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.
795,000 adults who have a stroke annually are vulnerable to reduced social interactions after stroke. This project introduces a new wearable social sensor, SocialBit, that can detect audio signatures of social interactions in real-world settings. In this study, we test whether SocialBit can accurately detect social interactions in stroke patients, which is the crucial first step in reducing social isolation and disability in this population.
