Exploring Heart Rate Variability and Heart Rate Dynamics Using Wearables Before, During, and After Speech Activity: Insights from a Controlled Study in a Low-Middle-Income Country

TL;DR

This study addresses the lack of objective physiological markers for Social Anxiety Disorder by analyzing heart rate and heart rate variability across Baseline, Anticipation, Speech Activity, and Reflection in a controlled Indian LMIC sample. It employs a three-phase protocol and multilevel modeling to reveal that HRV decreases and HR increases during anticipation and speech, with a reversal during reflection; SAD participants exhibit consistently lower HRV and higher HR than non-SAD peers. The analysis identifies RMSSD and SD1 (and, with gender considered, SD2 and TINN) as potential physiological markers for SAD, and shows gender modulates these relationships. A publicly released high-resolution HRV dataset from India aims to advance wearable-based mental health monitoring in LMICs.

Abstract

Conventional methods for diagnosing Social Anxiety Disorder (SAD), such as clinical interviews and self-reported questionnaires, often face accessibility barriers and subjective biases, underscoring the need for objective physiological markers. This study investigates heart rate (HR) and heart rate variability (HRV) as potential indicators of SAD by analyzing cardiovascular responses to anxiety-inducing speech tasks across four distinct phases: baseline, anticipation, speech activity, and reflection. In a controlled laboratory setting, we analyzed data from 51 participants and found that HRV decreased and HR increased during the anticipation and speech activity phases compared to baseline, while the reflection phase showed a reversal, with HRV increasing and HR decreasing. Participants with SAD exhibited lower HRV, higher HR, and greater self-reported anxiety than non-SAD participants across all phases. These findings enhance our understanding of the physiological signatures of social anxiety and have implications for developing wearable-based monitoring systems for SAD detection and intervention. To support further research, we also release a dataset capturing multi-phase anxiety responses, advancing physiological-based mental health assessment

Figures (12)

• Figure 1: Each study session involved a Research Associate (RA) and three participants, labeled P1, P2, and P3, represented by pink, blue, and yellow colors. Initially, ECG sensors were attached to the participants, and they provided self-reported SPIN responses. The participants then proceeded through four phases: Baseline, Anticipation, Speech Activity, and Reflection. After the Baseline phase and each subsequent phase, participants reported their perceived anxiety levels (PAL) using a single-item questionnaire. For data analysis, HR and HRV features—covering time-domain, frequency-domain, and non-linear metrics—were extracted from cleaned ECG signals and analyzed using a multilevel model to determine their potential as physiological markers for SAD.
• Figure 2: Participants participating in the controlled lab study
• Figure 3: Sequence of different phases in the study. PAL denotes instances at which participants reported perceived anxiety levels (PAL) through surveys. Shaded blocks , represent instances whenever RA gave instructions to the participants.
• Figure 4: Distribution of SPIN Scores. Participants with SPIN scores greater than 20 were labeled as SAD, while those with scores of 20 or below were labeled as non-SAD.
• Figure 5: Distribution of perceived anxiety level (PAL) at the baseline and different phases (anticipation, speech activity, and reflection).