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Baseline Variability Affects N-of-1 Intervention Effect: Simulation and Field Studies

The simulation study investigated the relationship between the local linear trend model’s data-comparison accuracy, baseline-data variability, and changes in level and slope after introducing the N-of-1 intervention. Contour maps were constructed, which included baseline-data variability, change in...

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Detalles Bibliográficos
Autores principales: Suzuki, Makoto, Tanaka, Satoshi, Saito, Kazuo, Cho, Kilchoon, Iso, Naoki, Okabe, Takuhiro, Suzuki, Takako, Yamamoto, Junichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10219212/
https://www.ncbi.nlm.nih.gov/pubmed/37240890
http://dx.doi.org/10.3390/jpm13050720
Descripción
Sumario:The simulation study investigated the relationship between the local linear trend model’s data-comparison accuracy, baseline-data variability, and changes in level and slope after introducing the N-of-1 intervention. Contour maps were constructed, which included baseline-data variability, change in level or slope, and percentage of non-overlapping data between the state and forecast values by the local linear trend model. Simulation results showed that baseline-data variability and changes in level and slope after intervention affect the data-comparison accuracy based on the local linear trend model. The field study investigated the intervention effects for actual field data using the local linear trend model, which confirmed 100% effectiveness of previous N-of-1 studies. These results imply that baseline-data variability affects the data-comparison accuracy using a local linear trend model, which could accurately predict the intervention effects. The local linear trend model may help assess the intervention effects of effective personalized interventions in precision rehabilitation.