Published on 10.08.17 in Vol 5, No 8 (2017): August
Works citing "Mobile Phone Detection of Semantic Location and Its Relationship to Depression and Anxiety"
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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Berman AL, Carter G. Technological Advances and the Future of Suicide Prevention: Ethical, Legal, and Empirical Challenges. Suicide and Life-Threatening Behavior 2020;50(3):643
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Thong MSY, Chan RJ, van den Hurk C, Fessele K, Tan W, Poprawski D, Fernández-Ortega P, Paterson C, Fitch MI. Going beyond (electronic) patient-reported outcomes: harnessing the benefits of smart technology and ecological momentary assessment in cancer survivorship research. Supportive Care in Cancer 2021;29(1):7
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Jones M, Johnson M, Shervey M, Dudley JT, Zimmerman N. Privacy-Preserving Methods for Feature Engineering Using Blockchain: Review, Evaluation, and Proof of Concept. Journal of Medical Internet Research 2019;21(8):e13600
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Yim SJ, Lui LM, Lee Y, Rosenblat JD, Ragguett R, Park C, Subramaniapillai M, Cao B, Zhou A, Rong C, Lin K, Ho RC, Coles AS, Majeed A, Wong ER, Phan L, Nasri F, McIntyre RS. The utility of smartphone-based, ecological momentary assessment for depressive symptoms. Journal of Affective Disorders 2020;274:602
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Montag C, Sindermann C, Baumeister H. Digital phenotyping in psychological and medical sciences: a reflection about necessary prerequisites to reduce harm and increase benefits. Current Opinion in Psychology 2020;36:19
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Jacobson NC, Chung YJ. Passive Sensing of Prediction of Moment-To-Moment Depressed Mood among Undergraduates with Clinical Levels of Depression Sample Using Smartphones. Sensors 2020;20(12):3572
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Trifan A, Oliveira M, Oliveira JL. Passive Sensing of Health Outcomes Through Smartphones: Systematic Review of Current Solutions and Possible Limitations. JMIR mHealth and uHealth 2019;7(8):e12649
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Jim HSL, Hoogland AI, Brownstein NC, Barata A, Dicker AP, Knoop H, Gonzalez BD, Perkins R, Rollison D, Gilbert SM, Nanda R, Berglund A, Mitchell R, Johnstone PAS. Innovations in research and clinical care using patient‐generated health data. CA: A Cancer Journal for Clinicians 2020;70(3):182
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Seppälä J, De Vita I, Jämsä T, Miettunen J, Isohanni M, Rubinstein K, Feldman Y, Grasa E, Corripio I, Berdun J, D'Amico E, Bulgheroni M. Mobile Phone and Wearable Sensor-Based mHealth Approaches for Psychiatric Disorders and Symptoms: Systematic Review. JMIR Mental Health 2019;6(2):e9819
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Boukhechba M, Chow P, Fua K, Teachman BA, Barnes LE. Predicting Social Anxiety From Global Positioning System Traces of College Students: Feasibility Study. JMIR Mental Health 2018;5(3):e10101
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Obuchi M, Huckins JF, Wang W, daSilva A, Rogers C, Murphy E, Hedlund E, Holtzheimer P, Mirjafari S, Campbell A. Predicting Brain Functional Connectivity Using Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(1):1
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Pisco Almeida AM, Almeida HS, Figueiredo-Braga M. Mobile solutions in depression: enhancing communication with patients using an SMS-based intervention. Procedia Computer Science 2018;138:89
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Liu T, Nicholas J, Theilig MM, Guntuku SC, Kording K, Mohr DC, Ungar L. Machine Learning for Phone-Based Relationship Estimation. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2019;3(4):1
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Nepal S, Mirjafari S, Martinez GJ, Audia P, Striegel A, Campbell AT. Detecting Job Promotion in Information Workers Using Mobile Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2020;4(3):1
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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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Burgess ER, Ringland KE, Nicholas J, Knapp AA, Eschler J, Mohr DC, Reddy MC. "I think people are powerful". Proceedings of the ACM on Human-Computer Interaction 2019;3(CSCW):1
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Miralles I, Granell C. Considerations for Designing Context-Aware Mobile Apps for Mental Health Interventions. International Journal of Environmental Research and Public Health 2019;16(7):1197
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Pastor N, Khalilian E, Caballeria E, Morrison D, Sanchez Luque U, Matrai S, Gual A, López-Pelayo H. Remote Monitoring Telemedicine (REMOTE) Platform for Patients With Anxiety Symptoms and Alcohol Use Disorder: Protocol for a Case-Control Study. JMIR Research Protocols 2020;9(6):e16964
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Pulantara IW, Parmanto B, Germain A. Development of a Just-in-Time Adaptive mHealth Intervention for Insomnia: Usability Study. JMIR Human Factors 2018;5(2):e21
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Pulantara IW, Parmanto B, Germain A. Clinical Feasibility of a Just-in-Time Adaptive Intervention App (iREST) as a Behavioral Sleep Treatment in a Military Population: Feasibility Comparative Effectiveness Study. Journal of Medical Internet Research 2018;20(12):e10124
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Luo X, Chen Z. English text quality analysis based on recurrent neural network and semantic segmentation. Future Generation Computer Systems 2020;112:507
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dos Santos Paula L, Barbosa JLV, Dias LPS. A model for assisting in the treatment of anxiety disorder. Universal Access in the Information Society 2022;21(2):533
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. Illuminating the Black Box: Interpreting Deep Neural Network Models for Psychiatric Research. Frontiers in Psychiatry 2020;11
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Maharjan SM, Poudyal A, van Heerden A, Byanjankar P, Thapa A, Islam C, Kohrt BA, Hagaman A. Passive sensing on mobile devices to improve mental health services with adolescent and young mothers in low-resource settings: the role of families in feasibility and acceptability. BMC Medical Informatics and Decision Making 2021;21(1)
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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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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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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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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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D’Mello R, Melcher J, Torous J. Similarity matrix-based anomaly detection for clinical intervention. Scientific Reports 2022;12(1)
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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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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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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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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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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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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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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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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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. Estimating Mental Health Using Human-generated Big Data and Machine Learning. The Brain & Neural Networks 2022;29(2):78
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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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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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Highland D, Zhou G. A review of detection techniques for depression and bipolar disorder. Smart Health 2022;24:100282
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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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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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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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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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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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Chia AZ, Zhang MW. Digital phenotyping in psychiatry: A scoping review. Technology and Health Care 2022;30(6):1331
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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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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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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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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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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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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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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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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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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
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According to Crossref, the following books are citing this article (DOI 10.2196/mhealth.7297):
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Iyawa GE, Ondiek CO, Osakwe JO. Smart Medical Data Sensing and IoT Systems Design in Healthcare. 2020. chapter 1:1
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Teles A, Barros F, Rodrigues I, Barbosa A, Silva F, Coutinho L, Teixeira S. IoT and ICT for Healthcare Applications. 2020. Chapter 4:33
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Heinz MV, Price GD, Song SH, Bhattacharya S, Jacobson NC. Digital Mental Health. 2023. Chapter 2:13
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