Clinical Trials for Digital Health Interventions: An Umbrella Review of Study Independence and the Developer Effect

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Unlike mandates and reporting guidelines established for traditional pharmaceutical trials, it remains unclear to what extent DHI trials vary in the degree of developer involvement in trial conduct or sponsorship, and whether such involvement should raise concerns about risk of bias in estimated trial effectiveness. This umbrella review aimed to narratively map and summarize published systematic reviews of randomized controlled trials (RCTs) evaluating DHIs, with a focus on quantifying developer involvement and its association with significance of reported trial outcomes. Systematic reviews published between 2013 and 2025 were identified from four databases, targeting interventions in nutrition, maternal health, mental health, and sleep. Data were extracted on trial characteristics, developer involvement, preregistration, participant population, DHI cost and public availability, and outcome significance. Across 229 trials from 29 systematic reviews, 73% of trials were conducted with direct developer involvement, whereas 27% were independent. Developer-involved trials were more likely to be preregistered than independent trials (OR = 2.47, p = 0.004). When weighted by sample size, developer-involved trials had higher odds of reporting statistically significant results than independent trials within each category (OR=1.23, 95% CI 1.16–1.31, p < 0.001). This review highlights the prevalence of developer involvement in DHI trials and its potential influence on reported outcomes, underscoring the need for greater transparency in publications, independent efficacy trials, and regulatory oversight for digital health technologies. Future research should examine how varying forms of developer engagement affect trial design, reporting, and effect sizes, to ensure credible, safe, and effective evidence generation in digital health. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Figures Figure 1 Figure 2 Figure 3 Introduction Technological advancements have enabled digital health technologies to expand across various healthcare domains, including prevention, health promotion, and disease management. According to the United States Food and Drugs Administration “[d]igital health technologies use computing platforms, connectivity, software, and sensors for health care and related uses 1 ”. Digital health interventions (DHIs) based on digital tools and technologies offer the potential to facilitate behavior change, increase healthcare access, and personalize health services 1 . Like all interventions, DHIs carry risks, such as adverse health outcomes and inaccurate clinical recommendations, particularly if based on biased algorithms and outdated data 2 . These risks underscore the importance of rigorous and independent evaluation of DHIs, especially as many are developed and tested by commercial entities with vested interests. Ideally, DHIs are evaluated through well-designed clinical trials in which both benefits and unintended consequences are systematically assessed. Yet, unlike traditional pharmaceutical and medical device trials, the extent to which conflicts of interest shape the design, conduct, and interpretation of DHI efficacy trials remains poorly examined. This gap is notable given longstanding evidence from other areas of healthcare showing that financial and institutional interests can meaningfully influence research outcomes. Pharmaceutical and medical device trials are conducted within regulatory frameworks that mandate independent trial design, ethical review, prespecified analytic protocols, and transparent reporting to minimize bias and protect patient safety. The U.S. Food and Drug Administration mandates independent study designs and oversight to minimize bias in drug trials 3 . Historical evidence shows that industry-sponsored clinical trials are more likely to report favorable outcomes compared with independently conducted trials, a phenomenon known as funding bias or sponsorship bias, meaning that studies directly conducted by pharmaceutical companies tend to report more favorable outcomes 4 . Industry-sponsored trials often involved company personnel in data access, publication approval, or authorship 5 . As a result, pharmaceutical trials are now typically conducted independently of the sponsor and must undergo rigorous ethical review with clear description of sponsor involvement. In contrast, many digital health apps are developed by for-profit companies that conduct or sponsor clinical trials of their own products. The boundaries between biomedical, behavioral, and product research have become increasingly blurred, as technology companies have historically been exempt from the regulatory standards designed to protect research participants 6 . For example, a systematic review of randomized controlled trials (RCTs) evaluating the popular mental health apps Headspace and Calm showed that nearly half of the Headspace trials reported a conflict of interest involving the app companies, and preregistration was uncommon 7 . Given the stringent requirements for all clinical trials to ensure patient safety and transparency, there is a critical need to investigate the independence of DHI clinical trials and the potential influence of their developers. While DHIs often combine both digital and non-digital components, evaluating their standalone effects is as essential as it is for pharmaceutical products. Although reporting guidelines exist for clinical trials, including extensions for specialized fields, there remains vagueness and under-adoption of standards specific to digital health. Traditional reporting frameworks such as the CONSORT Statement set minimum requirements for transparent reporting of randomized trials, aiming to facilitate critical appraisal and interpretability 8 . Recognizing unique aspects of digital health research, extensions such as CONSORT-EHEALTH have been developed to improve reporting of web-based and mobile health intervention trials by encouraging detailed descriptions of intervention components, delivery mechanisms, and outcomes 9 . However, adherence to these guidelines varied in practice, and they do not fully address transparency around commercial involvement, data infrastructures, software updates, or algorithmic decision rules that can influence study outcomes. While some interventions may be regulated as medical devices, regulatory approaches differ substantially across jurisdictions, requiring developers to engage with multiple, evolving governance systems. the National Institute for Health and Care Excellence (NICE) recommends that digital health products be regulated similarly to medical devices 10 , however, unlike for pharmaceuticals, a regulatory framework mandating standardized clinical trials for DHIs remains absent globally. Bodies such as the United Kingdom Medicines and Healthcare Products Regulatory Agency require manufacturers to self-certify the risk level of their products before market release, prompting cross-examination of evidence for digital mental health technologies 11 . In the United States, the Federal Trade Commission mandates transparency regarding any breaches of personal health information when managing digital health records 12 . These guidance provide important reference points for regulating digital health technologies, targeting transparency, evidence synthesis, and data protection 13 . However, current frameworks do not mandate that evidence supporting the effectiveness of digital health products must be generated independently of developer involvement or that potential developer involvement be disclosed, leaving potential sources of bias unaddressed. Additional frameworks such as the mHealth Evidence Reporting and Assessment (mERA) checklist 14 and the iCHECK-DH guidelines 15 aim to improve reporting of evidence and implementation processes in digital health, but uptake remains uneven and specific requirements for detailing stakeholder roles and conflicts of interest are still developing. This lack of consolidated and consistently applied reporting standards contributes to a landscape in which critical details about trial conduct, including funding sources, analytic independence, and developer involvement, are often incompletely reported or interpreted inconsistently across studies. Against this background, this review systematically examined the developer effect, defined as the influence of vested commercial or institutional interests on trial outcomes, and the independence of trial design and analyses from DHI stakeholders. Specifically, we quantify potential conflicts of interest by assessing the proportion of DHI trials that are conducted independently by third-party researchers compared with those involving direct developer funding, sponsorship, co-authorship, or operational roles. For this review, we adopted the National Institute for Health and Care Excellence (NICE) definition, which classifies DHIs based on their intended purpose 10 . DHIs are digital health technologies intended to be used alongside other treatments and have measurable user benefits on health 16 . This includes preventive behavior change, self-management, active monitoring, diagnosis and treatment 17 . These DHIs are subject to strict evidence requirements given a possible direct impact on health outcomes. Because DHIs may directly influence health outcomes and clinical decision-making, they are subject to more stringent evidence and evaluation requirements than digital tools designed solely for information provision or communication. Typically, DHIs incorporate automated processes, human–computer interaction, personalization or adaptability, and mechanisms to promote user engagement 18 . Digital tools limited to unidirectional health education, basic provider–patient communication, or static intervention delivery (e.g., non-adaptive content or simple SMS reminders) are therefore excluded from this review. This study was conducted as an umbrella-style narrative review with structured evidence mapping of published systematic reviews of RCTs evaluating DHIs. While we predefined the eligibility criteria, screened the studies screening and extracted the data in duplicate, the review was not intended to be a fully comprehensive systematic umbrella review due to the breadth of the field. We strategically selected fields with long established digital tools, including mental health, sleep, maternal health, and nutrition. This approach allowed focused coverage of DHIs commonly available on the market while keeping the scope tractable. Methodological reporting follows PRISMA-2020 principles where applicable, while recognizing the exploratory nature of the synthesis. The primary objective was to quantify developer involvement and conflicts of interest in DHI trials, rather than to estimate pooled intervention effects. By systematically characterizing conflicts of interest and stakeholder involvement, this review aims to inform the development of reporting standards and governance frameworks that better protect scientific credibility and public trust in digital health evidence. Methods Eligibility criteria We included systematic reviews of RCTs published between January 1, 2013, and January 1, 2023, with an updated search covering January 1, 2023 to January 1, 2025. Articles were selected for inclusion if they met the following Population, Intervention, Comparator, Outcome, Study design (PICOS) criteria: (1) Population: Individuals of any age, ethnicity, or health status in any geographic or care setting; (2) Intervention: DHIs defined according to NICE, including interventions for preventive behavior change, self-management, active monitoring, diagnosis, and treatment. Reviews were excluded if DHIs relied primarily on human coaching or involved only passive, non-interactive monitoring devices (e.g., standalone wearable trackers without digital intervention components); (3) Comparator: Paper-based