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BackgroundResearchers developing personal health tools employ a range of approaches to involve prospective users in design and development. ObjectiveThe aim of this paper was to develop a validated measure of the human- or user-centeredness of design and development processes for personal health tools. MethodsWe conducted a psychometric analysis of data from a previous systematic review of the design and development processes of 348 personal health tools. Using a conceptual framework of user-centered design, our team of patients, caregivers, health professionals, tool developers, and researchers analyzed how specific practices in tool design and development might be combined and used as a measure. We prioritized variables according to their importance within the conceptual framework and validated the resultant measure using principal component analysis with Varimax rotation, classical item analysis, and confirmatory factor analysis. ResultsWe retained 11 items in a 3-factor structure explaining 68% of the variance in the data. The Cronbach alpha was .72. Confirmatory factor analysis supported our hypothesis of a latent construct of user-centeredness. Items were whether or not: (1) patient, family, caregiver, or surrogate users were involved in the steps that help tool developers understand users or (2) develop a prototype, (3) asked their opinions, (4) observed using the tool or (5) involved in steps intended to evaluate the tool, (6) the process had 3 or more iterative cycles, (7) changes between cycles were explicitly reported, (8) health professionals were asked their opinion and (9) consulted before the first prototype was developed or (10) between initial and final prototypes, and (11) a panel of other experts was involved. ConclusionsThe User-Centered Design 11-item measure (UCD-11) may be used to quantitatively document the user/human-centeredness of design and development processes of patient-centered tools. By building an evidence base about such processes, we can help ensure that tools are adapted to people who will use them, rather than requiring people to adapt to tools.

BackgroundBased on high-quality evidence, guidelines recommend the long-term use of secondary prevention medications post-myocardial infarction (MI) to avoid recurrent cardiovascular events and death. Unfortunately, discontinuation of recommended medications post-MI is common. Observational evidence suggests that prescriptions covering a longer duration at discharge from hospital are associated with greater long-term medication adherence. The following is a proposal for the first interventional study to evaluate the impact of longer prescription duration at discharge post-MI on long-term medication adherence. ObjectiveThe overarching goal of this study is to reduce morbidity and mortality among post-MI patients through improved long-term cardiac medication adherence. The specific objectives include the following. First, we will assess whether long-term cardiac medication adherence improves among elderly, post-MI patients following the implementation of (1) standardized discharge prescription forms with 90-day prescriptions and 3 repeats for recommended cardiac medication classes, in combination with education and (2) education alone compared to (3) usual care. Second, we will assess the cost implications of prolonged initial discharge prescriptions compared with usual care. Third, we will compare clinical outcomes between longer (>60 days) versus shorter prescription durations. Fourth, we will collect baseline information to inform a multicenter interventional study. MethodsWe will conduct a quasiexperimental, interrupted time series design to evaluate the impact of a multifaceted intervention to implement longer duration prescriptions versus usual care on long-term cardiac medication adherence among post-MI patients. Intervention groups and their corresponding settings include: (1) intervention group 1: 1 cardiac center and 1 noncardiac hospital allocated to receive standardized discharge prescription forms supporting the dispensation of 90 days’ worth of cardiac medications with 3 repeats, coupled with education; (2) intervention group 2: 4 sites (including 1 cardiac center) allocated to receive education only; and (3) control group: all remaining hospitals within the province that did not receive an intervention (ie, usual care). Administrative databases will be used to measure all outcomes. Adherence to 4 classes of cardiac medications — statins, beta blockers, angiotensin system inhibitors, and secondary antiplatelets (ie, prasugrel, clopidogrel, or ticagrelor) — will be assessed. ResultsEnrollment began in September 2017, and results are expected to be analyzed in late 2020. ConclusionsThe results have the potential to redefine best practices regarding discharge prescribing policies for patients post-MI. A policy of standardized maximum-duration prescriptions at the time of discharge post-MI is a simple intervention that has the potential to significantly improve long-term medication adherence, thus decreasing cardiac morbidity and mortality. If effective, this low-cost intervention to implement longer duration prescriptions post-MI could be easily scaled. Trial RegistrationClinicalTrials.gov NCT03257579; https://clinicaltrials.gov/ct2/show/NCT03257579 International Registered Report Identifier (IRRID)DERR1-10.2196/18981

