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

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

Books/Policy Documents

  1. Wetter T. Consumer Health Informatics. View