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BACKGROUND Clinical trials are key to advancing evidence-based medical research. The medical research literature has identified the impact of publication bias in clinical trials. Selective publication for positive outcomes or nonpublication of negative results could misdirect subsequent research and result in literature reviews leaning toward positive outcomes. Digital health trials face specific challenges, including a high attrition rate, usability issues, and insufficient formative research. These challenges may contribute to nonpublication of the trial results. To our knowledge, no study has thus far reported the nonpublication rates of digital health trials. OBJECTIVE The primary research objective was to evaluate the nonpublication rate of digital health randomized clinical trials registered in ClinicalTrials.gov. Our secondary research objective was to determine whether industry funding contributes to nonpublication of digital health trials. METHODS To identify digital health trials, a list of 47 search terms was developed through an iterative process and applied to the “Title,” “Interventions,” and “Outcome Measures” fields of registered trials with completion dates between April 1, 2010, and April 1, 2013. The search was based on the full dataset exported from the ClinlicalTrials.gov database, with 265,657 trials entries downloaded on February 10, 2018, to allow publication of studies within 5 years of trial completion. We identified publications related to the results of the trials through a comprehensive approach that included an automated and manual publication-identification process. RESULTS In total, 6717 articles matched the a priori search terms, of which 803 trials matched our latest completion date criteria. After screening, 556 trials were included in this study. We found that 150 (27%) of all included trials remained unpublished 5 years after their completion date. In bivariate analyses, we observed statistically significant differences in trial characteristics between published and unpublished trials in terms of the intervention target condition, country, trial size, trial phases, recruitment, and prospective trial registration. In multivariate analyses, differences in trial characteristics between published and unpublished trials remained statistically significant for the intervention target condition, country, trial size, trial phases, and recruitment; the odds of publication for non-US–based trials were significant, and these trials were 3.3 (95% CI 1.845-5.964) times more likely to be published than US–based trials. We observed a trend of 1.5 times higher nonpublication rates for industry-funded trials. However, the trend was not statistically significant. CONCLUSIONS In the domain of digital health, 27% of registered clinical trials results are unpublished, which is lower than nonpublication rates in other fields. There are substantial differences in nonpublication rates between trials funded by industry and nonindustry sponsors. Further research is required to define the determinants and reasons for nonpublication and, more importantly, to articulate the impact and risk of publication bias in the field of digital health trials.

Background: Clinical trials are key to advancing evidence-based medical research. The medical research literature has identified the impact of publication bias in clinical trials. Selective publication for positive outcomes or nonpublication of negative results could misdirect subsequent research and result in literature reviews leaning toward positive outcomes. Digital health trials face specific challenges, including a high attrition rate, usability issues, and insufficient formative research. These challenges may contribute to nonpublication of the trial results. To our knowledge, no study has thus far reported the nonpublication rates of digital health trials. Objective: The primary research objective was to evaluate the nonpublication rate of digital health randomized clinical trials registered in ClinicalTrials.gov. Our secondary research objective was to determine whether industry funding contributes to nonpublication of digital health trials. Methods: To identify digital health trials, a list of 47 search terms was developed through an iterative process and applied to the “Title,” “Interventions,” and “Outcome Measures” fields of registered trials with completion dates between April 1, 2010, and April 1, 2013. The search was based on the full dataset exported from the ClinlicalTrials.gov database, with 265,657 trials entries downloaded on February 10, 2018, to allow publication of studies within 5 years of trial completion. We identified publications related to the results of the trials through a comprehensive approach that included an automated and manual publication-identification process. Results: In total, 6717 articles matched the a priori search terms, of which 803 trials matched our latest completion date criteria. After screening, 556 trials were included in this study. We found that 150 (27%) of all included trials remained unpublished 5 years after their completion date. In bivariate analyses, we observed statistically significant differences in trial characteristics between published and unpublished trials in terms of the intervention target condition, country, trial size, trial phases, recruitment, and prospective trial registration. In multivariate analyses, differences in trial characteristics between published and unpublished trials remained statistically significant for the intervention target condition, country, trial size, trial phases, and recruitment; the odds of publication for non-US–based trials were significant, and these trials were 3.3 (95% CI 1.845-5.964) times more likely to be published than US–based trials. We observed a trend of 1.5 times higher nonpublication rates for industry-funded trials. However, the trend was not statistically significant. Conclusions: In the domain of digital health, 27% of registered clinical trials results are unpublished, which is lower than nonpublication rates in other fields. There are substantial differences in nonpublication rates between trials funded by industry and nonindustry sponsors. Further research is required to define the determinants and reasons for nonpublication and, more importantly, to articulate the impact and risk of publication bias in the field of digital health trials.

