%0 Journal Article
%T Baseline Variability Affects N-of-1 Intervention Effect: Simulation and Field Studies.
%A Suzuki M
%A Tanaka S
%A Saito K
%A Cho K
%A Iso N
%A Okabe T
%A Suzuki T
%A Yamamoto J
%J J Pers Med
%V 13
%N 5
%D 2023 Apr 24
%M 37240890
%F 3.508
%R 10.3390/jpm13050720
%X 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.