mood and Cognitive Disorders. Frontiers in Psychiatry 2021;12 View
  44. Dwivedi R, Mehrotra D, Chandra S. Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review. Journal of Oral Biology and Craniofacial Research 2022;12(2):302 View
  45. Potier R. Revue critique sur le potentiel du numérique dans la recherche en psychopathologie : un point de vue psychanalytique. L'Évolution Psychiatrique 2022;87(4):729 View
  46. Castro R, Ribeiro-Alves M, Oliveira C, Romero C, Perazzo H, Simjanoski M, Kapciznki F, Balanzá-Martínez V, De Boni R. What Are We Measuring When We Evaluate Digital Interventions for Improving Lifestyle? A Scoping Meta-Review. Frontiers in Public Health 2022;9 View
  47. Langener A, Stulp G, Kas M, Bringmann L. Capturing the Dynamics of the Social Environment Through Experience Sampling Methods, Passive Sensing, and Egocentric Networks: Scoping Review. JMIR Mental Health 2023;10:e42646 View
  48. Moura I, Teles A, Coutinho L, Silva F. Towards identifying context-enriched multimodal behavioral patterns for digital phenotyping of human behaviors. Future Generation Computer Systems 2022;131:227 View
  49. Buck B, Munson J, Chander A, Wang W, Brenner C, Campbell A, Ben-Zeev D. The relationship between appraisals of auditory verbal hallucinations and real-time affect and social functioning. Schizophrenia Research 2022;250:112 View
  50. Morris M. Enhancing relationships through technology: directions in parenting, caregiving, romantic partnerships, and clinical practice. Dialogues in Clinical Neuroscience 2020;22(2):151 View
  51. Site A, Lohan E, Jolanki O, Valkama O, Hernandez R, Latikka R, Alekseeva D, Vasudevan S, Afolaranmi S, Ometov A, Oksanen A, Martinez Lastra J, Nurmi J, Fernandez F. Managing Perceived Loneliness and Social-Isolation Levels for Older Adults: A Survey with Focus on Wearables-Based Solutions. Sensors 2022;22(3):1108 View
  52. De La Fabián R, Jiménez-Molina Á, Pizarro Obaid F. A critical analysis of digital phenotyping and the neuro-digital complex in psychiatry. Big Data & Society 2023;10(1) View
  53. De Boer C, Ghomrawi H, Zeineddin S, Linton S, Kwon S, Abdullah F. A Call to Expand the Scope of Digital Phenotyping. Journal of Medical Internet Research 2023;25:e39546 View
  54. González-Pérez A, Matey-Sanz M, Granell C, Díaz-Sanahuja L, Bretón-López J, Casteleyn S. AwarNS: A framework for developing context-aware reactive mobile applications for health and mental health. Journal of Biomedical Informatics 2023;141:104359 View
  55. Ahmed M, Ahmed N. A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach. JMIR Formative Research 2023;7:e28848 View
  56. Hirten R, Suprun M, Danieletto M, Zweig M, Golden E, Pyzik R, Kaur S, Helmus D, Biello A, Landell K, Rodrigues J, Bottinger E, Keefer L, Charney D, Nadkarni G, Suarez-Farinas M, Fayad Z. A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort. JAMIA Open 2023;6(2) View
  57. Sarhaddi F, Azimi I, Niela-Vilen H, Axelin A, Liljeberg P, Rahmani A. Maternal Social Loneliness Detection Using Passive Sensing Through Continuous Monitoring in Everyday Settings: Longitudinal Study. JMIR Formative Research 2023;7:e47950 View
  58. Shui X, Chen Y, Hu X, Wang F, Zhang D. Personality in Daily Life: Multi-Situational Physiological Signals Reflect Big-Five Personality Traits. IEEE Journal of Biomedical and Health Informatics 2023;27(6):2853 View
  59. Wyant K, Moshontz H, Ward S, Fronk G, Curtin J. Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study. JMIR mHealth and uHealth 2023;11:e41833 View
  60. Thaker N, De B, Shah C, Manda S, Royce T, Beriwal S. Practical Applications of the Internet of Things in Radiation Oncology. Applied Radiation Oncology 2022 View
  61. Ahmed S, Khan S, Ahmed N. Is There Any Relation Between Smartphone Usage and Loneliness During the COVID-19 Pandemic?: A Study by Exploring Two Objective App Usage Datasets. EAI Endorsed Transactions on Pervasive Health and Technology 2023;9 View
  62. DeJohn A, Widener M, Mihailidis A. Who uses technology to socialize? Evidence from older Canadian adults. Discover Social Science and Health 2023;3(1) View
  63. Rajkishan S, Meitei A, Singh A. Role of AI/ML in the study of mental health problems of the students: a bibliometric study. International Journal of System Assurance Engineering and Management 2024;15(5):1615 View
