Study Design, Rationale, and Methodology for Promote Weight Loss in Patients With Peripheral Artery Disease Who Also Have Obesity: The PROVE Trial.

Background Overweight and obesity are associated with adverse functional outcomes in people with peripheral artery disease (PAD). The effects of weight loss in people with overweight/obesity and PAD are unknown. Methods The PROVE (Promote Weight Loss in Obese PAD Patients to Prevent Mobility Loss) Trial is a multicentered randomized clinical trial with the primary aim of testing whether a behavioral intervention designed to help participants with PAD lose weight and walk for exercise improves 6-minute walk distance at 12-month follow-up, compared with walking exercise alone. A total of 212 participants with PAD and body mass index ≥25 kg/m2 will be randomized. Interventions are delivered using a Group Mediated Cognitive Behavioral intervention model, a smartphone application, and individual telephone coaching. The primary outcome is 12-month change in 6-minute walk distance. Secondary outcomes include total minutes of walking exercise/wk at 12-month follow-up and 12-month change in accelerometer-measured physical activity, the Walking Impairment Questionnaire distance score, and the Patient-Reported Outcomes Measurement Information System mobility questionnaire. Tertiary outcomes include 12-month changes in perceived exertional effort at the end of the 6-minute walk, diet quality, and the Short Physical Performance Battery. Exploratory outcomes include changes in gastrocnemius muscle biopsy measures of mitochondrial cytochrome C oxidase activity, mitochondrial biogenesis, capillary density, and inflammatory markers. Conclusions The PROVE randomized clinical trial will evaluate the effects of exercise with an intervention of coaching and a smartphone application designed to achieve weight loss, compared with exercise alone, on walking performance in people with PAD and overweight/obesity. Results will inform optimal treatment for the growing number of patients with PAD who have overweight/obesity. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT04228978.

The Development of a Novel mHealth Tool for Obstructive Sleep Apnea: Tracking Continuous Positive Airway Pressure Adherence as a Percentage of Time in Bed.

BACKGROUND: Continuous positive airway pressure (CPAP) is the mainstay obstructive sleep apnea (OSA) treatment; however, poor adherence to CPAP is common. Current guidelines specify 4 hours of CPAP use per night as a target to define adequate treatment adherence. However, effective OSA treatment requires CPAP use during the entire time spent in bed to optimally treat respiratory events and prevent adverse health effects associated with the time spent sleeping without wearing a CPAP device. Nightly sleep patterns vary considerably, making it necessary to measure CPAP adherence relative to the time spent in bed. Weight loss is an important goal for patients with OSA. Tools are required to address these clinical challenges in patients with OSA. OBJECTIVE: This study aimed to develop a mobile health tool that combined weight loss features with novel CPAP adherence tracking (ie, percentage of CPAP wear time relative to objectively assessed time spent in bed) for patients with OSA. METHODS: We used an iterative, user-centered process to design a new CPAP adherence tracking module that integrated with an existing weight loss app. A total of 37 patients with OSA aged 20 to 65 years were recruited. In phase 1, patients with OSA who were receiving CPAP treatment (n=7) tested the weight loss app to track nutrition, activity, and weight for 10 days. Participants completed a usability and acceptability survey. In phase 2, patients with OSA who were receiving CPAP treatment (n=21) completed a web-based survey about their interpretations and preferences for wireframes of the CPAP tracking module. In phase 3, patients with recently diagnosed OSA who were CPAP naive (n=9) were prescribed a CPAP device (ResMed AirSense10 AutoSet) and tested the integrated app for 3 to 4 weeks. Participants completed a usability survey and provided feedback. RESULTS: During phase 1, participants found the app to be mostly easy to use, except for some difficulty searching for specific foods. All participants found the connected devices (Fitbit activity tracker and Fitbit Aria scale) easy to use and helpful. During phase 2, participants correctly interpreted CPAP adherence success, expressed as percentage of wear time relative to time spent in bed, and preferred seeing a clearly stated percentage goal ("Goal: 100%"). In phase 3, participants found the integrated app easy to use and requested push notification reminders to wear CPAP before bedtime and to sync Fitbit in the morning. CONCLUSIONS: We developed a mobile health tool that integrated a new CPAP adherence tracking module into an existing weight loss app. Novel features included addressing OSA-obesity comorbidity, CPAP adherence tracking via percentage of CPAP wear time relative to objectively assessed time spent in bed, and push notifications to foster adherence. Future research on the effectiveness of this tool in improving OSA treatment adherence is warranted.

Adaptation and study protocol of the evidence-based Make Better Choices (MBC2) multiple diet and activity change intervention for a rural Appalachian population.

