Dataset Information

Application of the National Institute for Health and Care Excellence Evidence Standards Framework for Digital Health Technologies in Assessing Mobile-Delivered Technologies for the Self-Management of Type 2 Diabetes Mellitus: Scoping Review.

ABSTRACT:

Background

There is a growing role of digital health technologies (DHTs) in the management of chronic health conditions, specifically type 2 diabetes. It is increasingly important that health technologies meet the evidence standards for health care settings. In 2019, the National Institute for Health and Care Excellence (NICE) published the NICE Evidence Standards Framework for DHTs. This provides guidance for evaluating the effectiveness and economic value of DHTs in health care settings in the United Kingdom.

Objective

The aim of this study is to assess whether scientific articles on DHTs for the self-management of type 2 diabetes mellitus report the evidence suggested for implementation in clinical practice, as described in the NICE Evidence Standards Framework for DHTs.

Methods

We performed a scoping review of published articles and searched 5 databases to identify systematic reviews and primary studies of mobile device-delivered DHTs that provide self-management support for adults with type 2 diabetes mellitus. The evidence reported within articles was assessed against standards described in the NICE framework.

Results

The database search yielded 715 systematic reviews, of which, 45 were relevant and together included 59 eligible primary studies. Within these, there were 39 unique technologies. Using the NICE framework, 13 technologies met best practice standards, 3 met minimum standards only, and 23 technologies did not meet minimum standards.

Conclusions

On the assessment of peer-reviewed publications, over half of the identified DHTs did not appear to meet the minimum evidence standards recommended by the NICE framework. The most common reasons for studies of DHTs not meeting these evidence standards included the absence of a comparator group, no previous justification of sample size, no measurable improvement in condition-related outcomes, and a lack of statistical data analysis. This report provides information that will enable researchers and digital health developers to address these limitations when designing, delivering, and reporting digital health technology research in the future.

SUBMITTER: Forsyth JR

PROVIDER: S-EPMC7925151 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Application of the National Institute for Health and Care Excellence Evidence Standards Framework for Digital Health Technologies in Assessing Mobile-Delivered Technologies for the Self-Management of Type 2 Diabetes Mellitus: Scoping Review.

Forsyth Jessica R JR Chase Hannah H Roberts Nia W NW Armitage Laura C LC Farmer Andrew J AJ

JMIR diabetes 20210216 1

<h4>Background</h4>There is a growing role of digital health technologies (DHTs) in the management of chronic health conditions, specifically type 2 diabetes. It is increasingly important that health technologies meet the evidence standards for health care settings. In 2019, the National Institute for Health and Care Excellence (NICE) published the NICE Evidence Standards Framework for DHTs. This provides guidance for evaluating the effectiveness and economic value of DHTs in health care setting ...[more]

PMID: 33591278

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Project description:BackgroundClinical governance of medical mobile apps is challenging, and there is currently no standard method for assessing the quality of such apps. In 2018, the National Institute for Health and Care Excellence (NICE) developed a framework for assessing the required level of evidence for digital health technologies (DHTs), as determined by their clinical function. The framework can potentially be used to assess mobile apps, which are a subset of DHTs. To be used reliably in this context, the framework must allow unambiguous classification of an app's clinical function.ObjectiveThe objective of this study was to determine whether mobile health apps could be reliably classified using the NICE evidence standards framework for DHTs.MethodsWe manually extracted app titles, screenshots, and content descriptions for all apps listed on the National Health Service (NHS) Apps Library website on July 12, 2019; none of the apps were downloaded. Using this information, 2 mobile health (mHealth) researchers independently classified each app to one of the 4 functional tiers (ie, 1, 2, 3a, and 3b) described in the NICE digital technologies evaluation framework. Coders also answered contextual questions from the framework to identify whether apps were deemed to be higher risk. Agreement between coders was assessed using Cohen ? statistic.ResultsIn total, we assessed 76 apps from the NHS Apps Library. There was classification agreement for 42 apps. Of these, 0 apps were unanimously classified into Tier 1; 24, into Tier 2; 15, into Tier 3a; and 3, into Tier 3b. There was disagreement between coders in 34/76 cases (45%); interrater agreement was poor (Cohen ?=0.32, 95% CI 0.16-0.47). Further investigation of disagreements highlighted 5 main explanatory themes: apps that did not correspond to any tier, apps that corresponded to multiple tiers, ambiguous tier descriptions, ambiguous app descriptions, and coder error.ConclusionsThe current iteration of the NICE evidence standards framework for DHTs did not allow mHealth researchers to consistently and unambiguously classify digital health mobile apps listed on the NHS app library according to their functional tier.

