Frameworks for Implementation, Uptake, and Use of Cardiometabolic Disease-Related Digital Health Interventions in Ethnic Minority Populations: Scoping Review.

cardiology cardiometabolic cultural digital health diverse diversity eHealth ethnicity framework health inequalities health inequality health technology metabolic metabolism minority review

Journal

JMIR cardio
ISSN: 2561-1011
Titre abrégé: JMIR Cardio
Pays: Canada
ID NLM: 101718325

Informations de publication

Date de publication:
11 Aug 2022
Historique:
received: 17 02 2022
accepted: 18 04 2022
revised: 17 04 2022
entrez: 15 8 2022
pubmed: 16 8 2022
medline: 16 8 2022
Statut: epublish

Résumé

Digital health interventions have become increasingly common across health care, both before and during the COVID-19 pandemic. Health inequalities, particularly with respect to ethnicity, may not be considered in frameworks that address the implementation of digital health interventions. We considered frameworks to include any models, theories, or taxonomies that describe or predict implementation, uptake, and use of digital health interventions. We aimed to assess how health inequalities are addressed in frameworks relevant to the implementation, uptake, and use of digital health interventions; health and ethnic inequalities; and interventions for cardiometabolic disease. SCOPUS, PubMed, EMBASE, Google Scholar, and gray literature were searched to identify papers on frameworks relevant to the implementation, uptake, and use of digital health interventions; ethnically or culturally diverse populations and health inequalities; and interventions for cardiometabolic disease. We assessed the extent to which frameworks address health inequalities, specifically ethnic inequalities; explored how they were addressed; and developed recommendations for good practice. Of 58 relevant papers, 22 (38%) included frameworks that referred to health inequalities. Inequalities were conceptualized as society-level, system-level, intervention-level, and individual. Only 5 frameworks considered all levels. Three frameworks considered how digital health interventions might interact with or exacerbate existing health inequalities, and 3 considered the process of health technology implementation, uptake, and use and suggested opportunities to improve equity in digital health. When ethnicity was considered, it was often within the broader concepts of social determinants of health. Only 3 frameworks explicitly addressed ethnicity: one focused on culturally tailoring digital health interventions, and 2 were applied to management of cardiometabolic disease. Existing frameworks evaluate implementation, uptake, and use of digital health interventions, but to consider factors related to ethnicity, it is necessary to look across frameworks. We have developed a visual guide of the key constructs across the 4 potential levels of action for digital health inequalities, which can be used to support future research and inform digital health policies.

Sections du résumé

BACKGROUND BACKGROUND
Digital health interventions have become increasingly common across health care, both before and during the COVID-19 pandemic. Health inequalities, particularly with respect to ethnicity, may not be considered in frameworks that address the implementation of digital health interventions. We considered frameworks to include any models, theories, or taxonomies that describe or predict implementation, uptake, and use of digital health interventions.
OBJECTIVE OBJECTIVE
We aimed to assess how health inequalities are addressed in frameworks relevant to the implementation, uptake, and use of digital health interventions; health and ethnic inequalities; and interventions for cardiometabolic disease.
METHODS METHODS
SCOPUS, PubMed, EMBASE, Google Scholar, and gray literature were searched to identify papers on frameworks relevant to the implementation, uptake, and use of digital health interventions; ethnically or culturally diverse populations and health inequalities; and interventions for cardiometabolic disease. We assessed the extent to which frameworks address health inequalities, specifically ethnic inequalities; explored how they were addressed; and developed recommendations for good practice.
RESULTS RESULTS
Of 58 relevant papers, 22 (38%) included frameworks that referred to health inequalities. Inequalities were conceptualized as society-level, system-level, intervention-level, and individual. Only 5 frameworks considered all levels. Three frameworks considered how digital health interventions might interact with or exacerbate existing health inequalities, and 3 considered the process of health technology implementation, uptake, and use and suggested opportunities to improve equity in digital health. When ethnicity was considered, it was often within the broader concepts of social determinants of health. Only 3 frameworks explicitly addressed ethnicity: one focused on culturally tailoring digital health interventions, and 2 were applied to management of cardiometabolic disease.
CONCLUSIONS CONCLUSIONS
Existing frameworks evaluate implementation, uptake, and use of digital health interventions, but to consider factors related to ethnicity, it is necessary to look across frameworks. We have developed a visual guide of the key constructs across the 4 potential levels of action for digital health inequalities, which can be used to support future research and inform digital health policies.

