Feasibility and success of cell-phone assisted remote observation of medication adherence (CAROMA) in clinical trials

DeWorsop, David O.; Creatura, Gina; Bluez, Grai; Thurnauer, Halle; Forselius-Bielen, Kimberlee; Ranganathan, Mohini; Deaso, Emma; Bhat, Jasra Ali

doi:10.1016/j.drugalcdep.2016.02.045

Cited by 23 publications

(12 citation statements)

References 47 publications

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“…While acknowledging that a national policy for investment in eHealth interventions is crucial to its adoption, Rozenblum et al [ 132 ] argued that financial incentives should be based on patient outcomes that might ensue from the eHealth intervention. DeWorsop et al [ 129 ] and Lee and Billings [ 133 ] reported on the importance of cost effectiveness to promoting the adoption of eHealth interventions.…”

Section: Resultsmentioning

confidence: 99%

Factors Determining the Success and Failure of eHealth Interventions: Systematic Review of the Literature

Granja¹,

Janssen²,

Johansen³

2018

J Med Internet Res

463

383

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BackgroundeHealth has an enormous potential to improve healthcare cost, effectiveness, and quality of care. However, there seems to be a gap between the foreseen benefits of research and clinical reality.ObjectiveOur objective was to systematically review the factors influencing the outcome of eHealth interventions in terms of success and failure.MethodsWe searched the PubMed database for original peer-reviewed studies on implemented eHealth tools that reported on the factors for the success or failure, or both, of the intervention. We conducted the systematic review by following the patient, intervention, comparison, and outcome framework, with 2 of the authors independently reviewing the abstract and full text of the articles. We collected data using standardized forms that reflected the categorization model used in the qualitative analysis of the outcomes reported in the included articles.ResultsAmong the 903 identified articles, a total of 221 studies complied with the inclusion criteria. The studies were heterogeneous by country, type of eHealth intervention, method of implementation, and reporting perspectives. The article frequency analysis did not show a significant discrepancy between the number of reports on failure (392/844, 46.5%) and on success (452/844, 53.6%). The qualitative analysis identified 27 categories that represented the factors for success or failure of eHealth interventions. A quantitative analysis of the results revealed the category quality of healthcare (n=55) as the most mentioned as contributing to the success of eHealth interventions, and the category costs (n=42) as the most mentioned as contributing to failure. For the category with the highest unique article frequency, workflow (n=51), we conducted a full-text review. The analysis of the 23 articles that met the inclusion criteria identified 6 barriers related to workflow: workload (n=12), role definition (n=7), undermining of face-to-face communication (n=6), workflow disruption (n=6), alignment with clinical processes (n=2), and staff turnover (n=1).ConclusionsThe reviewed literature suggested that, to increase the likelihood of success of eHealth interventions, future research must ensure a positive impact in the quality of care, with particular attention given to improved diagnosis, clinical management, and patient-centered care. There is a critical need to perform in-depth studies of the workflow(s) that the intervention will support and to perceive the clinical processes involved.

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Section: Resultsmentioning

confidence: 99%

Factors Determining the Success and Failure of eHealth Interventions: Systematic Review of the Literature

Granja¹,

Janssen²,

Johansen³

2018

J Med Internet Res

463

383

View full text Add to dashboard Cite

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“…Direct visualization of medication ingestion in real time by a human observer was studied in 4 interventions. A total of 2 studies used direct in-person observation as a gold standard to evaluate the accuracy of other measurement systems (A.3.1.1) [63,64], whereas 2 other studies used mobile videoconferencing technology to observe dosing remotely (A.3.1.2) [65,66]. Both of these studies had a standardized protocol for observed dosing, ostensibly to ensure adherence; the participants kept the pill container on camera while removing the medication and showed their open mouth following pill ingestion to prevent hiding the medication in the cheek.…”

Section: Resultsmentioning

confidence: 99%

Technological Interventions for Medication Adherence in Adult Mental Health and Substance Use Disorders: A Systematic Review

