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

Mobile Health in Psychiatry (426) Depression and Mood Disorders; Suicide Prevention (1363) mHealth for Wellness, Behavior Change and Prevention (2871) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1514) Usability of Apps and User Perceptions of mHealth (974) Wearable Devices and Sensors (888) Fitness Trackers and Smart Pedometers/Accelerometers (561) mHealth for Data Collection and Research (955) Mobile Health (mhealth) (2939) Web-based and Mobile Health Interventions (3297)

Published on in Vol 10 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/42866, first published .
The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement

The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement

The Feasibility of Implementing Remote Measurement Technologies in Psychological Treatment for Depression: Mixed Methods Study on Engagement

Authors of this article:

Valeria de Angel1, 2 Author Orcid Image ;   Fadekemi Adeleye3 Author Orcid Image ;   Yuezhou Zhang4 Author Orcid Image ;   Nicholas Cummins4 Author Orcid Image ;   Sara Munir5 Author Orcid Image ;   Serena Lewis1, 6 Author Orcid Image ;   Estela Laporta Puyal7, 8 Author Orcid Image ;   Faith Matcham1, 9 Author Orcid Image ;   Shaoxiong Sun4 Author Orcid Image ;   Amos A Folarin2, 4, 10, 11, 12 Author Orcid Image ;   Yatharth Ranjan13 Author Orcid Image ;   Pauline Conde13 Author Orcid Image ;   Zulqarnain Rashid13 Author Orcid Image ;   Richard Dobson1, 2 Author Orcid Image ;   Matthew Hotopf1, 2 Author Orcid Image
Article Authors Cited by (7) Tweetations (5) Metrics

Journals

  1. Lagera P, Chan S, Yellowlees P. Asynchronous Technologies in Mental Health Care and Education. Current Treatment Options in Psychiatry 2023;10(2):59 View
  2. Anmella G, Corponi F, Li B, Mas A, Sanabra M, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Giménez-Palomo A, Garriga M, Agasi I, Bastidas A, Cavero M, Fernández-Plaza T, Arbelo N, Bioque M, García-Rizo C, Verdolini N, Madero S, Murru A, Amoretti S, Martínez-Aran A, Ruiz V, Fico G, De Prisco M, Oliva V, Solanes A, Radua J, Samalin L, Young A, Vieta E, Vergari A, Hidalgo-Mazzei D. Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study. JMIR mHealth and uHealth 2023;11:e45405 View
  3. Abouali Z, Mohammadi S, Kazempour Z, Pirouzfar V. Removal of hydrocarbon compounds from the oil effluents of Abadan refinery by a biological method. International Journal of Energy and Water Resources 2024;8(4):475 View
  4. Zhang X, Lewis S, Carter L, Chen X, Zhou J, Wang X, Bucci S. Evaluating a smartphone-based symptom self-monitoring app for psychosis in China (YouXin): A non-randomised validity and feasibility study with a mixed-methods design. DIGITAL HEALTH 2024;10 View
  5. Rogan J, Bucci S, Firth J. Health Care Professionals’ Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta-Synthesis. JMIR Mental Health 2024;11:e49577 View
  6. Zhang Y, Folarin A, Dineley J, Conde P, de Angel V, Sun S, Ranjan Y, Rashid Z, Stewart C, Laiou P, Sankesara H, Qian L, Matcham F, White K, Oetzmann C, Lamers F, Siddi S, Simblett S, Schuller B, Vairavan S, Wykes T, Haro J, Penninx B, Narayan V, Hotopf M, Dobson R, Cummins N. Identifying depression-related topics in smartphone-collected free-response speech recordings using an automatic speech recognition system and a deep learning topic model. Journal of Affective Disorders 2024;355:40 View
  7. Zhang X, Lewis S, Chen X, Zhou J, Wang X, Bucci S. Acceptability and experience of a smartphone symptom monitoring app for people with psychosis in China (YouXin): a qualitative study. BMC Psychiatry 2024;24(1) View