Feasibility of Mobile Technology-Based Assessments of Community Reintegration in Homeless Veterans

退伍军人无家可归是一场全国性的危机，洛杉矶的无家可归退伍军人人数是世界上最高的 我们。尽管在为退伍军人提供住房方面取得了令人印象深刻的进展，特别是通过HUD-VASH 计划中，一个根本性的问题仍然存在：永久住房是必要的第一步，但不是 有足够的条件，才能成功地重新融入社区。社区重新融入，定义为完全重新融入 参与工作、社交、独立生活和娱乐活动，不是一次就会自动产生的 提供住房。尽管提供了住房，但累犯率(返回无家可归者)仍然很高。此外， 对非退伍军人事务部样本的少数相关研究表明，在提供有保障的住房后，未能蓬勃发展 (例如，职业和社会功能仍然很差)。HUD-VASH临床医生描述了同样糟糕的结果 退伍军人，但几乎没有任何关于社区问题的类型、严重性和原因的数据 重新融入最近安置的退伍军人(RHV)。这些信息对于发展复苏至关重要-- 可以在这一复杂、快速增长的退伍军人群体中实施的重点治疗。 2015年，我们的团队设立了退伍军人研究促进奖计划(REAP)，以增强 洛杉矶无家可归退伍军人的社区融合。《收获》致力于理解 重新融入社会问题的范围，确定其决定因素，并制定新的干预措施。我们有 确定了我们与RHV及其治疗提供者合作中的两个主要挑战。1.不足之处 措施：现有的社区融合措施缺乏必要的敏感度来确定具体的 RHV面临的挑战，而且还没有对RHV实际支出情况的细粒度评估 他们的时间。2.参与率低：让农村卫生组织参与治疗和研究极其困难 这需要反复访问退伍军人管理局的校园。因此，我们的研究评估只提供了一个 未能捕捉到他们生活中的动态波动的整合的横截面快照。此外， 不提供可用的治疗服务会导致累犯率和不良结果。因此，我们 我认为有必要超越传统的评估和治疗模式来解决这些问题 挑战。新的移动技术似乎非常适合这一目的。 这项建议的目标是评估通过移动设备提供数字表型(DP)的可行性 智能手机技术，用于评估患有严重精神疾病(SMI)的RHV的社区融合情况。我们 将同时使用主动(社交的生态瞬时评估[EMA])和被动(全球 定位系统测量社区的流动性)DP指数。活跃的均线指数涉及到暗示 参与者在一周内每天多次完成简短调查，以获得更细粒度的信息， 生态上有效的信息比传统的横断面测量方法更有效。被动指数会自动 使用标准的手机传感器在后台收集。DP指数从来没有在这里被检查过 人口。为了评估在这一具有挑战性的人群中的可行性，我们建议进行为期2年的混合量化/ 定性研究分为两个阶段。阶段1(目标1，1-3个月)包括重点小组和关键 利益相关者调整现有的EMA社区整合调查(最初是为SMI开发的)，以用于 了解RHV对通过智能手机进行被动数据收集的看法。阶段2(目标2，月 4-24)包括27名在HUD-VASH中有SMI的RHV，他们将完成(A)基线临床评估 社区融合，(B)为期7天(5次调查/天)的DP期，以评估可行性；(C)DP后量化/ 可接受性的定性评估。该提案旨在为移动电话的使用开辟新天地 技术，这些技术有可能为RHV提供创新的评估和治疗应用。

Veteran homelessness is a national crisis and Los Angeles has the highest homeless Veteran population in the US. Despite impressive progress in providing housing for Veterans, particularly through the HUD-VASH program, a fundamental problem remains: Permanent housing is a necessary first step, but not a sufficient condition, for successful community reintegration. Community reintegration, defined as full engagement in work, social, independent living, and recreational activities, does not arise automatically once housing is provided. Despite the provision of housing, recidivism (return to homelessness) is high. Further, the few relevant studies of non-VA samples demonstrate a failure to thrive after supported housing is provided (e.g., vocational and social functioning remain poor). HUD-VASH clinicians describe similarly poor outcomes in Veterans, but there are hardly any data on the types, severity, and causes of problems in community reintegration in recently-housed Veterans (RHVs). This information is essential for developing recovery- focused treatments that can be implemented in this complex, rapidly growing Veteran population. In 2015, our team established the VA Research Enhancement Award Program (REAP) on Enhancing Community Integration for Homeless Veterans in Los Angeles. The REAP is devoted to understanding the scope of reintegration problems, identifying their determinants, and developing novel interventions. We have identified two major challenges in our work with RHVs and their treatment providers. 1. Inadequacy of measures: Existing measures of community integration lack the sensitivity needed to identify the specific challenges faced by RHVs, and there have been no fine-grained assessments of how RHVs actually spend their time. 2. Poor rates of participation: It is extremely difficult to engage RHVs in treatment and research that require repeated visits to the VA campus. Consequently, our research assessments provide only single cross-sectional snapshots of integration that fail to capture the dynamic fluctuations in their lives. Furthermore, failure to engage in available treatment services contributes to recidivism and poor outcomes. We therefore believe it is necessary to look beyond traditional assessment and treatment modalities to address these challenges. New mobile technologies appear ideally suited this purpose. The goal of this proposal is to evaluate the feasibility of Digital Phenotyping (DP) delivered via mobile smartphone technology to assess community integration in RHVs with Serious Mental Illnesses (SMIs). We will use both active (Ecological Momentary Assessment [EMA] of social contact) and passive (Global Positioning System measures of mobility in the community) DP indices. Active EMA indices involve cueing participants to complete brief surveys multiple times per day over a week to obtain more fine-grained, ecologically valid information than traditional cross-sectional measures. Passive indices are automatically collected in the background using standard phone sensors. DP indices have never been examined in this population. To evaluate feasibility in this challenging population, we propose a 2-year mixed quantitative/ qualitative methods study with two phases. Phase 1 (Aim 1, Months 1-3) consists of focus groups with key stakeholders to adapt an existing EMA community integration survey (originally developed for SMI) for use in RHVs and to understand RHVs' views about passive data collection via smartphone. Phase 2 (Aim 2, Months 4-24) includes 27 RHVs with SMIs in HUD-VASH who will complete (a) baseline clinical assessments of community integration, (b) a 7-day (5 surveys/day) DP period to evaluate feasibility, (c) post-DP quantitative/ qualitative evaluations of acceptability. The proposal aims to break new ground in the use of mobile technologies, which have the potential for innovative assessment and treatment delivery applications for RHVs.