BACKGROUND: Rural Appalachian residents experience among the highest prevalence of chronic disease, premature mortality, and decreased life expectancy in the nation. Addressing these growing inequities while avoiding duplicating existing programming necessitates the development of appropriate adaptations of evidence-based lifestyle interventions. Yet few published articles explicate how to accomplish such contextual and cultural adaptation. METHODS: In this paper, we describe the process of adapting the Make Better Choices 2 (MBC2) mHealth diet and activity randomized trial and the revised protocol for intervention implementation in rural Appalachia. Deploying the NIH's Cultural Framework on Health and Aaron's Adaptation framework, the iterative adaptation process included convening focus groups (N = 4, 38 participants), conducting key informant interviews (N = 16), verifying findings with our Community Advisory Board (N = 9), and deploying usability surveys (N = 8), wireframing (N = 8), and pilot testing (N = 9. This intense process resulted in a comprehensive revision of recruitment, retention, assessment, and intervention components. For the main trial, 350 participants will be randomized to receive either the multicomponent MBC2 diet and activity intervention or an active control condition (stress and sleep management). The main outcome is a composite score of four behavioral outcomes: two outcomes related to diet (increased fruits and vegetables and decreased saturated fat intake) and two related to activity (increased moderate vigorous physical activity [MVPA] and decreased time spent on sedentary activities). Secondary outcomes include change in biomarkers, including blood pressure, lipids, A1C, waist circumference, and BMI. DISCUSSION: Adaptation and implementation of evidence-based interventions is necessary to ensure efficacious contextually and culturally appropriate health services and programs, particularly for underserved and vulnerable populations. This article describes the development process of an adapted, community-embedded health intervention and the final protocol created to improve health behavior and, ultimately, advance health equity. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT04309461. The trial was registered on 6/3/2020.

Relationships between changing communication networks and changing perceptions of psychological safety in a team science setting: Analysis with actor-oriented social network models.

A growing evidence base suggests that complex healthcare problems are optimally tackled through cross-disciplinary collaboration that draws upon the expertise of diverse researchers. Yet, the influences and processes underlying effective teamwork among independent researchers are not well-understood, making it difficult to fully optimize the collaborative process. To address this gap in knowledge, we used the annual NIH mHealth Training Institutes as a testbed to develop stochastic actor-oriented models that explore the communicative interactions and psychological changes of its disciplinarily and geographically diverse participants. The models help investigate social influence and social selection effects to understand whether and how social network interactions influence perceptions of team psychological safety during the institute and how they may sway communications between participants. We found a degree of social selection effects: in particular years, scholars were likely to choose to communicate with those who had more dissimilar levels of psychological safety. We found evidence of social influence, in particular, from scholars with lower psychological safety levels and from scholars with reciprocated communications, although the sizes and directions of the social influences somewhat varied across years. The current study demonstrated the utility of stochastic actor-oriented models in understanding the team science process which can inform team science initiatives. The study results can contribute to theory-building about team science which acknowledges the importance of social influence and selection.

The EVO study protocol for a randomized controlled evaluation trial of an optimized weight management intervention.

BACKGROUND: Obesity is a substantial public health concern; however, gold-standard behavioral treatments for obesity are costly and burdensome. Existing adaptations to the efficacious Diabetes Prevention Program (DPP) demonstrate mixed results. Our prior research applying the Multiphase Optimization Strategy (MOST) to DPP identifies a more parsimonious, less costly intervention (EVO) resulting in significant weight loss. OBJECTIVE: The aim of the remotely conducted EVO trial is to test the non-inferiority of EVO against DPP. We will conduct economic evaluations alongside the trial to estimate delivery and patient costs, cost-effectiveness, and lifetime healthcare costs of EVO as compared to DPP. Exploratory analyses will examine maintenance, moderators, and mediators of the treatment effect. STUDY DESIGN: The EVO trial will recruit nationally to randomize 524 participants with obesity. Participants will receive either EVO or DPP over a 6 month period. EVO participants will be provided online lessons, a smartphone application to self-monitor diet, physical activity, and weight, and attend 12 brief calls with a Health Promotionist. DPP participants will receive the first 6 months of the Center for Disease Control's T2D materials and attend 16 one-hour video call sessions with staff certified in DPP delivery. Weight will be measured at baseline, 3-, 6-, and 12-months. Itemized delivery cost will be collected. Staff and participants will also provide information to estimate costs for intervention-related activities. SIGNIFICANCE: The EVO trial could establish evidence supporting dissemination of a scalable, cost-effective behavioral treatment with potential to shift clinical practice guidelines, inform policy, and reduce the prevalence of obesity.

