The everyday acoustic environment and its association with human heart rate: evidence from real-world data logging with hearing aids and wearables

Abstract

We investigate the short-term association between multidimensional acoustic characteristics of everyday ambient sound and continuous mean heart rate. We used in-market data from hearing aid users who logged ambient acoustics via smartphone-connected hearing aids and continuous mean heart rate in 5 min intervals from their own wearables. We find that acoustic characteristics explain approximately 4% of the fluctuation in mean heart rate throughout the day. Specifically, increases in ambient sound pressure intensity are significantly related to increases in mean heart rate, corroborating prior laboratory and short-term real-world data. In addition, increases in ambient sound quality-that is, more favourable signal to noise ratios-are associated with decreases in mean heart rate. Our findings document a previously unrecognized mixed influence of everyday sounds on cardiovascular stress, and that the relationship is more complex than is seen from an examination of sound intensity alone. Thus, our findings highlight the relevance of ambient environmental sound in models of human ecophysiology.

Keywords: data logging; everyday sounds; hearing aids; hearing loss; linear mixed-effects models; real-world heart rates.

Figure 1.

Cumulative distribution functions of acoustic data separated by soundscape. Median sound pressure level (a), sound modulation level (b) and signal-to-noise ratio (c) for each percentile across participants. Shaded area represents the 95% confidence interval.

Figure 2.

Everyday acoustic environment. (a–c) quartiles of the continuous acoustic data for each hour of the day computed as the grand median across all participants (solid, dashed and dotted lines). (d) Relative occurrence of each soundscape for each hour of the day computed as the mean percentage across all participants. Shaded area represents the standard error.

Figure 3.

Overview of data records for statistically associating mean HR and ambient sound. (a) Total counts of data records for each hour of the day (i) and weekday (ii) for each soundscape class (colours). (b) Density distributions for each acoustic data variable and the heart rates. Note that data records with HR below the 5th or above the 95th percentile were excluded prior to visualizing (see text for details).

Figure 4.

Marginal mean heart rate (HR) grouped in non-overlapping decile bins (bin-centres on x-axis) of the acoustic characteristics. Solid lines represent best fitting linear regression with 95% CI for the prediction (shaded area). (a) HR versus SPL Leq (β = 0.05, F_1,8 = 886.70, p < 0.001, R² = 0.99). (b) HR versus SML at low intensities (β = −0.01, F_1,8 = 2.30, p = 0.132, R² = 0.27). (c) HR versus SML at high intensities (β = −0.04, F_1,8 = 61.86, p < 0.001, R² = 0.86). (d) HR versus SNR at low intensities (β = 0.04, F_1,8 = 45.39.31, p < 0.001, R² = 0.85). (e) HR versus SNR at high intensities (β = −0.02, F_1,8 = 20.76, p = 0.002, R² = 0.72). See text for details about computing the marginal means.

Figure 5.

Relationship between each acoustic variable and the soundscape in the data records as two-dimensional density distributions. (a) SML versus SPL. (b) SNR versus SPL. (c) SNR versus SML.

Figure 6.

Predicted regression lines from the LME interaction model of the coefficients SPL (a), SML (b) and SNR (c). Shaded area represents the standard error of prediction.