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For: Onnela JP. Opportunities and challenges in the collection and analysis of digital phenotyping data. Neuropsychopharmacology 2021;46:45-54. [PMID: 32679583 DOI: 10.1038/s41386-020-0771-3] [Cited by in Crossref: 53] [Cited by in F6Publishing: 35] [Article Influence: 26.5] [Reference Citation Analysis]
Number Citing Articles
1 Lee J, Turchioe MR, Creber RM, Biviano A, Hickey K, Bakken S. Phenotypes of engagement with mobile health technology for heart rhythm monitoring. JAMIA Open 2021;4:ooab043. [PMID: 34131638 DOI: 10.1093/jamiaopen/ooab043] [Reference Citation Analysis]
2 Kilshaw RE, Adamo C, Butner JE, Deboeck PR, Shi Q, Bulik CM, Flatt RE, Thornton LM, Argue S, Tregarthen J, Baucom BRW. Passive Sensor Data for Characterizing States of Increased Risk for Eating Disorder Behaviors in the Digital Phenotyping Arm of the Binge Eating Genetics Initiative: Protocol for an Observational Study. JMIR Res Protoc 2022;11:e38294. [PMID: 35653175 DOI: 10.2196/38294] [Reference Citation Analysis]
3 Lee J, Solomonov N, Banerjee S, Alexopoulos GS, Sirey JA. Use of Passive Sensing in Psychotherapy Studies in Late Life: A Pilot Example, Opportunities and Challenges. Front Psychiatry 2021;12:732773. [PMID: 34777042 DOI: 10.3389/fpsyt.2021.732773] [Reference Citation Analysis]
4 Breasail MÓ, Biswas B, Smith MD, Mazhar MKA, Tenison E, Cullen A, Lithander FE, Roudaut A, Henderson EJ. Wearable GPS and Accelerometer Technologies for Monitoring Mobility and Physical Activity in Neurodegenerative Disorders: A Systematic Review. Sensors (Basel) 2021;21:8261. [PMID: 34960353 DOI: 10.3390/s21248261] [Reference Citation Analysis]
5 Ryu J, Sükei E, Norbury A, H Liu S, Campaña-Montes JJ, Baca-Garcia E, Artés A, Perez-Rodriguez MM. Shift in Social Media App Usage During COVID-19 Lockdown and Clinical Anxiety Symptoms: Machine Learning-Based Ecological Momentary Assessment Study. JMIR Ment Health 2021;8:e30833. [PMID: 34524091 DOI: 10.2196/30833] [Reference Citation Analysis]
6 Beukenhorst AL, Collins E, Burke KM, Rahman SM, Clapp M, Konanki SC, Paganoni S, Miller TM, Chan J, Onnela JP, Berry JD. Smartphone data during the COVID-19 pandemic can quantify behavioral changes in people with ALS. Muscle Nerve 2021;63:258-62. [PMID: 33118628 DOI: 10.1002/mus.27110] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
7 De Boer CJ, Ghomrawi H, Zeineddin S, Linton S, Kwon S, Abdullah F. A Call to Expand the Scope of Digital Phenotyping (Preprint). Journal of Medical Internet Research. [DOI: 10.2196/39546] [Reference Citation Analysis]
8 Beierle F, Schobel J, Vogel C, Allgaier J, Mulansky L, Haug F, Haug J, Schlee W, Holfelder M, Stach M, Schickler M, Baumeister H, Cohrdes C, Deckert J, Deserno L, Edler JS, Eichner FA, Greger H, Hein G, Heuschmann P, John D, Kestler HA, Krefting D, Langguth B, Meybohm P, Probst T, Reichert M, Romanos M, Störk S, Terhorst Y, Weiß M, Pryss R. Corona Health-A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic. Int J Environ Res Public Health 2021;18:7395. [PMID: 34299846 DOI: 10.3390/ijerph18147395] [Reference Citation Analysis]
9 Alfalahi H, Khandoker AH, Chowdhury N, Iakovakis D, Dias SB, Chaudhuri KR, Hadjileontiadis LJ. Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Sci Rep 2022;12:7690. [PMID: 35546606 DOI: 10.1038/s41598-022-11865-7] [Reference Citation Analysis]
10 Bougeard A, Guay Hottin1 R, Houde V, Jean T, Piront T, Potvin S, Bernard P, Tourjman V, De Benedictis L, Orban P. Le phénotypage digital pour une pratique clinique en santé mentale mieux informée. smq 2021;46:135-56. [DOI: 10.7202/1081513ar] [Reference Citation Analysis]