materials, text-messaging communications, or interpersonal communication modes (face-to-face or telephone), standard care, wait-list control, or no intervention; (4) Outcome: Any health-related or behavioral outcome in the fields of nutrition, maternal health, and mental health; (5) Studies design: Systematic reviews, with or without meta-analysis, of randomized controlled trials. We excluded narrative reviews, scoping reviews without systematic methods, conference abstracts, protocols, and reviews not restricted to RCTs. Information sources and search strategy The initial search was conducted in PubMed, Scopus, Web of Science, and Google Scholar using predefined keyword combinations relating to health domains (“sleep,” “nutrition,” “mental health,” “maternal health”) and digital technologies (“digital,” “app,” “mobile,” “web-based,” “technology”) in combination with “systematic review” or “meta-analysis.” Reference lists of included reviews were manually screened to identify additional eligible articles. Because the objective was to map conflicts of interest rather than to estimate intervention effectiveness comprehensively, the search strategy prioritized breadth across domains rather than exhaustive retrieval within a single clinical condition. Study selection and extraction Results were manually reviewed by one of the authors to determine if they met inclusion criteria based on titles and abstracts. The second reviewer retrieved full texts to confirm potentially eligible articles. A PRISMA flow diagram (Fig. 1 ) documents the screening and selection process. Data extraction was conducted in duplicate. The full text of each article was independently reviewed and extracted by two researchers (HZ and SK). In the event of discrepancies, a final evaluation was carried out by the senior author and reached consensus. For each systematic review, we extracted title of the review, citation details (e.g., author list, journal, year of publication), country, PICOS elements, number of included trials, and meta-analysis method (where applicable). The methodological quality of included systematic reviews was assessed using the updated 16-item AMSTAR 2 (A MeaSurement Tool to Assess systematic Reviews) instrument. Each review was assessed by one reviewer. AMSTAR 2 ratings were used descriptively and were not applied as exclusion criteria. For each individual trial included in each meta-analysis, we extracted: Basic information: first author, year of publication, country, study design, preregistration status, study population; DHI-related information: type of DHI, components of intervention (digital parts, non-digital parts, monitoring device), evidence of developer involvement (see definition below), cost and public availability of the DHI; Effectiveness of DHI: Effective estimates could not be meaningfully pooled across interventions. Given the narrative scope of this review and its primary focus on conflicts of interest and trial independence, effectiveness data were thus summarized using a single indicator per trial reflecting whether the reported results favored the intervention. When a primary outcome was explicitly prespecified, only the statistical significance and direction of effect for that primary outcome were considered. When no primary outcome was prespecified, overall trial effectiveness was determined based on whether the majority of reported outcomes demonstrated statistically significant effects in favor of the intervention. This approach was adopted to enable consistent classification of trial findings across heterogeneous interventions and outcome measures while minimizing selective emphasis on secondary outcomes. Key definition Definition of developer involvement in the DHI trial is categorized into three types: Independent: No evidence of developer funding, author employment, or developer involvement in trial conduct, analysis, or manuscript preparation. Developer-involved: The trial was sponsored by developer of the intervention; or at least one author employed by or formally affiliated with the developer; or developers directly involved in trial design, data analysis, or manuscript drafting; or trials primarily conducted by company employees; or the authors adapted a preexisting digital health intervention and evaluated it themselves. Unknown: Insufficient information available after full-text review and supplementary searches. Classification was based on funding statements, author affiliations, conflict of interest disclosures, trial registrations, and supplementary materials. Data synthesis Because of heterogeneity across domains, interventions, and outcomes, results were synthesized narratively and descriptively. No pooled effect estimates were calculated. We summarized proportions of trials in each developer involvement category, distribution of developer involvement across health domains, patterns of reporting transparency, and associations between developer involvement and trial outcomes. We examined whether significance of trial outcomes and trial preregistration differed by developer involvement using a chi-square test of independence. The chi-square test was conducted on a 2×2 contingency table. Statistical significance was assessed using a two-sided α level of 0.05. To further account for heterogeneity across trial categories (namely, nutrition, sleep, maternal health, and mental health) and differences in trial population size, we fit a generalized linear mixed-effects model with a binomial distribution and logit link. Trial category was included as a random intercept, and the trials were weighted by population size. Effect estimates are reported as odds ratios with 95% confidence intervals. All analyses were performed using R (version 4.4.2). Results Overview Out of 294 randomized controlled trials examined for eligibility in full texts, 229 trials from 29 systematic reviews met the inclusion criteria with digital health interventions meeting the NICE definition, including 18 trials for sleep outcomes, 19 trials for maternal health outcomes, 86 trials for mental health outcomes, and 106 trials for nutrition outcomes. In terms of publication year, Figure 2 demonstrated the distribution of trials included in this review. The majority of the trials were published between 2017 and 2020 suggesting a publishing and reporting lag of the systematic reviews. Nearly half of the trials (n = 97, 42%) were conducted in the United States, followed by trials in Australia (n = 30, 13%) and in United Kingdom (n = 20, 9%). Other countries and regions with 5 or more trials include Netherlands (n = 8), Sweden (n = 8), New Zealand (n = 7), China (n = 7), and Germany (n = 5). Quality appraisal Sixteen reviews stated that they had registered or otherwise published a review protocol. 19–34 All reviews searched at least two databases and provided their full search strategy, adequately described characteristics of included studies, and reported conflicts of interest. However, only two reviews 35,36 reported on the sources of funding for the studies included in the review, leaving potential bias from funding unexamined in most trials. Among 15 reviews that present a meta-analysis, four reviews did not discuss publication bias 28–30,37 . Figure 3 summarizes the methodological quality of all 29 systematic reviews that we assessed using the AMSTAR 2 tool. In this study, AMSTAR 2 assessments were used descriptively rather than as eligibility criteria. Nevertheless, the findings highlight a recurring methodological gap in the systematic review literature, particularly the limited examination of trial funding sources and the potential biases associated with developer involvement. Assessment of developer involvement To understand whether there is a difference in the significance of results for trials that are developer-involved or independent, we extracted and identified the developer involvement in trials. Of all 229 trials, 167 (72.9%) were not conducted independently, indicating that the DHI trial was either designed or sponsored by the DHI developer, or that the author was affiliated with the DHI developer, and the remaining 62 (27.1%) were conducted independent. Of the 167 developer-involved trials, 117 (70.1%) reported positive results, while of the 62 independent trials, 43 (69.4%) reported positive results. Table 1 shows the 2x2 table for Chi-square test. A chi-square test of independence showed no significant association between developer involvement and likelihood of reporting a statistically significant outcome (OR=1.03, χ²(1)=0.01, p =0.92). Table. 1 | Distribution of independent v.s. developer-involved trials and the significance of trial outcomes Significant Not significant Total All trials Independent 43 19 62 Developer-involved 117 50 167 Total 160 69 229 Nutrition Independent 18 10 28 Developer-involved 50 28 78 Total 68 38 106 Mental health Independent 24 9 33 Developer-involved 38 15 53 Total 62 24 86 Maternal Health Independent 0 0 0 Developer-involved 15 4 19 Total 15 4 19 Sleep Independent 1 0 1 Developer-involved 15 2 17 Total 16 2 18 To account for heterogeneity in trial sample size and clustering of trials within different categories of digital health interventions, weighted mixed-effects logistic regression model was conducted. When weighted by sample size in each trial, developer involvement was associated with a modest but statistically significant increase in the odds of reporting a significant outcome (OR = 1.09, 95% CI 1.03–1.15, p =0.004). This positive finding was driven primarily by larger trials, which contribute disproportionately to the population represented across trials. When further accounting for clustering by categories (nutrition, mental health, maternal health, and sleep) by adding the random intercept, the association was, if anything, even stronger (OR = 1.23, 95% CI 1.16–1.31, p <0.001), indicating that developer-involved trials were more likely to report significant results if categories are accounted for. Preregistration Of 167 developer-involved trials, 112 trials (67%) were preregistered clinical trials, and the remaining 55 trials did not mention any preregistration or protocol published before the trial. Of 62 independent trials, 28 trials (45%) were preregistered clinical trials. A chi-square test showed that developer-involved trials were significantly more likely to be preregistered than independent trials (OR = 2.47, χ²(1) = 8.23, p = 0.004). Setting In total, 79 trials (35%) were conducted in clinical or outpatient settings, 3 trials recruited mixed samples from both clinical settings and the general population; 147 trials (64%) were conducted in general population using recruitment methods such as flyers, websites, or social media. Developer-involved trials were more frequently conducted in clinical settings, with 63 of 167 developer-involved trials (38%) recruiting participants from clinical settings compared with 16 of 62 independent trials (26%). Most trials (149/229, 65%) were conducted among participants with clinical symptoms. A few trials (n=3) included mixed samples with and without clinical symptoms, and the remaining 77 trials were conducted in populations without clinical symptoms. Developer-involved trials were more likely to enroll symptomatic populations, with 112 of 167 developer-involved trials (67%) involving participants with clinical symptoms, compared with 37 of 62 independent trials (60%). Cost Of 167 developer-involved trials, 111 (66.5%) DHIs were not publicly available, 30 (18.0%) available trials were free, and 26 (15.6%) required payments for full functionality. Of 62 independent trials, only 5 (8.1%) DHIs were not publicly available, 8 (12.9%) available trials were free, and 49 (79.0%) required (some levels of) paid subscription. The cost of the app ranged from $6.99 to $129 for one-time use and from $1.99 to $59 per month for monthly subscription. Discussion This umbrella review evaluated the extent of developer involvement in digital health intervention (DHI) trials and its association with reported trial outcomes. Our