Usability and feasibility of PreventS-MD web app for stroke prevention.

Stroke epidemiology hypertension prevention risk factors stroke facilities

Journal

International journal of stroke : official journal of the International Stroke Society
ISSN: 1747-4949
Titre abrégé: Int J Stroke
Pays: United States
ID NLM: 101274068

Informations de publication

Date de publication:
19 Aug 2023
Historique:
pubmed: 24 7 2023
medline: 24 7 2023
entrez: 24 7 2023
Statut: aheadofprint

Résumé

Most strokes and cardiovascular diseases (CVDs) are potentially preventable if their risk factors are identified and well controlled. Digital platforms, such as the PreventS-MD web app (PreventS-MD) may aid health care professionals (HCPs) in assessing and managing risk factors and promoting lifestyle changes for their patients. This is a mixed-methods cross-sectional two-phase survey using a largely positivist (quantitative and qualitative) framework. During Phase 1, a prototype of PreventS-MD was tested internationally by 59 of 69 consenting HCPs of different backgrounds, age, sex, working experience, and specialties using hypothetical data. Collected comments/suggestions from the study HCPs in Phase 1 were reviewed and implemented. In Phase 2, a near-final version of PreventS-MD was developed and tested by 58 of 72 consenting HCPs using both hypothetical and real patient (n = 10) data. Qualitative semi-structured interviews with real patients (n = 10) were conducted, and 1 month adherence to the preventive recommendations was assessed by self-reporting. The four System Usability Scale (SUS) groups of scores (0-50 unacceptable; 51-68 poor; 68-80.3 good; >80.3 excellent) were used to determine usability of PreventS-MD. Ninety-nine HCPs from 27 countries (45% from low- to middle-income countries) participated in the study, and out of them, 10 HCPs were involved in the development of PreventS before the study, and therefore were not involved in the survey. Of the remaining 89 HCPs, 69 consented to the first phase of the survey, and 59 of them completed the first phase of the survey (response rate 86%), and 58 completed the second phase of the survey (response rate 84%). The SUS scores supported good usability of the prototype (mean score = 80.2; 95% CI [77.0-84.0]) and excellent usability of the final version of PreventS-MD (mean score = 81.7; 95% CI [79.1-84.3]) in the field. Scores were not affected by the age, sex, working experience, or specialty of the HCPs. One-month follow-up of the patients confirmed the high level of satisfaction/acceptability of PreventS-MD and (100%) adherence to the recommendations. The PreventS-MD web app has a high level of usability, feasibility, and satisfaction by HCPs and individuals at risk of stroke/CVD. Individuals at risk of stroke/CVD demonstrated a high level of confidence and motivation in following and adhering to preventive recommendations generated by PreventS-MD.

Sections du résumé

BACKGROUND UNASSIGNED
Most strokes and cardiovascular diseases (CVDs) are potentially preventable if their risk factors are identified and well controlled. Digital platforms, such as the PreventS-MD web app (PreventS-MD) may aid health care professionals (HCPs) in assessing and managing risk factors and promoting lifestyle changes for their patients.
METHODS UNASSIGNED
This is a mixed-methods cross-sectional two-phase survey using a largely positivist (quantitative and qualitative) framework. During Phase 1, a prototype of PreventS-MD was tested internationally by 59 of 69 consenting HCPs of different backgrounds, age, sex, working experience, and specialties using hypothetical data. Collected comments/suggestions from the study HCPs in Phase 1 were reviewed and implemented. In Phase 2, a near-final version of PreventS-MD was developed and tested by 58 of 72 consenting HCPs using both hypothetical and real patient (n = 10) data. Qualitative semi-structured interviews with real patients (n = 10) were conducted, and 1 month adherence to the preventive recommendations was assessed by self-reporting. The four System Usability Scale (SUS) groups of scores (0-50 unacceptable; 51-68 poor; 68-80.3 good; >80.3 excellent) were used to determine usability of PreventS-MD.
FINDINGS UNASSIGNED
Ninety-nine HCPs from 27 countries (45% from low- to middle-income countries) participated in the study, and out of them, 10 HCPs were involved in the development of PreventS before the study, and therefore were not involved in the survey. Of the remaining 89 HCPs, 69 consented to the first phase of the survey, and 59 of them completed the first phase of the survey (response rate 86%), and 58 completed the second phase of the survey (response rate 84%). The SUS scores supported good usability of the prototype (mean score = 80.2; 95% CI [77.0-84.0]) and excellent usability of the final version of PreventS-MD (mean score = 81.7; 95% CI [79.1-84.3]) in the field. Scores were not affected by the age, sex, working experience, or specialty of the HCPs. One-month follow-up of the patients confirmed the high level of satisfaction/acceptability of PreventS-MD and (100%) adherence to the recommendations.
INTERPRETATION UNASSIGNED
The PreventS-MD web app has a high level of usability, feasibility, and satisfaction by HCPs and individuals at risk of stroke/CVD. Individuals at risk of stroke/CVD demonstrated a high level of confidence and motivation in following and adhering to preventive recommendations generated by PreventS-MD.

