Measuring the Implementation of Behavioral Intervention Technologies: Recharacterization of Established Outcomes

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Abstract

Behavioral intervention technologies (BITs) are websites, software, mobile apps, and sensors designed to help users address or change behaviors, cognitions, and emotional states. BITs have the potential to transform health care delivery, and early research has produced promising findings of efficacy. BITs also favor new models of health care delivery and provide novel data sources for measurement. However, there are few examples of successful BIT implementation and a lack of consensus on as well as inadequate descriptions of BIT implementation measurement. The aim of this viewpoint paper is to provide an overview and characterization of implementation outcomes for the study of BIT use in routine practice settings. Eight outcomes for the evaluation of implementation have been previously described: acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration, and sustainability. In a proposed recharacterization of these outcomes with respect to BIT implementation, definitions are clarified, expansions to the level of analysis are identified, and unique measurement characteristics are discussed. Differences between BIT development and implementation, an increased focus on consumer-level outcomes, the expansion of providers who support BIT use, and the blending of BITs with traditional health care services are specifically discussed. BITs have the potential to transform health care delivery. Realizing this potential, however, will hinge on high-quality research that consistently and accurately measures how well such technologies have been integrated into health services. This overview and characterization of implementation outcomes support BIT research by identifying and proposing solutions for key theoretical and practical measurement challenges.

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A compilation of strategies for implementing clinical innovations in health and mental health.

Byron J. Powell, J. McMillen, Enola K. Proctor … (2012)

Efforts to identify, develop, refine, and test strategies to disseminate and implement evidence-based treatments have been prioritized in order to improve the quality of health and mental health care delivery. However, this task is complicated by an implementation science literature characterized by inconsistent language use and inadequate descriptions of implementation strategies. This article brings more depth and clarity to implementation research and practice by presenting a consolidated compilation of discrete implementation strategies, based on a review of 205 sources published between 1995 and 2011. The resulting compilation includes 68 implementation strategies and definitions, which are grouped according to six key implementation processes: planning, educating, financing, restructuring, managing quality, and attending to the policy context. This consolidated compilation can serve as a reference to stakeholders who wish to implement clinical innovations in health and mental health care and can facilitate the development of multifaceted, multilevel implementation plans that are tailored to local contexts.

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Beyond the Trial: Systematic Review of Real-World Uptake and Engagement With Digital Self-Help Interventions for Depression, Low Mood, or Anxiety

Theresa Fleming, Lynda M. Bavin, Mathijs Lucassen … (2018)

Background Digital self-help interventions (including online or computerized programs and apps) for common mental health issues have been shown to be appealing, engaging, and efficacious in randomized controlled trials. They show potential for improving access to therapy and improving population mental health. However, their use in the real world, ie, as implemented (disseminated) outside of research settings, may differ from that reported in trials, and implementation data are seldom reported. Objective This study aimed to review peer-reviewed articles reporting user uptake and/or ongoing use, retention, or completion data (hereafter usage data or, for brevity, engagement) from implemented pure self-help (unguided) digital interventions for depression, anxiety, or the enhancement of mood. Methods We conducted a systematic search of the Scopus, Embase, MEDLINE, and PsychINFO databases for studies reporting user uptake and/or usage data from implemented digital self-help interventions for the treatment or prevention of depression or anxiety, or the enhancement of mood, from 2002 to 2017. Additionally, we screened the reference lists of included articles, citations of these articles, and the titles of articles published in Internet Interventions, Journal of Medical Internet Research (JMIR), and JMIR Mental Health since their inception. We extracted data indicating the number of registrations or downloads and usage of interventions. Results After the removal of duplicates, 970 papers were identified, of which 10 met the inclusion criteria. Hand searching identified 1 additional article. The included articles reported on 7 publicly available interventions. There was little consistency in the measures reported. The number of registrants or downloads ranged widely, from 8 to over 40,000 per month. From 21% to 88% of users engaged in at least minimal use (eg, used the intervention at least once or completed one module or assessment), whereas 7-42% engaged in moderate use (completing between 40% and 60% of modular fixed-length programs or continuing to use apps after 4 weeks). Indications of completion or sustained use (completion of all modules or the last assessment or continuing to use apps after six weeks or more) varied from 0.5% to 28.6%. Conclusions Available data suggest that uptake and engagement vary widely among the handful of implemented digital self-help apps and programs that have reported this, and that usage may vary from that reported in trials. Implementation data should be routinely gathered and reported to facilitate improved uptake and engagement, arguably among the major challenges in digital health.

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Mobile Applications for Diabetics: A Systematic Review and Expert-Based Usability Evaluation Considering the Special Requirements of Diabetes Patients Age 50 Years or Older

Madlen Arnhold, Mandy Quade, Wilhelm Kirch (2014)

Background A multitude of mhealth (mobile health) apps have been developed in recent years to support effective self-management of patients with diabetes mellitus type 1 or 2. Objective We carried out a systematic review of all currently available diabetes apps for the operating systems iOS and Android. We considered the number of newly released diabetes apps, range of functions, target user groups, languages, acquisition costs, user ratings, available interfaces, and the connection between acquisition costs and user ratings. Additionally, we examined whether the available applications serve the special needs of diabetes patients aged 50 or older by performing an expert-based usability evaluation. Methods We identified relevant keywords, comparative categories, and their specifications. Subsequently, we performed the app review based on the information given in the Google Play Store, the Apple App Store, and the apps themselves. In addition, we carried out an expert-based usability evaluation based on a representative 10% sample of diabetes apps. Results In total, we analyzed 656 apps finding that 355 (54.1%) offered just one function and 348 (53.0%) provided a documentation function. The dominating app language was English (85.4%, 560/656), patients represented the main user group (96.0%, 630/656), and the analysis of the costs revealed a trend toward free apps (53.7%, 352/656). The median price of paid apps was €1.90. The average user rating was 3.6 stars (maximum 5). Our analyses indicated no clear differences in the user rating between free and paid apps. Only 30 (4.6%) of the 656 available diabetes apps offered an interface to a measurement device. We evaluated 66 apps within the usability evaluation. On average, apps were rated best regarding the criterion “comprehensibility” (4.0 out of 5.0), while showing a lack of “fault tolerance” (2.8 out of 5.0). Of the 66 apps, 48 (72.7%) offered the ability to read the screen content aloud. The number of functions was significantly negative correlated with usability. The presence of documentation and analysis functions reduced the usability score significantly by 0.36 and 0.21 points. Conclusions A vast number of diabetes apps already exist, but the majority offer similar functionalities and combine only one to two functions in one app. Patients and physicians alike should be involved in the app development process to a greater extent. We expect that the data transmission of health parameters to physicians will gain more importance in future applications. The usability of diabetes apps for patients aged 50 or older was moderate to good. But this result applied mainly to apps offering a small range of functions. Multifunctional apps performed considerably worse in terms of usability. Moreover, the presence of a documentation or analysis function resulted in significantly lower usability scores. The operability of accessibility features for diabetes apps was quite limited, except for the feature “screen reader”.