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

Mobile Health (mhealth) (5301) Depression and Mood Disorders; Suicide Prevention (2518) Methods and New Tools in Mental Health Research (940) Diagnostic Tools in Mental Health (454) Transdiagnostic Mental Health Interventions (274) Digital Biomarkers and Digital Phenotyping (470) Clinical Mental Health Informatics (151)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/51269, first published .
Woman using a smartphone on a textured table

Digital Phenotypes of Mobile Keyboard Backspace Rates and Their Associations With Symptoms of Mood Disorder: Algorithm Development and Validation

Digital Phenotypes of Mobile Keyboard Backspace Rates and Their Associations With Symptoms of Mood Disorder: Algorithm Development and Validation

Authors of this article:

Qimin Liu1, 2 Author Orcid Image ;   Emma Ning3 Author Orcid Image ;   Mindy K Ross4 Author Orcid Image ;   Andrea Cladek2 Author Orcid Image ;   Sarah Kabir2 Author Orcid Image ;   Amruta Barve2 Author Orcid Image ;   Ellyn Kennelly5 Author Orcid Image ;   Faraz Hussain2 Author Orcid Image ;   Jennifer Duffecy2 Author Orcid Image ;   Scott A Langenecker6 Author Orcid Image ;   Theresa M Nguyen4 Author Orcid Image ;   Theja Tulabandhula7 Author Orcid Image ;   John Zulueta2 Author Orcid Image ;   Alexander P Demos3 Author Orcid Image ;   Alex Leow2, 4 Author Orcid Image ;   Olusola Ajilore2 Author Orcid Image
Article Authors Cited by (8) Tweetations (7) Metrics

Journals

  1. Bahaabadi Z, Karav S, Sahebkar A. Advances in Apolipoprotein-A4 Biosensing Assays for Depression Diagnosis. Critical Reviews in Analytical Chemistry 2026;56(5):1410 View
  2. Knol L, Ross M, Nagpal A, Burns A, Morrissey Z, Hussain F, Eisenlohr-Moul T, Beckmann C, Leow A, Marquand A. Unobtrusive inference of diurnal rhythms from smartphone data. npj Digital Medicine 2025;9(1) View
  3. Jacobucci R, Shao W, Kobrinsky V, Ammerman B. Predicting Momentary Suicidal Ideation From Smartphone Screenshots Using Vision-Language Models: Prospective Machine Learning Study. JMIR Mental Health 2026;13:e90581 View
  4. Jacobson S, Carling H, Sarraf L, Draper E, Jacobson S, St-James M, Misiasz C, Williamson S, Thibaudeau E, Sauvé G, Lavigne K, Raucher-Chéné D. Digital tools for assessing bipolar disorder: A scoping review of the current landscape. Neuroscience Applied 2026;5:107004 View
  5. Kim E, Park D, Lee J, Kim J, Ko S, Kim Y, Lee S. Inferring Goal-Directed Cognitive States from Everyday Smartphone Interaction for Adaptive Human–Computer Interaction. International Journal of Human–Computer Interaction 2026:1 View
  6. Kutcher T, Chakraborty I, Kostick-Quenet K, Sano A, Moukaddam N, Herron J, Goodman W, Sheth S, Sabharwal A, Provenza N. Digital Markers for Passive Remote Monitoring of Bipolar Disorder: Systematic Review. JMIR Mental Health 2026;13:e94819 View
  7. Strauss G, Raugh I, Cowan T, Luther L, Zhang Z, Luck J, Dhima A, Ehm J, Cohen A, Kirkpatrick B, Pinkham A, Ackerman R, Depp C, Moore R, Harvey P. Toward the third generation of negative symptom assessment in schizophrenia: A systematic review of digital phenotyping measures. Schizophrenia Research 2026;298:54 View
  8. Ren Q, Wu Y, Chi J, Li Y, Zhao P, Luo M, Sun J, He Y, Liu J, Luo L. Online Behavioral Digital Phenotyping for Depression: Scoping Review of Behavioral Features and Exploratory Assessment of RDoC Construct Relevance (Preprint). Journal of Medical Internet Research 2026 View