Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Harvard Medical School, Boston, Massachusetts, USA.
BMJ Open. 2023 Aug 28;13(8):e076297. doi: 10.1136/bmjopen-2023-076297.
Social isolation has been found to be a significant risk factor for health outcomes, on par with traditional risk factors. This isolation is characterised by reduced social interactions, which can be detected acoustically. To accomplish this, we created a machine learning algorithm called SocialBit. SocialBit runs on a smartwatch and detects minutes of social interaction based on vocal features from ambient audio samples without natural language processing.
In this study, we aim to validate the accuracy of SocialBit in stroke survivors with varying speech, cognitive and physical deficits. Training and testing on persons with diverse neurological abilities allows SocialBit to be a universally accessible social sensor. We are recruiting 200 patients and following them for up to 8 days during hospitalisation and rehabilitation, while they wear a SocialBit-equipped smartwatch and engage in naturalistic daily interactions. Human observers tally the interactions via a video livestream (ground truth) to analyse the performance of SocialBit against it. We also examine the association of social interaction time with stroke characteristics and outcomes. If successful, SocialBit would be the first social sensor available on commercial devices for persons with diverse abilities.
This study has received ethical approval from the Institutional Review Board of Mass General Brigham (Protocol #2020P003739). The results of this study will be published in a peer-reviewed journal.
社交孤立已被发现是影响健康结果的一个重要因素，与传统风险因素相当。这种孤立的特点是社交互动减少，可以通过声学检测到。为此，我们创建了一个名为 SocialBit 的机器学习算法。SocialBit 在智能手表上运行，根据环境音频样本中的语音特征检测社交互动分钟数，无需自然语言处理。
在这项研究中，我们旨在验证 SocialBit 在言语、认知和身体功能障碍程度不同的中风幸存者中的准确性。在具有不同神经功能能力的人群中进行训练和测试，使 SocialBit 成为一种普遍适用的社交传感器。我们正在招募 200 名患者，在他们住院和康复期间最多 8 天佩戴配备 SocialBit 的智能手表，并进行自然的日常互动。人类观察者通过视频直播（真实数据）来计算互动次数，以分析 SocialBit 的性能。我们还研究了社交互动时间与中风特征和结果之间的关联。如果成功，SocialBit 将成为第一款可用于具有不同能力人群的商业设备上的社交传感器。
这项研究已获得麻省总医院布列根和妇女医院机构审查委员会的伦理批准（Protocol #2020P003739）。这项研究的结果将发表在同行评议的期刊上。