findings indicate that a substantial majority of DHI trials (73%) were conducted with direct developer involvement, whether through funding, authorship, or operational roles. Developer involvement was associated with a higher likelihood of reporting statistically significant outcomes once weighted by trial size and accounted for trial category. To our knowledge, this is the first review to investigate the developer involvement in digital health interventions. In traditional biomedical research, industry sponsorship and author conflicts of interest have long been linked to favorable study outcomes and reporting biases 38 . For example, a Cochrane review examining studies of drugs and devices demonstrated less evidence of harm and more favorable results in industry-sponsored studies compared with non-industry-sponsored studies 39 . The explanations for favorable outcomes can be heterogenous across studies, however, a review in opioid use disorder interventions pointed out that a substantial portion of systematic review authors had undisclosed conflicts of interest, although the association with outcome favorability was not statistically significant in that sample 40 . This highlights that conflicts of interest across all trials, regardless of pharmaceuticals, medical devices, or digital health interventions demands rigorous disclosure and methodological transparency to preserve public trust and interpretability in evidence. Our cost findings illustrate that academic–industry collaborations (AICs) in digital health take multiple structural forms. Among developer-involved trials, two-thirds of DHIs were not publicly available, suggesting that many trials evaluated academically or industry-developed prototypes still in the development phase, where clinical trials functioned as validation or refinement prior to potential commercialization. Independent trials, by comparison, overwhelmingly evaluated commercially available DHIs, most of which required paid subscriptions. This pattern indicates that independent researchers more often assess interventions that have already entered the market. Interestingly, several widely studied commercial apps, such as Headspace, offer free research access to academic investigators, which lowers barriers to evaluation and increases their likelihood of appearing in published trials 7 . While such arrangements facilitate evidence generation, they also introduce a market advantage: interventions that can afford to provide free access are more likely to be studied, whereas equally or more effective products without such resources may remain underrepresented in the literature. Qualitative research on AICs in digital health further contextualizes these findings. Academic researchers have cautioned against expecting industry to “pay for everything,” noting that such expectations risk reducing collaborations to transactional relationships rather than genuine partnerships 41 . While developer funding and access support the production of empirical evidence, they also shape which interventions are evaluated and how evidence is accumulated. Our results offer a first explorations of how cost structures and access arrangements may drive the evidence formation in digital health. Future research is needed to reflect commercial partnership models to support independent evaluation of subscription-based and less commercially available interventions. Only 62% of the trials preregistered the clinical trials. The absence of regulations allows most trials to be conducted by individuals or organizations with direct ties to DHI development, opening the door for potential conflicts of interest. Furthermore, DHI trials undergo less scrutiny compared to pharmaceutical studies, despite the fact that digital tools may provide misdiagnosis to vulnerable populations and delay access to effective care 42 , 43 . The lack of standardized protocols for evaluating DHIs highlights the need for greater transparency and accountability in trial design and reporting. More trials of the developer-involved trials were conducted in clinical settings than of the independent trials. In traditional pharmaceutical research, Phase I trials are typically conducted with healthy volunteers to establish safety and tolerability. This raises the concern that participants in DHI trials receive no direct therapeutic benefit while still being exposed to risk. In pharmaceutical research, only Phase II and III trials enroll individuals with the disease of interest, offering a more favorable risk–benefit balance 44 . In contrast, many digital health intervention trials in our sample recruited symptomatic participants outside conventional clinical care pathways, often with limited ongoing clinical supervision beyond initial ethical approval. Although such trials are typically reviewed by institutional review boards and include safety protocols, the distributed and self-guided nature of many DHIs may reduce opportunities for continuous risk monitoring. While digital interventions are often assumed to carry low physical risk, they can still generate meaningful psychosocial harms, especially in the absence of regular clinical contact. Consequently, DHI trials warrant risk–benefit scrutiny comparable to that applied in pharmaceutical research. Ethical frameworks for clinical research underscores that individuals in studies with no direct therapeutic benefit requires careful justification and rigorous evaluation of potential harm 45 . By applying ethical principles similar to those used in phase I pharmaceutical trials, DHI researchers can more robustly protect participants and strengthen ethical rigor. Limitations This review has several limitations that should be considered when interpreting the findings. First, due to the substantial heterogeneity in intervention types, outcome measures, and reporting practices across digital health trials, it was not feasible to calculate pooled effect estimates or conduct quantitative meta-analytic comparisons between developer-involved and independent trials. Instead, trial outcomes were classified based on statistical significance of prespecified primary outcomes or, when unavailable, the overall direction of reported results. While this approach enabled consistent classification across diverse trials, statistical significance does not necessarily reflect clinical relevance or true intervention efficacy, particularly for complex behavioral and digital interventions. As a result, our findings cannot determine whether developer involvement influences the magnitude or durability of intervention effects. To partially address this limitation, we applied a standardized and conservative classification rule across all trials to minimize selective emphasis on favorable outcomes. Moreover, we included many trials to increase statistical power. More specialized meta-analytic research with comparable outcome definitions is needed to precisely assess whether developer involvement is associated with effect size inflation in DHI trials. Second, this review did not employ a fully comprehensive systematic search strategy. Instead, it relied on structured evidence mapping of published systematic reviews using predefined but non-exhaustive search terms. This approach was chosen to capture a broad landscape of DHI research across multiple health domains to discuss the generalizability of developer involvement in the field rather than to maximize retrieval within a single condition. However, this design may have resulted in omission of relevant reviews or trials, potentially introducing some selection effects. To mitigate this risk, multiple major databases were searched, reference lists were manually screened, and duplicate review procedures were used for study selection. Nevertheless, the findings should be interpreted as descriptive of sampled literature rather than fully representative of all DHI RCTs. Third, this review was not preregistered prior to study selection and data extraction. Lack of preregistration increases the risk of bias in this review. To reduce potential bias, all inclusion criteria, definitions, and classification rules were explicitly defined prior to data synthesis, and discrepancies were resolved through senior author consensus. Fourth, assessment of developer involvement and conflicts of interest was limited to information disclosed in published literature, trial registrations, and supplementary materials. Incomplete or inconsistent reporting of funding sources, author affiliations, and developer roles may have resulted in misclassification, particularly underestimation of developer involvement. To address this limitation, supplementary searches and cross-checking of author affiliations were conducted when possible; however, undisclosed relationships cannot be ruled out. This limitation reflects a broader systemic issue in digital health reporting and further reinforces the need for standardized disclosure requirements. Finally, the classification of trials into independent and developer-involved categories, while based on transparent criteria, simplifies a complex continuum of academic–industry relationships. Some collaborations may involve partial independence or varying degrees of influence before or during the trial that could not be fully captured through binary categorization or published documents. This framework was necessary to enable a reliable comparison across a large and heterogeneous body of literature. Future Directions Our review identifies several important gaps and opportunities for future research on developer involvement in digital health intervention trials. First, future studies should move beyond binary classifications of developer involvement and adopt more granular frameworks that distinguish among different forms of academic–industry collaboration, including early-stage co-development, sponsored validation trials, post-market evaluations, and independent replications. Such distinctions would allow clearer understanding of how varying levels and types of developer engagement influence trial design, analysis, reporting practices, and interpretation of findings. Second, future research should examine whether developer involvement is associated not only with statistical significance but also with effect size magnitude. Harmonization of outcome measures and reporting standards across DHI trials would enable quantitative synthesis and meta-analysis, allowing more precise assessment of potential effect size inflation or selective outcome reporting in developer-involved studies. Third, future work should investigate how cost structures, access arrangements, and commercialization pathways shape the digital health evidence landscape. Comparative studies could examine whether subscription-based interventions differ in effectiveness, engagement, or safety compared with free or prototype-stage apps, and whether access provided to researchers influences publication likelihood. Based on our results, we make the following careful recommendations for trial reporting in digital health research. For regulatory authorities, the interventions targeting clinical populations or intended for integration into healthcare systems should be warranted with oversight by regulatory authorities such as the FDA in the United States. Independent trials should be prioritized when interventions are considered for reimbursement or clinical decision-making, providing robust, unbiased evidence of safety and efficacy. Regulatory guidance could also encourage standardized reporting of trial design, participant selection, and outcome measures to ensure consistency and credibility. As the involvement of developers in the research process may introduce a bias in evaluating effectiveness, more transparent disclosure is needed. Journals should require authors to transparently report developer involvement, including financial or intellectual interests, software ownership, and post-trial commercialization plans. Preregistration of trials and comprehensive disclosure of all stages of developer engagement will improve