  64. Qirtas M, White E, Zafeiridi E, Pesch D. Personalizing Loneliness Detection Through Behavioral Grouping of Passive Sensing Data From College Students. IEEE Access 2023;11:88841 View
  65. Lim S, Kim C, Cho B, Choi S, Lee H, Jang D. Investigation of daily patterns for smartphone keystroke dynamics based on loneliness and social isolation. Biomedical Engineering Letters 2024;14(2):235 View
  66. Sethi M, Kumar C, Math S. The Vanguard of Psychiatry: Artificial Intelligence as a Catalyst for Change. Journal of Psychiatry Spectrum 2024;3(1):1 View
  67. Villar A, de Andrade C. Supervised machine learning algorithms for predicting student dropout and academic success: a comparative study. Discover Artificial Intelligence 2024;4(1) View
  68. Santos R, Ventura S, Nogueira Y, Mendes C, Paula J, Miranda D, Romano-Silva M. The Associations Between Screen Time and Mental Health in Adults: A Systematic Review. Journal of Technology in Behavioral Science 2024;9(4):825 View
  69. Hughes G, Moore L, Hennessy M, Sandset T, Jentoft E, Haldar M. What kind of a problem is loneliness? Representations of connectedness and participation from a study of telepresence technologies in the UK. Frontiers in Digital Health 2024;6 View
  70. Mullick T, Shaaban S, Radovic A, Doryab A. Framework for Ranking Machine Learning Predictions of Limited, Multimodal, and Longitudinal Behavioral Passive Sensing Data: Combining User-Agnostic and Personalized Modeling. JMIR AI 2024;3:e47805 View
  71. Jafarlou S, Azimi I, Lai J, Wang Y, Labbaf S, Nguyen B, Qureshi H, Marcotullio C, Borelli J, Dutt N, Rahmani A, Chang L. Objective monitoring of loneliness levels using smart devices: A multi-device approach for mental health applications. PLOS ONE 2024;19(6):e0298949 View
  72. Wang N, Goel S, Ibrahim S, Badal V, Depp C, Bilal E, Subbalakshmi K, Lee E. Decoding loneliness: Can explainable AI help in understanding language differences in lonely older adults?. Psychiatry Research 2024;339:116078 View
  73. Wallace M, Frank E, McClung C, Cote S, Kendrick J, Payne S, Frost-Pineda K, Leach J, Matthews M, Choudhury T, Kupfer D. A translationally informed approach to vital signs for psychiatry: a preliminary proof of concept. NPP—Digital Psychiatry and Neuroscience 2024;2(1) View
  74. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View
  75. Slade C, Benzo R, Washington P. Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study. Journal of Medical Internet Research 2024;26:e55694 View
  76. Müller S, Peters H, Matz S, Wang W, Harari G. Investigating the Relationships between Mobility Behaviours and Indicators of Subjective Well–Being Using Smartphone–Based Experience Sampling and GPS Tracking. European Journal of Personality 2020;34(5):714 View
  77. Zhang J, Feng X, Wang W, Liu S, Zhang Q, Wu D, Liu Q. Predicting the Risk of Loneliness in Children and Adolescents: A Machine Learning Study. Behavioral Sciences 2024;14(10):947 View
  78. Dwyer L. Canadian perspectives on loneliness; digital communication as meaningful connection. Frontiers in Public Health 2024;12 View

Books/Policy Documents

  1. Nishiyama Y, Ferreira D, Eigen Y, Sasaki W, Okoshi T, Nakazawa J, Dey A, Sezaki K. Distributed, Ambient and Pervasive Interactions. View
  2. Carretero P, Campana-Montes J, Artes-Rodriguez A. Behavioral Neurobiology of Suicide and Self Harm. View
  3. Ahmed M, Ahmed N. Pervasive Computing Technologies for Healthcare. View
  4. Marastoni N, Oliboni B, Quintarelli E. Big Data Analytics and Knowledge Discovery. View
  5. Oliboni B, Dalla Vecchia A, Marastoni N, Quintarelli E. Advances in Smart Healthcare Paradigms and Applications. View
  6. Demetriou D, Mathabe K, Lolas G, Dlamini Z. Society 5.0 and Next Generation Healthcare. View
  7. Ahmed M, Ahmed N. Pervasive Computing Technologies for Healthcare. View
  8. Kapdi T, Shah A. Advances in Data-Driven Computing and Intelligent Systems. View
  9. Sjöberg J, Bergdahl N, Sjödén B, Nouri J. Design, Learning, and Innovation. View
  10. Mettouris C, Vanezi E, Cammerlander L, Schober P, Hadjicosta A, Moza S, Rohringer M, Zemaitaityte I, Kellerer J, Schulc E, Bardauskiene R, Zaramella P, Maistrello A, Papadopoulos G. Advances in Computational Collective Intelligence. View