Browsing by Subject "digital health"

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Background: Digital divide among elderly people is an emerging problem. With more adoption of technology in the health care field, we should be aware of the health inequity generated by the adoption of digital health as the norm after COVID-19 crisis. Objectives: The primary aim of this study is to evaluate the participants’ comfort with technology (self-reported digital literacy) prior to using VERA, a digital health platform for exercise therapy. The secondary aim is to examine the association of patient characteristics and digital literacy with the acceptability, treatment adherence, accuracy of exercises performed, and change in exercise over 90-day intervention. Methods: This cohort study used secondary data from the VERITAS clinical trial (clinicaltrials.gov identifier: NCT02914210). The research analyzed socio-demographics, digital health determinants, and process outcomes at 90-days. Descriptive statistics were conducted, Prevalence Ratio (PR) was used as a measure of association. Results: Participants who were older in age and had less than 16 years of education were less comfortable with technology. Less comfort with technology prior to starting therapy was not associated with lower acceptability, adherence, accuracy, nor change in days per week exercised over 90 days. We found that having a preexisting condition of neurological disorders was associated with lower self-reported adherence. Conclusion: Age and education are related to comfort using technology. We should take them into consideration at the digital health design stage. Besides, we didn’t find unequal use of VERA with the process outcomes among different strata, which means by personalized health intervention and improved usability, people who have disadvantages can also adopt technology to achieve better health. By advocating the human-centered design, digital health can benefit more people to achieve health equity on a large scale.

Background: Hypertension is a primary risk factor for cardiovascular disease. Studies have shown that hypertension may have a more severe effect on cardiovascular disease outcomes among women. To mitigate this risk of hypertension in women, evidence supports that the DASH (Dietary Approaches to Stop Hypertension) diet as an effective treatment. Despite decades of evidence supporting the efficacy of the DASH diet to treat hypertension, compliance to DASH remains consistently low across all populations. However, given the importance of self-monitoring for producing behavior change, innovative efforts that utilize this strategy are needed to improve adoption of DASH on a population level. Methods: This thesis is a secondary analysis of 3-month digital health intervention (DASH Cloud) to improve adoption of DASH among high risk women conducted in Durham, NC at Duke University. Participants (N= 59) were allocated into the DASH Cloud arm (N= 30), or the attention control arm (N = 29). Both groups received publicly available booklets about the DASH diet and were asked to self-monitor their diet using a commercially available diet tracking app. Only the intervention arm received personalized feedback about their compliance to the DASH diet and skills training videos via text message. The goal of this study was to understand patterns and predictors of self-monitoring via the diet tracking app. Results: Overall, the median proportion of self-monitoring engagement was 76%. By treatment arm, the median proportion of engagement in the DASH Cloud arm and attention control arm was 68% and 85%, respectively. Significant predictors of self-monitoring include those who had 100% engagement during the first two weeks and first month of the study, had marital support, lower BMI, at least a college degree, insurance, a negative perception of their food environment, were somewhat comfortable with using apps and less likely to use an app to track medication adherence. Conclusion: This study provided evidence that self-monitoring of diet is high within the context of an intervention aimed at using digital health to promote compliance to the DASH diet among high risk women. The strongest predictor of self-monitoring engagement was early engagement. This suggests that interventions aiming to improve rates of self-monitoring an improve uptake of DASH using diet tracking apps should aim to improve early engagement to achieve success overall. However, given the small sample size of the study, future studies should include a larger population to describe patterns of self-monitoring and ascertain other predictors of engagement.

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Digital health interventions are widely considered a highly promising solution to issues with access to evidence-based care. However, digital health interventions are limited by lower rates of engagement than are typically seen in traditional, face-to-face interventions. Despite the importance of engagement for intervention efficacy, engagement is rarely a primary outcome in intervention studies, and few studies have empirically tested intervention changes to improve engagement. Hazel was a two-arm, randomized controlled exploratory study designed to investigate the impact of intervention design on engagement as a primary outcome. We conducted a trial of a mental health intervention, based on the Unified Protocol, delivered entirely online. Participants were randomized to complete the 12-week intervention self-guided (the unsupported arm) or with 4 weeks of therapist support (the supported arm). We sought to measure engagement as comprehensively as possible and therefore collected numerous self-report, behavioral, and objective measures of multiple facets of engagement, including how participants felt about the intervention, how they used the intervention technology, and how their behavior changed over the course of the intervention. We collected engagement outcomes at baseline, weekly during the intervention, and immediately following completion of the intervention. We hypothesized that a low dose of therapist support would improve engagement and subsequent mental health outcomes while being more scalable and feasible to implement than offering therapist support during all intervention weeks. The aims of this small, exploratory study were to learn more about patterns of engagement with the intervention and identify issues with the current intervention and trial design. We used descriptive statistics and visualizations to understand and describe trends in the data rather than p-value significance testing, in line with best practices for pilot studies. We recruited 23 North Carolina-based adults with clinically elevated depression and/or anxiety symptoms. Overall, the intervention and trial design appeared acceptable to participants. Participants in both arms had relatively positive attitudes toward the intervention at all time points, and there was no evidence that the response burden of our outcome measures was too high. However, several of our measures showed little variation between participants or over time. Our findings provides guidance for future studies to select alternative measures and/or to administer some engagement measures less frequently. This represents a meaningful step forward for the field of engagement research, as prior to this study, there was almost no empirical guidance on how to select measures of engagement or how frequently to administer them. We observed a substantial drop in engagement for participants in the supported arm when video sessions began, contrary to our expectations that video sessions would improve engagement. Notably, video sessions began partway through the intervention and participants were blinded to condition until the onset of these sessions; changes to blinding and/or timing of video sessions might improve engagement. At the same time, those participants in the supported arm who did engage in video sessions (n=4) completed more of each weekly lesson than participants in the unsupported arm, indicating that there is evidence that therapist support can increase engagement. Secondary outcomes were sample diversity and depression and anxiety symptom change. Our sample was comparably diverse to other digital health studies in terms of race and socioeconomic status, and we overrecruited LGBTQ+-identified participants. Despite finding little evidence of changes in behavior or the theoretical mechanisms underlying the intervention, the majority of participants showed improvement in their symptoms.