Clinician Perceptions Related to the Use of the CBT-I Coach Mobile App

Miller, Katherine E.; Kuhn, Eric; Owen, Jason E.; Taylor, Katherine; Yu, Jessica; Weiß, Bernd; Crowley, Jill J.; Trockel, Mickey

doi:10.1080/15402002.2017.1403326

Cited by 34 publications

(26 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The studies also cover diverse disease areas and medical specialties, including ambulatory care [ 58 ], cognitive behavioral therapy [ 40 ], cardiovascular disease [ 56 , 83 ], dermatology [ 73 ], diabetes [ 50 , 63 , 65 ], general practice [ 35 , 52 , 59 ], intensive care [ 36 ], neurology [ 48 ], pediatric [ 45 ], primary and acute care [ 46 , 49 , 55 , 71 , 74 ], psychiatry and mental health [ 41 , 53 , 68 ], residential aged care [ 57 ], reproductive health [ 37 ], and substance use recovery [ 39 , 44 ].…”

Section: Resultsmentioning

confidence: 99%

“…DOI was used in 16% (8/50) of the studies [ 37 , 40 , 50 , 53 , 56 , 62 , 76 , 78 ], and UTAUT extensions were used in 12% (6/50) of the studies; some of these UTAUT extensions were based on the research context and previous research [ 64 , 66 , 84 ], and other included extensions from other frameworks such as the De Lone and McLean information success model [ 54 ], use of technology [ 65 ], and a combination of TAM, TPB, and DOI [ 82 ]. Furthermore, certain studies used a combination of the TAM and DOI frameworks (3/50, 6%) [ 42 , 43 , 59 ]; others used the consolidated framework for implementation research (CFIR; 3/50, 6%) [ 39 , 41 , 67 ] and the STS (2/50, 4%) [ 49 , 55 ].…”

Section: Resultsmentioning

confidence: 99%

“…Compatibility (CFIR-DOI), adaptability (CFIR), practicability (APEASE), and job relevance (TAM2) were mentioned in eight studies and mostly related to the degree to which mHealth fits into, can be adapted, tailored, or refined to meet local needs [ 39 , 40 , 57 , 61 , 62 , 67 , 71 , 82 ]. Furthermore, four studies discussed a factor that was not specified as a distinct construct in most of the frameworks used, which is the impact of the perception of job security, job autonomy, perceived threat (dual-factor model), and empowerment.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Understanding Clinicians’ Adoption of Mobile Health Tools: A Qualitative Review of the Most Used Frameworks

Jacob¹,

Sánchez-Vázquez²,

Ivory³

2020

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

Background Although there is a push toward encouraging mobile health (mHealth) adoption to harness its potential, there are many challenges that sometimes go beyond the technology to involve other elements such as social, cultural, and organizational factors. Objective This review aimed to explore which frameworks are used the most, to understand clinicians’ adoption of mHealth as well as to identify potential shortcomings in these frameworks. Highlighting these gaps and the main factors that were not specifically covered in the most frequently used frameworks will assist future researchers to include all relevant key factors. Methods This review was an in-depth subanalysis of a larger systematic review that included research papers published between 2008 and 2018 and focused on the social, organizational, and technical factors impacting clinicians’ adoption of mHealth. The initial systematic review included 171 studies, of which 50 studies used a theoretical framework. These 50 studies are the subject of this qualitative review, reflecting further on the frameworks used and how these can help future researchers design studies that investigate the topic of mHealth adoption more robustly. Results The most commonly used frameworks were different forms of extensions of the Technology Acceptance Model (TAM; 17/50, 34%), the diffusion of innovation theory (DOI; 8/50, 16%), and different forms of extensions of the unified theory of acceptance and use of technology (6/50, 12%). Some studies used a combination of the TAM and DOI frameworks (3/50, 6%), whereas others used the consolidated framework for implementation research (3/50, 6%) and sociotechnical systems (STS) theory (2/50, 4%). The factors cited by more than 20% of the studies were usefulness, output quality, ease of use, technical support, data privacy, self-efficacy, attitude, organizational inner setting, training, leadership engagement, workload, and workflow fit. Most factors could be linked to one framework or another, but there was no single framework that could adequately cover all relevant and specific factors without some expansion. Conclusions Health care technologies are generally more complex than tools that address individual user needs as they usually support patients with comorbidities who are typically treated by multidisciplinary teams who might even work in different health care organizations. This special nature of how the health care sector operates and its highly regulated nature, the usual budget deficits, and the interdependence between health care organizations necessitate some crucial expansions to existing theoretical frameworks usually used when studying adoption. We propose a shift toward theoretical frameworks that take into account implementation challenges that factor in the complexity of the sociotechnical structure of health care organizations and the interplay between the technical, social, and organizational aspects. Our consolidated framework offers recommendations on which factors to include when investigating clinicians’ adoption of mHealth, taking into account all three aspects.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Understanding Clinicians’ Adoption of Mobile Health Tools: A Qualitative Review of the Most Used Frameworks

