(49) VALIDITY OF A SMART GARMENT TO COLLECT MEASURES OF HEART RATE VARIABILITY DURING REST AND POST-EXERCISE RECOVERY

Friday, July 14, 2023

10:30 AM – 12:00 PM East Coast USA Time

Location: Milano III-IV

Poster Presenter(s)

David J. Cornell, PhD, DPT, CSCS, TSAC-F, FACSM

Assistant Professor
University of Massachusetts Lowell
Lowell, Massachusetts, United States

Author(s)

TS

Thomas J. Sherriff

Student Physical Therapist
University of Massachusetts Lowell
Lowell, Massachusetts, United States
QP

Quentin J. Proulx

Student Physical Therapist
University of Massachusetts Lowell
Lowell, Massachusetts, United States
DR

Donald W. Rogers

BS in Exercise Science Student
University of Massachusetts Lowell
Lowell, Massachusetts, United States
MD

Megan T. Duong

Student Physical Therapist
University of Massachusetts Lowell
Lowell, Massachusetts, United States
JH

Julia J. Hill

Student Physical Therapist
University of Massachusetts Lowell
Lowell, Massachusetts, United States
AH

Andreas T. Himariotis

Student Physical Therapist
University of Massachusetts Lowell
Lowell, Massachusetts, United States

Abstract Details: Although there has been a rise in wearable technology, the ability to assess autonomic nervous system function in field-based settings remains limited. A smart garment prototype embedded with laminate conductive electrocardiography electrodes may offer a novel method of collecting measures of heart rate variability (HRV).

Purpose: To determine the concurrent validity of a smart garment when collecting HRV data during rest and post-exercise recovery conditions compared to an industry standard heart rate monitor (HRM).

Methods: 5 minutes of R-R interval data were simultaneously collected from 16 male participants (age: 24.6 ± 4.3 yrs; height: 180.7 ± 6.7 cm; body mass: 88.0 ± 10.0 kg) using smart garment and HRM devices in both the Supine and Seated positions. Data were collected before (Resting) and after (Recovery) a 15-minute treadmill exercise protocol. Data from both devices were then processed using Kubios HRV 3.5 software and an automatic correction filter to derive mean measures of R-R intervals and natural log of the root mean square of successive R-R interval differences (lnRMSSD) for each condition.

Results: Trivial to small and non-significant (p > 0.05) differences in R-R interval and lnRMSSD data were observed between the smart garment and the HRM during the Resting – Supine, Resting – Seated, and Recovery – Supine conditions (Table). However, although a trivial difference in R-R interval data (p = 0.348) was noted between the smart garment and the HRM in the Recovery – Seated position, a moderate and significant difference was observed in lnRMSSD data (p = 0.011). In addition, very strong to nearly-perfect and statistically significant (p < 0.05) correlations were identified between the Smart Garment and HRM in R-R interval data during the Resting – Supine (r = 0.999, R² = 0.998), Recovery – Seated (r = 0.902, R² = 0.814), and Recovery – Supine (r = 0.759, R² = 0.576) conditions, along with very strong to nearly-perfect correlations in lnRMSSD data during the Resting – Supine (r = 0.985, R² = 0.970) and Recovery – Supine (r = 0.704, R² = 0.496) conditions. However, only strong correlations in R-R interval data and lnRMSSD data were identified between the smart garment and HRM during the Resting – Seated (r = 0.658, R² = 0.433; r = 0.608, R² = 0.370, respectively) condition. Finally, although statistically significant (p = 0.043), only a strong correlation in lnRMSSD data was identified between the smart garment and HRM during the Recovery – Seated (r = 0.529, R² = 0.280) condition.

Conclusions: The smart garment prototype demonstrated concurrent validity with collecting R-R interval data during rest and post-exercise recovery, but the degree of validity decreased after exercise in the seated position. PRACTICAL APPLICATIONS: This novel smart garment prototype may prove to be an effective method for strength and conditioning professionals to assess HRV measures for longer periods of time and in a variety of field-based environments.

Acknowledgements: This study was funded by the Massachusetts Manufacturing Innovation Initiative (M2I2), Commonwealth of Massachusetts. We would also like to thank Human Systems Integration, Inc. for providing the smart garments for this study, as well as the U.S. Army Research Institute for Environmental Medicine (USARIEM) for their technical support.