interpretability, reproducibility, and trust in published findings. Researchers conducting trials should prioritize independent replication, active control designs rather than relying solely on passive or no-intervention controls, and open-access interventions to generate robust evidence. Developers, meanwhile, should support independent evaluation of their products and collaborate with researchers to provide access for replication studies while maintaining transparency regarding their role in trial design, analysis, and reporting. Proactive disclosure of potential conflicts and commercial interests will help mitigate bias and enhance public confidence in digital health evidence. While digital mental health technologies are not designed to be replacement for medications or professional healthcare, the benefits of digital health tools can be foreseeable. The evidence generation of digital health tools thus should ensure credibility, safety, and efficacy for the population. Regulatory authorities, journals, researchers, and developers all have complementary roles in this effort. Together, these measures will support better conversation between developers, clinicians, and patients to help make the decision that benefit all. Declarations Competing interests We declare no competing interests. Funding statement The work of HZ on this project was supported by a grant from the Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) under grant number and title for grant amount (T76 MC000010, Maternal and Child Health Training Grant). This information or content and conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by HRSA, HHS or the U.S. Government. Author Contribution H.Z. conducted data curation and formal analysis and wrote the original draft of the manuscript. S.K. contributed to conceptualization and data curation and reviewed and edited the manuscript. H.T. contributed to conceptualization and supervision and reviewed and edited the manuscript. All authors reviewed and approved the final manuscript. Data Availability The datasets generated and analyzed during the current study are not publicly available but are available on reasonable request. References US Food and Drugs Administration, D. H. C. of E. What is Digital Health? (2020). Pan, C.-C., Urban, M. & Schüz, B. Unintended Consequences of Digital Behavior Change Interventions: A Social–Ecological Perspective. Eur. J. Health Psychol. 31 , 141–149 (2024). Food and Drug Administration, F. 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Perski, O., Blandford, A., West, R. & Michie, S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl. Behav. Med. 7 , 254–267 (2017). Zachariae, R., Lyby, M. S., Ritterband, L. M. & O’Toole, M. S. Efficacy of internet-delivered cognitive-behavioral therapy for insomnia – A systematic review and meta-analysis of randomized controlled trials. Sleep Med. Rev. 30 , 1–10 (2016). Bonvicini, L. et al. Effectiveness of mobile health interventions targeting parents to prevent and treat childhood Obesity: Systematic review. Prev. Med. Rep. 29 , 101940 (2022). Scarry, A., Rice, J., O’Connor, E. M. & Tierney, A. C. Usage of Mobile Applications or Mobile Health Technology to Improve Diet Quality in Adults. Nutrients 14 , 2437 (2022). Wang, L. et al. mHealth Interventions to Promote a Healthy Diet and Physical Activity among Cancer Survivors: A Systematic Review of Randomized Controlled Trials. Cancers 14 , 3816 (2022). Cruz-Cobo, C., Bernal-Jiménez, M. Á., Vázquez-García, R. & Santi-Cano, M. J. Effectiveness of mHealth Interventions in the Control of Lifestyle and Cardiovascular Risk Factors in Patients After a Coronary Event: Systematic Review and Meta-analysis. JMIR MHealth UHealth 10 , e39593 (2022). Alnooh, G., Alessa, T., Hawley, M. & De Witte, L. The Use of Dietary Approaches to Stop Hypertension (DASH) Mobile Apps for Supporting a Healthy Diet and Controlling Hypertension in Adults: Systematic Review. JMIR Cardio 6 , e35876 (2022). Ang, S. M. et al. Efficacy of Interventions That Incorporate Mobile Apps in Facilitating Weight Loss and Health Behavior Change in the Asian Population: Systematic Review and Meta-analysis. J. Med. Internet Res. 23 , e28185 (2021). Chew, H. S. J., Koh, W. L., Ng, J. S. H. Y. & Tan, K. K. Sustainability of Weight Loss Through Smartphone Apps: Systematic Review and Meta-analysis on Anthropometric, Metabolic, and Dietary Outcomes. J. Med. Internet Res. 24 , e40141 (2022). Daly, L. M., Horey, D., Middleton, P. F., Boyle, F. M. & Flenady, V. The effect of mobile application interventions on influencing healthy maternal behaviour and improving perinatal health outcomes: a systematic review protocol. Syst. Rev. 6 , 26 (2017). Tsai, Z. et al. Evaluating the effectiveness and quality of mobile applications for perinatal depression and anxiety: A systematic review and meta-analysis. J. Affect. Disord. 296 , 443–453 (2022). Rahman, M. O., Yamaji, N., Nagamatsu, Y. & Ota, E. Effects of mHealth Interventions on Improving Antenatal Care Visits and Skilled Delivery Care in Low- and Middle-Income Countries: Systematic Review and Meta-analysis. J. Med. Internet Res. 24 , e34061 (2022). Hanach, N., De Vries, N., Radwan, H. & Bissani, N. The effectiveness of telemedicine interventions, delivered exclusively during the postnatal period, on postpartum depression in mothers without history or existing mental disorders: A systematic review and meta-analysis. Midwifery 94 , 102906 (2021). Zhou, C. et al. The effectiveness of mHealth interventions on postpartum depression: A systematic review and meta-analysis. J. Telemed. Telecare 28 , 83–95 (2022). Eisenstadt, M., Liverpool, S., Infanti, E., Ciuvat, R. M. & Carlsson, C. Mobile Apps That Promote Emotion Regulation, Positive Mental Health, and Well-being in the General Population: Systematic Review and Meta-analysis. JMIR Ment. Health 8 , e31170 (2021). Serrano-Ripoll, M. J., Zamanillo-Campos, R., Fiol-DeRoque, M. A., Castro, A. & Ricci-Cabello, I. Impact of Smartphone App–Based Psychological Interventions for Reducing Depressive Symptoms in People With Depression: Systematic Literature Review and Meta-analysis of Randomized Controlled Trials. JMIR MHealth UHealth 10 , e29621 (2022). Gál, É., Ștefan, S. & Cristea, I. A. The efficacy of mindfulness meditation apps in enhancing users’ well-being and mental health related outcomes: a meta-analysis of randomized controlled trials. J. Affect. Disord. 279 , 131–142 (2021). Villinger, K., Wahl, D. R., Boeing, H., Schupp, H. T. & Renner, B. The effectiveness of app‐based mobile interventions on nutrition behaviours and nutrition‐related health outcomes: A systematic review and meta‐analysis. Obes. Rev. 20 , 1465–1484 (2019). Feldman, N., Back, D., Boland, R. & Torous, J. A systematic review of mHealth application interventions for peripartum mood disorders: trends and evidence in academia and industry. Arch. Womens Ment. Health 24 , 881–892 (2021). Jiang, A., Rosario, M., Stahl, S., Gill, J. M. & Rusch, H. L. The Effect of Virtual Mindfulness-Based Interventions on Sleep Quality: A Systematic Review of Randomized Controlled Trials. Curr. Psychiatry Rep. 23 , 62 (2021). Lexchin, J., Bero, L. A., Djulbegovic, B. & Clark, O. Pharmaceutical industry sponsorship and research outcome and quality: systematic review. BMJ 326 , 1167–1170 (2003). Lundh, A., Lexchin, J., Sismondo, S., Busuioc, O. A. & Bero, L. Industry sponsorship and research outcome. in Cochrane Database of Systematic Reviews (ed. The Cochrane Collaboration) MR000033 (John Wiley & Sons, Ltd, Chichester, UK, 2011). doi:10.1002/14651858.MR000033. Ferrell, S. et al. Association between industry sponsorship and author conflicts of interest with outcomes of systematic reviews and meta-analyses of interventions for opioid use disorder. J. Subst. Abuse Treat. 132 , 108598 (2022). Ford, K. L. et al. “It depends:” a qualitative study on digital health academic-industry collaboration. mHealth 7 , 57–57 (2021). Akbar, S., Coiera, E. & Magrabi, F. Safety concerns with consumer-facing mobile health applications and their consequences: a scoping review. J. Am. Med. Inform. Assoc. 27 , 330–340 (2020). Taher, R. et al. The Safety of Digital Mental Health Interventions: Systematic Review and Recommendations. JMIR Ment. Health 10 , e47433 (2023). Shamoo, A. E. & Resnik, D. B. Strategies to Minimize Risks and Exploitation in Phase One Trials on Healthy Subjects*. Am. J. Bioeth. 6 , W1–W13 (2006). Resnik, D. B. Limits on risks for healthy volunteers in biomedical research. Theor. Med. Bioeth. 33 , 137–149 (2012). Baptista, P. M. et al. A systematic review of smartphone applications and devices for obstructive sleep apnea. Braz. J. Otorhinolaryngol. 88 , S188–S197 (2022). Arroyo, A. C. & Zawadzki, M. J. The Implementation of Behavior Change Techniques in mHealth Apps for Sleep: Systematic Review. JMIR MHealth UHealth 10 , e33527 (2022). Aji, M. et al. Framework for the Design Engineering and Clinical Implementation and Evaluation of mHealth Apps for Sleep Disturbance: Systematic Review. J. Med. Internet Res. 23 , e24607 (2021). Yau, K. W. et al. Effectiveness of Mobile Apps in Promoting Healthy Behavior Changes and Preventing Obesity in Children: Systematic Review. JMIR Pediatr. Parent. 5 , e34967 (2022). Hussain, T., Smith, P. & Yee, L. M. Mobile Phone–Based Behavioral Interventions in Pregnancy to Promote Maternal and Fetal Health in High-Income Countries: Systematic Review. JMIR MHealth UHealth 8 , e15111 (2020). Sowon, K., Maliwichi, P. & Chigona, W. The Influence of Design and Implementation Characteristics on the Use of Maternal Mobile Health Interventions in Kenya: Systematic Literature Review. JMIR MHealth UHealth 10 , e22093 (2022). Yadav, P., Kant, R., Kishore, S., Barnwal, S. & Khapre, M. The Impact of Mobile Health Interventions on Antenatal and Postnatal Care Utilization in Low- and Middle-Income Countries: A Meta-Analysis. Cureus https://doi.org/10.7759/cureus.21256 (2022) doi:10.7759/cureus.21256. Miralles, I. et al. Smartphone Apps for the Treatment of Mental Disorders: Systematic Review. JMIR MHealth UHealth 8 , e14897 (2020). Lu, S.-C. et al. Effectiveness and Minimum Effective Dose of App-Based Mobile Health Interventions for Anxiety and Depression Symptom Reduction: Systematic Review and Meta-Analysis. JMIR Ment. Health 9 , e39454 (2022). Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 31 Mar, 2026 Reviews received at journal 27 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviews received at journal 06 Mar, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers invited by journal 12 Feb, 2026 Editor assigned by journal 12 Feb, 2026 Submission checks completed at journal 12 Feb, 2026 First submitted to journal 10 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8841172","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":592014234,"identity":"e68a8296-28b0-4692-b7d4-5db855dd1168","order_by":0,"name":"Hui Zhou","email":"","orcid":"","institution":"Harvard T.H. Chan School of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhou","suffix":""},{"id":592014236,"identity":"1c849296-94ad-4d61-9c01-f028994bbb94","order_by":1,"name":"Sajeev Kohli","email":"","orcid":"","institution":"Harvard College","correspondingAuthor":false,"prefix":"","firstName":"Sajeev","middleName":"","lastName":"Kohli","suffix":""},{"id":592014239,"identity":"5b768951-d27e-4d29-896d-b44977960bc7","order_by":2,"name":"Henning Tiemeier","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIie3RMWvCQBTA8RcCl+VB1kiRfoVXDhIEq19FCaSLUKGLo1sXsesJ+RBCofPBUVz6AU7iJtzkIBRKB8FeFKEFTxw73H8IjyM/7sEB+Hz/sbD+EECcHGZ5PEWAYOwQeCINcTU5DaSvJd0ofJ9vh+1HXj2b9RBWt5nIzXYD7eZcuhZjxVJQ0XpbfWRcgLkrdcFnJRTcTTCtkBSlesBuEFQgUPLQDn03ib+qHe2JiwdTk67Axacl+wsEWQUkiZJeWpO+iCb1LdJNFEuXE8op0YOM2w1zS56CknI+c5DoRRn9vetQbBdb40jdizB6hc2o05w6yO/Y4U2P0aUf/xCfz+fznekHhIhYfQFmXnYAAAAASUVORK5CYII=","orcid":"","institution":"Harvard T.H. Chan School of Public Health","correspondingAuthor":true,"prefix":"","firstName":"Henning","middleName":"","lastName":"Tiemeier","suffix":""}],"badges":[],"createdAt":"2026-02-10 12:41:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8841172/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8841172/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102982011,"identity":"0bf74e12-9a33-466f-82ae-6b8f95176256","added_by":"auto","created_at":"2026-02-19 09:12:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37821,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flow diagram illustrating the screening process.