Project description:BackgroundAn increasing number of patients expect and want to play a greater role in their treatment and care decisions. This emphasizes the need to adopt collaborative health care practices, which implies collaboration among interprofessional health care teams and patients, their families, caregivers, and communities. In recent years, digital health technologies that support self-care and collaboration between the community and health care providers (ie, participatory health technologies) have received increasing attention. However, knowledge regarding the features of such technologies that support effective patient-professional partnerships is still limited.ObjectiveThis study aimed to map and assess published studies on participatory health technologies intended to support partnerships among patients, caregivers, and health care professionals in chronic care, focusing specifically on identifying the main features of these technologies.MethodsA scoping review covering scientific publications in English between January 2008 and December 2020 was performed. We searched PubMed and Web of Science databases. Peer-reviewed qualitative, quantitative, and mixed methods studies that evaluated digital health technologies for patient-professional partnerships in chronic care settings were included. The data were charted and analyzed thematically. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was used.ResultsThis review included 32 studies, reported in 34 papers. The topic of participatory health technologies experienced a slightly increasing trend across publication years, with most papers originating from the United States and Norway. Diabetes and cardiovascular diseases were the most common conditions addressed. Of the 32 studies, 12 (38%) evaluated the influence of participatory health technologies on partnerships, mostly with positive outcomes, although we also identified how partnership relationships and the nature of collaborative work could be challenged when the roles and expectations between users were unclear. Six common features of participatory health technologies were identified: patient-professional communication, self-monitoring, tailored self-care support, self-care education, care planning, and community forums for peer-to-peer interactions.ConclusionsOur findings emphasize the importance of clarifying mutual expectations and carefully considering the implications that the introduction of participatory health technologies may have on the work of patients and health care professionals, both individually and in collaboration. A knowledge gap remains regarding the use of participatory health technologies to effectively support patient-professional partnerships in chronic care management.

Project description:IntroductionAn estimated one billion people globally are currently suffering from hypertension. Prevention and management of hypertension are suboptimal especially in low- and middle-income countries leading to increased complications and deaths. With increased mobile phone coverage globally, this study aims to review mobile health technologies used for the prevention and management of hypertension.MethodsWe conducted a literature search on electronic databases using identified keywords involving "hypertension", "mobile health technology" and their synonyms. Snowballing technique was also used. Papers were screened at two levels by independent reviewers. The targets were studies published in peer-reviewed journals reporting mobile health interventions for hypertension prevention and management. Only primary research studies published in English from January 2017 to April 2024 were included. Google Forms were used to extract the data along with other characteristics, and selected articles were categorised into: mobile application, web-based solutions, and Short Message Service (SMS) and other offline solutions.ResultThe search yielded 184 articles, and 44 studies were included in the review. Most (n = 26) were randomised control trials. Twenty-two studies (22) focused only on mobile applications solutions, 12 on SMS and other offline mHealth, 5 web-based solutions, and 5 combined more than one type of mobile health technology. The United States of America had the majority of studies (n = 17), with 6 studies from other American countries, 11 from Asia and nine from Europe, while only one from Africa. A total of 36 studies reported that mobile health technology significantly improved hypertension care through reduced blood pressure, improved adherence to follow-up visits and medications, and lifestyle changes. SMS and offline mHealth strategies have also demonstrated effectiveness in promoting self-management and reducing racial disparities in hypertension care.ConclusionMobile health technology has the potential to play a significant role in the prevention and management of hypertension. However, there is a need for mobile health solutions for hypertension prevention and management in African countries and other developing countries. Integrating mHealth into primary healthcare delivery would also go a long way in strengthening patient care and reducing the burden on healthcare systems.

