Objectives To evaluate the compliance with prospective registration and inclusion of the trial registration number (TRN) in published randomised controlled trials (RCTs), and to analyse the rationale behind, and detect selective registration bias in, retrospective trial registration. Design Cross sectional analysis. Data sources PubMed, the 17 World Health Organization’s trial registries, University of Toronto library, International Committee of Medical Journal Editors (ICMJE) list of member journals, and the InCites Journal Citation Reports. Study selection criteria RCTs registered in any WHO trial registry and published in any PubMed indexed journal in 2018. Results This study included 10 500 manuscripts published in 2105 journals. Overall, 71.2% (7473/10500) reported the TRN and 41.7% (3013/7218) complied with prospective trial registration. The univariable and multivariable analyses reported significant relations (P<0.05) between reporting the TRN and the impact factor and ICMJE membership of the publishing journal. A significant relation (P<0.05) was also observed between prospective trial registration and the registry, region, condition, funding, trial size, interval between paper registration and submission dates, impact factor, and ICMJE membership of the publishing journal. A manuscript published in an ICMJE member journal was 5.8 times more likely to include the TRN (odds ratio 5.8, 95% confidence interval 4.0 to 8.2), and a published trial was 1.8 times more likely to be registered prospectively (1.8, 1.5 to 2.2) when published in an ICMJE member journal compared with other journals. This study detected a new form of bias, selective registration bias, with a higher proportion (85.2% (616/723)) of trials registered retrospectively within a year of submission for publication. Higher rates of retrospective registrations were observed within the first three to eight weeks after enrolment of study participants. Within the 286 RCTs registered retrospectively and published in an ICMJE member journal, only 2.8% (8/286) of the authors included a statement justifying the delayed registration. Reasons included lack of awareness, error of omission, and the registration process taking longer than anticipated. Conclusions This study found a high compliance in reporting of the TRN for trial papers published in ICMJE member journals, but prospective trial registration was low.

BackgroundThe high prevalence rate of asthma represents a major societal burden. Advancements in information technology continue to affect the delivery of patient care in all areas of medicine. Internet-based solutions, social media, and mobile technology could address some of the problems associated with increasing asthma prevalence.ObjectiveThis review evaluates Internet-based asthma interventions that were published between 2004 and October 2014 with respect to the use of behavioral change theoretical frameworks, applied clinical guidelines, and assessment tools.MethodsThe search term (Asthma AND [Online or Internet or Mobile or Application or eHealth or App]) was applied to six bibliographic databases (Ovid MEDLINE, PubMed, BioMed Central, ProQuest Computing, Web of Knowledge, and ACM Digital Library) including only English-language articles published between 2004 and October 2014. In total, 3932 articles matched the priori search terms and were reviewed by the primary reviewer based on their titles, index terms, and abstracts. The matching articles were then screened by the primary reviewer for inclusion or exclusion based on their abstract, study type, and intervention objectives with respect to the full set of priori inclusion and exclusion criteria; 331 duplicates were identified and removed. A total of 85 articles were included for in-depth review and the remaining 3516 articles were excluded. The primary and secondary reviewer independently reviewed the complete content of the 85 included articles to identify the applied behavioral change theories, clinical guidelines, and assessment tools. Findings and any disagreement between reviewers were resolved by in-depth discussion and through a consolidation process for each of the included articles.ResultsThe reviewers identified 17 out of 85 interventions (20%) where at least one model, framework, and/or construct of a behavioral change theory were applied. The review identified six clinical guidelines that were applied across 30 of the 85 interventions (35%) as well as a total of 21 assessment tools that were applied across 32 of the 85 interventions (38%).ConclusionsThe findings of this literature review indicate that the majority of published Internet-based interventions do not use any documented behavioral change theory, clinical guidelines, and/or assessment tools to inform their design. Further, it was found that the application of clinical guidelines and assessment tools were more salient across the reviewed interventions. A consequence, as such, is that many Internet-based asthma interventions are designed in an ad hoc manner, without the use of any notable evidence-based theoretical frameworks, clinical guidelines, and/or assessment tools.

BackgroundClinical 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.ObjectiveThe 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.MethodsTo 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.ResultsIn 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.ConclusionsIn 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 ...

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.

Registration of clinical trials was introduced to mitigate the risk of publication and selective reporting bias in the realm of clinical research. The prevalence of publication and selective reporting bias in trial results has been evidenced through scientific research. This bias may compromise the ethical and methodological conduct in the design, implementation and dissemination of evidence-based healthcare interventions. Principal investigators of digital health trials may be overwhelmed with challenges that are unique to digital health research, such as the usability of the intervention under test, participant recruitment, and retention challenges that may contribute to non-publication rate and prospective trial registration. Our primary research objective was to examine the prevalence of prospective registration and publication rates in digital health trials. We included 417 trials that enrolled participants in 2012 and were registered in any of the seventeen WHO registries. The prospective registration and publication rates were at (38.4%) and (65.5%) respectively. We identified a statistically significant (P<.001) "Selective Registration Bias" with 95.7% of trials published within a year after registration, were registered retrospectively. We reported a statistically significant relationship (P=.003) between prospective registration and funding sources, with industry-funded trials having the lowest compliance with prospective registration at (14.3%). The lowest non-publication rates were in the Middle East (26.7%) and Europe (28%), and the highest were in Asia (56.5%) and the U.S. (42.5%). We found statistically significant differences (P<.001) between trial location and funding sources with the highest percentage of industry funded trials in Asia (17.3%) and the U.S. (3.3%).

Objectives We sought to examine the prevalence of prospective registration and publication rates in digital health trials. Materials and Methods We included 417 trials that enrolled participants in 2012 and were registered in any of the 17 WHO data provider registries. The evaluation of the prospective trial registration was based on the actual difference between the registration and enrollment dates. We identified existing publications through an automated PubMed search by every trial registration number as well as a pragmatic search in PubMed and Google based on extracted metadata from the trial registries. Results The prospective registration and publication rates were at (38.4%) and (65.5%), respectively. We identified a statistically significant ( p < 0.001) “Selective Registration Bias” with 95.7% of trials published within a year after registration, were registered retrospectively. We reported a statistically significant relationship ( p = 0.003) between prospective registration and funding sources, with industry-funded trials having the lowest compliance with prospective registration at (14.3%). The lowest non-publication rates were in the Middle East (26.7%) and Europe (28%), and the highest were in Asia (56.5%) and the U.S. (42.5%). We found statistically significant differences ( p < 0.001) between trial location and funding sources with the highest percentage of industry-funded trials in Asia (17.4%) and the U.S. (3.3%). Conclusion The adherence of investigators to the best practices of trial registration and result dissemination is still evolving in digital health trials. Further research is required to identify contributing factors and mitigation strategies to low compliance rate with trial publication and prospective registration in digital health trials.