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

Mobile Health (mhealth) (2939) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1514)

Published on in Vol 15, No 7 (2013): July

Maximizing the Value of Mobile Health Monitoring by Avoiding Redundant Patient Reports: Prediction of Depression-Related Symptoms and Adherence Problems in Automated Health Assessment Services

Maximizing the Value of Mobile Health Monitoring by Avoiding Redundant Patient Reports: Prediction of Depression-Related Symptoms and Adherence Problems in Automated Health Assessment Services

Maximizing the Value of Mobile Health Monitoring by Avoiding Redundant Patient Reports: Prediction of Depression-Related Symptoms and Adherence Problems in Automated Health Assessment Services

Authors of this article:

John D Piette1 ;   Jeremy B Sussman1 ;   Paul N Pfeiffer2 ;   Maria J Silveira1 ;   Satinder Singh3 ;   Mariel S Lavieri4
Article Authors Cited by (11) Tweetations (5) Metrics

Journals

  1. Piette J, Krein S, Striplin D, Marinec N, Kerns R, Farris K, Singh S, An L, Heapy A. Patient-Centered Pain Care Using Artificial Intelligence and Mobile Health Tools: Protocol for a Randomized Study Funded by the US Department of Veterans Affairs Health Services Research and Development Program. JMIR Research Protocols 2016;5(2):e53 View
  2. Aikens J, Zivin K, Trivedi R, Piette J. Diabetes self-management support using mHealth and enhanced informal caregiving. Journal of Diabetes and its Complications 2014;28(2):171 View
  3. Bashshur R, Shannon G, Bashshur N, Yellowlees P. The Empirical Evidence for Telemedicine Interventions in Mental Disorders. Telemedicine and e-Health 2016;22(2):87 View
  4. Monteith S, Glenn T, Geddes J, Whybrow P, Bauer M. Big data for bipolar disorder. International Journal of Bipolar Disorders 2016;4(1) View
  5. Kim S, Lee K. Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods. Neuropsychiatric Disease and Treatment 2021;Volume 17:3415 View
  6. Bohlmann A, Mostafa J, Kumar M. Machine Learning and Medication Adherence: Scoping Review. JMIRx Med 2021;2(4):e26993 View
  7. Elkefi S. Access and usage of mobile health (mHealth) for communication, health monitoring, and decision-making among patients with multiple chronic diseases (comorbidities). IISE Transactions on Healthcare Systems Engineering 2024;14(3):179 View
  8. Abdallah S, Khalil A. Using a hybrid methodology for literature review: a case study in depression research. Information Discovery and Delivery 2024;52(3):305 View

Books/Policy Documents

  1. Wetter T. Consumer Health Informatics. View