Evidence from ClinicalTrials.gov on the growth of Digital Health Technologies in neurology trials.
Ontology highlight
ABSTRACT:
SUBMITTER: Masanneck L
PROVIDER: S-EPMC9918454 | biostudies-literature | 2023 Feb
REPOSITORIES: biostudies-literature
Publications
Evidence from ClinicalTrials.gov on the growth of Digital Health Technologies in neurology trials.
Masanneck Lars L Gieseler Pauline P Gordon William J WJ Meuth Sven G SG Stern Ariel D AD
NPJ digital medicine 20230210 1
Digital Health Technologies (DHTs) such as connected sensors offer particular promise for improving data collection and patient empowerment in neurology research and care. This study analyzed the recent evolution of the use of DHTs in trials registered on ClinicalTrials.gov for four chronic neurological disorders: epilepsy, multiple sclerosis, Alzheimer's, and Parkinson's disease. We document growth in the collection of both more established digital measures (e.g., motor function) and more novel ...[more]
Similar Datasets
Project description:BackgroundDigital health technologies (DHTs) can facilitate the execution of de-centralized trials that can offer opportunities to reduce the burden on participants, collect outcome data in a real-world setting, and potentially make trial populations more diverse and inclusive. However, DHTs can also be a significant source of electronic waste (e-waste). In recognition of the potential health and environmental impact from DHT use in trials, private and public institutions have recently launched initiatives to help measure and manage this e-waste. But in order to develop sound e-waste management policies, it will be necessary to first estimate the current volume of e-waste that results from the use of DHTs in trials.Materials and methodsA Web Ontology Language (OWL)-compliant ontology of DHTs was created using a list of 500 DHT device names derived from a mixture of public and private sources. The U.S. clinical trials registry, ClinicalTrials.gov, was then queried to identify and classify trials using any of the devices in the ontology. The ClinicalTrials.gov records from this search were then analyzed to characterize the volume and properties of trials using DHTs, as well as estimating the total volume of individual DHT units that have been provisioned (or are planned to be provisioned) for clinical research.ResultsOur ontology-driven search identified 2326 unique clinical trials with a reported "actual" enrollment of 200,947 participants and a "planned" enrollment of an additional 4,094,748 participants. The most-used class of DHTs in our ontology was "wearables," (1852 trials), largely driven by the use of smart watches and other wrist-worn sensors (estimated to involve 149,391 provisioned devices). The most-used subtype of DHTs in trials was "subcutaneous" devices (367 trials), driven by the prevalent use and testing of glucose monitors (estimated to involve 17,666 provisioned devices).ConclusionThousands of trials, involving hundreds of thousands of devices, have already been completed, and many more trials (potentially involving millions more devices) are planned. Despite the great opportunities that are afforded by DHTs to the clinical trial enterprise, if the industry lacks the ability to track DHT use with sufficient resolution, the result is likely to be a great deal of e-waste. A new ontology of DHTs, combined with rigorous data science methods like those described in this paper, can be used to provide better information across the industry, and in turn, help create a more sustainable and equitable clinical trials enterprise.
| S-EPMC10579130 | biostudies-literature
Project description:Sensor data from digital health technologies (DHTs) used in clinical trials provides a valuable source of information, because of the possibility to combine datasets from different studies, to combine it with other data types, and to reuse it multiple times for various purposes. To date, there exist no standards for capturing or storing DHT biosensor data applicable across modalities and disease areas, and which can also capture the clinical trial and environment-specific aspects, so-called metadata. In this perspectives paper, we propose a metadata framework that divides the DHT metadata into metadata that is independent of the therapeutic area or clinical trial design (concept of interest and context of use), and metadata that is dependent on these factors. We demonstrate how this framework can be applied to data collected with different types of DHTs deployed in the WATCH-PD clinical study of Parkinson's disease. This framework provides a means to pre-specify and therefore standardize aspects of the use of DHTs, promoting comparability of DHTs across future studies.
