Published on in Vol 6 , No 10 (2018) :October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11557, first published .
A Mobile Sleep-Management Learning System for Improving Students’ Sleeping Habits by Integrating a Self-Regulated Learning Strategy: Randomized Controlled Trial

A Mobile Sleep-Management Learning System for Improving Students’ Sleeping Habits by Integrating a Self-Regulated Learning Strategy: Randomized Controlled Trial

A Mobile Sleep-Management Learning System for Improving Students’ Sleeping Habits by Integrating a Self-Regulated Learning Strategy: Randomized Controlled Trial

Authors of this article:

Hui-Chun Chu 1 Author Orcid Image ;   Yi-Meng Liu 1 Author Orcid Image ;   Fan-Ray Kuo 1 Author Orcid Image

Journals

  1. de Zambotti M, Cellini N, Menghini L, Sarlo M, Baker F. Sensors Capabilities, Performance, and Use of Consumer Sleep Technology. Sleep Medicine Clinics 2020;15(1):1 View
  2. Nelson E, Verhagen T, Vollenbroek-Hutten M, Noordzij M. Is Wearable Technology Becoming Part of Us? Developing and Validating a Measurement Scale for Wearable Technology Embodiment. JMIR mHealth and uHealth 2019;7(8):e12771 View
  3. Glazer Baron K, Culnan E, Duffecy J, Berendson M, Cheung Mason I, Lattie E, Manalo N. How are Consumer Sleep Technology Data Being Used to Deliver Behavioral Sleep Medicine Interventions? A Systematic Review. Behavioral Sleep Medicine 2022;20(2):173 View
  4. Liang Z. Context-Aware Sleep Health Recommender Systems (CASHRS): A Narrative Review. Electronics 2022;11(20):3384 View
  5. Edney S, Chua X, Müller A, Kui K, Müller‐Riemenschneider F. mHealth interventions targeting movement behaviors in Asia: A scoping review. Obesity Reviews 2022;23(4) View
  6. Arroyo A, Zawadzki M. The Implementation of Behavior Change Techniques in mHealth Apps for Sleep: Systematic Review. JMIR mHealth and uHealth 2022;10(4):e33527 View

Books/Policy Documents

  1. Kang S, Kim Y. Frontiers in Psychiatry. View