While type 2 diabetes (T2D) rates have soared, the number of Americans classified as ‘prediabetic’ has also increased. Despite this, current preventative approaches are costly and often not without undue side-effects. Instead, behavioral lifestyle approaches hold promise in reducing conversion rates of T2D as the latest treatment option that could mitigate and transform disease management. However, present interventions do not possess the scope necessary for implementation in a realistic, scalable way that can target the large at-risk population.
The application (app) “BeWell24” mitigates this diabetes risk through targeting sleep, physical activity, sedentary behavior, and diet, and is being delivered through mHealth technology to attenuate the higher-risk of the prediabetic Veteran population. In order for full scale dissemination, this thesis examines a provider perspective of the ‘Post-intervention interview guide’, performed with a Phoenix Veterans Affairs Health Care System (PVAHCS) provider. It then suggests revisions to the interview guide based on the provider’s interview and existing literature. This thesis also emphasizes the rationale behind these proposed changes to be organized in line with the iPARIHS framework (integrated Promoting Action on Research Implementation in Health Services).
Overall, the provider responded positively to BeWell24 and the ‘Post-intervention interview guide’, with constructive suggestions for each question in the interview guide. The main theme of the provider’s answers and comments were to prioritize efficiency and preserve standard clinical flow. A revised interview guide is provided, which prospectively presents as a more brief and focused interview organized by the iPARIHS framework. This revised interview guide could aid in the clarity of provider responses, specifically for the prospective interviews of the ongoing larger BeWell24 study and future studies.

Mobile health or "mHealth" defines a broad spectrum of medical or public health practice supported by mobile devices. The patient's perception of mobile health applications is the key point in confronting whether or not patients will utilize the tools at their disposal As such, the primary aim of this study was to examine participant feedback through quantitative and qualitative measures using the Therapy Evaluation Questionnaire and a patient interview, respectively, to further understand the patient rated acceptability of using BeWell24 and SleepWell24 for improving health outcomes. For BeWell24, it was hypothesized that patients who received the Multicomponent version would report higher acceptability scores than those randomized to the Health Education version. Furthermore, in regard to SleepWell24, it was hypothesized that the SleepWell24 patient would provide positive feedback and suggestions regarding their own experience with the SleepWell24 app. Data from this thesis was pulled from two ongoing randomized controlled trials currently being conducted at the Phoenix Veteran Affairs Health Care Service (PVACHS) and Mayo Clinic hospitals. Means, standard deviations, frequencies, and percentages were commuted to summarize demographics and TEQ scores. In addition, key concepts from a qualitative interview with a SleepWell24 participant were derived. The results showed a greater acceptability of the multicomponent versions of BeWell24 and SleepWell24 but a lower TEQ score of perceived usability. mHealth implementations pose a potential to become an important part of the health sector for establishing innovative approaches to delivering care, and while benefits have been highly praised, it is clear that the perceptions of mHealth must be positive if the technology is to transcend into a practical clinical setting.

Background: Although current technological advancements have allowed for objective measurements of sedentary behavior via accelerometers, these devices do not provide the contextual information needed to identify targets for behavioral interventions and generate public health guidelines to reduce sedentary behavior. Thus, self-reports still remain an important method of measurement for physical activity and sedentary behaviors.

Objective: This study evaluated the reliability, validity, and sensitivity to change of a smartphone app in assessing sitting, light-intensity physical activity (LPA), and moderate-vigorous physical activity (MVPA).
Methods: Adults (N=28; 49.0 years old, standard deviation [SD] 8.9; 85% men; 73% Caucasian; body mass index=35.0, SD 8.3 kg/m2) reported their sitting, LPA, and MVPA over an 11-week behavioral intervention. During three separate 7-day periods, participants wore the activPAL3c accelerometer/inclinometer as a criterion measure. Intraclass correlation (ICC; 95% CI) and bias estimates (mean difference [δ] and root of mean square error [RMSE]) were used to compare app-based reported behaviors to measured sitting time (lying/seated position), LPA (standing or stepping at <100 steps/minute), and MVPA (stepping at >100 steps/minute).

Results: Test-retest results suggested moderate agreement with the criterion for sedentary time, LPA, and MVPA (ICC=0.65 [0.43-0.82], 0.67 [0.44-0.83] and 0.69 [0.48-0.84], respectively). The agreement between the two measures was poor (ICC=0.05-0.40). The app underestimated sedentary time (δ=-45.9 [-67.6, -24.2] minutes/day, RMSE=201.6) and overestimated LPA and MVPA (δ=18.8 [-1.30 to 38.9] minutes/day, RMSE=183; and δ=29.3 [25.3 to 33.2] minutes/day, RMSE=71.6, respectively). The app underestimated change in time spent during LPA and MVPA but overestimated change in sedentary time. Both measures showed similar directions in changed scores on sedentary time and LPA.

Conclusions: Despite its inaccuracy, the app may be useful as a self-monitoring tool in the context of a behavioral intervention. Future research may help to clarify reasons for under- or over-reporting of behaviors.