| S-EPMC8954603 | biostudies-literature
Project description:BackgroundClinical trials for obesity have traditionally focused on weight loss and resolution of comorbidities as primary outcomes. However, secondary outcomes, such as the impact of weight reduction on patient experience, like health-related quality of life (HRQoL), have increasingly been recognized as important. Therefore, a review was conducted to determine the Clinical Outcome Assessments (COAs) and Digital Health Technologies (DHTs) used in clinical trials for obesity to assess the patient experience.MethodsTwo clinical trial databases (United States & European Union) were reviewed to identify Phase 2-4 clinical trials for obesity (2018-2023). A targeted literature review was also conducted using the OVID database to identify clinical trial for obesity publications which included COAs/DHTs (2010-2023).ResultTrials from the databases (n = 53) and publications (n = 42) were included in data extraction (N = 73). This resulted in identification of 108 COAs, the majority being patient-reported outcome (PRO) measures (n = 83), but also 24 performance outcomes (PerfO) measures, and 1 composite PRO-clinician-reported outcomes (ClinRO) measure, as well as 2 DHTs. The most frequently identified PRO measures were the Short Form 36 and the Impact of Weight on Quality of Life Lite Clinical Trials. Twenty-four PerfO measures were also identified, with the 6-minute walk test being most common. These measures were most often used to construct secondary endpoints, with physical function (PF) being the most frequently specified domain. PRO measures assessing eating-related thoughts/behaviours, physical activity, and disordered eating were also frequently included, although individual measures varied widely across trials.ConclusionReview of COAs and DHTs in registered clinical trials and publications for obesity found that PRO measures were the most common type of COA used to develop endpoints with current use of DHTs limited. Specifically, the physical function domain of multidimensional patient-reported outcome measures assessing health-related quality of life were often used to construct secondary endpoints. Further work is warranted to assess how the COAs and DHT data collected in clinical trials are viewed by regulators and payers.
| S-EPMC11842654 | biostudies-literature
Project description: Not available
| S-EPMC8956713 | biostudies-literature
Project description:The recent pandemic ushered in a marked surge in the adoption of digital health technologies (DHTs), necessitating remote approaches aiming to safeguard both patient and healthcare provider well-being. These technologies encompass an array of terms, including e-health, m-health, telemedicine, wearables, sensors, smartphone apps, digital therapeutics, virtual and augmented reality, and artificial intelligence (AI). Notably, some DHTs employed in critical healthcare decisions may transition into the realm of medical devices, subjecting them to more stringent regulatory scrutiny. Consequently, it is imperative to understand the validation processes of these technologies within clinical studies. Our study summarizes an extensive examination of clinical trials focusing on cardiovascular (CV) diseases and digital health (DH) interventions, with particular attention to those incorporating elements of AI. A dataset comprising 107 eligible trials, registered on clinicaltrials.gov and International Clinical Trials Registry Platform (ICTRP) databases until 19 June 2023, forms the basis of our investigation. We focused on clinical trials employing DHTs in the European context, revealing a diverse landscape of interventions. Devices constitute the predominant category (45.8%), followed by behavioral interventions (17.8%). Within the CV domain, trials predominantly span pivotal or confirmatory phases, with a notable presence of smaller feasibility and exploratory studies. Notably, a majority of trials exhibit randomized, parallel assignment designs. When analyzing the multifaceted landscape of trial outcomes, we identified various categories such as physiological and functional measures, diagnostic accuracy, CV events and mortality, patient outcomes, quality of life, treatment adherence and effectiveness, quality of hospital processes, and usability/feasibility measures. Furthermore, we delve into a subset of 15 studies employing AI and machine learning, describing various study design features, intended purposes and the validation strategies employed. In summary, we aimed to elucidate the diverse applications, study design features, and objectives of the evolving CV-related DHT clinical trials field.