11 Van Assche E, Antoni Ramos-quiroga J, Pariante CM, Sforzini L, Young AH, Flossbach Y, Gold SM, Hoogendijk WJ, Baune BT, Maron E. Digital tools for the assessment of pharmacological treatment for depressive disorder: State of the art. European Neuropsychopharmacology 2022;60:100-16. [DOI: 10.1016/j.euroneuro.2022.05.007] [Reference Citation Analysis]
12 Kiang MV, Chen JT, Krieger N, Buckee CO, Alexander MJ, Baker JT, Buckner RL, Coombs G 3rd, Rich-Edwards JW, Carlson KW, Onnela JP. Sociodemographic characteristics of missing data in digital phenotyping. Sci Rep 2021;11:15408. [PMID: 34326370 DOI: 10.1038/s41598-021-94516-7] [Reference Citation Analysis]
13 Hayes CJ, Cucciare MA, Martin BC, Hudson TJ, Bush K, Lo-Ciganic W, Yu H, Charron E, Gordon AJ. Using data science to improve outcomes for persons with opioid use disorder. Subst Abus 2022;43:956-63. [PMID: 35420927 DOI: 10.1080/08897077.2022.2060446] [Reference Citation Analysis]
14 Beukenhorst AL, Sergeant JC, Schultz DM, McBeth J, Yimer BB, Dixon WG. Understanding the Predictors of Missing Location Data to Inform Smartphone Study Design: Observational Study. JMIR Mhealth Uhealth 2021;9:e28857. [PMID: 34783661 DOI: 10.2196/28857] [Reference Citation Analysis]
15 Onnela J, Dixon C, Griffin K, Jaenicke T, Minowada L, Esterkin S, Siu A, Zagorsky J, Jones E. Beiwe: A data collection platform for high-throughput digital phenotyping. JOSS 2021;6:3417. [DOI: 10.21105/joss.03417] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Ball TM, Gunaydin LA. Measuring maladaptive avoidance: from animal models to clinical anxiety. Neuropsychopharmacology 2022. [PMID: 35034097 DOI: 10.1038/s41386-021-01263-4] [Reference Citation Analysis]
17 Spottswood M, Lim CT, Davydow D, Huang H. Improving Suicide Prevention in Primary Care for Differing Levels of Behavioral Health Integration: A Review. Front Med (Lausanne) 2022;9:892205. [PMID: 35712115 DOI: 10.3389/fmed.2022.892205] [Reference Citation Analysis]
18 Keusch F, Bähr S, Haas G, Kreuter F, Trappmann M, Eckman S. Non‐participation in smartphone data collection using research apps. Royal Stats Society Series A. [DOI: 10.1111/rssa.12827] [Reference Citation Analysis]
19 Mangalam M, Fragaszy DM, Wagman JB, Day BM, Kelty-Stephen DG, Bongers RM, Stout DW, Osiurak F. On the psychological origins of tool use. Neurosci Biobehav Rev 2022;:104521. [PMID: 34998834 DOI: 10.1016/j.neubiorev.2022.104521] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
20 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. Front Psychiatry 2021;12:618442. [PMID: 34108893 DOI: 10.3389/fpsyt.2021.618442] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
21 Vidal Bustamante CM, Coombs G 3rd, Rahimi-Eichi H, Mair P, Onnela JP, Baker JT, Buckner RL. Fluctuations in behavior and affect in college students measured using deep phenotyping. Sci Rep 2022;12:1932. [PMID: 35121741 DOI: 10.1038/s41598-022-05331-7] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
22 Inomata T, Nakamura M, Iwagami M, Sung J, Nakamura M, Ebihara N, Fujisawa K, Muto K, Nojiri S, Ide T, Okano M, Okumura Y, Fujio K, Fujimoto K, Nagao M, Hirosawa K, Akasaki Y, Murakami A. Individual characteristics and associated factors of hay fever: A large-scale mHealth study using AllerSearch. Allergol Int 2022:S1323-8930(22)00001-6. [PMID: 35105520 DOI: 10.1016/j.alit.2021.12.004] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
23 Choudhary S, Thomas N, Ellenberger J, Srinivasan G, Cohen R. A Machine Learning Approach Detecting Digital Behavioural Patterns of Depression Using Non-intrusive Smartphone Data - A Complementary Path to PHQ-9 Assessment: A Prospective Observational Study. JMIR Form Res 2022. [PMID: 35420993 DOI: 10.2196/37736] [Reference Citation Analysis]
