Characteristics of Digital Health Studies Registered in ClinicalTrials.gov | Research, Methods, Statistics | JAMA Internal Medicine | JAMA Network
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Table 1.  Digital Health Studies Registered in ClinicalTrials.gov
Digital Health Studies Registered in ClinicalTrials.gov
Table 2.  Study Characteristics Stratified by Study Design Characteristics
Study Characteristics Stratified by Study Design Characteristics
1.
Turakhia  MP, Desai  SA, Harrington  RA.  The outlook of digital health for cardiovascular medicine: challenges but also extraordinary opportunities.  JAMA Cardiol. 2016;1(7):743-744. doi:10.1001/jamacardio.2016.2661PubMedGoogle ScholarCrossref
2.
Ahern  DK.  Challenges and opportunities of ehealth research.  Am J Prev Med. 2007;32(5)(suppl):S75-S82. doi:10.1016/j.amepre.2007.01.016PubMedGoogle ScholarCrossref
3.
O’Neil  A, Cocker  F, Rarau  P,  et al.  Using digital interventions to improve the cardiometabolic health of populations: a meta-review of reporting quality.  J Am Med Inform Assoc. 2017;24(4):867-879. doi:10.1093/jamia/ocw166PubMedGoogle ScholarCrossref
4.
National Institutes of Health. ClinicalTrials.gov. https://clinicaltrials.gov/. Accessed June 19, 2017.
5.
National Institutes of Health. ClinicalTrials.gov FDAAA 801 requirements. https://clinicaltrials.gov/ct2/search/advanced?cond=&term=&state1=&cntry1=. Accessed June 19, 2017.
6.
National Institutes of Health. ClinicalTrials.gov advanced search. https://clinicaltrials.gov/ct2/search/advanced?cond=&term=&state1=&cntry1=. Accessed June 19, 2017.
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    Research Letter
    February 25, 2019

    Characteristics of Digital Health Studies Registered in ClinicalTrials.gov

    Author Affiliations
    • 1Department of Medicine, Stanford University School of Medicine, Stanford, California
    • 2Center for Digital Health, Stanford University School of Medicine, Stanford, California
    • 3Stanford Center for Clinical Research, Department of Medicine, Stanford University School of Medicine, Stanford, California
    • 4Veterans Affairs Palo Alto Health Care System, Palo Alto, California
    JAMA Intern Med. 2019;179(6):838-840. doi:10.1001/jamainternmed.2018.7235

    Digital health is the application of software or hardware, often using mobile smartphone or sensor technologies to improve patient or population health and health care delivery.1 In contrast to drugs and traditional medical devices, which have strict regulatory guidelines on safety and efficacy, the clinical evidence generation for digital health tools may be motivated by other factors, including adoption, utilization, and value, that may influence study design and quality. The landscape of clinical evidence underlying digital health interventions has not been well characterized.2,3 We sought to evaluate the characteristics of digital health studies registered in ClinicalTrials.gov.

    Methods

    We performed a cross-sectional analysis of digital health studies in ClinicalTrials.gov.4,5 To identify studies evaluating mobile-, web-, and electronic-based tools as well as digital medical devices, we searched ClinicalTrials.gov on January 22, 2017, using the Medical Subject Heading concepts (mobile health, mHealth, ehealth, telehealth, and telemedicine) and commonly used lay terms (digital health, consumer health, mobile application, and wireless technology). Variables were exported as structured fields when downloaded from ClinicalTrials.gov.6 A single reviewer (C.E.C.) verified studies for inclusion, removed duplicates, and assigned each study to 1 of 13 clinical areas determined by iterative qualitative clustering against commonly accepted medicine domains. Descriptive statistics were calculated for key study characteristics, with additional stratification by study type (interventional vs observational, randomization status). We used the χ2 test to compare proportions, and P < .05 was considered to be statistically significant.

    Results

    We identified 1783 studies that met our inclusion criteria (from the top-level search of 3833 and after deduplication); 1570 studies (88.1%) were interventional, and 1257 (70.8%) were randomized. Among interventional studies, 107 (6.9%) were double-blinded and 417 (26.7%) single-blinded. Among observational studies, the most common study designs were case-control (26 [14.4%]), case-only (39 [21.7%]), and cohort (100 [55.6%]) (Table 1).

    Most studies consisted of adults or elderly individuals; 374 (20.1%) enrolled children. The most common clinical areas were cardiometabolic (382 [21.4%]), mental health (216 [12.1%]), and wellness (183 [10.2%]). Funding sources included federal and National Institutes of Health (369 [20.7%]) and industry (214 [12.0%]). Median enrollment was 120 (interquartile range, 50-300), although study sample size varied from fewer than 100 individuals (829 [46.5%]) to more than 1000 (142 [8.0%]) (Table 2). A higher proportion of publicly funded studies were interventional (338 [21.5%]) or randomized (294 [23.4%]), whereas a higher proportion of industry-funded studies were observational (48 [22.5%]) or nonrandomized (88 [17.0%]) (Table 2). Overall, 692 of 1783 studies (38.9%) had completed recruitment, and 85 completed or terminated studies (11.3%) had reported results. After multivariate adjustment, federally funded trials were more likely to have reported results (odds ratio, 4.9; 95% CI, 3.1-7.8; P < .001).

