Toledo Meynard John, Hekler Eric, Hollingshead Kevin, Epstein Dana, Buman Matthew
Arizona State University, School of Nutrition and Health Promotion, Phoenix, AZ, United States.
Phoenix Veterans Affairs Health Care System, Phoenix, AZ, United States.
JMIR Mhealth Uhealth. 2017 Aug 9;5(8):e119. doi: 10.2196/mhealth.6974.
Although current technological advancements have allowed for objective measurements of sedentary behavior via accelerometers, these devices do not provide the contextual information needed to identify targets for behavioral interventions and generate public health guidelines to reduce sedentary behavior. Thus, self-reports still remain an important method of measurement for physical activity and sedentary behaviors.
This study evaluated the reliability, validity, and sensitivity to change of a smartphone app in assessing sitting, light-intensity physical activity (LPA), and moderate-vigorous physical activity (MVPA).
Adults (N=28; 49.0 years old, standard deviation [SD] 8.9; 85% men; 73% Caucasian; body mass index=35.0, SD 8.3 kg/m2) reported their sitting, LPA, and MVPA over an 11-week behavioral intervention. During three separate 7-day periods, participants wore the activPAL3c accelerometer/inclinometer as a criterion measure. Intraclass correlation (ICC; 95% CI) and bias estimates (mean difference [δ] and root of mean square error [RMSE]) were used to compare app-based reported behaviors to measured sitting time (lying/seated position), LPA (standing or stepping at <100 steps/minute), and MVPA (stepping at >100 steps/minute).
Test-retest results suggested moderate agreement with the criterion for sedentary time, LPA, and MVPA (ICC=0.65 [0.43-0.82], 0.67 [0.44-0.83] and 0.69 [0.48-0.84], respectively). The agreement between the two measures was poor (ICC=0.05-0.40). The app underestimated sedentary time (δ=-45.9 [-67.6, -24.2] minutes/day, RMSE=201.6) and overestimated LPA and MVPA (δ=18.8 [-1.30 to 38.9] minutes/day, RMSE=183; and δ=29.3 [25.3 to 33.2] minutes/day, RMSE=71.6, respectively). The app underestimated change in time spent during LPA and MVPA but overestimated change in sedentary time. Both measures showed similar directions in changed scores on sedentary time and LPA.
Despite its inaccuracy, the app may be useful as a self-monitoring tool in the context of a behavioral intervention. Future research may help to clarify reasons for under- or over-reporting of behaviors.
尽管当前的技术进步使得通过加速度计对久坐行为进行客观测量成为可能，但这些设备无法提供识别行为干预目标以及制定减少久坐行为的公共卫生指南所需的背景信息。因此，自我报告仍然是身体活动和久坐行为测量的重要方法。
本研究评估了一款智能手机应用程序在评估坐姿、轻度身体活动（LPA）和中度至剧烈身体活动（MVPA）方面的可靠性、有效性和对变化的敏感性。
成年人（N = 28；年龄49.0岁，标准差[SD] 8.9；85%为男性；73%为白种人；体重指数 = 35.0，SD 8.3 kg/m²）在为期11周的行为干预期间报告了他们的坐姿、LPA和MVPA。在三个独立的7天时间段内，参与者佩戴activPAL3c加速度计/倾角仪作为标准测量工具。组内相关系数（ICC；95% CI）和偏差估计值（平均差异[δ]和均方根误差[RMSE]）用于比较基于应用程序报告的行为与测量的久坐时间（躺/坐姿）、LPA（站立或每分钟步数<100步的行走）和MVPA（每分钟步数>100步的行走）。
重测结果表明，在久坐时间、LPA和MVPA方面与标准测量结果的一致性为中等（ICC分别为0.65 [0.43 - 0.82]、0.67 [0.44 - 0.83]和0.69 [0.48 - 0.84]）。两种测量方法之间的一致性较差（ICC = 0.05 - 0.40）。该应用程序低估了久坐时间（δ = -45.9 [-67.6, -24.2]分钟/天，RMSE = 201.6），高估了LPA和MVPA（δ分别为18.8 [-1.30至38.9]分钟/天，RMSE = 183；以及δ = 29.3 [25.3至33.2]分钟/天，RMSE = 71.6）。该应用程序低估了LPA和MVPA期间花费时间的变化，但高估了久坐时间的变化。两种测量方法在久坐时间和LPA的变化分数方向上显示出相似性。
尽管该应用程序存在不准确之处，但在行为干预的背景下，它可能作为一种自我监测工具有用。未来的研究可能有助于阐明行为报告不足或过度报告的原因。
