Abstract

Social interaction supports brain health and recovery after neurological injury. Yet no validated tool exists for real-time measurement in individuals with and without neurological deficits. We developed SocialBit, a lightweight, privacy-preserving machine learning algorithm that detects social interactions using ambient audio features on a commercial smartwatch. In a prospective validation study, we evaluated SocialBit against livestream minute-by-minute human-coded ground truth in 153 hospitalized stroke patients who wore the device for up to 8 days, generating 88,918 min of observation. In these patients, the stroke severity and cognition spanned broad clinical ranges (NIH Stroke Scale 0–25; Montreal Cognitive Assessment 8–30), and 24 patients had aphasia across diverse subtypes, including severe presentations. SocialBit achieved high overall performance (sensitivity 0.87, specificity 0.88, area under the …

Date

2026

Authors

Amar Dhand, Samuel Tate, Cade Mack, Sofia Carozza, David Farynyk, Mehdi Bourahla, Oluwamayomikun Adeboye, Grace Cooke, Olivia Berglund, Riya Dahima, Melinda Luo, Vrushali Dhongade, George S Usmanov, Kelly White, Amanda M Bernal, Ross Zafonte, Shrikanth Narayanan, Minwoo Lee, Matthias R Mehl, Min Shin

Journal

Scientific Reports

Volume

16

Issue

1

Pages

4529

Publisher

Nature Publishing Group UK