BackgroundAs the increasing prevalence of type 2 diabetes mellitus has put pressure on health systems to appropriately manage these patients, there have been a growing number of mobile apps designed to improve the self-management of diabetes. One such app, BlueStar, has been shown to significantly reduce hemoglobin A1c (HbA1c) levels in small studies and is the first app in the United States to receive Food and Drug Administration approval as a mobile prescription therapy. However, the impact of the app across real-world population among different clinical sites and health systems remains unclear. ObjectiveThe primary objective of this study was to conduct a pragmatic randomized controlled trial of the BlueStar mobile app to determine if app usage leads to improved HbA1c levels among diverse participants in real-life clinical contexts. We hypothesized that this mobile app would improve self-management and HbA1c levels compared with controls. MethodsThe study consisted of a multicenter pragmatic randomized controlled trial. Overall, 110 participants randomized to the immediate treatment group (ITG) received the intervention for 6 months, and 113 participants randomized to the wait-list control (WLC) group received usual care for the first 3 months and then received the intervention for 3 months. The primary outcome was glucose control measured by HbA1c levels at 3 months. Secondary outcomes assessed intervention impact on patient self-management, experience of care, and self-reported health utilization using validated scales, including the Problem Areas in Diabetes, the Summary of Diabetes Self-Care Activities, and the EuroQol-5D. Intervention usage data were collected directly from the app. ResultsThe results of an analysis of covariance controlling for baseline HbA1c levels did not show evidence of intervention impact on HbA1c levels at 3 months (mean difference [ITG−WLC] −0.42, 95% CI −1.05 to 0.21; P=.19). Similarly, there was no intervention effect on secondary outcomes measuring diabetes self-efficacy, quality of life, and health care utilization behaviors. An exploratory analysis of 57 ITG participants investigating the impact of app usage on HbA1c levels showed that each additional day of app use corresponded with a 0.016-point decrease in participants’ 3-month HbA1c levels (95% CI −0.03 to −0.003). App usage varied significantly by site, as participants from 1 site logged in to the app a median of 36 days over 14 weeks (interquartile range [IQR] 10.5-124); those at another site used the app significantly less (median 9; IQR 6-51). ConclusionsThe results showed no difference between intervention and control arms for the primary clinical outcome of glycemic control measured by HbA1c levels. Although there was low usage of the app among participants, results indicate contextual factors, particularly site, had a significant impact on overall usage. Future research into the patient and site-specific factors that increase app utilization are needed. Trial RegistrationClinicaltrials.gov NCT02813343; https://clinicaltrials.gov/ct2/show/NCT02813343 (Archived by WebCite at https://clinicaltrials.gov/ct2/show/NCT02813343)

BackgroundAtrial fibrillation (AF) is a common and preventable cause of stroke. Barriers to reducing stroke risk through appropriate prescribing have been identified at the system, provider, and patient levels. To ensure a multifaceted initiative to address these barriers is effective, it is essential to incorporate user-centered design to ensure all intervention components are optimized for users. ObjectiveTo test the usability of an electronic medical record (EMR) toolkit for AF in primary care with the goal of further refining the intervention to meet the needs of primary care clinicians. MethodsAn EMR-based toolkit for AF was created and optimized through usability testing and iterative redesign incorporating a human factors approach. A mixed-methods pilot study consisting of observations, semi-structured interviews, and surveys was conducted to examine usability and perceived impact on patient care and workflow. ResultsA total of 14 clinicians (13 family physicians and 1 nurse practitioner) participated in the study. Nine iterations of the toolkit were created in response to feedback from clinicians and the research team; interface-related changes were made, additional AF-related resources were added, and functionality issues were fixed to make the toolkit more effective. After improvements were made, clinicians expressed that the toolkit improved accessibility to AF-related information and resources, served as a reminder for guideline-concordant AF management, and was easy to use. Most clinicians intended to continue using the toolkit for patient care. With respect to impact on care, clinicians believed the toolkit increased the thoroughness of their assessments for patients with AF and improved the quality of AF-related care received by their patients. ConclusionsThe positive feedback surrounding the EMR toolkit for AF and its perceived impact on patient care can be attributed to the adoption of a user-centered design that merged clinically important information about AF management with user needs. This study demonstrates the utility of a human factors approach to piloting and refining an intervention prior to wide-scale implementation.