Using factorial mediation analysis to better understand the effects of interventions.

To improve understanding of how interventions work or why they do not work, there is need for methods of testing hypotheses about the causal mechanisms underlying the individual and combined effects of the components that make up interventions. Factorial mediation analysis, i.e., mediation analysis applied to data from a factorial optimization trial, enables testing such hypotheses. In this commentary, we demonstrate how factorial mediation analysis can contribute detailed information about an intervention's causal mechanisms. We briefly review the multiphase optimization strategy (MOST) and the factorial experiment. We use an empirical example from a 25 factorial optimization trial to demonstrate how factorial mediation analysis opens possibilities for better understanding the individual and combined effects of intervention components. Factorial mediation analysis has important potential to advance theory about interventions and to inform intervention improvements.

Association of number of bites and eating speed with energy intake: Wearable technology results under free-living conditions.

Personalized weight management strategies are gaining interest. However, knowledge is limited regarding eating habits and association with energy intake, and current technologies limit assessment in free-living situations. We assessed associations between eating behavior and time of day with energy intake using a wearable camera under free-living conditions and explored if obesity modifies the associations. Sixteen participants (50% with obesity) recorded free-living eating behaviors using a wearable fish-eye camera for 14 days. Videos were viewed by trained annotators who confirmed number of bites, eating speed, and time of day for each eating episode. Energy intake was determined by a trained dietitian performing 24-h diet recalls. Greater number of bites, reduced eating speed, and increased BMI significantly predicted higher energy intake among all participants (P < 0.05, each). There were no significant interactions between obesity and number of bites, eating speed, or time of day (p > 0.05). Greater number of bites and reduced eating speed were significantly associated with higher energy intake in participants without obesity. Results show that under free-living conditions, more bites and slower eating speed predicted higher energy intake when examining consumption of foods with beverages. Obesity did not modify these associations. Findings highlight how eating behaviors can impact energy balance and can inform weight management interventions using wearable technology.

Using the Preparation Phase of the Multiphase Optimization Strategy to Develop a Messaging Component for Weight Loss: Formative and Pilot Research.

BACKGROUND: Mobile messaging is often used in behavioral weight loss interventions, yet little is known as to the extent to which they contribute to weight loss when part of a multicomponent treatment package. The multiphase optimization strategy (MOST) is a framework that researchers can use to systematically investigate interventions that achieve desirable outcomes given specified constraints. OBJECTIVE: This study describes the use of MOST to develop a messaging intervention as a component to test as part of a weight loss treatment package in a subsequent optimization trial. METHODS: On the basis of our conceptual model, a text message intervention was created to support self-regulation of weight-related behaviors. We tested the messages in the ENLIGHTEN feasibility pilot study. Adults with overweight and obesity were recruited to participate in an 8-week weight loss program. Participants received a commercially available self-monitoring smartphone app, coaching calls, and text messages. The number and frequency of text messages sent were determined by individual preferences, and weight was assessed at 8 weeks. RESULTS: Participants (n=9) in the feasibility pilot study lost 3.2% of their initial body weight over the 8-week intervention and preferred to receive 1.8 texts per day for 4.3 days per week. Researcher burden in manually sending messages was high, and the cost of receiving text messages was a concern. Therefore, a fully automated push notification system was developed to facilitate sending tailored daily messages to participants to support weight loss. CONCLUSIONS: Following the completion of specifying the conceptual model and the feasibility pilot study, the message intervention went through a final iteration. Theory and feasibility pilot study results during the preparation phase informed critical decisions about automation, frequency, triggers, and content before inclusion as a treatment component in a factorial optimization trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT01814072; https://clinicaltrials.gov/ct2/show/NCT01814072.

Online, cross-disciplinary team science training for health and medical professionals: Evaluation of COALESCE (teamscience.net).

