The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
CBT-I Coach: A Description and Clinician Perceptions of a Mobile App for Cognitive Behavioral Therapy for Insomnia
Open accessAbstract
Study objectives This paper describes CBT-I Coach, a patient-facing smartphone app designed to enhance cognitive behavioral therapy for insomnia (CBT-I). It presents findings of two surveys of U.S. Department of Veterans Affairs (VA) CBT-I trained clinicians regarding their perceptions of CBT-I Coach before it was released (n = 138) and use of it two years after it was released (n = 176). Methods VA-trained CBT-I clinicians completed web-based surveys before and two years after CBT-I Coach was publicly released. Results Prior to CBT-I Coach release, clinicians reported that it was moderately to very likely that the app could improve care and a majority (87.0%) intended to use it if it were available. Intention to use the app was predicted by smartphone ownership (β = 0.116, p < 0.05) and perceptions of relative advantage to existing CBT-I practices (β = 0.286, p < 0.01), compatibility with their own needs and values (β = 0.307, p < 0.01), and expectations about the complexity of the app (β = 0.245, p < 0.05). Two years after CBT-I Coach became available, 59.9% of participants reported using it with patients and had favorable impressions of its impact on homework adherence and outcomes. Conclusions Findings suggest that before release, CBT-I Coach was perceived to have potential to enhance CBT-I and address common adherence issues and clinicians would use it. These results are reinforced by findings two years after it was released suggesting robust uptake and favorable perceptions of its value.
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“Recent studies have shown that those who received Web-based CBTi had improved sleep outcomes [22-26].”
From Tamper-Resistant Mobile Health Using Blockchain Technology. (2017) · cites this paper as [25]
Citations
Cites
Valente, T. W. (2003) · Genetics in Medicine · doi:10.1097/01.gim.0000061743.67794.c4 · Cited by 20,559
Validation of the Insomnia Severity Index as an outcome measure for insomnia research
Bastien, C. (2001) · Sleep Medicine · doi:10.1016/s1389-9457(00)00065-4 · Cited by 8,067
Diffusion of Innovations in Service Organizations: Systematic Review and Recommendations
GREENHALGH, T., ROBERT, G., MACFARLANE, F., BATE, P., & KYRIAKIDOU, O. (2004) · The Milbank Quarterly · doi:10.1111/j.0887-378x.2004.00325.x · Cited by 7,448
The consensus sleep diary: standardizing prospective sleep self-monitoring
Carney, C. E., Buysse, D. J., Ancoli-Israel, S., Edinger, J. D., Krystal, A. D., Lichstein, K. L., & Morin, C. M. (2012) · Sleep · doi:10.5665/sleep.1642 · Cited by 2,172
Smartphones for smarter delivery of mental health programs: A systematic review
Donker, T., Petrie, K., Proudfoot, J., Clarke, J., Birch, M.-R., & Christensen, H. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2791 · Cited by 1,136 · cited 3×
“19 Treatment can be completed in fewer sessions if the patient makes sufficient improvement and relapse prevention strategies are discussed.”
Nonpharmacological interventions for insomnia: a meta-analysis of treatment efficacy
(1994) · American Journal of Psychiatry · doi:10.1176/ajp.151.8.1172 · Cited by 1,098 · cited 1×
“As behavioral intentions have been shown to relate to changes in clinician behavior (e.g., Perkins et al. 41 ), 2 additional items focused on participants’ intention to use the app (i.e., future use intention) if it were available.”
Behavioral and Pharmacological Therapies for Late-Life Insomnia: A Randomized Controlled Trial
Morin, C. M., Colecchi, C., Stone, J., Sood, R., & Brink, D. (1999) · JAMA · doi:10.1001/jama.281.11.991 · Cited by 1,095
Treatment of Chronic Insomnia by Restriction of Time in Bed
(1987) · Sleep · doi:10.1093/sleep/10.1.45 · Cited by 948
Manber, R., Edinger, J. D., Gress, J. L., Pedro-Salcedo, M. G. S., Kuo, T. F., & Kalista, T. (2008) · Sleep · doi:10.1093/sleep/31.4.489 · Cited by 868
Self-citationComparative Meta-Analysis of Pharmacotherapy and Behavior Therapy for Persistent Insomnia
Smith, M. T., Perlis, M. L., Park, A., Smith, M. S., Pennington, J., Giles, D. E., & Buysse, D. J. (2002) · American Journal of Psychiatry · doi:10.1176/appi.ajp.159.1.5 · Cited by 863
Research works printed but not linked: 4
- Pew Research Center. Cell Phone and Smartphone Ownership Demographics. Accessed February 2, 2015. Available at: http://www.pewinternet.org/data-trend/mobile/cell-phone-and-smartphone-ownership-demographics/.