    Discussion

    We characterized the digital health clinical research landscape. Although the number of registered studies increased by a mean of 29% per year from 2011 to 2017, many were small. Federally funded studies were more likely to use interventional designs and randomization. However, few studies have reported findings to date, even among studies marked completed or terminated.

    Our use of the ClinicalTrials.gov database has some limitations, most notably that submission of digital health trials, unlike that for drugs and devices, remains voluntary.5 Although most stakeholders and sponsors generally require that prospective interventional trials be reported to ClinicalTrials.gov, these standards do not always apply to observational studies. Selection bias could lead to overestimation of the proportion of studies that are randomized across the full landscape and mean trial size, particularly if small pilot and nonregulated validation studies are underascertained. Because these data were extracted in 2017, ongoing assessment of the state of digital health studies is warranted.

    Whether results will drive substantial clinical adoption is unknown because small studies, even if randomized, are unlikely to be significantly powered to demonstrate meaningful treatment effects. Although the pipeline of digital health studies appears to be promising, these factors could limit their ability to yield a high level of evidence, demonstrate value, or motivate stakeholder adoption.

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    Article Information

    Accepted for Publication: October 28, 2018.

    Corresponding Author: Mintu P. Turakhia, MD, MAS, Veterans Affairs Palo Alto Health Care System, 3801 Miranda Ave, 111C, Palo Alto, CA 94304 (mintu@stanford.edu).

    Published Online: February 25, 2019. doi:10.1001/jamainternmed.2018.7235

    Author Contributions: Dr Chen had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design:Chen, Harrington, Desai, Turakhia.

    Acquisition, analysis, or interpretation of data: Chen, Mahaffey, Turakhia.

    Drafting of the manuscript: Chen, Turakhia.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Chen, Turakhia.

    Administrative, technical, or material support: Harrington, Turakhia.

    Supervision: Harrington, Desai, Turakhia.

    Conflict of Interest Disclosures: Dr. Chen reported being employed by Lyra Health and serving as a consultant for and having equity in Vida Health. Dr Harrington reported serving as a consultant for Adverse Events, Amgen, Bayer, Gilead, Merck & Co, Vida Health, and WebMD; performing data safety monitoring for AstraZeneca, BMS, and Janssen; having equity in Element Science and MyoKardia; having a fiduciary role in Scanadu, Signal Path, the American Heart Association, College of the Holy Cross, and Stanford Healthcare; and receiving research grants from CSL Behring, GlaxoSmithKline, Merck & Co, Novartis, Portola, Sanofi, and The Medicines Company. Dr Desai reported being employed by Apple. Dr. Mahaffey reported receiving research grants from Afferent, Amgen, Apple Inc, AstraZeneca, Cardiva Medical Inc, Daiichi Sankyo, Ferring, Google (Verily), Johnson & Johnson, Luitpold, Medtronic, Merck & Co, the National Institutes of Health, Novartis, Sanofi, St. Jude, and Tenax; serving as a consultant for Abbott, Ablynx, Baim Institute, Boehringer Ingelheim, Bristol-Myers Squibb, Cardiometabolic Health Congress, Elsevier, GlaxoSmithKline, Medergy, Medscape, Mitsubishi, MyoKardia, Novo Nordisk, Oculeve, Portola, Radiometer, Springer Publishing, Theravance, University of California San Francisco, and WebMD; and having equity in BioPrint Fitness. Dr Turakhia reported receiving research grants from Medtronic Inc, Research, Janssen Pharmaceuticals, AstraZeneca, the American Heart Association, and Amazon; serving as a consultant for Medtronic Inc and Abbott; and having equity in AliveCor. No other disclosures were reported.

    References
    1.
    Turakhia  MP, Desai  SA, Harrington  RA.  The outlook of digital health for cardiovascular medicine: challenges but also extraordinary opportunities.  JAMA Cardiol. 2016;1(7):743-744. doi:10.1001/jamacardio.2016.2661PubMedGoogle ScholarCrossref
    2.
    Ahern  DK.  Challenges and opportunities of ehealth research.  Am J Prev Med. 2007;32(5)(suppl):S75-S82. doi:10.1016/j.amepre.2007.01.016PubMedGoogle ScholarCrossref
    3.
    O’Neil  A, Cocker  F, Rarau  P,  et al.  Using digital interventions to improve the cardiometabolic health of populations: a meta-review of reporting quality.  J Am Med Inform Assoc. 2017;24(4):867-879. doi:10.1093/jamia/ocw166PubMedGoogle ScholarCrossref
    4.
    National Institutes of Health. ClinicalTrials.gov. https://clinicaltrials.gov/. Accessed June 19, 2017.
    5.
    National Institutes of Health. ClinicalTrials.gov FDAAA 801 requirements. https://clinicaltrials.gov/ct2/search/advanced?cond=&term=&state1=&cntry1=. Accessed June 19, 2017.
    6.
    National Institutes of Health. ClinicalTrials.gov advanced search. https://clinicaltrials.gov/ct2/search/advanced?cond=&term=&state1=&cntry1=. Accessed June 19, 2017.
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