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Citing this Article

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Published on 10.08.17 in Vol 5, No 8 (2017): August

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

Works citing "Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety"

Sohrab Saeb, Emily G Lattie, Konrad P Kording, David C Mohr

JMIR Mhealth Uhealth 2017 (Aug 10); 5(8):e112 

According to Crossref, the following articles are citing this article (DOI 10.2196/mhealth.7297):

(note that this is only a small subset of citations)

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  15. Pratap A, Atkins DC, Renn BN, Tanana MJ, Mooney SD, Anguera JA, Areán PA. The accuracy of passive phone sensors in predicting daily mood. Depression and Anxiety 2019;36(1):72
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  25. Sheikh M, Qassem M, Kyriacou PA. Wearable, Environmental, and Smartphone-Based Passive Sensing for Mental Health Monitoring. Frontiers in Digital Health 2021;3
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  26. Vlisides-Henry RD, Gao M, Thomas L, Kaliush PR, Conradt E, Crowell SE. Digital Phenotyping of Emotion Dysregulation Across Lifespan Transitions to Better Understand Psychopathology Risk. Frontiers in Psychiatry 2021;12
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  27. Montag C, Rumpf H. The Potential of Digital Phenotyping and Mobile Sensing for Psycho-Diagnostics of Internet Use Disorders. Current Addiction Reports 2021;8(3):422
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  28. Teepe GW, Da Fonseca A, Kleim B, Jacobson NC, Salamanca Sanabria A, Tudor Car L, Fleisch E, Kowatsch T. Just-in-Time Adaptive Mechanisms of Popular Mobile Apps for Individuals With Depression: Systematic App Search and Literature Review. Journal of Medical Internet Research 2021;23(9):e29412
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  29. D’Mello R, Melcher J, Torous J. Similarity matrix-based anomaly detection for clinical intervention. Scientific Reports 2022;12(1)
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  30. Hart A, Reis D, Prestele E, Jacobson NC. Using Smartphone Sensor Paradata and Personalized Machine Learning Models to Infer Participants’ Well-being: Ecological Momentary Assessment. Journal of Medical Internet Research 2022;24(4):e34015
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  31. van Berkel N, D’Alfonso S, Kurnia Susanto R, Ferreira D, Kostakos V. AWARE-Light: a smartphone tool for experience sampling and digital phenotyping. Personal and Ubiquitous Computing 2023;27(2):435
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  32. MacLeod L, Suruliraj B, Gall D, Bessenyei K, Hamm S, Romkey I, Bagnell A, Mattheisen M, Muthukumaraswamy V, Orji R, Meier S. A Mobile Sensing App to Monitor Youth Mental Health: Observational Pilot Study. JMIR mHealth and uHealth 2021;9(10):e20638
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  33. Baglione AN, Cai L, Bahrini A, Posey I, Boukhechba M, Chow PI. 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
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  34. Montag C, Elhai JD, Dagum P. On Blurry Boundaries When Defining Digital Biomarkers: How Much Biology Needs to Be in a Digital Biomarker?. Frontiers in Psychiatry 2021;12
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  35. Li T, Zhang M, Li Y, Lagerspetz E, Tarkoma S, Hui P. The Impact of Covid-19 on Smartphone Usage. IEEE Internet of Things Journal 2021;8(23):16723
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  36. 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
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  37. Jacobson NC, Feng B. Digital phenotyping of generalized anxiety disorder: using artificial intelligence to accurately predict symptom severity using wearable sensors in daily life. Translational Psychiatry 2022;12(1)
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  38. . Estimating Mental Health Using Human-generated Big Data and Machine Learning. The Brain & Neural Networks 2022;29(2):78
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  39. Jacobson NC, Bhattacharya S. Digital biomarkers of anxiety disorder symptom changes: Personalized deep learning models using smartphone sensors accurately predict anxiety symptoms from ecological momentary assessments. Behaviour Research and Therapy 2022;149:104013
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  40. Montag C, Dagum P, Hall BJ, Elhai JD. How the study of digital footprints can supplement research in behavioral genetics and molecular psychology. Molecular Psychology: Brain, Behavior, and Society 2022;1:2