- Manber R, Friedman L, Siebern AT, et al. Cognitive behavioral therapy for insomnia in Veterans: therapist manual. Washington, DC: U.S. Department of Veterans Affairs; 2014.
- Rubinstein ML, Rothenberg SA, Maheswaran S, Tsai JS, Zozula R, Spielman AJ. Modified sleep restriction therapy in middle-aged and elderly chronic insomniacs. Sleep Res 1990;19,276.
- Nielsen. Mobile Millennials: Over 85% of Generation Y Owns Smartphones. Accessed September 5, 2014. Available at: http://www.nielsen.com/us/en/insights/news/2014/mobile-millennials-over-85-percent-of-generation-y-owns-smartphones.html.
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Koffel, E., Bramoweth, A. D., & Ulmer, C. S. (2018) · Journal of General Internal Medicine · doi:10.1007/s11606-018-4390-1 · Cited by 391
Tamper-Resistant Mobile Health Using Blockchain Technology.
Ichikawa, D., Kashiyama, M., & Ueno, T. (2017) · JMIR mHealth and uHealth · doi:10.2196/mhealth.7938 · Cited by 298
“In an mHealth system for CBTi, mobile devices and the host server are connected by a secured Internet network [25].”
Use of Social Robots in Mental Health and Well-Being Research: Systematic Review
Scoglio, A. A. J., Reilly, E. D., Gorman, J. A., & Drebing, C. E. (2019) · Journal of Medical Internet Research · doi:10.2196/13322 · Cited by 312
Hermes, E. D., Lyon, A. R., Schueller, S. M., & Glass, J. E. (2019) · Journal of Medical Internet Research · doi:10.2196/11752 · Cited by 149
Digital Cognitive Behavioral Therapy (dCBT) for Insomnia: a State-of-the-Science Review
Luik, A. I., Kyle, S. D., & Espie, C. A. (2017) · Current Sleep Medicine Reports · doi:10.1007/s40675-017-0065-4 · Cited by 160
Metadata
- Authors
- Kuhn, E., Weiss, B. J., Taylor, K. L., Hoffman, J. E., Ramsey, K. M., Manber, R., Gehrman, P., Crowley, J. J., Ruzek, J. I., & Trockel, M.
- Affiliations
- VA Palo Alto Health Care System (US)
- Stanford University (US)
- Philadelphia VA Medical Center (US)
- Corresponding author
- Kuhn, Eric
- ORCID
- Venue
- Journal of Clinical Sleep Medicine
- Published
- 2016-04-15
- Volume
- 12
- Issue
- 4
- Pages
- 597-606
- ISSN
- 1550-9389 (ISSN-L)
- 1550-9397
- PMC
- PMC4795288
- OpenAlex
- W2279773756
- Cited by
- 135
- References
- 40
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 15 · 2023: 11 · 2022: 15 · 2021: 21 · 2020: 10 · 2019: 16 · 2018: 14 · 2017: 11 · 2016: 6
- FWCI
- 22.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Clinical Sleep Medicine (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- cognitive behavioral therapy for insomnia, mobile health apps, mobile application, clinician perceptions, intention to use, technology acceptance, homework adherence, veterans, app uptake
- MeSH terms
- Smartphone, Attitude of Health Personnel, Female, Humans, Sleep Initiation and Maintenance Disorders, Male, Middle Aged, Veterans, Cognitive Behavioral Therapy, Treatment Outcome, Patient Satisfaction, Mobile Applications
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