{Reference Type}: Journal Article
{Title}: Baseline Variability Affects N-of-1 Intervention Effect: Simulation and Field Studies.
{Author}: Suzuki M;Tanaka S;Saito K;Cho K;Iso N;Okabe T;Suzuki T;Yamamoto J;
{Journal}: J Pers Med
{Volume}: 13
{Issue}: 5
{Year}: 2023 Apr 24
{Factor}: 3.508
{DOI}: 10.3390/jpm13050720
{Abstract}: 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.