Steinkamp¹,

Goldblatt²,

Borodovsky³

et al. 2019

JMIR Ment Health

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Background Medication adherence is critical to the effectiveness of psychopharmacologic therapy. Psychiatric disorders present special adherence considerations, notably an altered capacity for decision making and the increased street value of controlled substances. A wide range of interventions designed to improve adherence in mental health and substance use disorders have been studied; recently, many have incorporated information technology (eg, mobile phone apps, electronic pill dispensers, and telehealth). Many intervention components have been studied across different disorders. Furthermore, many interventions incorporate multiple components, making it difficult to evaluate the effect of individual components in isolation. Objective The aim of this study was to conduct a systematic scoping review to develop a literature-driven, transdiagnostic taxonomic framework of technology-based medication adherence intervention and measurement components used in mental health and substance use disorders. Methods This review was conducted based on a published protocol (PROSPERO: CRD42018067902) in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses systematic review guidelines. We searched 7 electronic databases: MEDLINE, EMBASE, PsycINFO, the Cochrane Central Register of Controlled Trials, Web of Science, Engineering Village, and ClinicalTrials.gov from January 2000 to September 2018. Overall, 2 reviewers independently conducted title and abstract screens, full-text screens, and data extraction. We included all studies that evaluate populations or individuals with a mental health or substance use disorder and contain at least 1 technology-delivered component (eg, website, mobile phone app, biosensor, or algorithm) designed to improve medication adherence or the measurement thereof. Given the wide variety of studied interventions, populations, and outcomes, we did not conduct a risk of bias assessment or quantitative meta-analysis. We developed a taxonomic framework for intervention classification and applied it to multicomponent interventions across mental health disorders. Results The initial search identified 21,749 results; after screening, 127 included studies remained (Cohen kappa: 0.8, 95% CI 0.72-0.87). Major intervention component categories include reminders, support messages, social support engagement, care team contact capabilities, data feedback, psychoeducation, adherence-based psychotherapy, remote care delivery, secure medication storage, and contingency management. Adherence measurement components include self-reports, remote direct visualization, fully automated computer vision algorithms, biosensors, smart pill bottles, ingestible sensors, pill counts, and utilization measures. Intervention modalities include short messaging service, mobile phone apps, websites, and interactive voice response. We provide graphical representations of intervention component categories and an element-wise breakdown of multicomponent interventions. Conclusions Many technology-based medication adherence and monitoring interventions have been studied across psychiatric disease contexts. Interventions that are useful in one psychiatric disorder may be useful in other disorders, and further research is necessary to elucidate the specific effects of individual intervention components. Our framework is directly developed from the substance use disorder and mental health treatment literature and allows for transdiagnostic comparisons and an organized conceptual mapping of interventions.

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“…Synchronous or asynchronous video observation of medication-taking is a recent advance in clinical trials and well suited for inclusion in remote trials. Medication-taking can be observed via video connection with a staff member in real time (ie, synchronously 58 , 59 ), recorded via video on a mobile device and uploaded for subsequent staff review (ie, asynchronously 32 , 60 , 61 ), or automatically detected using a mobile app with facial recognition software, such as AICure. 62 Limitations of these methods include high participant burden, required access to technology with video capabilities, and the need for staff to observe dosing in real time or review videos at a later date (for synchronous and asynchronous methods, respectively).…”

Section: Remote Medication Adherence Monitoringmentioning

confidence: 99%

Remote Methods for Conducting Tobacco-Focused Clinical Trials

Dahne

Tomko

McClure

et al. 2020

Nicotine &Amp; Tobacco Research

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Abstract Most tobacco-focused clinical trials are based on locally conducted studies that face significant challenges to implementation and successful execution. These challenges include the need for large, diverse, yet still representative study samples. This often means a protracted, costly, and inefficient recruitment process. Multisite clinical trials can overcome some of these hurdles but incur their own unique challenges. With recent advances in mobile health and digital technologies, there is now a promising alternative: Remote Trials. These trials are led and coordinated by a local investigative team, but are based remotely, within a given community, state, or even nation. The remote approach affords many of the benefits of multisite trials (more efficient recruitment of larger study samples) without the same barriers (cost, multisite management, and regulatory hurdles). The Coronavirus Disease 2019 (COVID-19) global health pandemic has resulted in rapid requirements to shift ongoing clinical trials to remote delivery and assessment platforms, making methods for the conduct of remote trials even more timely. The purpose of the present review is to provide an overview of available methods for the conduct of remote tobacco-focused clinical trials as well as illustrative examples of how these methods have been implemented across recently completed and ongoing tobacco studies. We focus on key aspects of the clinical trial pipeline including remote: (1) study recruitment and screening, (2) informed consent, (3) assessment, (4) biomarker collection, and (5) medication adherence monitoring. Implications With recent advances in mobile health and digital technologies, remote trials now offer a promising alternative to traditional in-person clinical trials. Remote trials afford expedient recruitment of large, demographically representative study samples, without undo burden to a research team. The present review provides an overview of available methods for the conduct of remote tobacco-focused clinical trials across key aspects of the clinical trial pipeline.

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Feasibility and success of cell-phone assisted remote observation of medication adherence (CAROMA) in clinical trials

Cited by 23 publications

References 47 publications

Factors Determining the Success and Failure of eHealth Interventions: Systematic Review of the Literature

Factors Determining the Success and Failure of eHealth Interventions: Systematic Review of the Literature

Technological Interventions for Medication Adherence in Adult Mental Health and Substance Use Disorders: A Systematic Review

Remote Methods for Conducting Tobacco-Focused Clinical Trials

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