The Health4Life e-health intervention for modifying lifestyle risk behaviours of adolescents: secondary outcomes of a cluster randomised controlled trial.

OBJECTIVES: To investigate the effectiveness of a school-based multiple health behaviour change e-health intervention for modifying risk factors for chronic disease (secondary outcomes). STUDY DESIGN: Cluster randomised controlled trial. SETTING, PARTICIPANTS: Students (at baseline [2019]: year 7, 11-14 years old) at 71 Australian public, independent, and Catholic schools. INTERVENTION: Health4Life: an e-health school-based multiple health behaviour change intervention for reducing increases in the six major behavioural risk factors for chronic disease: physical inactivity, poor diet, excessive recreational screen time, poor sleep, and use of alcohol and tobacco. It comprises six online video modules during health education class and a smartphone app. MAIN OUTCOME MEASURES: Comparison of Health4Life and usual health education with respect to their impact on changes in twelve secondary outcomes related to the six behavioural risk factors, assessed in surveys at baseline, immediately after the intervention, and 12 and 24 months after the intervention: binge drinking, discretionary food consumption risk, inadequate fruit and vegetable intake, difficulty falling asleep, and light physical activity frequency (categorical); tobacco smoking frequency, alcohol drinking frequency, alcohol-related harm, daytime sleepiness, and time spent watching television and using electronic devices (continuous). RESULTS: A total of 6640 year 7 students completed the baseline survey (Health4Life: 3610; control: 3030); 6454 (97.2%) completed at least one follow-up survey, 5698 (85.8%) two or more follow-up surveys. Health4Life was not statistically more effective than usual school health education for influencing changes in any of the twelve outcomes over 24 months; for example: fruit intake inadequate: odds ratio [OR], 1.08 (95% confidence interval [CI], 0.57-2.05); vegetable intake inadequate: OR, 0.97 (95% CI, 0.64-1.47); increased light physical activity: OR, 1.00 (95% CI, 0.72-1.38); tobacco use frequency: relative difference, 0.03 (95% CI, -0.58 to 0.64) days per 30 days; alcohol use frequency: relative difference, -0.34 (95% CI, -1.16 to 0.49) days per 30 days; device use time: relative difference, -0.07 (95% CI, -0.29 to 0.16) hours per day. CONCLUSIONS: Health4Life was not more effective than usual school year 7 health education for modifying adolescent risk factors for chronic disease. Future e-health multiple health behaviour change intervention research should examine the timing and length of the intervention, as well as increasing the number of engagement strategies (eg, goal setting) during the intervention. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry: ACTRN12619000431123 (prospective).

Delivering COVID-19 Vaccine via Trusted Social Services: Program Evaluation Results from the Chicagoland CEAL Program.

To describe the reach, implementation, and sustainability of COVID-19 vaccination programs delivered by social service community organizations. Five academic institutions in the Chicagoland CEAL (Community Engagement Alliance) program partnered with 17 community organizations from September 2021-April 2022. Interviews, community organizations program implementation tracking documents, and health department vaccination data were used to conduct the evaluation. A total of 269 events were held and 5,432 COVID-19 vaccines delivered from May 2021-April 2022. Strategies that worked best included offering vaccinations in community settings with flexible and reliable hours; pairing vaccinations with ongoing social services; giving community organizations flexibility to adjust programs; offering incentives; and vaccinating staff first. These strategies and partnership structures supported vaccine uptake, community organization alignment with their missions and communities' needs, and trust. Community organizations delivering social services are local community experts and trusted messengers. Pairing social service delivery with COVID-19 vaccination built individual and community agency. Giving COs creative control over program implementation enhanced trust and vaccine delivery. When given appropriate resources and control, community organizations can quickly deliver urgently needed health services in a public health crisis.

An Adaptive Behavioral Intervention for Weight Loss Management: A Randomized Clinical Trial.