Identifiants

pubmed: 35969455
pii: v6i2e37360
doi: 10.2196/37360
pmc: PMC9412726
doi:

Types de publication

Journal Article Review

Langues

eng

Pagination

e37360

Informations de copyright

©Mel Ramasawmy, Lydia Poole, Zareen Thorlu-Bangura, Aneesha Chauhan, Mayur Murali, Parbir Jagpal, Mehar Bijral, Jai Prashar, Abigail G-Medhin, Elizabeth Murray, Fiona Stevenson, Ann Blandford, Henry W W Potts, Kamlesh Khunti, Wasim Hanif, Paramjit Gill, Madiha Sajid, Kiran Patel, Harpreet Sood, Neeraj Bhala, Shivali Modha, Manoj Mistry, Vinod Patel, Sarah N Ali, Aftab Ala, Amitava Banerjee. Originally published in JMIR Cardio (https://cardio.jmir.org), 11.08.2022.

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Auteurs

Mel Ramasawmy (M)

Institute of Health Informatics, University College London, London, United Kingdom.

Lydia Poole (L)

Institute of Health Informatics, University College London, London, United Kingdom.

Zareen Thorlu-Bangura (Z)

Institute of Health Informatics, University College London, London, United Kingdom.

Aneesha Chauhan (A)

Charing Cross Hospital, Imperial College Healthcare NHS Trust, London, United Kingdom.

Mayur Murali (M)

Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom.

Parbir Jagpal (P)

School of Pharmacy, University of Birmingham, Birmingham, United Kingdom.

Mehar Bijral (M)

University College London Medical School, University College London, London, United Kingdom.

Jai Prashar (J)

University College London Medical School, University College London, London, United Kingdom.

Abigail G-Medhin (A)

Department of Population Health Sciences, King's College London, London, United Kingdom.

Elizabeth Murray (E)

eHealth Unit, Research Department of Primary Care and Population Health, University College London Medical School, London, United Kingdom.

Fiona Stevenson (F)

eHealth Unit, Research Department of Primary Care and Population Health, University College London Medical School, London, United Kingdom.

Ann Blandford (A)

University College London Interaction Centre, University College London, London, United Kingdom.

Henry W W Potts (HWW)

Institute of Health Informatics, University College London, London, United Kingdom.

Kamlesh Khunti (K)

Diabetes Research Centre, Leicester General Hospital, University of Leicester, Leicester, United Kingdom.

Wasim Hanif (W)

Department of Diabetes and Institute of Translational Medicine, University Hospital Birmingham, Birmingham, United Kingdom.

Paramjit Gill (P)

Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, United Kingdom.

Madiha Sajid (M)

Patient and Public Involvement Representative, DISC Study (UK), United Kingdom.

Kiran Patel (K)

Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, United Kingdom.
University Hospitals Coventry and Warwickshire, Coventry, United Kingdom.

Harpreet Sood (H)

Health Education England, London, United Kingdom.
Hurley Group Practice, London, United Kingdom.

Neeraj Bhala (N)

Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom.
Institute of Applied Health Research, University of Birmingham, Birmingham, United Kingdom.

Shivali Modha (S)

Patient and Public Involvement Representative, DISC Study (UK), United Kingdom.

Manoj Mistry (M)

Patient and Public Involvement Representative, DISC Study (UK), United Kingdom.

Vinod Patel (V)

Warwick Medical School, University of Warwick, Coventry, United Kingdom.

Sarah N Ali (SN)

Department of Diabetes and Endocrinology, Royal Free London NHS Foundation Trust, London, United Kingdom.

Aftab Ala (A)

Department of Access and Medicine, Royal Surrey NHS Foundation Trust, Guildford, United Kingdom.
Department of Clinical and Experimental Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.
Institute of Liver Studies, King's College Hospital NHS Foundation Trust, London, United Kingdom.

Amitava Banerjee (A)

Institute of Health Informatics, University College London, London, United Kingdom.

Classifications MeSH