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8841172/v1/94c084fdf16a9a947ae0fac9.png"},{"id":102981991,"identity":"20b2f6b9-5dcd-4453-8f25-02e9260a083e","added_by":"auto","created_at":"2026-02-19 09:12:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":36610,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of trials\u003csup\u003ea\u003c/sup\u003e included by publication year\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: a. All the trials were retrieved from systematic reviews published from 2013 to 2025 after careful screening for eligibility.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8841172/v1/c6ed90530bc16a871a12d33c.png"},{"id":102982069,"identity":"e11ba311-b565-4b5b-a76f-95fc0a44da02","added_by":"auto","created_at":"2026-02-19 09:12:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64651,"visible":true,"origin":"","legend":"\u003cp\u003eQuality of evidence based on AMSTAR 2 for systematic reviews\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: The horizontal stacked bar chart displays the distribution of risk-of-bias ratings for the 29 included systematic reviews (100%) across the 16 AMSTAR2 items (y-axis). Ratings are categorized as low risk (green), some concerns (yellow), or high risk (red). Gray bars indicate reviews without a meta-analysis, for which AMSTAR2 items 11, 12, and 15 were not applicable (“NA”). In the first AMSTAR2 item, PICO refers to Population, Intervention, Comparator, and Outcome.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8841172/v1/640589ac9556f8f78708c42f.png"},{"id":102982094,"identity":"ae2bce16-74fc-42d4-a10c-fd5528f5c979","added_by":"auto","created_at":"2026-02-19 09:12:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":774150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8841172/v1/8b9c5b24-d6c5-4414-91e0-9379e001653e.pdf"},{"id":102981969,"identity":"bae983a8-4b9d-4b6f-805e-3a8bab39d761","added_by":"auto","created_at":"2026-02-19 09:11:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":65564,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8841172/v1/3d3d2cc3ce6af9ed5149987d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical Trials for Digital Health Interventions: An Umbrella Review of Study Independence and the Developer Effect","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTechnological advancements have enabled digital health technologies to expand across various healthcare domains, including prevention, health promotion, and disease management. According to the United States Food and Drugs Administration \u0026ldquo;[d]igital health technologies use computing platforms, connectivity, software, and sensors for health care and related uses\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e\u0026rdquo;. Digital health interventions (DHIs) based on digital tools and technologies offer the potential to facilitate behavior change, increase healthcare access, and personalize health services\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Like all interventions, DHIs carry risks, such as adverse health outcomes and inaccurate clinical recommendations, particularly if based on biased algorithms and outdated data\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These risks underscore the importance of rigorous and independent evaluation of DHIs, especially as many are developed and tested by commercial entities with vested interests.\u003c/p\u003e \u003cp\u003eIdeally, DHIs are evaluated through well-designed clinical trials in which both benefits and unintended consequences are systematically assessed. Yet, unlike traditional pharmaceutical and medical device trials, the extent to which conflicts of interest shape the design, conduct, and interpretation of DHI efficacy trials remains poorly examined. This gap is notable given longstanding evidence from other areas of healthcare showing that financial and institutional interests can meaningfully influence research outcomes.\u003c/p\u003e \u003cp\u003ePharmaceutical and medical device trials are conducted within regulatory frameworks that mandate independent trial design, ethical review, prespecified analytic protocols, and transparent reporting to minimize bias and protect patient safety. The U.S. Food and Drug Administration mandates independent study designs and oversight to minimize bias in drug trials\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Historical evidence shows that industry-sponsored clinical trials are more likely to report favorable outcomes compared with independently conducted trials, a phenomenon known as funding bias or sponsorship bias, meaning that studies directly conducted by pharmaceutical companies tend to report more favorable outcomes\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Industry-sponsored trials often involved company personnel in data access, publication approval, or authorship\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. As a result, pharmaceutical trials are now typically conducted independently of the sponsor and must undergo rigorous ethical review with clear description of sponsor involvement.\u003c/p\u003e \u003cp\u003eIn contrast, many digital health apps are developed by for-profit companies that conduct or sponsor clinical trials of their own products. The boundaries between biomedical, behavioral, and product research have become increasingly blurred, as technology companies have historically been exempt from the regulatory standards designed to protect research participants\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. For example, a systematic review of randomized controlled trials (RCTs) evaluating the popular mental health apps Headspace and Calm showed that nearly half of the Headspace trials reported a conflict of interest involving the app companies, and preregistration was uncommon\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Given the stringent requirements for all clinical trials to ensure patient safety and transparency, there is a critical need to investigate the independence of DHI clinical trials and the potential influence of their developers. While DHIs often combine both digital and non-digital components, evaluating their standalone effects is as essential as it is for pharmaceutical products.\u003c/p\u003e \u003cp\u003e Although reporting guidelines exist for clinical trials, including extensions for specialized fields, there remains vagueness and under-adoption of standards specific to digital health. Traditional reporting frameworks such as the CONSORT Statement set minimum requirements for transparent reporting of randomized trials, aiming to facilitate critical appraisal and interpretability\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Recognizing unique aspects of digital health research, extensions such as CONSORT-EHEALTH have been developed to improve reporting of web-based and mobile health intervention trials by encouraging detailed descriptions of intervention components, delivery mechanisms, and outcomes\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, adherence to these guidelines varied in practice, and they do not fully address transparency around commercial involvement, data infrastructures, software updates, or algorithmic decision rules that can influence study outcomes. While some interventions may be regulated as medical devices, regulatory approaches differ substantially across jurisdictions, requiring developers to engage with multiple, evolving governance systems. the National Institute for Health and Care Excellence (NICE) recommends that digital health products be regulated similarly to medical devices \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, however, unlike for pharmaceuticals, a regulatory framework mandating standardized clinical trials for DHIs remains absent globally. Bodies such as the United Kingdom Medicines and Healthcare Products Regulatory Agency require manufacturers to self-certify the risk level of their products before market release, prompting cross-examination of evidence for digital mental health technologies\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In the United States, the Federal Trade Commission mandates transparency regarding any breaches of personal health information when managing digital health records\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. These guidance provide important reference points for regulating digital health technologies, targeting transparency, evidence synthesis, and data protection\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, current frameworks do not mandate that evidence supporting the effectiveness of digital health products must be generated independently of developer involvement or that potential developer involvement be disclosed, leaving potential sources of bias unaddressed. Additional frameworks such as the mHealth Evidence Reporting and Assessment (mERA) checklist\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and the iCHECK-DH guidelines\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e aim to improve reporting of evidence and implementation processes in digital health, but uptake remains uneven and specific requirements for detailing stakeholder roles and conflicts of interest are still developing. This lack of consolidated and consistently applied reporting standards contributes to a landscape in which critical details about trial conduct, including funding sources, analytic independence, and developer involvement, are often incompletely reported or interpreted inconsistently across studies.\u003c/p\u003e \u003cp\u003e Against this background, this review systematically examined the developer effect, defined as the influence of vested commercial or institutional interests on trial outcomes, and the independence of trial design and analyses from DHI stakeholders. Specifically, we quantify potential conflicts of interest by assessing the proportion of DHI trials that are conducted independently by third-party researchers compared with those involving direct developer funding, sponsorship, co-authorship, or operational roles.\u003c/p\u003e \u003cp\u003eFor this review, we adopted the National Institute for Health and Care Excellence (NICE) definition, which classifies DHIs based on their intended purpose \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. DHIs are digital health technologies intended to be used alongside other treatments and have measurable user benefits on health \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. This includes preventive behavior change, self-management, active monitoring, diagnosis and treatment \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. These DHIs are subject to strict evidence requirements given a possible direct impact on health outcomes. Because DHIs may directly influence health outcomes and clinical decision-making, they are subject to more stringent evidence and evaluation requirements than digital tools designed solely for information provision or communication. Typically, DHIs incorporate automated processes, human\u0026ndash;computer interaction, personalization or adaptability, and mechanisms to promote user engagement\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Digital tools limited to unidirectional health education, basic provider\u0026ndash;patient communication, or static intervention delivery (e.g., non-adaptive content or simple SMS reminders) are therefore excluded from this review.