Importance: Lifestyle interventions for weight loss are difficult to implement in clinical practice. Self-managed mobile health implementations without or with added support after unsuccessful weight loss attempts could offer effective population-level obesity management. Objective: To test whether a wireless feedback system (WFS) yields noninferior weight loss vs WFS plus telephone coaching and whether participants who do not respond to initial treatment achieve greater weight loss with more vs less vigorous step-up interventions. Design, Setting, and Participants: In this noninferiority randomized trial, 400 adults aged 18 to 60 years with a body mass index of 27 to 45 were randomized in a 1:1 ratio to undergo 3 months of treatment initially with WFS or WFS plus coaching at a US academic medical center between June 2017 and March 2021. Participants attaining suboptimal weight loss were rerandomized to undergo modest or vigorous step-up intervention. Interventions: The WFS included a Wi-Fi activity tracker and scale transmitting data to a smartphone app to provide daily feedback on progress in lifestyle change and weight loss, and WFS plus coaching added 12 weekly 10- to 15-minute supportive coaching calls delivered by bachelor's degree-level health promotionists viewing participants' self-monitoring data on a dashboard; step-up interventions included supportive messaging via mobile device screen notifications (app-based screen alerts) without or with coaching or powdered meal replacement. Participants and staff were unblinded and outcome assessors were blinded to treatment randomization. Main Outcomes and Measures: The primary outcome was the between-group difference in 6-month weight change, with the noninferiority margin defined as a difference in weight change of -2.5 kg; secondary outcomes included between-group differences for all participants in weight change at 3 and 12 months and between-group 6-month weight change difference among nonresponders exposed to modest vs vigorous step-up interventions. Results: Among 400 participants (mean [SD] age, 40.5 [11.2] years; 305 [76.3%] women; 81 participants were Black and 266 were White; mean [SD] body mass index, 34.4 [4.3]) randomized to undergo WFS (n = 199) vs WFS plus coaching (n = 201), outcome data were available for 342 participants (85.5%) at 6 months. Six-month weight loss was -2.8 kg (95% CI, -3.5 to -2.0) for the WFS group and -4.8 kg (95% CI, -5.5 to -4.1) for participants in the WFS plus coaching group (difference in weight change, -2.0 kg [90% CI, -2.9 to -1.1]; P < .001); the 90% CI included the noninferiority margin of -2.5 kg. Weight change differences were comparable at 3 and 12 months and, among nonresponders, at 6 months, with no difference by step-up therapy. Conclusions and Relevance: A wireless feedback system (Wi-Fi activity tracker and scale with smartphone app to provide daily feedback) was not noninferior to the same system with added coaching. Continued efforts are needed to identify strategies for weight loss management and to accurately select interventions for different individuals to achieve weight loss goals. Trial Registration: ClinicalTrials.gov Identifier: NCT02997943.

Design of experiments with sequential randomizations on multiple timescales: the hybrid experimental design.

Psychological interventions, especially those leveraging mobile and wireless technologies, often include multiple components that are delivered and adapted on multiple timescales (e.g., coaching sessions adapted monthly based on clinical progress, combined with motivational messages from a mobile device adapted daily based on the person's daily emotional state). The hybrid experimental design (HED) is a new experimental approach that enables researchers to answer scientific questions about the construction of psychological interventions in which components are delivered and adapted on different timescales. These designs involve sequential randomizations of study participants to intervention components, each at an appropriate timescale (e.g., monthly randomization to different intensities of coaching sessions and daily randomization to different forms of motivational messages). The goal of the current manuscript is twofold. The first is to highlight the flexibility of the HED by conceptualizing this experimental approach as a special form of a factorial design in which different factors are introduced at multiple timescales. We also discuss how the structure of the HED can vary depending on the scientific question(s) motivating the study. The second goal is to explain how data from various types of HEDs can be analyzed to answer a variety of scientific questions about the development of multicomponent psychological interventions. For illustration, we use a completed HED to inform the development of a technology-based weight loss intervention that integrates components that are delivered and adapted on multiple timescales.

Joint modeling the frequency and duration of accelerometer-measured physical activity from a lifestyle intervention trial.

