ArticleThe journal of prevention of Alzheimer's disease2021
A UK-Wide Study Employing Natural Language Processing to Determine What Matters to People about Brain Health to Improve Drug Development: The Electronic Person-Specific Outcome Measure (ePSOM) Programme.
Article in The journal of prevention of Alzheimer's disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Towards a lifelong personalized brain health program: empowering individuals to define, pursue, and monitor meaningful outcomes.Frontiers in neurology · 2024Article
- Meaningful benefits: a framework to assess disease-modifying therapies in preclinical and early Alzheimer's disease.Alzheimer's research & therapy · 2022Review
- Natural language processing in Alzheimer's disease research: Systematic review of methods, data, and efficacy.Alzheimer's & dementia (Amsterdam, Netherlands)Review
- Incorporating individually defined brain health priorities in clinical trial outcomes: the electronic Person-Specific Outcome Measure approach in the United States.Alzheimer's & dementia (New York, N. Y.)Review
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Authors and funding
5 authors.
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Abstract
backgroundIt is important to use outcome measures for novel interventions in Alzheimer's disease (AD) that capture the research participants' views of effectiveness. The electronic Person-Specific Outcome Measure (ePSOM) development programme is underpinned by the need to identify and detect change in early disease manifestations and the possibilities of incorporating artificial intelligence in outcome measures.
objectivesThe aim of the ePSOM programme is to better understand what outcomes matter to patients in the AD population with a focus on those at the pre-dementia stages of disease. Ultimately, we aim to develop an app with robust psychometric properties to be used as a patient reported outcome measure in AD clinical trials.
designWe designed and ran a nationwide study (Aug 2019 - Nov 2019, UK), collecting primarily free text responses in five pre-defined domains. We collected self-reported clinical details and sociodemographic data to analyse responses by key variables whilst keeping the survey short (around 15 minutes). We used clustering and Natural Language Processing techniques to identify themes which matter most to individuals when developing new treatments for AD.
resultsThe study was completed by 5,808 respondents, yielding over 80,000 free text answers. The analysis resulted in 184 themes of importance. An analysis focusing on key demographics to explore how priorities differed by age, gender and education revealed that there are significant differences in what groups consider important about their brain health. DISCUSSION: The ePSOM data has generated evidence on what matters to people when developing new treatments for AD that target secondary prevention and therein maintenance of brain health. These results will form the basis for an electronic outcome measure to be used in AD clinical research and clinical practice.
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