Neighborhood Risk Factors for Falls in the Elderly

项目摘要 福尔斯是老年人受伤的主要原因。预防跌伤是国家公共卫生 要务迄今为止，大多数关于老年人福尔斯跌倒的研究都是在非西班牙裔白色人群中进行的 在城市地区。关于福尔斯跌倒的发生率、情况和后果， 生活在农村和郊区社区以及少数种族/民族中的老年人。据我们所知， 没有关于福尔斯的研究调查过农村、郊区和城市的老年人在空间和时间使用上的差异 社区，以及这些差异如何与跌倒风险相关。为了填补这一知识空白，该项目 将调查（1）城乡、性别和种族/民族在时间和空间使用方面的差异以及 特定地点和活动的福尔斯; 2）时间和空间使用如何影响室内和室外福尔斯的风险 生活在城市，郊区和农村社区的老年人中;以及3）个人和 邻里因素可预测空间和时间的使用以及地点和活动的具体福尔斯。使用 综合数据，4）我们将开发个性化的预测模型的位置和活动特定的福尔斯。 我们建议建立一个种族和民族多样化、性别平衡的纵向队列，共1，252人。 马萨诸塞州中部65岁及以上的成年人，包括500名来自市区、500名来自郊区和252名来自郊区的成年人。 600名（48%）非西班牙裔白人和652名（52%）少数民族（218名非西班牙裔 黑人，218名西班牙裔和216名亚洲人/其他种族）。参与者将每6个月随访一次，持续3年， 跟踪他们的福尔斯、移动性、活动模式、残疾、健康和健康行为。秋季事件将被跟踪 使用每月福尔斯日历和后续电话调查，如果跌倒发生。参与人流动模式， 关于空间、频率和持续时间，将使用全球定位系统（GPS）单元进行测量，以及 参与者的时间，频率，持续时间和室内和室外活动的强度将同时进行 在基线、6个月、24个月和30个月时使用加速计测量。在随访的第2年和第3年， 参与者将通过每年两次的家庭访问、邮件或电话调查来跟踪他们的健康状况 习惯和健康状况。GPS和加速度计数据将与参与者报告的 健康、感知和行为数据以及邻近环境数据。这些数据将 综合分析，实现上述分析目标。 这些研究结果将为设计以社区为基础的促进积极生活的方案提供信息， 在所有种族/族裔群体和城乡之间有效预防男女福尔斯跌倒 连续体基于本研究开发的个性化跌倒风险预测模型，我们将设计 并在随后的随机临床试验研究中测试个性化（精确性）福尔斯预防方法。

Project summary Falls are the leading cause of injuries in older adults. Prevention of fall injuries is a national public health priority. To date, most of the studies on falls in older adults were conducted in non-Hispanic White populations in urban areas. Little is known about the occurrence rates, circumstances and consequences of falls among older adults living in rural and suburban neighborhoods, and among racial/ethnic minorities. To our knowledge, no study on falls has examined how older adults’ space and time use differ in rural, suburban and urban neighborhoods, and how such differences are related to risk of falling. To fill in this knowledge gap, this project will investigate 1) the rural-urban, gender and racial/ethnic differences in time and space use and rates of location- and activity-specific falls; 2) how time and space use influence risks for indoor and outdoor falls among older adults living in urban, suburban and rural neighborhoods; and 3) what personal and neighborhood-level factors are predictive of space and time use and location- and activity-specific falls. Using the integrated data, 4) we will develop personalized prediction models for location- and activity-specific falls. We propose to establish a racially and ethnically diverse, gender-balanced longitudinal cohort of 1,252 adults age 65 years and older in Central Massachusetts, including 500 from urban, 500 from suburban and 252 from rural areas, and 600 (48%) non-Hispanic Whites and 652 (52%) racial/ethnic minorities (218 non-Hispanic Blacks, 218 Hispanics and 216 Asians/other races). Participants will be followed every 6 months for 3 years to track their falls, mobility, activity patterns, disability, health and health behaviors. Fall events will be tracked using monthly falls calendars and follow-up telephone surveys if a fall occurs. Participant mobility patterns with respect to space, frequency and duration will be measured using a global positioning system (GPS) unit, and participant timing, frequency, duration and intensity of indoor and outdoor activities will be concurrently measured using an accelerometer, at baseline, 6, 24 and 30 months. During the follow-up years 2 and 3, participants will be followed using in-home visits, mail or telephone surveys twice a year querying their health habits and health status. The GPS and accelerometer data will be integrated with the participant’s reported health, perception and behavioral data as well as neighborhood environment data. These data will be integrated and analyzed to achieve the above analytic goals. These study results will inform the design of community-based programs for promoting active living and preventing falls that will be effective in both genders, among all racial/ethnic groups and across the rural-urban continuum. Based on the personalized fall risk prediction models to be developed in this study, we will design and test personalized (precision) falls prevention approaches in a subsequent randomized clinical trial study.