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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. 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. 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. 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. 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.

Predicting the likelihood of success of weight loss interventions using machine learning (ML) models may enhance intervention effectiveness by enabling timely and dynamic modification of intervention components for nonresponders to treatment. However, a lack of understanding and trust in these ML models impacts adoption among weight management experts. Recent advances in the field of explainable artificial intelligence enable the interpretation of ML models, yet it is unknown whether they enhance model understanding, trust, and adoption among weight management experts. This study aimed to build and evaluate an ML model that can predict 6-month weight loss success (ie, ≥7% weight loss) from 5 engagement and diet-related features collected over the initial 2 weeks of an intervention, to assess whether providing ML-based explanations increases weight management experts’ agreement with ML model predictions, and to inform factors that influence the understanding and trust of ML models to advance explainability in early prediction of weight loss among weight management experts. We trained an ML model using the random forest (RF) algorithm and data from a 6-month weight loss intervention (N=419). We leveraged findings from existing explainability metrics to develop Prime Implicant Maintenance of Outcome (PRIMO), an interactive tool to understand predictions made by the RF model. We asked 14 weight management experts to predict hypothetical participants’ weight loss success before and after using PRIMO. We compared PRIMO with 2 other explainability methods, one based on feature ranking and the other based on conditional probability. We used generalized linear mixed-effects models to evaluate participants’ agreement with ML predictions and conducted likelihood ratio tests to examine the relationship between explainability methods and outcomes for nested models. We conducted guided interviews and thematic analysis to study the impact of our tool on experts’ understanding and trust in the model. Our RF model had 81% accuracy in the early prediction of weight loss success. Weight management experts were significantly more likely to agree with the model when using PRIMO (χ[sup.2]=7.9; P=.02) compared with the other 2 methods with odds ratios of 2.52 (95% CI 0.91-7.69) and 3.95 (95% CI 1.50-11.76). From our study, we inferred that our software not only influenced experts’ understanding and trust but also impacted decision-making. Several themes were identified through interviews: preference for multiple explanation types, need to visualize uncertainty in explanations provided by PRIMO, and need for model performance metrics on similar participant test instances. Our results show the potential for weight management experts to agree with the ML-based early prediction of success in weight loss treatment programs, enabling timely and dynamic modification of intervention components to enhance intervention effectiveness. Our findings provide methods for advancing the understandability and trust of ML models among weight management experts.

Poor diet, low physical activity, high stress, and poor sleep are prevalent modifiable risk factors for chronic diseases. Synergistic effects of interventions targeting diet and activity on other health risk behaviors such as stress and sleep are understudied. To conduct a secondary data analysis to investigate whether interventions targeting diet and activity produce collateral improvements in stress and sleep. Make Better Choices 2 was a randomized clinical trial to test a technology-assisted coaching intervention with modest incentives to improve diet and activity, as compared to a matched intervention targeting improved stress and sleep. Participants (n = 212) were adults (76.4% female, 59% non-white minority, mean age = 40.8 years) with multiple diet and activity risk behaviors. For 7 days at baseline, 3-, 6-, and 9-months, participants reported perceived sleep duration, stress, diet, and activity through a smartphone application. Outcomes were evaluated by linear mixed models. The study was registered on clinicaltrials.gov (NCT01249989). Both interventions produced significant, statistically comparable improvements in average daily stress rating (z = 1.35, p = .177). Reduction in average daily stress rating was 1.68 following diet/activity intervention (z = -12.25, P < .001) and 2.08 following stress/sleep intervention (z = -7.83, P < .001) on an 11-point Likert scale. Though changes in sleep duration for both groups were clinically meaningful, the stress/sleep intervention produced statistically larger improvements in sleep as compared to the diet/activity intervention (z = -3.79, P < .001). Sleep duration increased 26.39 minutes following diet/activity intervention (z = 3.16, P = 0.002) and 92.65 minutes following stress/sleep intervention (z = 5.912, P < 0.001). Findings suggest that diet and activity behavior change interventions can effectively improve outcomes within and between the behavior domains they directly target. Future research should identify common mechanistic pathways to inform development of interventions that efficiently change multiple health behaviors implicated in chronic disease morbidity and mortality.

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.

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. The Make Better Choices 2 trial tested whether a multicomponent intervention integrating mHealth, modest incentives, and remote coaching could sustainably improve diet and activity. 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. 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. Multicomponent mHealth diet and activity intervention involving connected coaching and modest initial performance incentives holds potential to reduce chronic disease risk. ClinicalTrials.gov NCT01249989; https://clinicaltrials.gov/ct2/show/NCT01249989 (Archived by WebCite at https://clinicaltrials.gov/ct2/show/NCT01249989).

