Ivan Liu1, Shiguang Ni2, Kaiping Peng1,3. 1. Data Science and Information Technology Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, China. 2. Graduate School at Shenzhen, Tsinghua University, Shenzhen, China. 3. Department of Psychology, Tsinghua University, Beijing, China.
Abstract
Background: Heart rate variability (HRV) provides essential mental health information for clinical diagnosis, telemedicine, preventive medicine, and public health; however, the lack of a convenient detection method limits its potential. Objective: This study aims to investigate the feasibility and credibility of smartphone photoplethysmogram (PPG)-based HRV analysis for mental well-being and health assessment. Methods: Data were collected from 93 students and university employees in Shenzhen, China. Forty-six percent were male, and the average age was 23.71 years (σ = 4.33). An app recorded a 4-min video of their fingertips and converted the frames into five HRV measures, including the root mean square of successive differences (rMSSD), standard deviation of the normal-to-normal (NN) intervals (SDNN), percentage of successive NN intervals differing by ≥50 ms (pNN50), log high-frequency (HF) HRV, and log low-frequency (LF) HRV. Results: The data verify the positive relationship between mental well-being and HRV measures. Participants with higher Satisfaction With Life Scale (SWLS) scores have a higher rMSSD (p = 0.047), SDNN (p = 0.009), log HF (p = 0.02), and log LF (p = 0.003). Participants who suffer from depression have lower log HF (p = 0.048) and log LF (p = 0.02). Participants in the high-anxiety group have lower pNN50 (p = 0.04) and log HF (p = 0.03). Conclusions: The results of this study validate the feasibility of using the smartphone PPG by demonstrating similar results to previous findings. Our data also support the theorized positive link between mental health and HRV.
Background: Heart rate variability (HRV) provides essential mental health information for clinical diagnosis, telemedicine, preventive medicine, and public health; however, the lack of a convenient detection method limits its potential. Objective: This study aims to investigate the feasibility and credibility of smartphone photoplethysmogram (PPG)-based HRV analysis for mental well-being and health assessment. Methods: Data were collected from 93 students and university employees in Shenzhen, China. Forty-six percent were male, and the average age was 23.71 years (σ = 4.33). An app recorded a 4-min video of their fingertips and converted the frames into five HRV measures, including the root mean square of successive differences (rMSSD), standard deviation of the normal-to-normal (NN) intervals (SDNN), percentage of successive NN intervals differing by ≥50 ms (pNN50), log high-frequency (HF) HRV, and log low-frequency (LF) HRV. Results: The data verify the positive relationship between mental well-being and HRV measures. Participants with higher Satisfaction With Life Scale (SWLS) scores have a higher rMSSD (p = 0.047), SDNN (p = 0.009), log HF (p = 0.02), and log LF (p = 0.003). Participants who suffer from depression have lower log HF (p = 0.048) and log LF (p = 0.02). Participants in the high-anxiety group have lower pNN50 (p = 0.04) and log HF (p = 0.03). Conclusions: The results of this study validate the feasibility of using the smartphone PPG by demonstrating similar results to previous findings. Our data also support the theorized positive link between mental health and HRV.
Authors: Lourdes Díaz-Rodríguez; Keyla Vargas-Román; Juan Carlos Sanchez-Garcia; Raquel Rodríguez-Blanque; Guillermo Arturo Cañadas-De la Fuente; Emilia I De La Fuente-Solana Journal: Int J Environ Res Public Health Date: 2021-01-13 Impact factor: 3.390
Authors: Vicente Javier Clemente-Suárez; Pablo Ruisoto; Manuel Isorna-Folgar; Jesús Cancelo-Martínez; Ana Isabel Beltrán-Velasco; José Francisco Tornero-Aguilera Journal: Appl Psychophysiol Biofeedback Date: 2021-12-27