Physical activity (PA) guidelines recommend that PA be accumulated in bouts of 10 minutes or more in duration. Recently, researchers have sought to better understand how participants in PA interventions increase their activity. Participants can increase their daily PA by increasing the number of PA bouts per day while keeping the duration of the bouts constant; they can keep the number of bouts constant but increase the duration of each bout; or participants can increase both the number of bouts and their duration. We propose a novel joint modeling framework for modeling PA bouts and their duration over time. Our joint model is comprised of two sub-models: a mixed-effects Poisson hurdle sub-model for the number of bouts per day and a mixed-effects location scale gamma regression sub-model to characterize the duration of the bouts and their variance. The model allows us to estimate how daily PA bouts and their duration vary together over the course of an intervention and by treatment condition and is specifically designed to capture the unique distributional features of bouted PA as measured by accelerometer: frequent measurements, zero-inflated bouts, and skewed bout durations. We apply our methods to the Make Better Choices study, a longitudinal lifestyle intervention trial to increase PA. We perform a simulation study to evaluate how well our model is able to estimate relationships between outcomes.

Effect of the Fit2Thrive Intervention on Patient-reported Outcomes in Breast Cancer Survivors: A Randomized Full Factorial Trial.

BACKGROUND: Moderate to vigorous physical activity (MVPA) interventions improve patient-reported outcomes (PROs) of physical and psychological health among breast cancer survivors (BCS); however, the effects of specific intervention components on PROs are unknown. PURPOSE: To use the Multiphase Optimization Strategy (MOST) to examine overall effects of the Fit2Thrive MVPA promotion intervention on PROs in BCS and explore whether there are intervention component-specific effects on PROs. METHODS: Physically inactive BCS [n = 269; Mage = 52.5 (SD = 9.9)] received a core intervention (Fitbit + Fit2Thrive smartphone app) and were randomly assigned to one of 32 conditions in a full factorial experiment of five components ("on" vs. "off"): (i) support calls, (ii) deluxe app, (iii) text messages, (iv) online gym, and (v) buddy. Patient-Reported Outcomes Measurement Information System (PROMIS) questionnaires assessed anxiety, depression, fatigue, physical functioning, sleep disturbance and sleep-related impairment at baseline, post-intervention (12-week), and 24-week follow-up. Main effects for all components at each time point were examined using an intention to treat mixed-effects model. RESULTS: All PROMIS measures except sleep disturbance significantly improved (p's < .008 for all) from baseline to 12-weeks. Effects were maintained at 24-weeks. The "on" level of each component did not result in significantly greater improvements on any PROMIS measure compared to the "off" level. CONCLUSIONS: Participation in Fit2Thrive was associated with improved PROs in BCS, but improvements did not differ for "on" vs. "off" levels for any component tested. The low-resource Fit2Thrive core intervention is a potential strategy to improve PROs among BCS. Future studies should test the core in an RCT and examine various intervention component effects in BCS with clinically elevated PROs.

An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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 (χ2=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. CONCLUSIONS: 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.

Optimization of a technology-supported physical activity promotion intervention for breast cancer survivors: Results from Fit2Thrive.

BACKGROUND: The benefits of moderate to vigorous physical activity (MVPA) for breast cancer survivors are well established. However, most are insufficiently active. Fit2Thrive used the Multiphase Optimization Strategy methodology to determine the effect of 5 intervention components on MVPA in this population. METHODS: Two hundred sixty-nine participants (mean age, 52.5 years; SD, 9.9 years) received a core intervention (the Fit2Thrive self-monitoring app and Fitbit) and were randomly assigned to 5 intervention components set to on/off in a full factorial experiment: support calls, deluxe app, buddy, online gym, and text messages. The intervention was delivered over 12 weeks with a 12-week follow-up. MVPA was measured via accelerometry at the baseline (T1), at 12 weeks (T2), and at 24 weeks (T3). The main effects and interaction effects at each time point were examined for all components. RESULTS: Trial retention was high: 91.8% had valid accelerometer data at T2 or T3. Across all conditions, there were significant increases in MVPA (+53.6 min/wk; P < .001) and in the proportion of survivors meeting MVPA guidelines (+22.3%; P < .001) at T2 that were maintained but attenuated at T3 (MVPA, +24.6 min/wk; P < .001; meeting guidelines, +12.6%; P < .001). No individual components significantly improved MVPA, although increases were greater for the on level versus the off level for support calls, buddy, and text messages at T2 and T3. CONCLUSIONS: The Fit2Thrive core intervention (the self-monitoring app and Fitbit) is promising for increasing MVPA in breast cancer survivors, but the components provided no additional increases in MVPA. Future research should evaluate the core intervention in a randomized trial and determine what components optimize MVPA behaviors in breast cancer survivors.

