Driving Transitions and Mental Health in Disabled Elders

推动残疾老年人的转变和心理健康

• 批准号: 6774368
• 负责人: AMY HOROWITZ
• 金额: $ 40.39万
• 依托单位: LIGHTHOUSE INTERNATIONAL
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-05-01 至 2009-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6774368
• 关键词: aging; behavioral /social science research tag; clinical research; decision making; depression; health behavior; human old age (65+); human subject; interview; longitudinal human study; mental health; person with disability; psychological aspect of aging; psychological stressor; travel; vehicular accident; vision disorders

DESCRIPTION (provided by applicant): Most prior research in driving and aging has focused on demographic and medical factors associated with driving cessation. However, little is known about the psychosocial influences on driving transitions, the severity or trajectory of subsequent mental health problems, or why some older adults are better able than others to make these transitions with less psychological distress. Driving decisions are especially salient for the growing numbers of older adults with vision problems. The primary aim of the proposed longitudinal study is to examine how the transition from driver to ex-driver may be more or less difficult for older drivers with vision problems, depending on their personal, social, and contextual resources. Conceptually, the study draws upon recent health behavior change models that examine the conditions under which a person is willing and able to change a health behavior (e.g., driving self-regulation and cessation). The proposed study will extend prior research on driving in later life by using a prospective and theoretically guided approach. Specific aims of the proposed research are: 1. To identify the extent of driving self-regulation and the occurrence of driving cessation, and to examine both their short- and long-term impact on mental health outcomes (i.e., depression, vision-specific mental health, psychological well-being) among visually impaired older adults. 2. To examine the influence of personal resources (e.g., personal goal structures, self-regulatory coping tendencies), social resources (e.g., influence of family, friends and professionals) and contextual resources (e.g., alternative transportation) on decisions regarding driving self regulation and cessation over time. 3. To identify the key personal, social and contextual resources that, concurrently and over time, mediate the impact of driving self-regulation and cessation on mental health outcomes. 4. To explore the extent to which participants and informants are congruent in reports of driving behaviors and interactions regarding decision-making, and to explore the influence of congruence on driving cessation and mental health outcomes. Participants will be 380 older adult drivers (age 55+) recruited from a community-based vision rehabilitation agency. Data will be collected through in-person interviews at baseline and at 1- and 2-year follow-ups, as well as brief quarterly telephone interviews at 4, 8, 16, and 20 months. In addition, baseline and 1- and 2-year telephone interviews with an informant (family or friend) will be conducted. Analyses will address concurrent associations (cross sectional) and prospective relationships (longitudinal) using Regression-, Individual Growth-, and Structural Equation Modeling techniques.

描述（由申请人提供）：大多数先前在驾驶和衰老方面的研究都集中在与驾驶停止相关的人口统计和医疗因素上。但是，对于对驾驶过渡的影响，随后的心理健康问题的严重性或轨迹的影响知之甚少，或者为什么某些老年人比其他成年人更有能力使这些过渡的心理困扰更少。对于越来越多的视力问题的老年人，驾驶决策尤其重要。拟议的纵向研究的主要目的是检查从驾驶员到前驾驶员的过渡对于具有视力问题的老年驾驶员或多或少的困难，具体取决于他们的个人，社交和上下文资源。从概念上讲，该研究借鉴了最近的健康行为变化模型，该模型检查了一个人愿意并能够改变健康行为的条件（例如，驱动自我调节和停止）。拟议的研究将通过使用前瞻性和理论指导的方法来扩展有关以后生活的先前研究。拟议的研究的具体目的是：1。确定驱动自我调节的程度和戒烟的发生程度，并检查视觉损害老年人对他们对心理健康结果的短期和长期影响（即抑郁症，特定于视力的心理健康，心理健康，心理健康）。 2。检查个人资源（例如个人目标结构，自我调节倾向）的影响，社会资源（例如，家人，朋友和专业人士的影响）以及上下文资源（例如，替代运输）对推动自我调节和随时间戒烟的决策的影响。 3。确定关键的个人，社会和上下文资源，同时并随着时间的流逝，可以调解自我调节和停止对心理健康结果的影响。 4。探讨参与者和线人在有关决策方面的驾驶行为和互动的报告中的一致性，并探讨一致性对驾驶停止和心理健康成果的影响。参与者将是从社区视力康复机构招募的380名老年人司机（55岁以上）。数据将通过基线和1年和2年的随访以及4、8、16和20个月的简短季度电话采访来收集数据。此外，还将对线人（家人或朋友）进行基线以及1年和2年的电话采访。分析将使用回归，个人增长和结构方程建模技术解决并发关联（横截面）和前瞻性关系（纵向）。