Population Health Research Institute, St George's, University of London, Cranmer Terrace, London, SW17 0RE, UK.
Int J Behav Nutr Phys Act. 2020 Oct 9;17(1):129. doi: 10.1186/s12966-020-01020-8.
Step-count monitors (pedometers, body-worn trackers and smartphone applications) can increase walking, helping to tackle physical inactivity. We aimed to assess the effect of step-count monitors on physical activity (PA) in randomised controlled trials (RCTs) amongst community-dwelling adults; including longer-term effects, differences between step-count monitors, and between intervention components.
Systematic literature searches in seven databases identified RCTs in healthy adults, or those at risk of disease, published between January 2000-April 2020. Two reviewers independently selected studies, extracted data and assessed risk of bias. Outcome was mean differences (MD) with 95% confidence intervals (CI) in steps at follow-up between treatment and control groups. Our preferred outcome measure was from studies with follow-up steps adjusted for baseline steps (change studies); but we also included studies reporting follow-up differences only (end-point studies). Multivariate-meta-analysis used random-effect estimates at different time-points for change studies only. Meta-regression compared effects of different step-count monitors and intervention components amongst all studies at ≤4 months.
Of 12,491 records identified, 70 RCTs (at generally low risk of bias) were included, with 57 trials (16,355 participants) included in meta-analyses: 32 provided change from baseline data; 25 provided end-point only. Multivariate meta-analysis of the 32 change studies demonstrated step-counts favoured intervention groups: MD of 1126 steps/day 95%CI [787, 1466] at ≤4 months, 1050 steps/day [602, 1498] at 6 months, 464 steps/day [301, 626] at 1 year, 121 steps/day [- 64, 306] at 2 years and 434 steps/day [191, 676] at 3-4 years. Meta-regression of the 57 trials at ≤4 months demonstrated in mutually-adjusted analyses that: end-point were similar to change studies (+ 257 steps/day [- 417, 931]); body-worn trackers/smartphone applications were less effective than pedometers (- 834 steps/day [- 1542, - 126]); and interventions providing additional counselling/incentives were not better than those without (- 812 steps/day [- 1503, - 122]).
Step-count monitoring leads to short and long-term step-count increases, with no evidence that either body-worn trackers/smartphone applications, or additional counselling/incentives offer further benefit over simpler pedometer-based interventions. Simple step-count monitoring interventions should be prioritised to address the public health physical inactivity challenge.
PROSPERO number CRD42017075810 .
计步器（计步器、可穿戴跟踪器和智能手机应用程序）可以增加步行量，有助于解决身体活动不足的问题。我们旨在评估计步器在社区居住的成年人中随机对照试验（RCT）中对身体活动（PA）的影响；包括长期影响、计步器之间的差异以及干预成分之间的差异。
在七个数据库中进行系统文献检索，以确定 2000 年 1 月至 2020 年 4 月期间发表的健康成年人或有患病风险的成年人的 RCT。两名审查员独立选择研究、提取数据并评估偏倚风险。结局是治疗组和对照组在随访期间的平均差异（MD），用步长表示（调整基线步长后的变化研究）；但我们还包括仅报告随访差异的研究（终点研究）。只有变化研究才使用多变量荟萃分析来估计不同时间点的随机效应估计值。荟萃回归比较了所有研究中≤4 个月时不同计步器和干预成分的效果。
从 12491 条记录中确定了 70 项 RCT（总体偏倚风险较低），其中 57 项 RCT（16355 名参与者）纳入荟萃分析：32 项提供了从基线数据的变化；25 项仅提供终点数据。32 项变化研究的多变量荟萃分析表明，计步器组的计步效果较好：≤4 个月时，每天增加 1126 步，95%CI[787,1466]；6 个月时，每天增加 1050 步，95%CI[602,1498]；1 年时，每天增加 464 步，95%CI[301,626]；2 年时，每天增加 121 步，95%CI[-64,306]；3-4 年时，每天增加 434 步，95%CI[191,676]。≤4 个月时 57 项试验的荟萃回归分析表明，在相互调整的分析中，终点与变化研究相似（增加 257 步，95%CI[-417,931]）；可穿戴跟踪器/智能手机应用程序的效果不如计步器（减少 834 步，95%CI[-1542,-126]）；提供额外咨询/激励的干预措施并不比没有这些措施的干预措施更好（减少 812 步，95%CI[-1503,-122]）。
计步器监测可导致短期和长期计步增加，没有证据表明可穿戴跟踪器/智能手机应用程序或额外咨询/激励措施在简单计步器为基础的干预措施之外提供进一步的益处。应优先考虑简单的计步监测干预措施，以应对公共卫生领域身体活动不足的挑战。
PROSPERO 编号 CRD42017075810 。
