Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, United States.
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, United States; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States; Division of Population Sciences, Dana Farber Cancer Institute, Boston, MA, United States.
Environ Res. 2023 Nov 15;237(Pt 2):116864. doi: 10.1016/j.envres.2023.116864. Epub 2023 Aug 28.
Inconsistent results have been found in the literature on associations of greenness, or vegetation quantity, and physical activity. However, few studies have assessed associations between mobility-based greenness and physical activity from mobile health data from smartphone and wearable devices with fine spatial and temporal resolution.
We assessed mobility-based greenness exposure and wearable accelerometer data from participants in the US-based prospective Nurses' Health Study 3 cohort Mobile Health (mHealth) Substudy (2018-2020). We recruited 500 female participants with instructions to wear devices over four 7-day sampling periods equally spaced throughout the year. After restriction criteria there were 337 participants (mean age 36 years) with n = 639,364 unique observations. Normalized Difference Vegetation Index (NDVI) data were derived from 30 m x 30 m Landsat-8 imagery and spatially joined to GPS points recorded every 10 min. Fitbit proprietary algorithms provided physical activity summarized as mean number of steps per minute, which we averaged during the 10-min period following a GPS-based greenness exposure assessment. We utilized Generalized Additive Mixed Models to examine associations (every 10 min) between greenness and physical activity adjusting for neighborhood and individual socioeconomic status, Census region, season, neighborhood walkability, daily mean temperature and precipitation. We assessed effect modification through stratification and interaction models and conducted sensitivity analyses.
Mean 10-min step count averaged 7.0 steps (SD 14.9) and greenness (NDVI) averaged 0.3 (SD 0.2). Contrary to our hypotheses, higher greenness exposure was associated non-linearly with lower mean steps per minute after adjusting for confounders. We observed statistically significant effect modification by Census region and season.
We utilized objective physical activity data at fine temporal and spatial scales to present novel estimates of the association between mobility-based greenness and step count. We found higher levels of greenness were inversely associated with steps per minute.
关于绿色植被数量与身体活动之间的关联,文献中的结果并不一致。然而,很少有研究从智能手机和可穿戴设备的移动健康数据中,以精细的时空分辨率评估基于移动性的绿化与身体活动之间的关联。
我们评估了美国前瞻性护士健康研究 3 队列移动健康 (mHealth) 子研究(2018-2020 年)中参与者的基于移动性的绿化暴露和可穿戴加速度计数据。我们招募了 500 名女性参与者,要求她们在一年中均匀分布的四个 7 天采样期内佩戴设备。经过限制条件后,有 337 名参与者(平均年龄 36 岁),有 n=639364 个唯一观察值。归一化差异植被指数 (NDVI) 数据来自 30m x 30m 的 Landsat-8 图像,并与每 10 分钟记录的 GPS 点进行空间连接。Fitbit 专有算法提供了身体活动的汇总数据,以每分钟的平均步数表示,我们在基于 GPS 的绿化暴露评估后 10 分钟的时间段内对其进行平均。我们利用广义加性混合模型,在调整了邻里和个人社会经济地位、人口普查区域、季节、邻里步行性、日平均温度和降水的情况下,检查了绿化与身体活动之间的关联(每 10 分钟)。我们通过分层和交互模型评估了效应修饰,并进行了敏感性分析。
平均 10 分钟的步数值平均为 7.0 步(标准差 14.9),绿化值(NDVI)平均为 0.3(标准差 0.2)。与我们的假设相反,在调整了混杂因素后,较高的绿化暴露与平均每分钟步数呈非线性负相关。我们观察到人口普查区域和季节的统计学显著效应修饰。
我们利用精细的时间和空间尺度的客观身体活动数据,提出了基于移动性的绿化与步数之间关联的新估计。我们发现,较高的绿化水平与每分钟的步数呈反比。