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

Design and Usability of Consumer Health Tech and Home Monitoring Devices (228) Telehealth and Telemonitoring (1713) Usability of Apps and User Perceptions of mHealth (993) mHealth for Wellness, Behavior Change and Prevention (2922) mHealth for Telemedicine and Homecare (418) Insomnia and Sleep Hygiene (177)

Published on in Vol 5, No 2 (2018): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/8905, first published .
Development of a Just-in-Time Adaptive mHealth Intervention for Insomnia: Usability Study

Development of a Just-in-Time Adaptive mHealth Intervention for Insomnia: Usability Study

Development of a Just-in-Time Adaptive mHealth Intervention for Insomnia: Usability Study

Authors of this article:

I Wayan Pulantara1 Author Orcid Image ;   Bambang Parmanto1 Author Orcid Image ;   Anne Germain2 Author Orcid Image
Article Authors Cited by (21) Tweetations (7) Metrics

Journals

  1. Pulantara I, Parmanto B, Germain A. Clinical Feasibility of a Just-in-Time Adaptive Intervention App (iREST) as a Behavioral Sleep Treatment in a Military Population: Feasibility Comparative Effectiveness Study. Journal of Medical Internet Research 2018;20(12):e10124 View
  2. Rodenburg F, Sawada Y, Hayashi N. Improving RNN Performance by Modelling Informative Missingness with Combined Indicators. Applied Sciences 2019;9(8):1623 View
  3. Germain A, Markwald R, King E, Bramoweth A, Wolfson M, Seda G, Han T, Miggantz E, O’Reilly B, Hungerford L, Sitzer T, Mysliwiec V, Hout J, Wallace M. Enhancing behavioral sleep care with digital technology: study protocol for a hybrid type 3 implementation-effectiveness randomized trial. Trials 2021;22(1) View
  4. Aji M, Gordon C, Stratton E, Calvo R, Bartlett D, Grunstein R, Glozier N. Framework for the Design Engineering and Clinical Implementation and Evaluation of mHealth Apps for Sleep Disturbance: Systematic Review. Journal of Medical Internet Research 2021;23(2):e24607 View
  5. Ravuri V, Paromita P, Mundnich K, Nadarajan A, Booth B, Narayanan S, Chaspari T. Investigating Group-Specific Models of Hospital Workers’ Well-Being: Implications for Algorithmic Bias. International Journal of Semantic Computing 2020;14(04):477 View
  6. 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
  7. Abo-Tabik M, Benn Y, Costen N. Are Machine Learning Methods the Future for Smoking Cessation Apps?. Sensors 2021;21(13):4254 View
  8. Takeuchi H, Suwa K, Kishi A, Nakamura T, Yoshiuchi K, Yamamoto Y. The Effects of Objective Push-Type Sleep Feedback on Habitual Sleep Behavior and Momentary Symptoms in Daily Life: mHealth Intervention Trial Using a Health Care Internet of Things System. JMIR mHealth and uHealth 2022;10(10):e39150 View
  9. Montenegro T, Ali R, Arle J. Closed-Loop Systems in Neuromodulation. Neurosurgery Clinics of North America 2022;33(3):297 View
  10. Kuhn E, Miller K, Puran D, Wielgosz J, YorkWilliams S, Owen J, Jaworski B, Hallenbeck H, McCaslin S, Taylor K. A Pilot Randomized Controlled Trial of the Insomnia Coach Mobile App to Assess Its Feasibility, Acceptability, and Potential Efficacy. Behavior Therapy 2022;53(3):440 View
  11. Slavish D, Briggs M, Fentem A, Messman B, Contractor A. Bidirectional associations between daily PTSD symptoms and sleep disturbances: A systematic review. Sleep Medicine Reviews 2022;63:101623 View
  12. Liang Z. Context-Aware Sleep Health Recommender Systems (CASHRS): A Narrative Review. Electronics 2022;11(20):3384 View
  13. Tobin S, Williams P, Baron K, Halliday T, Depner C. Challenges and Opportunities for Applying Wearable Technology to Sleep. Sleep Medicine Clinics 2021;16(4):607 View
  14. Zerlik M, Jung I, Sehr T, Hennings F, Kamann C, Brandt M, Sedlmayr M, Sedlmayr B. A pragmatic methodical framework for the user-centred development of an electronic process support for the sleep laboratory patients’ management. DIGITAL HEALTH 2022;8:205520762211344 View
  15. Bramoweth A, Hough C, McQuillan A, Spitznogle B, Thorpe C, Lickel J, Boudreaux-Kelly M, Hamm M, Germain A. Reduction of Sleep Medications via a Combined Digital Insomnia and Pharmacist-Led Deprescribing Intervention: Protocol for a Feasibility Trial. JMIR Research Protocols 2023;12:e47636 View
  16. McCrae C, Curtis A, Stearns M, Nair N, Golzy M, Shenker J, Beversdorf D, Cottle A, Rowe M. Development and Initial Evaluation of Web-Based Cognitive Behavioral Therapy for Insomnia in Rural Family Caregivers of People With Dementia (NiteCAPP): Mixed Methods Study. JMIR Aging 2023;6:e45859 View
  17. Wang W, Khalajzadeh H, Grundy J, Madugalla A, McIntosh J, Obie H. Adaptive user interfaces in systems targeting chronic disease: a systematic literature review. User Modeling and User-Adapted Interaction 2024;34(3):853 View
  18. Moorthy P, Weinert L, Schüttler C, Svensson L, Sedlmayr B, Müller J, Nagel T. Attributes, Methods, and Frameworks Used to Evaluate Wearables and Their Companion mHealth Apps: Scoping Review. JMIR mHealth and uHealth 2024;12:e52179 View
  19. Pulantara I, Wang Y, Burke L, Sereika S, Bizhanova Z, Kariuki J, Cheng J, Beatrice B, Loar I, Cedillo M, Conroy M, Parmanto B. Data Collection and Management of mHealth, Wearables, and Internet of Things in Digital Behavioral Health Interventions With the Awesome Data Acquisition Method (ADAM): Development of a Novel Informatics Architecture. JMIR mHealth and uHealth 2024;12:e50043 View
  20. Tran T, Abonyi J, Ruppert T. Technology-enabled cognitive resilience: what can we learn from military operation to develop Operator 5.0 solutions?. Production & Manufacturing Research 2024;12(1) View

Books/Policy Documents

  1. Baron K. Adapting Cognitive Behavioral Therapy for Insomnia. View