INTRODUCTION: The National Academies of Sciences (NAS) emphasize the need for interdisciplinary team science (TS) training, but few training resources are available. COALESCE, an open-access tool developed with National Institutes of Health support and located at teamscience.net, is considered a gold standard resource but has not previously been evaluated. COALESCE launched four learning modules in 2011. The Science of TS (SciTS) module, an interactive encyclopedia, introduces foundational concepts. Three scenario-based modules simulate TS challenges in behavioral, clinical, and basic biomedical sciences. This study examined user characteristics, usage patterns, and effects of completing the four modules on TS knowledge, attitudes, and skills. METHODS: Repeated measures ANOVA tested for pre-post changes in performance and compared learning by users with biomedical versus other disciplinary backgrounds. RESULTS: From 2011 through 2017, the site attracted 16,280 new users who engaged in 6461 sessions that lasted more than 1 min. The modal registrant identified as working in a biomedical field (47%), in an academic institution (72%), and expressed greater interest in the practice than the SciTS (67%). Those completing pre- and post-tests (n = 989) showed significant improvement in knowledge, attitudes, and skills after taking all scenario-based modules (p < 0.005); knowledge and attitudes were unchanged after the SciTS encyclopedia. Biomedical and other health professionals improved comparably. CONCLUSION: Evaluation of the TS training tool at teamscience.net indicates broad dissemination and positive TS-related outcomes. Site upgrades implemented between 2018 and 2020, including adding five new modules, are expected to increase the robustness and accessibility of the COALESCE training resource.

Toward a health-promoting system for cancer survivors: Patient and provider multiple behavior change.

OBJECTIVE: This paper examines how and why to improve care systems for disease management and health promotion for the growing population of cancer survivors with cardiovascular multi-morbidities. METHOD: We reviewed research characterizing cancer survivors' and their multiple providers' common sense cognitive models of survivors' main health threats, preventable causes of adverse health events, and optimal coping strategies. RESULTS: Findings indicate that no entity in the health care system self-identifies as claiming primary responsibility to address longstanding unhealthy lifestyle behaviors that heighten survivors' susceptibility to both cancer and cardiovascular disease (CVD) and whose improvement could enhance quality of life. CONCLUSIONS: To address this gap, we propose systems-level changes that integrate health promotion into existing survivorship services by including behavioral risk factor vital signs in the electronic medical record, with default proactive referral to a health promotionist (a paraprofessional coach adept with mobile technologies and supervised by a professional expert in health behavior change). By using the patient's digital tracking data to coach remotely and periodically report progress to providers, the health promotionist closes a gap, creating a connected care system that supports, reinforces, and maintains accountability for healthy lifestyle improvement. No comparable resource solely dedicated to treatment of chronic disease risk behaviors (smoking, obesity, physical inactivity, treatment nonadherence) exists in current models of integrated care. Integrating health promotionists into care delivery channels would remove burden from overtaxed PCPs and instantiate a comprehensive, actionable systems-level schema of health risks and coping strategies needed to have preventive impact with minimal interference to clinical work flow. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

SMART: Study protocol for a sequential multiple assignment randomized controlled trial to optimize weight loss management.

BACKGROUND: Stepped care is a rational resource allocation approach to reduce population obesity. Evidence is lacking to guide decisions on use of low cost treatment components such as mobile health (mHealth) tools without compromising weight loss of those needing more expensive traditional treatment components (e.g., coaching, meal replacement). A sequential multiple assignment randomization trial (SMART) will be conducted to inform the development of an empirically based stepped care intervention that incorporates mHealth and traditional treatment components. OBJECTIVE: The primary aim tests the non-inferiority of app alone, compared to app plus coaching, as first line obesity treatment, measured by weight change from baseline to 6 months. Secondary aims are to identify the best tactic to address early treatment non-response and the optimal treatment sequence for resource efficient weight loss. STUDY DESIGN: Four hundred participants, 18-60 years old with Body Mass Index between 27 and 45 kg/m2 will be randomized to receive a weight loss smartphone app (APP) or the app plus weekly coaching (APP + C) for a 12 week period. Those achieving <0.5 lb. weight loss on average per week, assessed by wireless scale at 2, 4, and 8 weeks, will be classified as non-responders and re-randomized once to step-up modestly (adding another mHealth component) or vigorously (adding mHealth and traditional treatment components) for the remaining treatment period. Weight will be assessed in person at baseline, 3, 6, and 12 months. SIGNIFICANCE: Results will inform construction of an obesity treatment algorithm that balances weight loss outcomes with resource consumption.

Multicomponent mHealth Intervention for Large, Sustained Change in Multiple Diet and Activity Risk Behaviors: The Make Better Choices 2 Randomized Controlled Trial.