Background: Elevated blood pressure is one of the main risk factors for death globally. Behavioral neurocardiac training (BNT) is a complementary approach to blood pressure and stress management that is intended to exercise the autonomic reflexes, improve stress recovery, and lower blood pressure. BNT involves cognitive-behavioral therapy with a paced breathing technique and heart rate variability biofeedback. BNT is limited to in-clinic delivery and faces an accessibility barrier because of the need for clinical oversight and the use of complex monitoring tools. Objective: The objective of this project was to design, develop, and evaluate a wearable electrocardiographic (ECG) sensor system for the delivery of BNT in a home setting. Methods: The wearable sensor system, Beat , consists of an ECG sensor and a mobile app. It was developed iteratively using the principles of test-driven Agile development and user-centered design. A usability study was conducted at Toronto General Hospital to evaluate feasibility and user experience and identify areas of improvement. Results: The Beat sensor was designed as a modular patch to be worn on the user’s chest and uses standard ECG electrodes. It streams a single-lead ECG wirelessly to a mobile phone using Bluetooth Low Energy. The use of small, low-power electronics, a low device profile, and a tapered enclosure allowed for a device that can be unobtrusively worn under clothing. The sensor was designed to operate with a mobile app that guides users through the BNT exercises to train them to a slow-paced breathing technique for stress recovery. The BNT app uses the ECG captured by the sensor to provide heart rate variability biofeedback in the form of a real-time heart rate waveform to complement and reinforce the impact of the training. Usability testing (n=6) indicated that the overall response to the design and user experience of the system was perceived positively. All participants indicated that the system had a positive effect on stress management and that they would use it at home. Areas of improvement were identified, which focused primarily on the delivery of training and education on BNT through the app. Conclusions: The outcome of this project was a wearable sensor system to deliver BNT at home. The system has the potential to offer a complementary approach to blood pressure and stress management at home and reduce current accessibility barriers. [JMIR Mhealth Uhealth 2016;4(2):e45]

Background: Practice guidelines advocate combining pharmacotherapy with lifestyle counseling for patients with hypertension. To allow for appropriate tailoring of interventions to meet individual patient needs, a comprehensive understanding of baseline patient characteristics is essential. However, few studies have empirically assessed behavioral profiles of hypertensive patients in Web-based lifestyle counseling programs. Objective: The objectives of this study were to (1) specify baseline psychobehavioral profiles of patients with hypertension who were enrolled in a Web-based lifestyle counseling trial, and (2) examine mean differences among the identified profile groups in demographics, psychological distress, self-reported self-care behaviors, physiological outcomes, and program engagement to determine prognostic implications. Methods: Participants (N=264; mean age 57.5 years; 154/264, 58.3% female; 193/264, 73.1% white) were recruited into a longitudinal, double-blind, randomized controlled trial, designed to evaluate an online lifestyle intervention for hypertensive patients. A series of latent profile analyses identified psychobehavioral profiles, indicated by baseline measures of mood, motivation, and health behaviors. Mean differences between profile groups were then explored. Results: A 2-class solution provided the best model fit (the Bayesian information criterion (BIC) is 10,133.11; sample-size adjusted BIC is 10,006.54; Lo-Mendell-Rubin likelihood ratio test is 65.56, P=.001). The 2 profile groups were (1) adaptive adjustment, marked by low distress, high motivation, and somewhat satisfactory engagement in health behaviors and (2) affectively distressed, marked by clinically significant distress. At baseline, on average, affectively distressed patients had lower income, higher body mass index, and endorsed higher stress compared with their adaptive adjustment counterparts. At 12-months post intervention, treatment effects were sustained for systolic blood pressure and Framingham risk index in the adaptive adjustment group, and those in the adaptive adjustment group were 2.4 times more likely to complete the 12-month intervention study, compared with their affectively distressed counterparts. Conclusions: Interventions for patients who are adaptively adjusted may differ in focus from those designed for the affectively distressed patients. As such, this study underscores the importance of identifying psychobehavioral profiles, as they allow for evidence-based tailoring of lifestyle counseling programs for patients with hypertension. Trial Registration: ClinicalTrials.gov NCT01541540; https://clinicaltrials.gov/ct2/show/NCT01541540 (Archived by WebCite at http://www.webcitation.org/6yzZYZcWF)