Identifiants

pubmed: 37485871
doi: 10.1177/17474930231190745
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

17474930231190745

Investigateurs

Foad Abd-Allah (F)
Rufus Akinyemi (R)
Reza Azarpazhooh (R)
Anjali Bhatia (A)
Philip M Bath (PM)
Carol Brayne (C)
Hrvoje Budincevic (H)
Nicholas Child (N)
Kamil Chwojnicki (K)
Manuel Correia (M)
Alan Davis (A)
Gerry Devlin (G)
Vida Demarin (V)
Rajinder K Dhamija (RK)
Ding Ding (D)
Klara Dokova (K)
Makarena Dudley (M)
Jesse Dyer (J)
Misty Edmonds (M)
Marcela Ely (M)
Mehdi Farhoudi (M)
Svetlana Feigin (S)
Caroline Fornolles (C)
Aznida Firzah Abdul Aziz (A)
Denis Gabriel (D)
Seana Gall (S)
Artyom Gil (A)
Elena Gnedovskaya (E)
Ann George (A)
Michal Haršány (M)
Matire Harwood (M)
Argye Hillis (A)
Zeng-Guang Hou (ZG)
Kevin Hwang (K)
Norlinah Ibrahim (N)
Tania Ka'ai (T)
Nidhi Kalra (N)
Judith Katzenellenbogen (J)
Law Zhe Kang (L)
Arindam Kar (A)
Bartosz Karaszewski (B)
Vitalij Kazin (V)
Miia Kivipelto (M)
Saltanat Kamenova (S)
Aida Kondybaeva (A)
Pablo Lavados (P)
Tsong-Hai Lee (TH)
Liping Liu (L)
Karim Mahawish (K)
Michal Maluchnik (M)
Sheila Martins (S)
Farrah Mateen (F)
Nahal Mavaddat (N)
Man Mohan Mehndiratta (M)
Robert Mikulik (R)
Angela Oliver (A)
Serefnur Özturk (S)
Nikhil Patel (N)
Michael Piradov (M)
Binita Prakash (B)
Tara Purvis (T)
Ulf-Dietrich Reips (UD)
Kev Roos (K)
Jonathan Rosand (J)
Ramesh Sahathevan (R)
Lakshmanan Sekaran (L)
Nikolay Shamalov (N)
Deidre Anne De Silva (D)
Vinod Singh (V)
Alina Solomon (A)
Padma Srivastava (P)
Nijasri C Suwanwela (NC)
Denise Taylor (D)
Thomas Truelsen (T)
Narayanaswamy Venketasubramanian (N)
Ekaterina Volevach (E)
Ondřej Volný (O)
Joyce Wan (J)
Katila Withanapathirana (K)
Tamara Welte (T)
David Wiebers (D)
Andrea S Winkler (AS)
Tissa Wijeratne (T)
Teddy Wu (T)
Wan Asyraf Wan Zaidi (W)

Auteurs

Valery L Feigin (VL)

National Institute for Stroke and Applied Neurosciences, School of Clinical Sciences, Auckland University of Technology, Auckland, New Zealand.

Rita Krishnamurthi (R)

National Institute for Stroke and Applied Neurosciences, School of Clinical Sciences, Auckland University of Technology, Auckland, New Zealand.

Oleg Medvedev (O)

School of Psychology, University of Waikato, Hamilton, New Zealand.

Alexander Merkin (A)

National Institute for Stroke and Applied Neurosciences, School of Clinical Sciences, Auckland University of Technology, Auckland, New Zealand.

Balakrishnan Nair (B)

National Institute for Stroke and Applied Neurosciences, School of Clinical Sciences, Auckland University of Technology, Auckland, New Zealand.

Michael Kravchenko (M)

Research Center of Neurology, Moscow, Russia.

Shabnam Jalili-Moghaddam (S)

National Institute for Stroke and Applied Neurosciences, School of Clinical Sciences, Auckland University of Technology, Auckland, New Zealand.

Suzanne Barker-Collo (S)

School of Psychology, The University of Auckland, Auckland, New Zealand.

Yogini Ratnasabapathy (Y)

Te Whatu Ora-Health New Zealand, Waitematā, Auckland, New Zealand.

Luke Skinner (L)

Te Whatu Ora-Health New Zealand, Waitematā, Auckland, New Zealand.

Mayowa Owolabi (M)

Center for Genomic and Precision Medicine, College of Medicine, University of Ibadan, Ibadan, Nigeria.

Bo Norrving (B)

Section of Neurology, Lund University, Skåne University Hospital, Lund, Sweden.

Perminder S Sachdev (PS)

Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales (UNSW), Sydney, NSW, Australia.
Neuropsychiatric Institute, The Prince of Wales Hospital, Sydney, NSW, Australia.

Bruce Arroll (B)

Department of General Practice and Primary Health Care, The University of Auckland, Auckland, New Zealand.

Michael Brainin (M)

Danube University Krems, Krems an der Donau, Austria.

Amanda Thrift (A)

Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC, Australia.

Graeme J Hankey (GJ)

Medical School, Faculty of Health and Medical Sciences, The University of Western Australia, Perth, WA, Australia.
Perron Institute for Neurological and Translational Science, Perth, WA, Australia.

Classifications MeSH