Oral Presentation Australian and New Zealand Stroke Organisation Conference 2025

Stroke Riskometer™ mHealth Application increases Stroke Knowledge in A Randomised Controlled Trial (123117)

Addisu Dabi Wake 1 2 , Rita Krishnamurthi 3 , Katherine Chappell 1 , Valery Feigin 3 , Amanda G Thrift 4 , Timothy J Kleinig 4 5 , Dominique A Cadilhac 6 , Derrick A Bennett 7 8 , Mark R Nelson 1 , Tara Purvis 6 , Shabnam Jalili-Moghaddam 3 , Gemma Kitsos 1 , Seana L Gall 1 3
  1. Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia.
  2. Nursing Department, College of Health Sciences, Arsi University, Asella, Oromia , Ethiopia.
  3. National Institute of Stroke and Applied Neurosciences, Auckland University of Technology, Auckland, New Zealand.
  4. Department of Medicine, The University of Adelaide, Adelaide, SA, Australia.
  5. Department of Neurology, Royal Adelaide Hospital, Adelaide, SA, Australia.
  6. Translational Public Health and Evaluation Division, Stroke and Ageing Research, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia.
  7. Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
  8. Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.

Background: Improving knowledge about stroke symptoms, risk factors, and management may reduce its burden. We examine the effect of Stroke Riskometer™ app on stroke knowledge.  

Methods: A phase III, prospective, double-blinded, 2-arm randomised controlled trial in Australia and New Zealand from August 2021 to November 2023. Participants recruited were aged 35-75 years with ≥2 stroke modifiable risk factors, but no history of stroke/myocardial infarction/cognitive impairment/terminal illness. The intervention group (IG) was given the App; usual care group (UCG) received generic online risk factor information. Stroke knowledge was measured at baseline, 3-, 6- and 12-months using six validated questions summed to total score from 0 to 19. Linear mixed-effects modelling assessed changes in stroke knowledge between IG and UCG over time. Interactions tested effect modification by baseline characteristics.

Results: Among 862 randomised participants (IG n=429; UCG n=433) the mean age was 58 years; 63% female; 62% tertiary educated; and 15% most disadvantaged area-level socioeconomic status. Stroke knowledge improved in IG from baseline (mean [SE] 13.41 [0.16]) to 3-months (14.21 [0.18]) to 6-months (14.49 [0.17]) to 12-months (14.23 [0.18]) compared to UCG at baseline (13.16 [0.16]) to 3-months (13.99 [0.17]) to 6-months (13.99 [0.17]) to 12-months (14.39 [0.17]).The time-IG interaction showed a greater increased stroke knowledge (β =0.57, 95%CI:0.07, 1.06) than UCG at 6-months. The interaction analysis revealed a significantly higher intervention effect in tertiary educated, non-European and non-Indigenous ethnic group, and the least disadvantaged area.

Conclusions: The Stroke Riskometer™ app significantly improved stroke knowledge at 6-months post-randomisation compared to UCG.