2017
DOI: 10.3414/me16-02-0040
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Patient-driven N-of-1 in Parkinson’s Disease

Abstract: KeywordsParkinson's disease, N-of-1, levodopa induced dyskinesia, patient-driven, Quantified Self Summary Background: New insights and knowledge in biomedical science often come from observation and experimentation. Methods traditionally used include self-experimentation, case reports, randomised controlled trials, and N-of-1 studies. Technological advances have lead to an increasing number of individuals and patients engaging in self-tracking. We use the term patient-driven N-of-1 for self-tracking performed … Show more

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Cited by 17 publications
(20 citation statements)
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“…Used in this way, the learning healthcare system [92] will provide the most precise version of “what works for whom, when” we can think of: personalized value equations in patient- or provider-driven n = 1 studies [93]. Aggregation of all n = 1 studies will then provide the basis for accumulation of knowledge of “what works for whom, when,” thereby bridging personalized medicine and the ideas for systemizing knowledge about adaptations as outlined in the Adaptome [26].…”
Section: Discussionmentioning
confidence: 99%
“…Used in this way, the learning healthcare system [92] will provide the most precise version of “what works for whom, when” we can think of: personalized value equations in patient- or provider-driven n = 1 studies [93]. Aggregation of all n = 1 studies will then provide the basis for accumulation of knowledge of “what works for whom, when,” thereby bridging personalized medicine and the ideas for systemizing knowledge about adaptations as outlined in the Adaptome [26].…”
Section: Discussionmentioning
confidence: 99%
“…N-of-1 designs have received attention for their potential to facilitate understanding and management of health conditions that require individualized insights and approaches (1,2). N-of-1 studies come in a variety of designs [e.g., Heyvaert and Onghena (3) and Daskalova et al, (4)], including self-monitoring, in which people track data related to their condition to examine progress toward goals or changes over time [e.g., Mishra et al, (5), Ayobi et al, (6), and Consolvo et al, (7)]; correlational analyses, in which people investigate what factors may affect their symptoms [e.g., behavioral, environmental, medical; (8)]; and self-experiments, in which people determine causality between factors and symptoms [e.g., Karkar et al, (9) and Riggare et al, (10)]. Each of these designs can support a range of goals.…”
Section: Introductionmentioning
confidence: 99%
“…Together with other patients, I have published articles about how we can contribute to our health and care when our healthcare systems have not yet responded to the latest research. One article describes an approach to multidisciplinary teams10; another our patient driven study 11…”
Section: #Wearenotwaitingmentioning
confidence: 99%