IntroductionLifestyle risk behaviours, including alcohol use, smoking, poor diet, physical inactivity, poor sleep (duration and/or quality) and sedentary recreational screen time (‘the Big 6’), are strong determinants of chronic disease. These behaviours often emerge during adolescence and co-occur. School-based interventions have the potential to address risk factors prior to the onset of disease, yet few eHealth school-based interventions target multiple behaviours concurrently. This paper describes the protocol of the Health4Life Initiative, an eHealth school-based intervention that concurrently addresses the Big 6 risk behaviours among secondary school students.Methods and analysisA multisite cluster randomised controlled trial will be conducted among year 7 students (11–13 years old) from 72 Australian schools. Stratified block randomisation will be used to assign schools to either the Health4Life intervention or an active control (health education as usual). Health4Life consists of (1) six web-based cartoon modules and accompanying activities delivered during health education (once per week for 6 weeks), and a smartphone application (universal prevention), and (2) additional app content, for students engaging in two or more risk behaviours when they are in years 8 and 9 (selective prevention). Students will complete online self-report questionnaires at baseline, post intervention, and 12, 24 and 36 months after baseline. Primary outcomes are consumption of sugar-sweetened beverages, moderate-to-vigorous physical activity, sleep duration, sedentary recreational screen time and uptake of alcohol and tobacco use.Ethics and disseminationThis study has been approved by the University of Sydney (2018/882), NSW Department of Education (SERAP no. 2019006), University of Queensland (2019000037), Curtin University (HRE2019-0083) and relevant Catholic school committees. Results will be presented to schools and findings disseminated via peer-reviewed journals and scientific conferences. This will be the first evaluation of an eHealth intervention, spanning both universal and selective prevention, to simultaneously target six key lifestyle risk factors among adolescents.Trial registration numberAustralian New Zealand Clinical Trials Registry (ACTRN12619000431123), 18 March 2019.

Both genetic and lifestyle factors contribute to an individual’s disease risk, suggesting a multi-omic approach is essential for personalized prevention. Studies have examined the effectiveness of lifestyle coaching on clinical outcomes, however, little is known about the impact of genetic predisposition on the response to lifestyle coaching. Here we report on the results of a real-world observational study in 2531 participants enrolled in a commercial “Scientific Wellness” program, which combines multi-omic data with personalized, telephonic lifestyle coaching. Specifically, we examined: 1) the impact of this program on 55 clinical markers and 2) the effect of genetic predisposition on these clinical changes. We identified sustained improvements in clinical markers related to cardiometabolic risk, inflammation, nutrition, and anthropometrics. Notably, improvements in HbA1c were akin to those observed in landmark trials. Furthermore, genetic markers were associated with longitudinal changes in clinical markers. For example, individuals with genetic predisposition for higher LDL-C had a lesser decrease in LDL-C on average than those with genetic predisposition for average LDL-C. Overall, these results suggest that a program combining multi-omic data with lifestyle coaching produces clinically meaningful improvements, and that genetic predisposition impacts clinical responses to lifestyle change.

Background Studies demonstrate the impact of diet and physical activity on epigenetic biomarkers, specifically DNA methylation. However, no intervention studies have examined the combined impact of dietary and activity changes on the blood epigenome. The objective of this study was to examine the impact of the Make Better Choices 2 (MBC2) healthy diet and activity intervention on patterns of epigenome-wide DNA methylation. The MBC2 study was a 9-month randomized controlled trial among adults aged 18–65 with non-optimal levels of health behaviors. The study compared three 12-week interventions to (1) simultaneously increase exercise and fruit/vegetable intake, while decreasing sedentary leisure screen time; (2) sequentially increase fruit/vegetable intake and decrease leisure screen time first, then increase exercise; (3) increase sleep and decrease stress (control). We collected blood samples at baseline, 3 and 9 months, and measured DNA methylation using the Illumina EPIC (850 k) BeadChip. We examined region-based differential methylation patterns using linear regression models with the false discovery rate of 0.05. We also conducted pathway analysis using gene ontology (GO), KEGG, and IPA canonical pathway databases. Results We found no differences between the MBC2 population ( n = 340) and the subsample with DNA methylation measured ( n = 68) on baseline characteristics or the impact of the intervention on behavior change. We identified no differentially methylated regions at baseline between the control versus intervention groups. At 3 versus 9 months, we identified 154 and 298 differentially methylated regions, respectively, between controls compared to pooled samples from sequential and simultaneous groups. In the GO database, we identified two gene ontology terms related to hemophilic cell adhesion and cell-cell adhesion. In IPA analysis, we found pathways related to carcinogenesis including PI3K/AKT, Wnt/β-catenin, sonic hedgehog, and p53 signaling. We observed an overlap between 3 and 9 months, including the GDP- l -fucose biosynthesis I, methylmalonyl metabolism, and estrogen-mediated cell cycle regulation pathways. Conclusions The results demonstrate that the MBC2 diet and physical activity intervention impacts patterns of DNA methylation in gene regions related to cell cycle regulation and carcinogenesis. Future studies will examine DNA methylation as a biomarker to identify populations that may particularly benefit from incorporating health behavior change into plans for precision prevention.