BACKGROUND: Prevalent co-occurring poor diet and physical inactivity convey chronic disease risk to the population. Large magnitude behavior change can improve behaviors to recommended levels, but multiple behavior change interventions produce small, poorly maintained effects. OBJECTIVE: The Make Better Choices 2 trial tested whether a multicomponent intervention integrating mHealth, modest incentives, and remote coaching could sustainably improve diet and activity. METHODS: Between 2012 and 2014, the 9-month randomized controlled trial enrolled 212 Chicago area adults with low fruit and vegetable and high saturated fat intakes, low moderate to vigorous physical activity (MVPA) and high sedentary leisure screen time. Participants were recruited by advertisements to an open-access website, screened, and randomly assigned to either of two active interventions targeting MVPA simultaneously with, or sequentially after other diet and activity targets (N=84 per intervention) or a stress and sleep contact control intervention (N=44). They used a smartphone app and accelerometer to track targeted behaviors and received personalized remote coaching from trained paraprofessionals. Perfect behavioral adherence was rewarded with an incentive of US $5 per week for 12 weeks. Diet and activity behaviors were measured at baseline, 3, 6, and 9 months; primary outcome was 9-month diet and activity composite improvement. RESULTS: Both simultaneous and sequential interventions produced large, sustained improvements exceeding control (P<.001), and brought all diet and activity behaviors to guideline levels. At 9 months, the interventions increased fruits and vegetables by 6.5 servings per day (95% CI 6.1-6.8), increased MVPA by 24.7 minutes per day (95% CI 20.0-29.5), decreased sedentary leisure by 170.5 minutes per day (95% CI -183.5 to -157.5), and decreased saturated fat intake by 3.6% (95% CI -4.1 to -3.1). Retention through 9-month follow-up was 82.1%. Self-monitoring decreased from 96.3% of days at baseline to 72.3% at 3 months, 63.5% at 6 months, and 54.6% at 9 months (P<.001). Neither attrition nor decline in self-monitoring differed across intervention groups. CONCLUSIONS: Multicomponent mHealth diet and activity intervention involving connected coaching and modest initial performance incentives holds potential to reduce chronic disease risk. TRIAL REGISTRATION: ClinicalTrials.gov NCT01249989; https://clinicaltrials.gov/ct2/show/NCT01249989 (Archived by WebCite at https://clinicaltrials.gov/ct2/show/NCT01249989).

Daily and Seasonal Influences on Dietary Self-monitoring Using a Smartphone Application.

OBJECTIVE: To examine within-person variation in dietary self-monitoring during a 6-month technology-supported weight loss trial as a function of time-varying factors including time in the study, day of the week, and month of the year. METHODS: Smartphone self-monitoring data were examined from 31 obese adults (aged 18-60 years) who participated in a 6-month technology-supported weight loss program. Multilevel regression modeling was used to examine within-person variation in dietary self-monitoring. RESULTS: Participants recorded less as time in the study progressed. Fewer foods were reported on the weekends compared with weekdays. More foods were self-monitored in January compared with October; however, a seasonal effect was not observed. CONCLUSIONS AND IMPLICATIONS: The amount of time in a study and day of the week were associated with dietary self-monitoring but not season. Future studies should examine factors that influence variations in self-monitoring and identify methods to improve technology-supported dietary self-monitoring adherence.

Evaluating and improving recruitment and retention in an mHealth clinical trial: an example of iterating methods during a trial.

BACKGROUND: Recruitment and retention strategy investigations in mHealth clinical trials are rare. Technology presents an opportunity to intensely and remotely evaluate recruitment, use of mobile apps, and retention, leading to new insights for continuous improvement of mHealth trials. The objective of this paper is to present a case study in which a trial evaluated and changed strategies during a clinical trial to improve recruitment, adherence to study protocols, and retention in the mHealth trial. METHODS: In Fall 2015, the NUYou trial enrolled 150 college freshmen in an mHealth protocol. Three months after study initiation, NUYou struggled to meet recruitment goals and maintain anticipated usage levels of the study smartphone application. Two sets of data were collected to improve recruitment and retention: a survey about recruitment was sent to the target population and surveys regarding usability of the app was sent to the study sample. Survey results informed improvements in recruitment strategies, the study retention protocol, and the smartphone application. RESULTS: Survey results revealed several insights including misunderstanding components of the trial by potential participants, low perceived usefulness of the app, and little recall or impact of the incentive structure. After implementation of user-centered improvements, the second cohort of NUYou recruitment in the fall of 2016 produced an equal sample size in 4 weeks less time. Winter quarter of 2016 compared to 2017 demonstrated an improvement in retention via app use and completion of weekly in-app surveys. CONCLUSIONS: Recruitment and retention in clinical trials continues to be a critical challenge and mHealth trials may present both unique challenges and opportunities. To our knowledge, this is the first study to describe a systematic evaluation followed by changes and further evaluation to recruitment, use of the mHealth application, adherence to study protocol, and retention during an mHealth clinical trial. Future work should adopt and explicitly study these processes to optimize both enrollment and retention in these types of trials to preserve validity and reliability of research results.