Parent-based interventions to improve multiple lifestyle risk behaviors among adolescents: A systematic review and meta-analysis.

Lifestyle risk behaviors often co-occur and are prevalent among adolescents. Parent-based interventions addressing risk behaviors concurrently have the potential to improve youth and parent outcomes. This systematic review evaluated the efficacy of parent-based interventions targeting multiple lifestyle risk behaviors among adolescents and parents. MEDLINE (Ovid), Embase (Ovid), PsycInfo (Ovid), Scopus, CINAHL, the Cochrane Database of Systematic Reviews (CDSR) and Cochrane Central Register of Controlled Trials (CENTRAL) were searched from 2010-May 2021. Eligible studies were randomised controlled trials (RCTs) of parent-based interventions addressing 2+ risk behaviors: alcohol use, smoking, poor diet, physical inactivity, sedentary behaviors, and poor sleep. Studies directly targeting parents, and that assessed adolescent outcomes (11-18 years) were eligible. Where possible, random-effects meta-analysis was conducted. From 11,975 identified records, 46 publications of 36 RCTs (n = 28,322 youth, n = 7385 parents) were eligible. Parent-based interventions were associated with improved adolescent moderate-to-vigorous physical activity (MVPA) [Odds Ratio (OR) = 1.82, 95% CI = 1.18, 2.81; p = 0.007], and reduced screen time (SMD = -0.39, 95% CI = -0.62, -0.16, p = 0.0009) and discretionary food intake (SMD = -0.18; 95% CI = -0.30, -0.06; p = 0.002) compared to controls. However, there was some evidence that interventions increased the odds of ever using tobacco in the medium-term (OR = 1.47, 95% CI = 0.99, 2.18, p = 0.06) and of past month tobacco use in the long-term (OR = 1.46, 95% CI = 1.12, 1.90; p = 0.005). Overall, the quality of evidence was moderate. Parent-based interventions targeting multiple risk behaviors improved adolescent MVPA, and reduced screen time discretionary food intake. Further research is needed to address sleep problems and increase intervention efficacy, particularly for alcohol and tobacco use.

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.

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.

Sustained physical activity in peripheral artery disease: Associations with disease severity, functional performance, health-related quality of life, and subsequent serious adverse events in the LITE randomized clinical trial.

This study investigated cross-sectional associations of peripheral artery disease (PAD) severity (defined by the ankle-brachial index (ABI)) and amounts of daily sustained physical activity (PA) (defined as > 100 activity counts per minute lasting 5 consecutive minutes or more). This study also investigated associations of amounts of daily sustained PA with 6-minute walk (6MW) distance and the Short Form-36 physical functioning domain (SF-36 PF) score in cross-sectional analyses and with serious adverse events (SAEs) in longitudinal analyses of people with PAD. PA was measured continuously for 10 days using a tri-axial accelerometer at baseline in 277 participants with PAD randomized to the LITE clinical trial. In regression analyses, each 0.15 lower ABI value was associated with a 5.67% decrease in the number of daily bouts of sustained PA (95% CI: 3.85-6.54; p < 0.001). Every additional bout of sustained PA per day was associated with a 4.56-meter greater 6MW distance (95% CI: 2.67-6.46; p < 0.0001), and a 0.81-point improvement in SF-36 PF score (95% CI: 0.34-1.28; p < 0.001). Participants with values of daily bouts of sustained PA below the median had higher rates of SAEs during follow-up, compared to participants above the median (41% vs 24%; p = 0.002). In conclusion, among participants with PAD, lower ABI values were associated with fewer bouts of daily sustained PA. A greater number of bouts of daily sustained PA were associated with better 6MW performance and SF-36 PF score, and, in longitudinal analyses, lower rates of SAEs. Clinicaltrials.gov ID: NCT02538900.

Effect of Low-Intensity vs High-Intensity Home-Based Walking Exercise on Walk Distance in Patients With Peripheral Artery Disease: The LITE Randomized Clinical Trial.

