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

Mobile Health (mhealth) (3464) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2111)

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

Books/Policy Documents

  1. Saiz Manzanares M, Martínez Martín M, Escolar Llamazares M, Ortiz Huerta J, Santamaría Vázquez M, Mercado Val E, Marticorena Sánchez R, Arnáiz González Á, Díez Pastor J, Rodríguez Arribas S. Training and specialisation in early intervention: use of technological resources and artificial intelligence. View
  2. Saiz Manzanares M, Martínez Martín M, Escolar Llamazares M, Ortiz Huerta J, Santamaría Vázquez M, Mercado Val E, Marticorena Sánchez R, Arnáiz González Á, Díez Pastor J, Rodríguez Arribas S. Formación y especialización en atención temprana: uso de recursos tecnológicos y de inteligencia artificial. View