\u003c/p\u003e \u003cp\u003eThis study was conducted as an umbrella-style narrative review with structured evidence mapping of published systematic reviews of RCTs evaluating DHIs. While we predefined the eligibility criteria, screened the studies screening and extracted the data in duplicate, the review was not intended to be a fully comprehensive systematic umbrella review due to the breadth of the field. We strategically selected fields with long established digital tools, including mental health, sleep, maternal health, and nutrition. This approach allowed focused coverage of DHIs commonly available on the market while keeping the scope tractable. Methodological reporting follows PRISMA-2020 principles where applicable, while recognizing the exploratory nature of the synthesis. The primary objective was to quantify developer involvement and conflicts of interest in DHI trials, rather than to estimate pooled intervention effects. By systematically characterizing conflicts of interest and stakeholder involvement, this review aims to inform the development of reporting standards and governance frameworks that better protect scientific credibility and public trust in digital health evidence.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEligibility criteria\u003c/h2\u003e \u003cp\u003eWe included systematic reviews of RCTs published between January 1, 2013, and January 1, 2023, with an updated search covering January 1, 2023 to January 1, 2025.\u003c/p\u003e \u003cp\u003eArticles were selected for inclusion if they met the following Population, Intervention, Comparator, Outcome, Study design (PICOS) criteria: (1) Population: Individuals of any age, ethnicity, or health status in any geographic or care setting; (2) Intervention: DHIs defined according to NICE, including interventions for preventive behavior change, self-management, active monitoring, diagnosis, and treatment. Reviews were excluded if DHIs relied primarily on human coaching or involved only passive, non-interactive monitoring devices (e.g., standalone wearable trackers without digital intervention components); (3) Comparator: Paper-based materials, text-messaging communications, or interpersonal communication modes (face-to-face or telephone), standard care, wait-list control, or no intervention; (4) Outcome: Any health-related or behavioral outcome in the fields of nutrition, maternal health, and mental health; (5) Studies design: Systematic reviews, with or without meta-analysis, of randomized controlled trials.\u003c/p\u003e \u003cp\u003e We excluded narrative reviews, scoping reviews without systematic methods, conference abstracts, protocols, and reviews not restricted to RCTs.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInformation sources and search strategy\u003c/h3\u003e\n\u003cp\u003eThe initial search was conducted in PubMed, Scopus, Web of Science, and Google Scholar using predefined keyword combinations relating to health domains (\u0026ldquo;sleep,\u0026rdquo; \u0026ldquo;nutrition,\u0026rdquo; \u0026ldquo;mental health,\u0026rdquo; \u0026ldquo;maternal health\u0026rdquo;) and digital technologies (\u0026ldquo;digital,\u0026rdquo; \u0026ldquo;app,\u0026rdquo; \u0026ldquo;mobile,\u0026rdquo; \u0026ldquo;web-based,\u0026rdquo; \u0026ldquo;technology\u0026rdquo;) in combination with \u0026ldquo;systematic review\u0026rdquo; or \u0026ldquo;meta-analysis.\u0026rdquo;\u003c/p\u003e \u003cp\u003eReference lists of included reviews were manually screened to identify additional eligible articles. Because the objective was to map conflicts of interest rather than to estimate intervention effectiveness comprehensively, the search strategy prioritized breadth across domains rather than exhaustive retrieval within a single clinical condition.\u003c/p\u003e\n\u003ch3\u003eStudy selection and extraction\u003c/h3\u003e\n\u003cp\u003eResults were manually reviewed by one of the authors to determine if they met inclusion criteria based on titles and abstracts. The second reviewer retrieved full texts to confirm potentially eligible articles. A PRISMA flow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) documents the screening and selection process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eData extraction was conducted in duplicate. The full text of each article was independently reviewed and extracted by two researchers (HZ and SK). In the event of discrepancies, a final evaluation was carried out by the senior author and reached consensus. For each systematic review, we extracted title of the review, citation details (e.g., author list, journal, year of publication), country, PICOS elements, number of included trials, and meta-analysis method (where applicable). The methodological quality of included systematic reviews was assessed using the updated 16-item AMSTAR 2 (A MeaSurement Tool to Assess systematic Reviews) instrument. Each review was assessed by one reviewer. AMSTAR 2 ratings were used descriptively and were not applied as exclusion criteria.\u003c/p\u003e \u003cp\u003eFor each individual trial included in each meta-analysis, we extracted:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBasic information: first author, year of publication, country, study design, preregistration status, study population;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDHI-related information: type of DHI, components of intervention (digital parts, non-digital parts, monitoring device), evidence of developer involvement (see definition below), cost and public availability of the DHI;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEffectiveness of DHI: Effective estimates could not be meaningfully pooled across interventions. Given the narrative scope of this review and its primary focus on conflicts of interest and trial independence, effectiveness data were thus summarized using a single indicator per trial reflecting whether the reported results favored the intervention. When a primary outcome was explicitly prespecified, only the statistical significance and direction of effect for that primary outcome were considered. When no primary outcome was prespecified, overall trial effectiveness was determined based on whether the majority of reported outcomes demonstrated statistically significant effects in favor of the intervention. This approach was adopted to enable consistent classification of trial findings across heterogeneous interventions and outcome measures while minimizing selective emphasis on secondary outcomes.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e\n\u003ch3\u003eKey definition\u003c/h3\u003e\n\u003cp\u003eDefinition of developer involvement in the DHI trial is categorized into three types:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIndependent: No evidence of developer funding, author employment, or developer involvement in trial conduct, analysis, or manuscript preparation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDeveloper-involved: The trial was sponsored by developer of the intervention; or at least one author employed by or formally affiliated with the developer; or developers directly involved in trial design, data analysis, or manuscript drafting; or trials primarily conducted by company employees; or the authors adapted a preexisting digital health intervention and evaluated it themselves.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eUnknown: Insufficient information available after full-text review and supplementary searches.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eClassification was based on funding statements, author affiliations, conflict of interest disclosures, trial registrations, and supplementary materials.\u003c/p\u003e\n\u003ch3\u003eData synthesis\u003c/h3\u003e\n\u003cp\u003eBecause of heterogeneity across domains, interventions, and outcomes, results were synthesized narratively and descriptively. No pooled effect estimates were calculated. We summarized proportions of trials in each developer involvement category, distribution of developer involvement across health domains, patterns of reporting transparency, and associations between developer involvement and trial outcomes.\u003c/p\u003e \u003cp\u003eWe examined whether significance of trial outcomes and trial preregistration differed by developer involvement using a chi-square test of independence. The chi-square test was conducted on a 2\u0026times;2 contingency table. Statistical significance was assessed using a two-sided α level of 0.05. To further account for heterogeneity across trial categories (namely, nutrition, sleep, maternal health, and mental health) and differences in trial population size, we fit a generalized linear mixed-effects model with a binomial distribution and logit link. Trial category was included as a random intercept, and the trials were weighted by population size. Effect estimates are reported as odds ratios with 95% confidence intervals. All analyses were performed using R (version 4.4.2).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eOverview\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOut of 294 randomized controlled trials examined for eligibility in full texts, 229 trials from 29 systematic reviews met the inclusion criteria with digital health interventions meeting the NICE definition, including 18 trials for sleep outcomes, 19 trials for maternal health outcomes, 86 trials for mental health outcomes, and 106 trials for nutrition outcomes. In terms of publication year, Figure 2 demonstrated the distribution of trials included in this review. The majority of the trials were published between 2017 and 2020 suggesting a publishing and reporting lag of the systematic reviews. Nearly half of the trials (n = 97, 42%) were conducted in the United States, followed by trials in Australia (n = 30, 13%) and in United Kingdom (n = 20, 9%). Other countries and regions with 5 or more trials include Netherlands (n = 8), Sweden (n = 8), New Zealand (n = 7), China (n = 7), and Germany (n = 5).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQuality appraisal\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSixteen reviews stated that they had registered or otherwise published a review protocol.\u003csup\u003e19–34\u003c/sup\u003e All reviews searched at least two databases and provided their full search strategy, adequately described characteristics of included studies, and reported conflicts of interest. However, only two reviews\u003csup\u003e35,36\u003c/sup\u003e reported on the sources of funding for the studies included in the review, leaving potential bias from funding unexamined in most trials. Among 15 reviews that present a meta-analysis, four reviews did not discuss publication bias\u003csup\u003e28–30,37\u003c/sup\u003e. Figure 3 summarizes the methodological quality of all 29 systematic reviews that we assessed using the AMSTAR 2 tool. In this study, AMSTAR 2 assessments were used descriptively rather than as eligibility criteria. Nevertheless, the findings highlight a recurring methodological gap in the systematic review literature, particularly the limited examination of trial funding sources and the potential biases associated with developer involvement.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAssessment of developer involvement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo understand whether there is a difference in the significance of results for trials that are developer-involved or independent, we extracted and identified the developer involvement in trials. Of all 229 trials, 167 (72.9%) were not conducted independently, indicating that the DHI trial was either designed or sponsored by the DHI developer, or that the author was affiliated with the DHI developer, and the remaining 62 (27.1%) were conducted independent. Of the 167 developer-involved trials, 117 (70.1%) reported positive results, while of the 62 independent trials, 43 (69.4%) reported positive results. Table 1 shows the 2x2 table for Chi-square test. A chi-square test of independence showed no significant association between developer involvement and likelihood of reporting a statistically significant outcome (OR=1.03, χ²(1)=0.01, \u003cem\u003ep\u003c/em\u003e=0.92).\u003c/p\u003e\n\u003cp\u003eTable. 