Importance: Supervised high-intensity walking exercise that induces ischemic leg symptoms is the first-line therapy for people with lower-extremity peripheral artery disease (PAD), but adherence is poor. Objective: To determine whether low-intensity home-based walking exercise at a comfortable pace significantly improves walking ability in people with PAD vs high-intensity home-based walking exercise that induces ischemic leg symptoms and vs a nonexercise control. Design, Setting, and Participants: Multicenter randomized clinical trial conducted at 4 US centers and including 305 participants. Enrollment occurred between September 25, 2015, and December 11, 2019; final follow-up was October 7, 2020. Interventions: Participants with PAD were randomized to low-intensity walking exercise (n = 116), high-intensity walking exercise (n = 124), or nonexercise control (n = 65) for 12 months. Both exercise groups were asked to walk for exercise in an unsupervised setting 5 times per week for up to 50 minutes per session wearing an accelerometer to document exercise intensity and time. The low-intensity group walked at a pace without ischemic leg symptoms. The high-intensity group walked at a pace eliciting moderate to severe ischemic leg symptoms. Accelerometer data were viewable to a coach who telephoned participants weekly for 12 months and helped them adhere to their prescribed exercise. The nonexercise control group received weekly educational telephone calls for 12 months. Main Outcomes and Measures: The primary outcome was mean change in 6-minute walk distance at 12 months (minimum clinically important difference, 8-20 m). Results: Among 305 randomized patients (mean age, 69.3 [SD, 9.5] years, 146 [47.9%] women, 181 [59.3%] Black patients), 250 (82%) completed 12-month follow-up. The 6-minute walk distance changed from 332.1 m at baseline to 327.5 m at 12-month follow-up in the low-intensity exercise group (within-group mean change, -6.4 m [95% CI, -21.5 to 8.8 m]; P = .34) and from 338.1 m to 371.2 m in the high-intensity exercise group (within-group mean change, 34.5 m [95% CI, 20.1 to 48.9 m]; P < .001) and the mean change for the between-group comparison was -40.9 m (97.5% CI, -61.7 to -20.0 m; P < .001). The 6-minute walk distance changed from 328.1 m at baseline to 317.5 m at 12-month follow-up in the nonexercise control group (within-group mean change, -15.1 m [95% CI, -35.8 to 5.7 m]; P = .10), which was not significantly different from the change in the low-intensity exercise group (between-group mean change, 8.7 m [97.5% CI, -17.0 to 34.4 m]; P = .44). Of 184 serious adverse events, the event rate per participant was 0.64 in the low-intensity group, 0.65 in the high-intensity group, and 0.46 in the nonexercise control group. One serious adverse event in each exercise group was related to study participation. Conclusions and Relevance: Among patients with PAD, low-intensity home-based exercise was significantly less effective than high-intensity home-based exercise and was not significantly different from the nonexercise control for improving 6-minute walk distance. These results do not support the use of low-intensity home-based walking exercise for improving objectively measured walking performance in patients with PAD. Trial Registration: ClinicalTrials.gov Identifier: NCT02538900.

Melanoma Skin Self-Examination Education During Mammography: Health Burden of Women Impairs Implementation.

Identification of barriers to seeking health care for a concerning mole found during skin self-examination (SSE) by women educated during screening mammography. In this sequential mixed methods research, interviews with women who found a concerning mole and did not have health-care follow-up were analyzed and a survey was created. One year after SSE education, barriers to having health care for a self-identified concerning mole were assessed. The electronic medical records for all participants, who received education, were reviewed to ascertain who received health care related to a concerning mole or a screening mammogram. Among the 280 women who performed SSE, 85 found a concerning mole. Nine months later 51 women did not receive health care for the mole. Barriers were the burden of other medical concerns, fear of what the doctor will find, feeling like nothing is wrong, and being too busy. A positive screening mammogram (Fisher's two-sided exact test, p < 0.001) and a history of indoor tanning (Fisher's two-sided exact test, p = 0.011) were significantly associated with lack of follow-up for a concerning mole. Targeted melanoma self-identification with SSE relies upon participants initiating performance and seeking medical care for a concerning mole. The burden of a positive screening mammogram reported to women at about the same time as they identified the concerning mole was associated with failing to seek care for their concerning mole. Reminders to check moles for change 4 months after identifying a concerning mole may benefit women. Clinicaltrials.gov NCT03512457.