BackgroundChronic heart failure (CHF) is a public health priority. Its age-standardized prevalence has increased over the past decade. A major challenge for the management of CHF is to promote long-term adherence to self-care behaviors without overtaxing available health care resources. Counseling by multidisciplinary health care teams helps to improve adherence to self-care behaviors and to reduce the rate of death and hospitalization. In the absence of intervention, adherence to self-care is below recommended standards. ObjectiveThis trial aims to establish and evaluate a Canadian e-platform that will provide a core, standardized protocol of behavioral counseling and education to facilitate long-term adherence to self-care among patients with CHF. MethodsCanadian e-Platform to Promote Behavioral Self-Management in Chronic Heart Failure (CHF-CePPORT) is a multi-site, double blind, randomized controlled trial with a 2 parallel-group (e-Counseling + Usual Care vs e-Info Control + Usual Care) by 3 assessments (baseline, 4-, and 12-month) design. We will identify subjects with New York Heart Association Class II or III systolic heart failure from collaborating CHF clinics and then recruit them (n=278) by phone. Subjects will be randomized in blocks within each site (Toronto, Montreal, and Vancouver). The primary outcome will be improved quality of life, defined as an increased number of subjects with an improvement of ≥5 points on the summary score of the Kansas City Cardiomyopathy Questionnaire. We will also assess the following secondary outcomes: (1) diet habits, depression, anxiety, smoking history, stress level, and readiness for change using self-report questionnaires, (2) physical activity level, current smoking status, and vagal-heart rate modulation by physiological tests, and (3) exercise capacity, prognostic indicators of cardiovascular functioning, and medication adherence through medical chart review. The primary outcome will be analyzed using generalized estimation equations with repeated measures on an intention-to-treat basis. Secondary outcomes will be analyzed using repeated-measures linear mixed models with a random effects intercept. All significant main effects or interactions in the statistical models will be followed up with post hoc contrasts using a Bonferroni correction with a 2-sided statistical significance criterion of P

BackgroundInternet-based health programs have been shown to be effective in reducing risk for cardiovascular disease. However, their rates of enrollment and engagement remain low. It is currently unclear whether rewards from established loyalty programs can serve as a conditioned stimulus to improve the use of a freely available Internet-based program. ObjectiveThe objectives of the study were to (1) examine enrollment rates and levels of engagement with the My Health eSupport program between a Conditioned Reward group and a Control group, and (2) investigate the influence of loyalty rewards and participant characteristics on levels of enrollment and program engagement. MethodsThe study sample (n=142,726) consisted of individuals who were offered enrollment in an Internet-based health intervention (My Health eSupport) after completing the Heart&Stroke Risk Assessment on the Heart and Stroke Foundation website. My Health eSupport programs provided encouragement and tips for lifestyle change. This is a free, self-guided, fully automated program that proactively delivers tailored email messages at 2-week intervals based on the participant’s stage of motivational “readiness” and priority for lifestyle change. Participants in the Conditioned Reward group were offered a single exposure of 20 loyalty reward points from the Air Miles loyalty program for completing the Heart&Stroke Risk Assessment (10 reward points) and enrolling in the Internet-based program (10 reward points). Meanwhile, no rewards were given to the Control group participants. All data were collected between February 1, 2011 and February 10, 2012. ResultsIn total, 51.38% (73,327/142,726) of individuals in the Conditioned Reward group and 48.62% (69,399/142,726) of individuals in the Control group completed the Heart&Stroke Risk Assessment. Subsequently, significantly more individuals from the Conditioned Reward group (52.96%, 38,835/73,327) enrolled in the My Health eSupport program than Controls (4.07%, 2826/69,399). Regression analyses indicated that individuals were 27.9 times (95% CI 26.4-29.4; P