自由に生活する成人の腕に装着されたセンサーを使用して,毎日座っている時間を推定するアルゴリズムの性能評価
Charles E Matthews1, Pedro Saint-Maurice2, Joshua R Freeman1
1Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Journal for the measurement of physical behaviour
|August 21, 2025
まとめ
21のアルゴリズムのうち 座っている時間を正確に推定したのは 5つのアルゴリズムだけでした この研究は,現実世界の環境で 静止状態の行動を測定するための より良いアルゴリズムを特定します
科学分野:
- 身体活動と静止行動に関する研究
- 生物医学工学とセンサー技術
- 公衆衛生と疫学
背景:
- 座っている時間の正確な測定は 健康への影響を理解するために重要です
- 手首用アクセロメーターは便利ですが,静止時間の推定には検証されたアルゴリズムが必要です.
- 手首用センサーに適用されるアルゴリズムの実際のテストは限られている.
研究 の 目的:
- 座っている時間を推定する21のアルゴリズムの性能を評価する.
- アルゴリズムの推定値と,太ももで着用する基準装置 (activPAL) を比較する.
- 座っている時間の正確な推定に関連したアルゴリズムの特徴を特定します.
主な方法:
- 71人の成年者 (35歳から65歳) は,最大10日間,手首に装着されたGENEActivと,太ももに装着されたactivPALセンサーを装着しました.
- 静止状態の時間は21の異なる分類アルゴリズム (カットポイントと機械学習) を使用して推定されました.
- 同等性テスト (±10%) と線形回帰は,activPAL参照と比較してアルゴリズムの精度を分析した.
主要な成果:
- 21のアルゴリズムのうち5つ (24%) のアルゴリズムは,座っている時間を基準測定値の10%以内に推定した.
- 機械学習アルゴリズム (Trost Extended,OxWearables) と特定のカットポイント方法により精度が向上しました.
- アルゴリズムの性能は,姿勢ベースのグラウンド真実と自由生活データを用いた開発研究と関連付けられました.
結論:
- 現在利用可能なアルゴリズムの少数派は 座っている時間を 腕に付けられたセンサーで 正確に計測します
- 信頼性の高いアルゴリズムを特定し,将来のアルゴリズム開発に情報を提供するために,ベンチマーキング研究は不可欠です.
- 将来の研究は,堅牢な方法論を用いて開発され,検証されたアルゴリズムに焦点を当てるべきです.
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