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  41. Highland D, Zhou G. A review of detection techniques for depression and bipolar disorder. Smart Health 2022;24:100282
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  42. Paula LDS, Pfeiffer Salomão Dias L, Francisco R, Barbosa JLV. Analysing IoT Data for Anxiety and Stress Monitoring: A Systematic Mapping Study and Taxonomy. International Journal of Human–Computer Interaction 2024;40(5):1174
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  43. Hong J, Kim J, Kim S, Oh J, Lee D, Lee S, Uh J, Yoon J, Choi Y. Depressive Symptoms Feature-Based Machine Learning Approach to Predicting Depression Using Smartphone. Healthcare 2022;10(7):1189
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  44. Asani F, Patel B, Srikanthan S, Agu E. BioscoreNet: Traumatic Brain Injury (TBI) detection using a multimodal self-attention fusion neural network and a passive bioscore monitoring framework from smartphone sensor data. Smart Health 2023;27:100352
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  45. Niemeijer K, Mestdagh M, Verdonck S, Meers K, Kuppens P. Combining Experience Sampling and Mobile Sensing for Digital Phenotyping With m-Path Sense: Performance Study. JMIR Formative Research 2023;7:e43296
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  46. Meyerhoff J, Liu T, Kording KP, Ungar LH, Kaiser SM, Karr CJ, Mohr DC. Evaluation of Changes in Depression, Anxiety, and Social Anxiety Using Smartphone Sensor Features: Longitudinal Cohort Study. Journal of Medical Internet Research 2021;23(9):e22844
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  47. Chia AZ, Zhang MW. Digital phenotyping in psychiatry: A scoping review. Technology and Health Care 2022;30(6):1331
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  48. Memon A, Kilby J, Breñosa J, Espinosa JCM, Ashraf I. Analysis and Implementation of Human Mobility Behavior Using Similarity Analysis Based on Co-Occurrence Matrix. Sensors 2022;22(24):9898
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  49. Lee K, Lee TC, Yefimova M, Kumar S, Puga F, Azuero A, Kamal A, Bakitas MA, Wright AA, Demiris G, Ritchie CS, Pickering CE, Nicholas Dionne-Odom J. Using digital phenotyping to understand health-related outcomes: A scoping review. International Journal of Medical Informatics 2023;174:105061
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  50. Zou B, Zhang X, Xiao L, Bai R, Li X, Liang H, Ma H, Wang G. Sequence Modeling of Passive Sensing Data for Treatment Response Prediction in Major Depressive Disorder. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2023;31:1786
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  51. Matz SC, Beck ED, Atherton OE, White M, Rauthmann JF, Mroczek DK, Kim M, Bogg T. Personality Science in the Digital Age: The Promises and Challenges of Psychological Targeting for Personalized Behavior-Change Interventions at Scale. Perspectives on Psychological Science 2023;
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  52. Shin J, Bae SM. A Systematic Review of Location Data for Depression Prediction. International Journal of Environmental Research and Public Health 2023;20(11):5984
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  53. Zhuparris A, Maleki G, van Londen L, Koopmans I, Aalten V, Yocarini IE, Exadaktylos V, van Hemert A, Cohen A, Gal P, Doll R, Groeneveld GJ, Jacobs G, Kraaij W. A smartphone- and wearable-based biomarker for the estimation of unipolar depression severity. Scientific Reports 2023;13(1)
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  54. Panlilio LV, Burgess-Hull AJ, Feldman JD, Rogers JM, Tyburski M, Smith KE, Epstein DH. Activity space during treatment with medication for opioid use disorder: Relationships with personality, mood, and drug use. Journal of Substance Use and Addiction Treatment 2024;157:209219
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  55. Mao K, Wu Y, Chen J. A systematic review on automated clinical depression diagnosis. npj Mental Health Research 2023;2(1)
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  56. Alamoudi D, Nabney I, Crawley E. Evaluating the Effectiveness of the SleepTracker App for Detecting Anxiety- and Depression-Related Sleep Disturbances. Sensors 2024;24(3):722
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/mhealth.7297):

  1. Iyawa GE, Ondiek CO, Osakwe JO. Smart Medical Data Sensing and IoT Systems Design in Healthcare. 2020. chapter 1:1
    CrossRef
  2. Teles A, Barros F, Rodrigues I, Barbosa A, Silva F, Coutinho L, Teixeira S. IoT and ICT for Healthcare Applications. 2020. Chapter 4:33
    CrossRef
  3. Heinz MV, Price GD, Song SH, Bhattacharya S, Jacobson NC. Digital Mental Health. 2023. Chapter 2:13
    CrossRef