与SenseCam相比，PALMS GPS对主动和被动出行评分的有效性。
Validity of PALMS GPS scoring of active and passive travel compared with SenseCam.
作者信息
Carlson Jordan A, Jankowska Marta M, Meseck Kristin, Godbole Suneeta, Natarajan Loki, Raab Fredric, Demchak Barry, Patrick Kevin, Kerr Jacqueline
机构信息
Family and Preventive Medicine, University of California, San Diego, San Diego, CA.
出版信息
Med Sci Sports Exerc. 2015 Mar;47(3):662-7. doi: 10.1249/MSS.0000000000000446.
PURPOSE
The objective of this study is to assess validity of the personal activity location measurement system (PALMS) for deriving time spent walking/running, bicycling, and in vehicle, using SenseCam (Microsoft, Redmond, WA) as the comparison.
METHODS
Forty adult cyclists wore a Qstarz BT-Q1000XT GPS data logger (Qstarz International Co., Taipei, Taiwan) and SenseCam (camera worn around the neck capturing multiple images every minute) for a mean time of 4 d. PALMS used distance and speed between global positioning system (GPS) points to classify whether each minute was part of a trip (yes/no), and if so, the trip mode (walking/running, bicycling, or in vehicle). SenseCam images were annotated to create the same classifications (i.e., trip yes/no and mode). Contingency tables (2 × 2) and confusion matrices were calculated at the minute level for PALMS versus SenseCam classifications. Mixed-effects linear regression models estimated agreement (mean differences and intraclass correlation coefficients) between PALMS and SenseCam with regard to minutes/day in each mode.
RESULTS
Minute-level sensitivity, specificity, and negative predictive value were ≥88%, and positive predictive value was ≥75% for non-mode-specific trip detection. Seventy-two percent to 80% of outdoor walking/running minutes, 73% of bicycling minutes, and 74%-76% of in-vehicle minutes were correctly classified by PALMS. For minutes per day, PALMS had a mean bias (i.e., amount of over or under estimation) of 2.4-3.1 min (11%-15%) for walking/running, 2.3-2.9 min (7%-9%) for bicycling, and 4.3-5 min (15%-17%) for vehicle time. Intraclass correlation coefficients were ≥0.80 for all modes.
CONCLUSIONS
PALMS has validity for processing GPS data to objectively measure time spent walking/running, bicycling, and in vehicle in population studies. Assessing travel patterns is one of many valuable applications of GPS in physical activity research that can improve our understanding of the determinants and health outcomes of active transportation as well as its effect on physical activity.
目的
本研究的目的是评估个人活动位置测量系统（PALMS）在推算步行/跑步、骑自行车和乘车时间方面的有效性，并将其与SenseCam（微软公司，华盛顿州雷德蒙德）进行比较。
方法
40名成年自行车骑行者佩戴Qstarz BT-Q1000XT GPS数据记录器（Qstarz国际公司，中国台湾台北）和SenseCam（挂在脖子上的摄像头，每分钟拍摄多张图像），平均佩戴4天。PALMS利用全球定位系统（GPS）点之间的距离和速度来分类每分钟是否属于一次出行（是/否），如果是，则确定出行模式（步行/跑步、骑自行车或乘车）。对SenseCam图像进行标注以创建相同的分类（即出行是/否和模式）。在分钟层面计算PALMS与SenseCam分类的列联表（2×2）和混淆矩阵。混合效应线性回归模型估计了PALMS和SenseCam在每种模式下每天分钟数方面的一致性（平均差异和组内相关系数）。
结果
对于非模式特定的出行检测，分钟层面的灵敏度、特异度和阴性预测值≥88%，阳性预测值≥75%。PALMS正确分类了72%至80%的户外步行/跑步分钟数、73%的骑自行车分钟数以及74% - 76%的乘车分钟数。对于每天的分钟数，PALMS在步行/跑步方面的平均偏差（即高估或低估的量）为2.4 - 3.1分钟（11% - 15%），骑自行车为2.3 - 2.9分钟（7% - 9%），乘车时间为4.3 - 5分钟（15% - 17%）。所有模式的组内相关系数≥0.80。
结论
在人群研究中，PALMS在处理GPS数据以客观测量步行/跑步、骑自行车和乘车时间方面具有有效性。评估出行模式是GPS在体育活动研究中的众多有价值应用之一，有助于我们更好地理解积极出行的决定因素和健康结果，以及其对体育活动的影响。
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