| S-EPMC11406593 | biostudies-literature
Project description:Investigators of clinical trials in which the list of outcomes include patient-reported outcomes (PROs) - usually labeled quality of life (QoL) - have a large number of instruments from which to choose. The extent and manner in which PRO instruments are used in clinical trials can be assessed using data from clinical trial registries. Most medical journals now require a clinical trial be registered before its results are considered for publication. This requirement is intended to discourage publication bias, such as the reporting of tests of hypotheses different from those stipulated at the start of the trial and selective reporting of partial results.To assess the usage of PRO instruments in registered trials by various trial characteristics and to determine if the instruments are adequately identified in the registry.A local copy of the ClinicalTrials.gov database was made in September 2007. The outcomes of all interventional trials registered since September 2004 were assessed for usage of a PRO instrument. Odds ratios of PRO usage were estimated by a logistic regression model.Of 17,704 interventional trials, 2481 (14.0%) used at least one PRO instrument. However, less than half of those trials (41.0%) identified the instrument to be used. PRO usage is positively associated with phase (III), randomization type (randomized), intervention type (behavior) and sponsorship type (university/research organization).PRO instruments are used in a significant percentage but minority of clinical trials. Trial registries should require that all PRO instruments be identified, including the concepts or outcomes they are intended to measure.
| S-EPMC2683916 | biostudies-other
Project description:BackgroundProfound scientific evaluation of novel digital health technologies (DHTs) is key to enhance successful development and implementation. As such, we previously developed the eHealth evaluation cycle. The eHealth evaluation cycle contains 5 consecutive study phases: conceptual, development, feasibility, effectiveness, and implementation.ObjectiveThe aim of this study is to develop a better understanding of the daily practice of the eHealth evaluation cycle. Therefore, the objectives are to conduct a structured analysis of literature data to analyze the practice of the evaluation study phases and to determine which evaluation approaches are used in which study phase of the eHealth evaluation cycle.MethodsWe conducted a systematic literature search in PubMed including the MeSH term "telemedicine" in combination with a wide variety of evaluation approaches. Original peer-reviewed studies published in the year 2019 (pre-COVID-19 cohort) were included. Nonpatient-focused studies were excluded. Data on the following variables were extracted and systematically analyzed: journal, country, publication date, medical specialty, primary user, functionality, evaluation study phases, and evaluation approach. RStudio software was used to summarize the descriptive data and to perform statistical analyses.ResultsWe included 824 studies after 1583 titles and abstracts were screened. The majority of the evaluation studies focused on the effectiveness (impact; 304/824, 36.9%) study phase, whereas uptake (implementation; 70/824, 8.5%) received the least focus. Randomized controlled trials (RCTs; 170/899, 18.9%) were the most commonly used DHT evaluation method. Within the effectiveness (impact) study phase, RCTs were used in one-half of the studies. In the conceptual and planning phases, survey research (27/78, 35%) and interview studies (27/78, 35%) were most frequently used. The United States published the largest amount of DHT evaluation studies (304/824, 36.9%). Psychiatry and mental health (89/840, 10.6%) and cardiology (75/840, 8.9%) had the majority of studies published within the field.ConclusionsWe composed the first comprehensive overview of the actual practice of implementing consecutive DHT evaluation study phases. We found that the study phases of the eHealth evaluation cycle are unequally studied and most attention is paid to the effectiveness study phase. In addition, the majority of the studies used an RCT design. However, in order to successfully develop and implement novel DHTs, stimulating equal evaluation of the sequential study phases of DHTs and selecting the right evaluation approach that fits the iterative nature of technology might be of the utmost importance.