1 | Distribution of independent v.s. developer-involved trials and the significance of trial outcomes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSignificant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot significant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAll trials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDeveloper-involved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNutrition\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDeveloper-involved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMental health\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDeveloper-involved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal Health\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDeveloper-involved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndependent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDeveloper-involved\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTo account for heterogeneity in trial sample size and clustering of trials within different categories of digital health interventions, weighted mixed-effects logistic regression model was conducted. When weighted by sample size in each trial, developer involvement was associated with a modest but statistically significant increase in the odds of reporting a significant outcome (OR = 1.09, 95% CI 1.03–1.15, \u003cem\u003ep\u003c/em\u003e=0.004). This positive finding was driven primarily by larger trials, which contribute disproportionately to the population represented across trials. When further accounting for clustering by categories (nutrition, mental health, maternal health, and sleep) by adding the random intercept, the association was, if anything, even stronger (OR = 1.23, 95% CI 1.16–1.31, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), indicating that developer-involved trials were more likely to report significant results if categories are accounted for.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePreregistration\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOf 167 developer-involved trials, 112 trials (67%) were preregistered clinical trials, and the remaining 55 trials did not mention any preregistration or protocol published before the trial. \u0026nbsp;Of 62 independent trials, 28 trials (45%) were preregistered clinical trials. A chi-square test showed that developer-involved trials were significantly more likely to be preregistered than independent trials (OR = 2.47, χ²(1) = 8.23, \u003cem\u003ep\u003c/em\u003e = 0.004).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSetting\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn total, 79 trials (35%) were conducted in clinical or outpatient settings, 3 trials recruited mixed samples from both clinical settings and the general population; 147 trials (64%) were conducted in general population using recruitment methods such as flyers, websites, or social media.\u0026nbsp;Developer-involved trials were more frequently conducted in clinical settings, with 63 of 167 developer-involved trials (38%) recruiting participants from clinical settings compared with 16 of 62 independent trials (26%).\u003c/p\u003e\n\u003cp\u003eMost trials (149/229, 65%) were conducted among participants with clinical symptoms. A few trials (n=3) included mixed samples with and without clinical symptoms, and the remaining 77 trials were conducted in populations without clinical symptoms. Developer-involved trials were more likely to enroll symptomatic populations, with 112 of 167 developer-involved trials (67%) involving participants with clinical symptoms, compared with 37 of 62 independent trials (60%).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCost\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOf 167 developer-involved trials, 111 (66.5%) DHIs were not publicly available, 30 (18.0%) available trials were free, and 26 (15.6%) required payments for full functionality. Of 62 independent trials, only 5 (8.1%) DHIs were not publicly available, 8 (12.9%) available trials were free, and 49 (79.0%) required (some levels of) paid subscription. The cost of the app ranged from $6.99 to $129 for one-time use and from $1.99 to $59 per month for monthly subscription.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e This umbrella review evaluated the extent of developer involvement in digital health intervention (DHI) trials and its association with reported trial outcomes. Our findings indicate that a substantial majority of DHI trials (73%) were conducted with direct developer involvement, whether through funding, authorship, or operational roles. Developer involvement was associated with a higher likelihood of reporting statistically significant outcomes once weighted by trial size and accounted for trial category.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first review to investigate the developer involvement in digital health interventions. In traditional biomedical research, industry sponsorship and author conflicts of interest have long been linked to favorable study outcomes and reporting biases\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. For example, a Cochrane review examining studies of drugs and devices demonstrated less evidence of harm and more favorable results in industry-sponsored studies compared with non-industry-sponsored studies\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The explanations for favorable outcomes can be heterogenous across studies, however, a review in opioid use disorder interventions pointed out that a substantial portion of systematic review authors had undisclosed conflicts of interest, although the association with outcome favorability was not statistically significant in that sample\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. This highlights that conflicts of interest across all trials, regardless of pharmaceuticals, medical devices, or digital health interventions demands rigorous disclosure and methodological transparency to preserve public trust and interpretability in evidence.\u003c/p\u003e \u003cp\u003eOur cost findings illustrate that academic\u0026ndash;industry collaborations (AICs) in digital health take multiple structural forms. Among developer-involved trials, two-thirds of DHIs were not publicly available, suggesting that many trials evaluated academically or industry-developed prototypes still in the development phase, where clinical trials functioned as validation or refinement prior to potential commercialization. Independent trials, by comparison, overwhelmingly evaluated commercially available DHIs, most of which required paid subscriptions. This pattern indicates that independent researchers more often assess interventions that have already entered the market. Interestingly, several widely studied commercial apps, such as Headspace, offer free research access to academic investigators, which lowers barriers to evaluation and increases their likelihood of appearing in published trials\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. While such arrangements facilitate evidence generation, they also introduce a market advantage: interventions that can afford to provide free access are more likely to be studied, whereas equally or more effective products without such resources may remain underrepresented in the literature. Qualitative research on AICs in digital health further contextualizes these findings. Academic researchers have cautioned against expecting industry to \u0026ldquo;pay for everything,\u0026rdquo; noting that such expectations risk reducing collaborations to transactional relationships rather than genuine partnerships\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. While developer funding and access support the production of empirical evidence, they also shape which interventions are evaluated and how evidence is accumulated. Our results offer a first explorations of how cost structures and access arrangements may drive the evidence formation in digital health. Future research is needed to reflect commercial partnership models to support independent evaluation of subscription-based and less commercially available interventions.\u003c/p\u003e \u003cp\u003eOnly 62% of the trials preregistered the clinical trials. The absence of regulations allows most trials to be conducted by individuals or organizations with direct ties to DHI development, opening the door for potential conflicts of interest. Furthermore, DHI trials undergo less scrutiny compared to pharmaceutical studies, despite the fact that digital tools may provide misdiagnosis to vulnerable populations and delay access to effective care\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The lack of standardized protocols for evaluating DHIs highlights the need for greater transparency and accountability in trial design and reporting.\u003c/p\u003e \u003cp\u003eMore trials of the developer-involved trials were conducted in clinical settings than of the independent trials. In traditional pharmaceutical research, Phase I trials are typically conducted with healthy volunteers to establish safety and tolerability. This raises the concern that participants in DHI trials receive no direct therapeutic benefit while still being exposed to risk. In pharmaceutical research, only Phase II and III trials enroll individuals with the disease of interest, offering a more favorable risk\u0026ndash;benefit balance\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. In contrast, many digital health intervention trials in our sample recruited symptomatic participants outside conventional clinical care pathways, often with limited ongoing clinical supervision beyond initial ethical approval. Although such trials are typically reviewed by institutional review boards and include safety protocols, the distributed and self-guided nature of many DHIs may reduce opportunities for continuous risk monitoring. While digital interventions are often assumed to carry low physical risk, they can still generate meaningful psychosocial harms, especially in the absence of regular clinical contact. Consequently, DHI trials warrant risk\u0026ndash;benefit scrutiny comparable to that applied in pharmaceutical research. Ethical frameworks for clinical research underscores that individuals in studies with no direct therapeutic benefit requires careful justification and rigorous evaluation of potential harm\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. By applying ethical principles similar to those used in phase I pharmaceutical trials, DHI researchers can more robustly protect participants and strengthen ethical rigor.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis review has several limitations that should be considered when interpreting the findings. First, due to the substantial heterogeneity in intervention types, outcome measures, and reporting practices across digital health trials, it was not feasible to calculate pooled effect estimates or conduct quantitative meta-analytic comparisons between developer-involved and independent trials. Instead, trial outcomes were classified based on statistical significance of prespecified primary outcomes or, when unavailable, the overall direction of reported results. While this approach enabled consistent classification across diverse trials, statistical significance does not necessarily reflect clinical relevance or true intervention efficacy, particularly for complex behavioral and digital interventions. As a result, our findings cannot determine whether developer involvement influences the magnitude or durability of intervention effects. To partially address this limitation, we applied a standardized and conservative classification rule across all trials to minimize selective emphasis on favorable outcomes. Moreover, we included many trials to increase statistical power. More specialized meta-analytic research with comparable outcome definitions is needed to precisely assess whether developer involvement is associated with effect size inflation in DHI trials.