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
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