Kwan Rick Yiu Cho, Liu Justina Yat Wa, Lee Deborah, Tse Choi Yeung Andy, Lee Paul Hong
Centre for Gerontological Nursing, School of Nursing, The Hong Kong Polytechnic University, 11 Yuk Choi Road, Hung Hom, Kowloon, Hong Kong.
Department of Health and Physical Education, The Education University of Hong Kong, 10 Lo Ping Road, Tai Po, New Territories, Hong Kong.
Gait Posture. 2020 Oct;82:306-312. doi: 10.1016/j.gaitpost.2020.09.022. Epub 2020 Sep 28.
Physical activity promotes healthy ageing in older people. Accurate measurement of physical activity in free-living environment is important in evaluating physical activity interventions.
What is the criterion validity of 1)an ActiGraph to identify physical activity at different intensity levels and 2)an ActiGraph and a smartphone to measure step rate?
Community-dwelling older people aged≥60 were recruited. The index tests were using ActiGraph worn in different positions (i.e.,both wrists and hip) to measure physical activity intensity and step rate and using smartphone (i.e., Samsung J2 pro and Google Fit) worn in different positions (i.e.,trousers pocket and waist pouch) to measure the step rate. The reference standards were using indirect calorimetry (i.e.,CosMedK4b 2) to measure physical activity intensity and using direct observation for step rate. Subjects were exposed in different physical activity intensity levels (i.e.,sedentary:MET < 1.5,light: MET = 1.5-2.99, moderate:MET = 3.0-6.0, vigorous:MET>6) and step rates through walking on a treadmill at different speeds (i.e.,2-8 km) for approximately 30 min. Spearman's rho, ROC analysis, and percentage error were employed to report the criterion validity.
31 participants completed the tests. ActiGraphs worn in different body positions could significantly differentiate physical activity intensity at the levels of "light- or-above" (VM cut-off = 279.5-1959.1,AUC = 0.932-0.954), "moderate-or-above" (VM cut- off = 1051.0-4212.9,AUC = 0.918-0.932), and "vigorous" (VM cut-off = 3335.4-5093.0, AUC = 0.890-0.907) well with different cut-off points identified. The step rate measured by direct observation correlated significantly with ActiGraph and smartphone (rho = 0.415-0.791). Both ActiGraph and smartphone at different positions generally underestimated the step rate (%error= -20.5,-30.3).
A wrist-worn ActiGraph can accurately identify different physical activity intensity levels in older people, but lower cut-off points in older people should be adopted. To measure step rate, a hip-mounted ActiGraph is preferable than a wrist- worn one. A smartphone employing Google Fit generally underestimates step rate but it gives a relatively more accurate estimation of step rate when the older people walk at a speed of 4-8 km/h.
体育活动可促进老年人健康老龄化。在自由生活环境中准确测量体育活动对于评估体育活动干预措施很重要。
1)活动记录仪用于识别不同强度水平体育活动的标准效度如何,以及2)活动记录仪和智能手机用于测量步频的标准效度如何?
招募年龄≥60岁的社区居住老年人。指标测试包括使用佩戴在不同位置(即双腕和髋部)的活动记录仪测量体育活动强度和步频,以及使用佩戴在不同位置(即裤兜和腰包)的智能手机(即三星J2 pro和谷歌健身)测量步频。参考标准包括使用间接测热法(即CosMed K4b 2)测量体育活动强度以及使用直接观察法测量步频。受试者通过在跑步机上以不同速度(即2 - 8公里)行走约30分钟,暴露于不同的体育活动强度水平(即久坐:代谢当量<1.5,轻度:代谢当量=1.5 - 2.99,中度:代谢当量=3.0 - 6.0,剧烈:代谢当量>6)和步频下。采用斯皮尔曼等级相关系数、ROC分析和百分比误差来报告标准效度。
31名参与者完成了测试。佩戴在不同身体位置的活动记录仪能够在“轻度及以上”(垂直向量截断值=279.5 - 1959.1,曲线下面积=0.932 - 0.954)、“中度及以上”(垂直向量截断值=1051.0 - 4212.9,曲线下面积=0.918 - 0.932)和“剧烈”(垂直向量截断值=3335.4 - 5093.0,曲线下面积=0.890 - 0.907)水平上,通过确定不同的截断点很好地区分体育活动强度。直接观察测量的步频与活动记录仪和智能手机显著相关(等级相关系数=0.415 - 0.791)。不同位置的活动记录仪和智能手机通常都低估了步频(百分比误差=-20.5,-30.3)。
佩戴在手腕上的活动记录仪可以准确识别老年人不同的体育活动强度水平,但应采用针对老年人的较低截断点。为测量步频,佩戴在髋部的活动记录仪比佩戴在手腕上的更可取。使用谷歌健身的智能手机通常会低估步频,但当老年人以4 - 8公里/小时的速度行走时,它对步频的估计相对更准确。