Jacob¹,

Sánchez-Vázquez²,

Ivory³

2020

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

show abstract

“…Mobile technologies have been deployed successfully to reduce mental health problems, such as suicidal ideation (28), depression (29), sleep (30), and PTSD (31) to name a few. Moreover, mobile apps can be used to address PTSD-related needs at many different points of contact with the healthcare system: for use in screening for mental health conditions or tracking changes over time (32), as a means of providing immediate assistance while a patient is waiting for a higher level of care, for use in supporting the delivery of evidence-based treatment (33), for providing between-session homework exercises (34,35), to provide post-treatment support, and as a way to reach those who might not otherwise access mental health services. It follows that various large public health agencies have made policy to systematically increase the use of mobile apps to address public health problems.…”

Section: Review Articlementioning

confidence: 99%

“…After release of the app, 45% of providers were actively using the CBT-i Coach app. Providers who use the app, use it with over half of their patients Miller [2017] VA providers trained to deliver CBT-i (n=163)…”

Section: Cbt-i Coachmentioning

confidence: 99%

VA mobile apps for PTSD and related problems: public health resources for veterans and those who care for them

Owen

Kuhn

Jaworski

et al. 2018

mHealth

Self Cite

View full text Add to dashboard Cite

Many public health agencies, including the U.S. Department of Veterans Affairs (VA), have identified the use of mobile technologies as an essential part of a larger strategy to address major public health challenges. The VA's National Center for PTSD (NCPTSD), in collaboration with VA's Office of Mental Health and Suicide Prevention and the Defense Health Agency inside the U.S. Department of Defense (DoD), has been involved in the development, evaluation, and testing of 15 mobile apps designed specifically to address the needs and concerns of veterans and others experiencing symptoms of posttraumatic stress disorder (PTSD). These applications include seven treatment-companion apps (designed to be used with a provider, in conjunction with an evidence-based therapy) and eight self-management apps (designed to be used independently or as an adjunct or extender of traditional care). There is growing evidence for the efficacy of several of these apps for reducing PTSD and other symptoms, and studies of providers demonstrate that the apps are engaging, easy-to-use, and provide a relative advantage to traditional care without apps. While publicly available apps do not collect or share personal data, VA has created research-enabled versions of many of its mental health apps to enable ongoing product enhancement and continuous measurement of the value of these tools to veterans and frontline providers. VA and DoD are also collaborating on provider-based implementation networks to enable clinicians to optimize implementation of mobile technologies in care. Although there are many challenges to developing and integrating mHealth into care, including cost, privacy, and the need for additional research, mobile mental health technologies are likely here to stay and have the potential to reach large numbers of those with unmet mental health needs, including PTSD-related concerns.

show abstract

Management of Insomnia Disorder

Carlson

Zeidler

Martin

2022

Respiratory Medicine

View full text Add to dashboard Cite

Clinician Perceptions Related to the Use of the CBT-I Coach Mobile App

Abstract: Continued efforts are needed to increase adoption and enhance use of CBT-I Coach, as well as study if reported benefits can be evidenced more directly.

Cited by 34 publications

References 26 publications

Understanding Clinicians’ Adoption of Mobile Health Tools: A Qualitative Review of the Most Used Frameworks

Understanding Clinicians’ Adoption of Mobile Health Tools: A Qualitative Review of the Most Used Frameworks

VA mobile apps for PTSD and related problems: public health resources for veterans and those who care for them

Management of Insomnia Disorder

Contact Info

Product

Resources

About