Investigating the mechanisms of behavior change interventions provides a more fulsome understanding of how and why interventions work (or don't work). We assessed mechanisms of two interventions (mailouts alone, and mailouts plus telephone support, informed by the Health Action Process Approach (HAPA) and Habit Theory), designed to increase medication adherence after myocardial infarction. We conducted a process evaluation alongside a pragmatic trial. Medication adherence was assessed via self-report at 12-months in the trial, and participants in all trial groups were invited to contemporaneously complete an additional questionnaire assessing targeted mechanisms (HAPA constructs and automaticity). We used multiple regression-based mediation models to investigate indirect effects. Of 589 respondents, 497 were analyzed (92 excluded due to missing data). Mailouts plus telephone support had statistically significant but small effects on intention, social support, action planning, coping planning, and automaticity. There were no indirect effects of interventions on medication adherence via these constructs. Therefore, while this intervention led to changes in proposed mechanisms, these changes were not great enough to lead to behavior change. Refinements (and subsequent evaluation) of the interventions are warranted, and our findings indicate that this could involve offering more intensive support to form plans and identify cues for taking medications, in addition to providing physical supports to encourage self-monitoring, feedback, and habit formation. Trial registration: ClinicalTrials.gov: NCT02382731., Author(s): Nicola McCleary [sup.1] [sup.2] , Noah M. Ivers [sup.3] [sup.4] [sup.5] [sup.6] [sup.7] [sup.8] , J-D Schwalm [sup.9] [sup.10] , Holly O. Witteman [sup.1] [sup.11] , Monica Taljaard [sup.1] [...]

Objective: We aimed to better understand the challenges related to type 2 diabetes medication-taking through Theoretical Domains Framework (TDF)-guided interviews with people with type 2 diabetes with varying degrees of medication-taking. Methods: One-on-one qualitative interviews following a semistructured discussion guide informed by the TDF were conducted. Thirty people with type 2 diabetes in Canada were interviewed, with representation from across the country, of both sexes (47% female), of people with various diabetes durations (mean 12.9 ± 7.9 years), with different types of medication plans (n = 15 on polypharmacy), and with various medication-taking levels (n = 10 each for low-, medium-, and high-engagement groups). Results: Themes related to medication-taking from interviews mapped to 12 of the 14 TDF theme domains, with the exclusion of the knowledge and skills domains. The most prominent domains, as determined by high-frequency themes or themes for which people with low and high medication-taking had contrasting perspectives, were 1) emotion; 2) memory, attention, and decision processes; 3) behavioral regulation; 4) beliefs about consequences; 5) goals; and 6) environmental context and resources. Conclusion: Through our interviews, several areas of focus emerged that may help efforts to increase medication-taking. To validate these findings, future quantitative research is warranted to help support people with type 2 diabetes in overcoming psychological and behavioral barriers to medication-taking. [ABSTRACT FROM AUTHOR]

A goal of evidence synthesis for trials of complex interventions is to inform the design or implementation of novel versions of complex interventions by predicting expected outcomes with each intervention version. Conventional aggregate data meta-analyses of studies comparing complex interventions have limited ability to provide such information. We argue that evidence synthesis for trials of complex interventions should forgo aspirations of estimating causal effects and instead model the response surface of study results to 1) summarize the available evidence and 2) predict the average outcomes of future studies or in new settings. We illustrate this modeling approach using data from a systematic review of diabetes quality improvement (QI) interventions involving at least 1 of 12 QI strategy components. We specify a series of meta-regression models to assess the association of specific components with the posttreatment outcome mean and compare the results to conventional meta-analysis approaches. Compared with conventional approaches, modeling the response surface of study results can better reflect the associations between intervention components and study characteristics with the posttreatment outcome mean. Modeling study results using a response surface approach offers a useful and feasible goal for evidence synthesis of complex interventions that rely on aggregate data. [ABSTRACT FROM AUTHOR]