Physicians' perspectives on medication adherence and health promotion among cancer survivors.

BACKGROUND: Cancer survivors face an increased risk of cardiovascular events compared with the general population. Adopting a healthy lifestyle may reduce these risks, and guidelines encourage health-promotion counseling for cancer survivors, but the extent of physician adherence is unclear. METHODS: This mixed-method study surveyed 91 physicians, including 30 primary care physicians (PCPs), 30 oncologists, and 31 specialists (urologists, dermatologists, and gynecologists). Interviews also were conducted with 12 oncologists. RESULTS: Most PCPs (90%) reported recommending health promotion (eg, weight loss, smoking cessation) to at least some cancer survivors, whereas few oncologists (26.7%) and specialists (9.7%) said they ever did so (P < .001). Although most physicians believed that at least 50% of cancer survivors would be adherent to medication regimens to prevent cancer recurrence, they also believed that, if patients were trying to lose weight, they would not remain medication-adherent. In interviews, oncologists expressed fear that providing health-promotion advice would distress or overwhelm patients. Additional health-promotion barriers identified by thematic analysis included: identifying cancer as oncologists' focal concern, time pressure, insufficient behavior change training, and care coordination challenges. Facilitators included perceiving a patient benefit and having health-promotion resources integrated into the cancer care system. CONCLUSIONS: Physicians often do not have the time, expertise, or resources to address health promotion with cancer survivors. Research is needed to evaluate whether health-promotion efforts compromise medical regimen adherence, as physicians' responses suggest.

Six Month Abstinence Heterogeneity in the Best Quit Study.

BACKGROUND: Understanding the characteristics of smokers who are successful in quitting may help to increase smoking cessation rates. PURPOSE: To examine heterogeneity in cessation outcome at 6 months following smoking cessation behavioral counseling with or without weight management counseling. METHODS: 2,540 smokers were recruited from a large quitline provider and then randomized to receive proactive smoking cessation behavioral counseling without or with two versions of weight management counseling. A Classification and Regression Tree (CART) analysis was conducted to identify the individual pretreatment and treatment characteristics of groups of smokers with different quitting success (as measured by point prevalence of self-reported smoking of any amount at 6 months). RESULTS: CART analysis identified 10 subgroups ranging from 25.5% to 70.2% abstinent. The splits in the CART tree involved: the total number of counseling and control calls received, whether a smoking cessation pharmacotherapy was used, and baseline measures of cigarettes per day, confidence in quitting, expectation that the study would help the participant quit smoking, the motivation to quit, exercise minutes per week, anxiety, and lack of interest or pleasure in doing things. Costs per quitter ranged from a low of $US270 to a high of $US630. Specific treatment recommendations are made for each group. CONCLUSIONS: These results indicate the presence of a substantial variation in abstinence following treatment, and that the total extent of contact via counseling calls of any type and baseline characteristics, rather than assigned treatment, were most important to subgroup membership and abstinence. Tailored treatments to subgroups who are at high risk for smoking following a quit attempt could increase successful smoking cessation.

Perceptions of Health Behaviors and Mobile Health Applications in an Academically Elite College Population to Inform a Targeted Health Promotion Program.

BACKGROUND: College is a critical developmental time when many emerging adults engage in unhealthy behaviors (i.e., lack of exercise, poor diet, smoking) and consequently experience an increased risk for a decline in cardiovascular health. Understanding the beliefs and opinions of the target population is important to develop effective health promotion interventions. The goal of this study was to understand opinions regarding health and health-related mobile technology of college students at an academically elite Midwestern university in order to inform a mobile health promotion intervention following the integrated behavioral model framework. METHOD: Eighteen college students between the ages of 18 and 22 participated in one of four focus groups, where they discussed perceptions of health behaviors, technology use, and their college environment. Data were analyzed using inductive thematic analysis as well as consensus and conformity analysis. RESULTS: Students reported prioritizing academic success over health and believed in a cultural norm within the university that unhealthy behavioral practices lead to increased academic success. Other identified barriers to achieving good health were (a) low self-efficacy for engaging in healthy behaviors when presented with conflicting academic opportunities and (b) low estimation of the importance of engaging in health behaviors. Regarding mobile health applications (apps), students reported preferring apps that were visually attractive, personalized to each user, and that did not involve competing against other users. CONCLUSION: These results have implications for the development of mobile health promotion interventions for college students, as they highlight facilitators and barriers to health behavior change in an academically elite student body.

