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.
Prevention of fall injuries is a national public health priority. This study investigates the rural-urban, gender and racial/ethnic differences in time and space use in relation to falls among older adults. Personalized risk prediction models for location- and activity-specific falls will be created to support community-based programs that are effective across the rural-urban continuum, in both genders and among racial/ethnic groups for promoting active aging and preventing falls.