| S-EPMC11391152 | biostudies-literature
Project description:BackgroundDigital health technologies (DHTs) generate a large volume of information used in health care for administrative, educational, research, and clinical purposes. The clinical use of digital information for diagnostic, therapeutic, and prognostic purposes has multiple patient safety problems, some of which result from poor information quality (IQ).ObjectiveThis systematic review aims to synthesize an IQ framework that could be used to evaluate the extent to which digital health information is fit for clinical purposes.MethodsThe review was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines. We searched Embase, MEDLINE, PubMed, CINAHL, Maternity and Infant Care, PsycINFO, Global Health, ProQuest Dissertations and Theses Global, Scopus, and HMIC (the Health Management Information Consortium) from inception until October 2019. Multidimensional IQ frameworks for assessing DHTs used in the clinical context by health care professionals were included. A thematic synthesis approach was used to synthesize the Clinical Information Quality (CLIQ) framework for digital health.ResultsWe identified 10 existing IQ frameworks from which we developed the CLIQ framework for digital health with 13 unique dimensions: accessibility, completeness, portability, security, timeliness, accuracy, interpretability, plausibility, provenance, relevance, conformance, consistency, and maintainability, which were categorized into 3 meaningful categories: availability, informativeness, and usability.ConclusionsThis systematic review highlights the importance of the IQ of DHTs and its relevance to patient safety. The CLIQ framework for digital health will be useful in evaluating and conceptualizing IQ issues associated with digital health, thus forestalling potential patient safety problems.Trial registrationPROSPERO International Prospective Register of Systematic Reviews CRD42018097142; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=97142.International registered report identifier (irrid)RR2-10.1136/bmjopen-2018-024722.
| S-EPMC8167621 | biostudies-literature
Project description:The number of older adults with cancer is increasing. Over the past 3 decades, geriatric oncology research has focused on improving the assessment of frailty and fitness of older adults with cancer as well as methods of improving their outcomes. At the same time, advances in digital health technologies have opened new frontiers for reaching this goal. Digital health technologies encompass a variety of solutions, from electronic patient-reported outcomes (ePROs) to Big Data and wireless sensors. These solutions have the potential to further advance our understanding of patients' experiences during cancer treatment. Whereas the data on the feasibility and utility of such solutions in the care of older adults with cancer are limited, interest from digital health oncology researchers to further explore the benefits of these products is increasing. In this article, we describe the focus of geriatric oncology, the rationale behind the need to explore digital health technologies in this setting, and emerging data and ongoing studies, as well as provide guidelines for proper selection, implementation, and testing of digital health solutions in the context of geriatric oncology.
| S-EPMC7370237 | biostudies-literature
Project description:BackgroundThe global rise in type-2 diabetes (T2D) has prompted the development of new digital technologies for diabetes management. However, despite the proliferation of digital health companies for T2D care, scaling their solutions remains a critical challenge. This study investigates the digital transformation of T2D ecosystems and seeks to identify key innovation patterns. We examine: (1) What are emerging organizations in digital diabetes ecosystems? (2) What are the value streams in digital T2D ecosystems? (3) Which innovation patterns are present in digital T2D ecosystems?MethodsWe conducted a literature review and market analysis to characterize organizations and value streams in T2D ecosystems, pre- and post-digital transformation. We used the e3-value methodology to visualize T2D ecosystems (RQ1 and RQ2) and conducted expert interviews to identify emerging innovation patterns in digital diabetes ecosystems (RQ3).ResultsOur analyses revealed the emergence of eight organization segments in digital diabetes ecosystems: real-world evidence analytics, healthcare management platforms, clinical decision support, diagnostic and monitoring, digital therapeutics, wellness, online community, and online pharmacy (RQ1). Visualizing the value streams among these organizations highlights the crucial importance of individual health data (RQ2). Furthermore, our analysis revealed four major innovation patterns within the digital diabetes ecosystem: open ecosystem strategies, outcome-based payment models, platformization, and user-centric software (RQ3).ConclusionsOur findings illustrate the transition from traditional value chains in T2D care to platform-based and outcome-oriented models. These innovation patterns can inform strategic decisions for companies and healthcare providers, potentially helping anticipate new digital trends in diabetes care and across other chronic disease ecosystems.
| S-EPMC11795620 | biostudies-literature