24 Raugh IM, James SH, Gonzalez CM, Chapman HC, Cohen AS, Kirkpatrick B, Strauss GP. Digital phenotyping adherence, feasibility, and tolerability in outpatients with schizophrenia. J Psychiatr Res 2021;138:436-43. [PMID: 33964681 DOI: 10.1016/j.jpsychires.2021.04.022] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
25 Xia CH, Barnett I, Tapera TM, Adebimpe A, Baker JT, Bassett DS, Brotman MA, Calkins ME, Cui Z, Leibenluft E, Linguiti S, Lydon-Staley DM, Martin ML, Moore TM, Murtha K, Piiwaa K, Pines A, Roalf DR, Rush-Goebel S, Wolf DH, Ungar LH, Satterthwaite TD. Mobile footprinting: linking individual distinctiveness in mobility patterns to mood, sleep, and brain functional connectivity. Neuropsychopharmacology 2022. [PMID: 35660803 DOI: 10.1038/s41386-022-01351-z] [Reference Citation Analysis]
26 Wang X, Vouk N, Heaukulani C, Buddhika T, Martanto W, Lee J, Morris RJ. HOPES: An Integrative Digital Phenotyping Platform for Data Collection, Monitoring, and Machine Learning. J Med Internet Res 2021;23:e23984. [PMID: 33720028 DOI: 10.2196/23984] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
27 Beukenhorst AL, Burke KM, Scheier Z, Miller TM, Paganoni S, Keegan M, Collins E, Connaghan KP, Tay A, Chan J, Berry JD, Onnela JP. Using Smartphones to Reduce Research Burden in a Neurodegenerative Population and Assessing Participant Adherence: A Randomized Clinical Trial and Two Observational Studies. JMIR Mhealth Uhealth 2022;10:e31877. [PMID: 35119373 DOI: 10.2196/31877] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Liu T, Meyerhoff J, Eichstaedt JC, Karr CJ, Kaiser SM, Kording KP, Mohr DC, Ungar LH. The relationship between text message sentiment and self-reported depression. J Affect Disord 2021:S0165-0327(21)01359-8. [PMID: 34963643 DOI: 10.1016/j.jad.2021.12.048] [Reference Citation Analysis]
29 Perez-Pozuelo I, Spathis D, Gifford-Moore J, Morley J, Cowls J. Digital phenotyping and sensitive health data: Implications for data governance. J Am Med Inform Assoc 2021:ocab012. [PMID: 33647989 DOI: 10.1093/jamia/ocab012] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
30 Inomata T, Nakamura M, Sung J, Midorikawa-Inomata A, Iwagami M, Fujio K, Akasaki Y, Okumura Y, Fujimoto K, Eguchi A, Miura M, Nagino K, Shokirova H, Zhu J, Kuwahara M, Hirosawa K, Dana R, Murakami A. Smartphone-based digital phenotyping for dry eye toward P4 medicine: a crowdsourced cross-sectional study. NPJ Digit Med 2021;4:171. [PMID: 34931013 DOI: 10.1038/s41746-021-00540-2] [Reference Citation Analysis]
31 Stout D. The Cognitive Science of Technology. Trends Cogn Sci 2021:S1364-6613(21)00175-3. [PMID: 34362661 DOI: 10.1016/j.tics.2021.07.005] [Reference Citation Analysis]
32 Ressler KJ, Williams LM. Big data in psychiatry: multiomics, neuroimaging, computational modeling, and digital phenotyping. Neuropsychopharmacology 2021;46:1-2. [PMID: 32919403 DOI: 10.1038/s41386-020-00862-x] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
33 Neethirajan S, Kemp B. Digital Phenotyping in Livestock Farming. Animals (Basel) 2021;11:2009. [PMID: 34359137 DOI: 10.3390/ani11072009] [Reference Citation Analysis]
34 Keenoy KE, Lenze EJ, Nicol GE. Going remote: Implementing digital research methods at an academic medical center during COVID-19. J Clin Transl Sci 2021;5:e189. [PMID: 34812289 DOI: 10.1017/cts.2021.865] [Reference Citation Analysis]
35 Deng H, Abouzeid CA, Shepler LJ, Slavin MD, Taylor JA, Mercier HW, Herrera-escobar JP, Kazis LE, Ryan CM, Schneider JC. Using Digital Phenotyping to Characterize Psychosocial Trajectories for People with Burn Injury. Burns 2022. [DOI: 10.1016/j.burns.2022.04.003] [Reference Citation Analysis]
36 Huang EJ, Yan K, Onnela J. Smartphone-Based Activity Recognition Using Multistream Movelets Combining Accelerometer and Gyroscope Data. Sensors 2022;22:2618. [DOI: 10.3390/s22072618] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]