Implementation of a Substance Use Recovery Support Mobile Phone App in Community Settings: Qualitative Study of Clinician and Staff Perspectives of Facilitators and Barriers

Lord, Sarah; Moore, Sarah K.; Ramsey, Alex T.; Dinauer, Susan; Johnson, Kimberly

doi:10.2196/mental.4927

Cited by 53 publications

(59 citation statements)

References 42 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%

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%

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

“…We also tried to recruit from a range of different disciplines. Challenges for coding consensus have been identified as a limitation of the CFIR because of the large overlap of constructs within and between domains [ 56 ]. Another limitation in the application of the CFIR model to web-based interventions identified is the unidirectional (traditional face-to-face) process of implementation [ 56 ].…”

Section: Discussionmentioning

confidence: 99%

“…Challenges for coding consensus have been identified as a limitation of the CFIR because of the large overlap of constructs within and between domains [ 56 ]. Another limitation in the application of the CFIR model to web-based interventions identified is the unidirectional (traditional face-to-face) process of implementation [ 56 ]. The implementation of iGeriCare needs to be investigated longitudinally to analyze its long-term effects on organizations, professional roles, ways of working, and ultimately on caregiver and patient-related outcomes.…”

Section: Discussionmentioning

confidence: 99%

Barriers and Facilitators to Implementing Web-Based Dementia Caregiver Education From the Clinician’s Perspective: Qualitative Study

et al. 2020

View full text Add to dashboard Cite

Background Internet-based dementia caregiver interventions have been shown to be effective for a range of caregiver outcomes; however, little is known about how to best implement them. We developed iGeriCare, an evidence-based, multimedia, web-based educational resource for family caregivers of people living with dementia. Objective This study aims to obtain feedback and opinions from experts and clinicians involved in dementia care and caregiver education about 1 iGeriCare and 2 barriers and facilitators to implementing a web-based caregiver program. Methods We carried out semistructured interviews with individuals who had a role in dementia care and/or caregiver education in several key stakeholder settings in Southern Ontario, Canada. We queried participants’ perceptions of iGeriCare, caregiver education, the implementation process, and their experience with facilitators and barriers. Transcripts were coded and analyzed using a grounded theory approach. The themes that emerged were organized using the Consolidated Framework for Implementation Research. Results A total of 12 participants from a range of disciplines described their perceptions of iGeriCare and identified barriers and facilitators to the implementation of the intervention. The intervention was generally perceived as a high-quality resource for caregiver education and support, with many stakeholders highlighting the relative advantage of a web-based format. The intervention was seen to meet dementia caregiver needs, partially because of its flexibility, accessibility, and compatibility within existing clinical workflows. In addition, the intervention helps to overcome time constraints for both caregivers and clinicians. Conclusions Study findings indicate a generally positive response to the use of internet-based interventions for dementia caregiver education. Results suggest that iGeriCare may be a useful clinical resource to complement traditional face-to-face and print material–based caregiver education. More comprehensive studies are required to identify the effectiveness and longevity of web-based caregiver education interventions and to better understand barriers and facilitators with regard to the implementation of technology-enhanced caregiver educational interventions in various health care settings.

show abstract

“…In the post-treatment phase , additional interventions can be scheduled to maintain the outcome and prevent relapse ( Kessler and Chiu, 2005 ; Vittengl et al, 2009 ). Usually, Internet-based treatment during this phase also consists of symptom screening and interventions to guide clients after their active treatment ( Kok et al, 2014 ; Lord et al, 2016 ). Decisions that are seen in all phases are adherence measures that include motivational messages and feedback as well as reminders or contact to the therapist.…”

Section: Frameworkmentioning

confidence: 99%

Predictive modeling in e-mental health: A common language framework

Becker

Breda

Funk

et al. 2018

Internet Interventions

View full text Add to dashboard Cite

Recent developments in mobile technology, sensor devices, and artificial intelligence have created new opportunities for mental health care research. Enabled by large datasets collected in e-mental health research and practice, clinical researchers and members of the data mining community increasingly join forces to build predictive models for health monitoring, treatment selection, and treatment personalization. This paper aims to bridge the historical and conceptual gaps between the distant research domains involved in this new collaborative research by providing a conceptual model of common research goals. We first provide a brief overview of the data mining field and methods used for predictive modeling. Next, we propose to characterize predictive modeling research in mental health care on three dimensions: 1) time, relative to treatment (i.e., from screening to post-treatment relapse monitoring), 2) types of available data (e.g., questionnaire data, ecological momentary assessments, smartphone sensor data), and 3) type of clinical decision (i.e., whether data are used for screening purposes, treatment selection or treatment personalization). Building on these three dimensions, we introduce a framework that identifies four model types that can be used to classify existing and future research and applications. To illustrate this, we use the framework to classify and discuss published predictive modeling mental health research. Finally, in the discussion, we reflect on the next steps that are required to drive forward this promising new interdisciplinary field.

show abstract

Implementation of a Substance Use Recovery Support Mobile Phone App in Community Settings: Qualitative Study of Clinician and Staff Perspectives of Facilitators and Barriers

Cited by 53 publications

References 42 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

Barriers and Facilitators to Implementing Web-Based Dementia Caregiver Education From the Clinician’s Perspective: Qualitative Study

Predictive modeling in e-mental health: A common language framework

Contact Info

Product

Resources

About