SocialBit: Establishing the accuracy of a wearable sensor to detect social interactions after stroke

SocialBit：建立可穿戴传感器的准确性以检测中风后的社交互动

• 批准号: 10250357
• 负责人: Amar Dhand
• 金额: $ 53.97万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10250357
• 关键词: Address; Adult; Aphasia; Behavior; Behavior Therapy; Biological; Blood Vessels; Caregivers; Cognitive; Cognitive deficits; Communication; Data; Delirium; Detection; Ecological momentary assessment; Environment; Future; Growth; Health; Individual; Inpatients; Intervention; Intervention Trial; Intuition; Language; Language Disorders; Lesion; Life; Location; Loneliness; Measures; Medical; Mental Depression; Methods; Nature; Neurologic; Outcome; Patient Self-Report; Patients; Personal Satisfaction; Physical Function; Population; Recovery; Rehabilitation therapy; Reporting; Research; Risk; Risk Factors; Severities; Social Behavior; Social Change; Social Interaction; Social Network; Social isolation; Speech; Stroke; Surveys; Synapses; System; Technology; Testing; Time; United States National Institutes of Health; base; design; disability; improved; post stroke; privacy preservation; prototype; public health priorities; recruit; sensor; social; social deprivation; stroke outcome; stroke patient; stroke recovery; stroke survivor; tool; wearable sensor technology

Stroke survivors are vulnerable to reduced social interactions. Reduced interactions are related to worse physical recovery after stroke. Enhancing social interactions after stroke may be one of the most powerful strategies to improve stroke recovery. Social interactions are defined as the synchronous interactions, commonly verbal, between individuals who are usually co-present in the same physical location. Current ways to detect social interactions rely on self-report, which cannot be performed reliably by patients with language or cognitive deficits. Patients with such deficits are most vulnerable to social isolation. This project introduces a new wearable social sensor, SocialBit, that can detect audio signatures of social interactions in real-world settings. Our preliminary data show that SocialBit can detect social interactions accurately (~95%), and it can do so by processing select audio features without storing raw audio data. Therefore, the technology detects and measures the duration of the social interaction while preserving the privacy of the content during the interaction. Based on these findings, we have developed a research plan to establish the usefulness of SocialBit in stroke survivors in the immediate post-stroke period. The post-stroke period is apt for such a study because 1) patients are vulnerable to social deprivation in this time period, and 2) the bounded nature of an inpatient setting provides an ideal environment to test SocialBit against a ground truth of directly observed social interactions. Our central hypothesis is that SocialBit can accurately detect social interactions in stroke survivors in inpatient settings. This project is primarily designed to establish the accuracy of SocialBit to detect social interaction in patients with varying deficits against the ground truth of video-assisted, real- time observation in the post-stroke period. First, we will examine the accuracy of SocialBit to detect the social interaction time against direct observation in 200 patients (Aim 1). Second, we will determine the association of social interaction time to social isolation and stroke outcomes at 3 months (Aim 2). Finally, we will determine the medical factors associated with social interaction time (Aim 3). This study will establish the key criteria of quantifying social interaction in stroke recovery research. The project will (a) identify automatic and unobtrusive methods to measure social interaction, (b) determine key design and outcome criteria for a future intervention trial, and (c) increase our understanding of underlying mechanisms in social changes after stroke. In so doing, this study will address the public health priority of building better behavioral modification strategies for patients with stroke.

中风幸存者很容易受到社会交往减少的影响。减少相互作用与更差有关 中风后的身体恢复加强中风后的社会互动可能是最重要的因素之一， 改善中风恢复的有力策略。社会互动被定义为同步的 互动，通常是口头的，在通常共同存在于同一物质世界的个体之间。 位置.目前检测社交互动的方法依赖于自我报告，但无法可靠地执行 语言或认知缺陷的患者。有这种缺陷的患者最容易受到社会影响。 隔离这个项目引入了一种新的可穿戴社交传感器SocialBit，它可以检测音频 在现实世界中的社会互动的签名。我们的初步数据显示，SocialBit可以 准确地检测社交互动（~95%），它可以通过处理选定的音频特征来做到这一点， 存储原始音频数据。因此，该技术检测和测量社交活动的持续时间。 在交互过程中保护内容的隐私。基于这些发现，我们 已经制定了一项研究计划，以确定SocialBit在中风幸存者中的有用性。 中风后即刻。中风后时期适合这样的研究，因为1）患者 易受社会剥夺，在这段时间内，和2）有限的性质，住院设置 提供了一个理想的环境来测试SocialBit对直接观察到的社会 交互.我们的中心假设是，SocialBit可以准确地检测中风中的社交互动 住院治疗的幸存者。该项目的主要目的是建立SocialBit的准确性， 检测不同缺陷的患者的社会互动，与视频辅助的，真实的- 卒中后时间观察。首先，我们将检查SocialBit的准确性，以检测 200例患者的社会互动时间与直接观察（目标1）。第二，我们将确定 3个月时社会交往时间与社会隔离和卒中结局的相关性（目标2）。最后， 我们将确定与社会互动时间相关的医学因素（目标3）。本研究将 建立脑卒中康复研究中量化社会互动的关键标准。该项目将（a） 识别自动且不引人注目方法来测量社会交互，（B）确定关键设计， 未来干预试验的结果标准，以及（c）增加我们对潜在的 中风后社会变化的机制。在这样做的时候，这项研究将解决公共卫生优先事项， 为中风患者建立更好的行为矫正策略。