Counting Bites With Bits: Expert Workshop Addressing Calorie and Macronutrient Intake Monitoring.

BACKGROUND: Conventional diet assessment approaches such as the 24-hour self-reported recall are burdensome, suffer from recall bias, and are inaccurate in estimating energy intake. Wearable sensor technology, coupled with advanced algorithms, is increasingly showing promise in its ability to capture behaviors that provide useful information for estimating calorie and macronutrient intake. OBJECTIVE: This paper aimed to summarize current technological approaches to monitoring energy intake on the basis of expert opinion from a workshop panel and to make recommendations to advance technology and algorithms to improve estimation of energy expenditure. METHODS: A 1-day invitational workshop sponsored by the National Science Foundation was held at Northwestern University. A total of 30 participants, including population health researchers, engineers, and intervention developers, from 6 universities and the National Institutes of Health participated in a panel discussing the state of evidence with regard to monitoring calorie intake and eating behaviors. RESULTS: Calorie monitoring using technological approaches can be characterized into 3 domains: (1) image-based sensing (eg, wearable and smartphone-based cameras combined with machine learning algorithms); (2) eating action unit (EAU) sensors (eg, to measure feeding gesture and chewing rate); and (3) biochemical measures (eg, serum and plasma metabolite concentrations). We discussed how each domain functions, provided examples of promising solutions, and highlighted potential challenges and opportunities in each domain. Image-based sensor research requires improved ground truth (context and known information about the foods), accurate food image segmentation and recognition algorithms, and reliable methods of estimating portion size. EAU-based domain research is limited by the understanding of when their systems (device and inference algorithm) succeed and fail, need for privacy-protecting methods of capturing ground truth, and uncertainty in food categorization. Although an exciting novel technology, the challenges of biochemical sensing range from a lack of adaptability to environmental effects (eg, temperature change) and mechanical impact, instability of wearable sensor performance over time, and single-use design. CONCLUSIONS: Conventional approaches to calorie monitoring rely predominantly on self-reports. These approaches can gain contextual information from image-based and EAU-based domains that can map automatically captured food images to a food database and detect proxies that correlate with food volume and caloric intake. Although the continued development of advanced machine learning techniques will advance the accuracy of such wearables, biochemical sensing provides an electrochemical analysis of sweat using soft bioelectronics on human skin, enabling noninvasive measures of chemical compounds that provide insight into the digestive and endocrine systems. Future computing-based researchers should focus on reducing the burden of wearable sensors, aligning data across multiple devices, automating methods of data annotation, increasing rigor in studying system acceptability, increasing battery lifetime, and rigorously testing validity of the measure. Such research requires moving promising technological solutions from the controlled laboratory setting to the field.

Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support.

Background: The just-in-time adaptive intervention (JITAI) is an intervention design aiming to provide the right type/amount of support, at the right time, by adapting to an individual's changing internal and contextual state. The availability of increasingly powerful mobile and sensing technologies underpins the use of JITAIs to support health behavior, as in such a setting an individual's state can change rapidly, unexpectedly, and in his/her natural environment. Purpose: Despite the increasing use and appeal of JITAIs, a major gap exists between the growing technological capabilities for delivering JITAIs and research on the development and evaluation of these interventions. Many JITAIs have been developed with minimal use of empirical evidence, theory, or accepted treatment guidelines. Here, we take an essential first step towards bridging this gap. Methods: Building on health behavior theories and the extant literature on JITAIs, we clarify the scientific motivation for JITAIs, define their fundamental components, and highlight design principles related to these components. Examples of JITAIs from various domains of health behavior research are used for illustration. Conclusions: As we enter a new era of technological capacity for delivering JITAIs, it is critical that researchers develop sophisticated and nuanced health behavior theories capable of guiding the construction of such interventions. Particular attention has to be given to better understanding the implications of providing timely and ecologically sound support for intervention adherence and retention.