Project description:BackgroundDigital technologies have the potential to contribute to health promotion and disease prevention in the aging world.ObjectiveThis study aims to identify digital technologies for health promotion and disease prevention that could be used independently by older people in nonclinical settings using a scoping review.MethodsThrough database (MEDLINE, PsycINFO, CINAHL, and SCOPUS; to March 3, 2022) and manual searches (to June 14, 2022), 90 primary studies and 8 systematic reviews were included in this scoping review. The eligibility was based on the PCC (Population, Concept, and Context) criteria: (1) people aged 50 years or older (population), (2) any digital (health) technology (eg, smartphone apps, websites, virtual reality; concept), and (3) health promotion and disease prevention in daily life in nonclinical and noninstitutional settings (context). Data items included study characteristics, PCC criteria, opportunities versus challenges, and evidence gaps. Data were synthesized using descriptive statistics or narratively described by identifying common themes.ResultsThe studies were published in 2005-2022 and originated predominantly from North America and Europe. Most primary studies were nonrandomized, reported quantitative data, and investigated effectiveness or feasibility (eg, acceptance or usability) of digital technologies in older people. The participants were aged 50 years to 99 years, predominantly female, affluent (ie, with high income, education, and digital competence), and intended to use or used digital technologies for a median of 3 months independently at home or in community settings. The digital technologies included mobile or nonmobile technologies or virtual reality. The studies used "modern devices" (eg, smartphones, wearables, or gaming consoles) or modern and "older devices" (eg, computers or mobile phones). The users interacted with digital technologies via websites, emails, text messages, apps, or virtual reality. Health targets of digital technologies were mobility, mental health, nutrition, or cognition. The opportunities versus challenges of digital technologies were (1) potential health benefits versus unclear or no benefits for some outcomes, (2) monitoring of health versus ethical issues with data collection and management, (3) implications for functioning in daily life (ie, potential to prolong independent living) versus unclear application for clinical management or care, (4) tailoring of technical properties and content toward older users versus general use, (5) importance of human support for feasibility versus other factors required to improve feasibility, (6) reduction of social isolation versus access to digital technologies, and (7) improvement in digital competence versus digital divide.ConclusionsVarious digital technologies were independently used by people aged 50 years or older for health promotion and disease prevention. Future studies should focus on (1) more diverse populations of older people, (2) new digital technologies, (3) other (clinical and care) settings, and (4) outcome evaluation to identify factors that could enhance any health benefits of digital technologies.International registered report identifier (irrid)RR2-10.2196/37729.

Project description:BackgroundDue to growing pressure on the health care system, a shift in rehabilitation to home settings is essential. However, efficient support for home-based rehabilitation is lacking. The COVID-19 pandemic has further exacerbated these challenges and has affected individuals and health care professionals during rehabilitation. Digital rehabilitation (DR) could support home-based rehabilitation. To develop and implement DR solutions that meet clients' needs and ease the growing pressure on the health care system, it is necessary to provide an overview of existing, relevant, and future solutions shaping the constantly evolving market of technologies for home-based DR.ObjectiveIn this scoping review, we aimed to identify digital technologies for home-based DR, predict new or emerging DR trends, and report on the influences of the COVID-19 pandemic on DR.MethodsThe scoping review followed the framework of Arksey and O'Malley, with improvements made by Levac et al. A literature search was performed in PubMed, Embase, CINAHL, PsycINFO, and the Cochrane Library. The search spanned January 2015 to January 2022. A bibliometric analysis was performed to provide an overview of the included references, and a co-occurrence analysis identified the technologies for home-based DR. A full-text analysis of all included reviews filtered the trends for home-based DR. A gray literature search supplemented the results of the review analysis and revealed the influences of the COVID-19 pandemic on the development of DR.ResultsA total of 2437 records were included in the bibliometric analysis and 95 in the full-text analysis, and 40 records were included as a result of the gray literature search. Sensors, robotic devices, gamification, virtual and augmented reality, and digital and mobile apps are already used in home-based DR; however, artificial intelligence and machine learning, exoskeletons, and digital and mobile apps represent new and emerging trends. Advantages and disadvantages were displayed for all technologies. The COVID-19 pandemic has led to an increased use of digital technologies as remote approaches but has not led to the development of new technologies.ConclusionsMultiple tools are available and implemented for home-based DR; however, some technologies face limitations in the application of home-based rehabilitation. However, artificial intelligence and machine learning could be instrumental in redesigning rehabilitation and addressing future challenges of the health care system, and the rehabilitation sector in particular. The results show the need for feasible and effective approaches to implement DR that meet clients' needs and adhere to framework conditions, regardless of exceptional situations such as the COVID-19 pandemic.

