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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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 |
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author | Suzuki, Makoto Tanaka, Satoshi Saito, Kazuo Cho, Kilchoon Iso, Naoki Okabe, Takuhiro Suzuki, Takako Yamamoto, Junichi |
author_facet | Suzuki, Makoto Tanaka, Satoshi Saito, Kazuo Cho, Kilchoon Iso, Naoki Okabe, Takuhiro Suzuki, Takako Yamamoto, Junichi |
author_sort | Suzuki, Makoto |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10219212 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102192122023-05-27 Baseline Variability Affects N-of-1 Intervention Effect: Simulation and Field Studies Suzuki, Makoto Tanaka, Satoshi Saito, Kazuo Cho, Kilchoon Iso, Naoki Okabe, Takuhiro Suzuki, Takako Yamamoto, Junichi J Pers Med Article 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. MDPI 2023-04-24 /pmc/articles/PMC10219212/ /pubmed/37240890 http://dx.doi.org/10.3390/jpm13050720 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Suzuki, Makoto Tanaka, Satoshi Saito, Kazuo Cho, Kilchoon Iso, Naoki Okabe, Takuhiro Suzuki, Takako Yamamoto, Junichi Baseline Variability Affects N-of-1 Intervention Effect: Simulation and Field Studies |
title | Baseline Variability Affects N-of-1 Intervention Effect: Simulation and Field Studies |
title_full | Baseline Variability Affects N-of-1 Intervention Effect: Simulation and Field Studies |
title_fullStr | Baseline Variability Affects N-of-1 Intervention Effect: Simulation and Field Studies |
title_full_unstemmed | Baseline Variability Affects N-of-1 Intervention Effect: Simulation and Field Studies |
title_short | Baseline Variability Affects N-of-1 Intervention Effect: Simulation and Field Studies |
title_sort | baseline variability affects n-of-1 intervention effect: simulation and field studies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10219212/ https://www.ncbi.nlm.nih.gov/pubmed/37240890 http://dx.doi.org/10.3390/jpm13050720 |
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