Digital health behavior change interventions play an important role in helping cancer survivors improve their quality of life and reduce the risk of cancer recurrence. Clinician-patient communication is central to promoting the uptake of and adherence to digital health behavior change interventions. However, oncologists face significant barriers, including time constraints, knowledge gaps, and conversational uneasiness that limit risk behavior and health behavior change conversations. This qualitative study aims to explore oncologists' preferences for discussing and monitoring risk behaviors with cancer survivors, with a specific focus on conversations about digital health behavior change interventions. This study also aims to explore oncologists' informational and technological support requirements to facilitate these conversations. We conducted semistructured interviews with 18 oncologists who provide cancer care in a large National Cancer Institute-designated comprehensive cancer center. The transcripts and interview notes were analyzed through an iterative thematic analysis to generate relevant themes and categories. We identified 2 major themes with 7 subthemes. The first theme focused on oncologists' desired roles in promoting health behavior change, while the second theme addressed the support needs to facilitate conversations about risk and health promotion. Oncologists expressed a desire for 2 action-oriented communication mechanisms for promoting digital health behavior change with their patients: referring patients to interventions and reinforcing intervention goals longitudinally. To facilitate risk behavior and health behavior change conversations, their support needs included a preference for low-burden, electronic health record-integrated tools providing timely updates on patient enrollment and progress. The participating oncologists requested a tailored conversation aid for patient communication and parallel systems combining electronic health record messaging with print materials. They also emphasized the need for automated recommender systems to identify and refer eligible patients and reminder systems to prompt timely discussions with patients. Oncologists are motivated and well-positioned to support patients' health behavior change but have unmet informational and technological requirements. On the basis of oncologists' perspectives, our findings provide actionable, user-centered, low-burden strategies for facilitating oncologist-patient conversations about digital health behavior change interventions. We make recommendations for integrating these strategies directly into the electronic medical record system, with the goal of amplifying oncologists' influential roles in motivating health behavior change among survivors. These scalable strategies may be applicable beyond oncology to clinical contexts where greater promotion of patients' health behavior change is desired.

Melanoma survivors often do not engage in adequate sun protection, leading to sunburn and increasing their risk of future melanomas. Melanoma survivors do not accurately recall the extent of sun exposure they have received, thus, they may be unaware of their personal UV exposure, and this lack of awareness may contribute towards failure to change behavior. As a means of determining behavioral accuracy of recall of sun exposure, this study compared subjective self-reports of time outdoors to an objective wearable sensor. Analysis of the meaningful discrepancies between the self-report and sensor measures of time outdoors was made possible by using a network flow algorithm to align sun exposure events recorded by both measures. Aligning the two measures provides the opportunity to more accurately evaluate false positive and false negative self-reports of behavior and understand participant tendencies to over- and under-report behavior. 39 melanoma survivors wore an ultraviolet light (UV) sensor on their chest while outdoors for 10 consecutive summer days and provided an end-of-day subjective self-report of their behavior while outdoors. A Network Flow Alignment framework was used to align self-report and objective UV sensor data to correct misalignment. The frequency and time of day of under- and over-reporting were identified. For the 269 days assessed, the proposed framework showed a significant increase in the Jaccard coefficient (i.e. a measure of similarity between self-report and UV sensor data) by 63.64% (p < .001), and significant reduction in false negative minutes by 34.43% (p < .001). Following alignment of the measures, under-reporting of sun exposure time occurred on 51% of the days analyzed and more participants tended to under-report than to over-report sun exposure time. Rates of under-reporting of sun exposure were highest for events that began from 12-1pm, and second-highest from 5-6pm. These discrepancies may reflect lack of accurate recall of sun exposure during times of peak sun intensity (10am-2pm) that could ultimately increase the risk of developing melanoma. This research provides technical contributions to the field of wearable computing, activity recognition, and identifies actionable times to improve participants' perception of their sun exposure.