Project description:BackgroundIncreasing physical activity (PA) levels in adolescents aged 12 to 18 years is associated with prevention of unhealthy weight gain and improvement in cardiovascular fitness. The widespread availability of mobile health (mHealth) and wearable devices offers self-monitoring and motivational features for increasing PA levels and improving adherence to exercise programs.ObjectiveThe aim of this scoping review was to identify the efficacy or effectiveness of mHealth intervention strategies for facilitating PA among adolescents aged 12 to 18 years.MethodsWe conducted a systematic search for peer-reviewed studies published between 2008 and 2018 in the following electronic databases: PubMed, Google Scholar, PsychINFO, or SportDiscus. The search terms used included mHealth or "mobile health" or apps, "physical activity" or exercise, children or adolescents or teens or "young adults" or kids, and efficacy or effectiveness. Articles published outside of the date range (July 2008 to October 2018) and non-English articles were removed before abstract review. Three reviewers assessed all abstracts against the inclusion and exclusion criteria. Any uncertainties or differences in opinion were discussed as a group. The inclusion criteria were that the studies should (1) have an mHealth component, (2) target participants aged between 12 and 18 years, (3) have results on efficacy or effectiveness, and (4) assess PA-related outcomes. Reviews, abstracts only, protocols without results, and short message service text messaging-only interventions were excluded. We also extracted potentially relevant papers from reviews. At least 2 reviewers examined all full articles for fit with the criteria and extracted data for analysis. Data extracted from selected studies included study population, study type, components of PA intervention, and PA outcome results.ResultsOverall, 126 articles were initially identified. Reviewers pulled 18 additional articles from excluded review papers. Only 18 articles were passed onto full review, and 16 were kept for analysis. The included studies differed in the sizes of the study populations (11-607 participants), locations of the study sites (7 countries), study setting, and study design. Overall, 5 mHealth intervention categories were identified: website, website+wearable, app, wearable+app, and website+wearable+app. The most common measures reported were subjective weekly PA (4/13) and objective daily moderate-to-vigorous PA (5/13) of the 19 different PA outcomes assessed. Furthermore, 5 of 13 studies with a control or comparison group showed a significant improvement in PA outcomes between the intervention group and the control or comparison group. Of those 5 studies, 3 permitted isolation of mHealth intervention components in the analysis.ConclusionsPA outcomes for adolescents improved over time through mHealth intervention use; however, the lack of consistency in chosen PA outcome measures, paucity of significant outcomes via between-group analyses, and the various study designs that prevent separating the effects of intervention components calls into question their true effect.

Project description:BACKGROUND:Studies have shown the effectiveness and user acceptance of mobile health (mHealth) technologies in managing patients with chronic kidney disease (CKD). However, incorporating mHealth technology into the standard care of patients with CKD still faces many challenges. To our knowledge, there are no reviews on mHealth interventions and their assessments concerning the management of patients undergoing dialysis. OBJECTIVE:This study provided a scoping review on existing apps and interventions of mHealth technologies in adult patients undergoing chronic dialysis and identified the gaps in patient outcome assessment of mHealth technologies in the literature. METHODS:We systematically searched PubMed (MEDLINE), Scopus, and the Cumulative Index to Nursing and Allied Health Literature databases, as well as gray literature sources. Two keywords, "mHealth" and "dialysis," were combined to address the main concepts of the objectives. Inclusion criteria were as follows: (1) mHealth interventions, which are on a smartphone, tablet, or web-based portals that are accessible through mobile devices; and (2) adult patients (age ?18 years) on chronic dialysis. Only English papers published from January 2008 to October 2018 were included. Studies with mHealth apps for other chronic conditions, based on e-consultation or videoconferencing, non-English publications, and review papers were excluded. RESULTS:Of the 1054 papers identified, 22 met the inclusion and exclusion criteria. Most studies (n=20) were randomized controlled trials and cohort studies. These studies were carried out in 7 countries. The main purposes of these mHealth interventions were as follows: nutrition or dietary self-monitoring (n=7), remote biometric monitoring (n=7), web-based portal (n=4), self-monitoring of in-session dialysis-specific information (n=3), and self-monitoring of lifestyle or behavioral change (n=1). The outcomes of the 22 included studies were organized into five categories: (1) patient satisfaction and acceptance, (2) clinical effectiveness, (3) economic assessment, (4) health-related quality of life, and (5) impact on lifestyle or behavioral change. The mHealth interventions showed neutral to positive results in chronic dialysis patient management, reporting no to significant improvement of dialysis-specific measurements and some components of the overall quality of life assessment. Evaluation of these mHealth interventions consistently demonstrated evidence in patients' satisfaction, high level of user acceptance, and reduced use of health resources and cost savings to health care services. However, there is a lack of studies evaluating safety, organizational, sociocultural, ethical, and legal aspects of mHealth technologies. Furthermore, a comprehensive cost-effectiveness and cost-benefit analysis of adopting mHealth technologies was not found in the literature. CONCLUSIONS:The gaps identified in this study will inform the creation of health policies and organizational support for mHealth implementation in patients undergoing dialysis. The findings of this review will inform the development of a comprehensive service model that utilizes mHealth technologies for home monitoring and self-management of patients undergoing chronic dialysis.

Dataset Information

Application of the National Institute for Health and Care Excellence Evidence Standards Framework for Digital Health Technologies in Assessing Mobile-Delivered Technologies for the Self-Management of Type 2 Diabetes Mellitus: Scoping Review.

Background

Objective

Methods

Results

Conclusions

Publications

Application of the National Institute for Health and Care Excellence Evidence Standards Framework for Digital Health Technologies in Assessing Mobile-Delivered Technologies for the Self-Management of Type 2 Diabetes Mellitus: Scoping Review.

Similar Datasets

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