\u003c/p\u003e \u003cp\u003eSecond, this review did not employ a fully comprehensive systematic search strategy. Instead, it relied on structured evidence mapping of published systematic reviews using predefined but non-exhaustive search terms. This approach was chosen to capture a broad landscape of DHI research across multiple health domains to discuss the generalizability of developer involvement in the field rather than to maximize retrieval within a single condition. However, this design may have resulted in omission of relevant reviews or trials, potentially introducing some selection effects. To mitigate this risk, multiple major databases were searched, reference lists were manually screened, and duplicate review procedures were used for study selection. Nevertheless, the findings should be interpreted as descriptive of sampled literature rather than fully representative of all DHI RCTs.\u003c/p\u003e \u003cp\u003e Third, this review was not preregistered prior to study selection and data extraction. Lack of preregistration increases the risk of bias in this review. To reduce potential bias, all inclusion criteria, definitions, and classification rules were explicitly defined prior to data synthesis, and discrepancies were resolved through senior author consensus.\u003c/p\u003e \u003cp\u003eFourth, assessment of developer involvement and conflicts of interest was limited to information disclosed in published literature, trial registrations, and supplementary materials. Incomplete or inconsistent reporting of funding sources, author affiliations, and developer roles may have resulted in misclassification, particularly underestimation of developer involvement. To address this limitation, supplementary searches and cross-checking of author affiliations were conducted when possible; however, undisclosed relationships cannot be ruled out. This limitation reflects a broader systemic issue in digital health reporting and further reinforces the need for standardized disclosure requirements.\u003c/p\u003e \u003cp\u003eFinally, the classification of trials into independent and developer-involved categories, while based on transparent criteria, simplifies a complex continuum of academic\u0026ndash;industry relationships. Some collaborations may involve partial independence or varying degrees of influence before or during the trial that could not be fully captured through binary categorization or published documents. This framework was necessary to enable a reliable comparison across a large and heterogeneous body of literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFuture Directions\u003c/h2\u003e \u003cp\u003e Our review identifies several important gaps and opportunities for future research on developer involvement in digital health intervention trials. First, future studies should move beyond binary classifications of developer involvement and adopt more granular frameworks that distinguish among different forms of academic\u0026ndash;industry collaboration, including early-stage co-development, sponsored validation trials, post-market evaluations, and independent replications. Such distinctions would allow clearer understanding of how varying levels and types of developer engagement influence trial design, analysis, reporting practices, and interpretation of findings. Second, future research should examine whether developer involvement is associated not only with statistical significance but also with effect size magnitude. Harmonization of outcome measures and reporting standards across DHI trials would enable quantitative synthesis and meta-analysis, allowing more precise assessment of potential effect size inflation or selective outcome reporting in developer-involved studies. Third, future work should investigate how cost structures, access arrangements, and commercialization pathways shape the digital health evidence landscape. Comparative studies could examine whether subscription-based interventions differ in effectiveness, engagement, or safety compared with free or prototype-stage apps, and whether access provided to researchers influences publication likelihood.\u003c/p\u003e \u003cp\u003eBased on our results, we make the following careful recommendations for trial reporting in digital health research.\u003c/p\u003e \u003cp\u003eFor regulatory authorities, the interventions targeting clinical populations or intended for integration into healthcare systems should be warranted with oversight by regulatory authorities such as the FDA in the United States. Independent trials should be prioritized when interventions are considered for reimbursement or clinical decision-making, providing robust, unbiased evidence of safety and efficacy. Regulatory guidance could also encourage standardized reporting of trial design, participant selection, and outcome measures to ensure consistency and credibility.\u003c/p\u003e \u003cp\u003eAs the involvement of developers in the research process may introduce a bias in evaluating effectiveness, more transparent disclosure is needed. Journals should require authors to transparently report developer involvement, including financial or intellectual interests, software ownership, and post-trial commercialization plans. Preregistration of trials and comprehensive disclosure of all stages of developer engagement will improve interpretability, reproducibility, and trust in published findings. Researchers conducting trials should prioritize independent replication, active control designs rather than relying solely on passive or no-intervention controls, and open-access interventions to generate robust evidence.\u003c/p\u003e \u003cp\u003eDevelopers, meanwhile, should support independent evaluation of their products and collaborate with researchers to provide access for replication studies while maintaining transparency regarding their role in trial design, analysis, and reporting. Proactive disclosure of potential conflicts and commercial interests will help mitigate bias and enhance public confidence in digital health evidence.\u003c/p\u003e \u003cp\u003eWhile digital mental health technologies are not designed to be replacement for medications or professional healthcare, the benefits of digital health tools can be foreseeable. The evidence generation of digital health tools thus should ensure credibility, safety, and efficacy for the population. Regulatory authorities, journals, researchers, and developers all have complementary roles in this effort. Together, these measures will support better conversation between developers, clinicians, and patients to help make the decision that benefit all.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eWe declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e \u003cp\u003eThe work of HZ on this project was supported by a grant from the Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) under grant number and title for grant amount (T76 MC000010, Maternal and Child Health Training Grant). This information or content and conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by HRSA, HHS or the U.S. Government.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.Z. conducted data curation and formal analysis and wrote the original draft of the manuscript. S.K. contributed to conceptualization and data curation and reviewed and edited the manuscript. H.T. contributed to conceptualization and supervision and reviewed and edited the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available but are available on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUS Food and Drugs Administration, D. H. C. of E. What is Digital Health? 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The Impact of Mobile Health Interventions on Antenatal and Postnatal Care Utilization in Low- and Middle-Income Countries: A Meta-Analysis. \u003cem\u003eCureus\u003c/em\u003e https://doi.org/10.7759/cureus.21256 (2022) doi:10.7759/cureus.21256.\u003c/li\u003e\n\u003cli\u003eMiralles, I. \u003cem\u003eet al.\u003c/em\u003e Smartphone Apps for the Treatment of Mental Disorders: Systematic Review. \u003cem\u003eJMIR MHealth UHealth\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, e14897 (2020).\u003c/li\u003e\n\u003cli\u003eLu, S.-C. \u003cem\u003eet al.\u003c/em\u003e Effectiveness and Minimum Effective Dose of App-Based Mobile Health Interventions for Anxiety and Depression Symptom Reduction: Systematic Review and Meta-Analysis. \u003cem\u003eJMIR Ment. 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Unlike mandates and reporting guidelines established for traditional pharmaceutical trials, it remains unclear to what extent DHI trials vary in the degree of developer involvement in trial conduct or sponsorship, and whether such involvement should raise concerns about risk of bias in estimated trial effectiveness. This umbrella review aimed to narratively map and summarize published systematic reviews of randomized controlled trials (RCTs) evaluating DHIs, with a focus on quantifying developer involvement and its association with significance of reported trial outcomes. Systematic reviews published between 2013 and 2025 were identified from four databases, targeting interventions in nutrition, maternal health, mental health, and sleep. Data were extracted on trial characteristics, developer involvement, preregistration, participant population, DHI cost and public availability, and outcome significance. Across 229 trials from 29 systematic reviews, 73% of trials were conducted with direct developer involvement, whereas 27% were independent. Developer-involved trials were more likely to be preregistered than independent trials (OR = 2.47, \u003cem\u003ep\u003c/em\u003e = 0.004). When weighted by sample size, developer-involved trials had higher odds of reporting statistically significant results than independent trials within each category (OR=1.23, 95% CI 1.16–1.31, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001). This review highlights the prevalence of developer involvement in DHI trials and its potential influence on reported outcomes, underscoring the need for greater transparency in publications, independent efficacy trials, and regulatory oversight for digital health technologies. Future research should examine how varying forms of developer engagement affect trial design, reporting, and effect sizes, to ensure credible, safe, and effective evidence generation in digital health.\u003c/p\u003e","manuscriptTitle":"Clinical Trials for Digital Health Interventions: An Umbrella Review of Study Independence and the Developer Effect","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 09:09:38","doi":"10.21203/rs.3.rs-8841172/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-31T14:33:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-27T15:36:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65945234083705211663089700452007355751","date":"2026-03-20T13:10:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T14:08:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"138111579096168135968607617968924321648","date":"2026-02-20T19:16:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160695384643746777286693127927258873532","date":"2026-02-16T09:23:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-13T04:23:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-13T01:30:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-12T13:08:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Digital Medicine","date":"2026-02-10T11:53:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a0a74bc0-51a5-40f6-bb6f-14d1ce1477ee","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62993138,"name":"Health sciences/Diseases"},{"id":62993139,"name":"Health sciences/Health care"},{"id":62993140,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-05-05T04:38:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 09:09:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8841172","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8841172","identity":"rs-8841172","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}