How to Investigate the Effect of Music on Breathing during Exercise: Methodology and Tools

Innocenti, Lorenzo; Nicolò, Andrea; Massaroni, Carlo; Minganti, Carlo; Schena, Emiliano; Sacchetti, Massimo

doi:10.3390/s22062351

Cited by 4 publications

(3 citation statements)

References 48 publications

(142 reference statements)

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“…It is a pseudo-periodic signal with a negative phase corresponding to inhalation and a positive phase corresponding to exhalation (see Figure 3 b). In line with previous studies, the signal was integrated over time to obtain a respiratory volume (V) signal and then normalized [ 37 , 39 ]. Consequently, the V signal exhibited an ascending trend during the exhalation phase and a descending trend during the inhalation phase.…”

Section: Discussionmentioning

confidence: 99%

Respiratory Rate Estimation during Walking and Running Using Breathing Sounds Recorded with a Microphone

et al. 2023

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Emerging evidence suggests that respiratory frequency (fR) is a valid marker of physical effort. This has stimulated interest in developing devices that allow athletes and exercise practitioners to monitor this vital sign. The numerous technical challenges posed by breathing monitoring in sporting scenarios (e.g., motion artifacts) require careful consideration of the variety of sensors potentially suitable for this purpose. Despite being less prone to motion artifacts than other sensors (e.g., strain sensors), microphone sensors have received limited attention so far. This paper proposes the use of a microphone embedded in a facemask for estimating fR from breath sounds during walking and running. fR was estimated in the time domain as the time elapsed between consecutive exhalation events retrieved from breathing sounds every 30 s. Data were collected from ten healthy subjects (both males and females) at rest and during walking (at 3 km/h and 6 km/h) and running (at 9 km/h and 12 km/h) activities. The reference respiratory signal was recorded with an orifice flowmeter. The mean absolute error (MAE), the mean of differences (MOD), and the limits of agreements (LOAs) were computed separately for each condition. Relatively good agreement was found between the proposed system and the reference system, with MAE and MOD values increasing with the increase in exercise intensity and ambient noise up to a maximum of 3.8 bpm (breaths per minute) and −2.0 bpm, respectively, during running at 12 km/h. When considering all the conditions together, we found an MAE of 1.7 bpm and an MOD ± LOAs of −0.24 ± 5.07 bpm. These findings suggest that microphone sensors can be considered among the suitable options for estimating fR during exercise.

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Section: Discussionmentioning

confidence: 99%

Respiratory Rate Estimation during Walking and Running Using Breathing Sounds Recorded with a Microphone

et al. 2023

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“…Prior to data analysis, all the signals recorded with the facemask (T Exp signal, T E signal, motion module signal) were synchronized with the ϕ reference signal starting from the third deep breath of the starting sequence. The reference ϕ provides a pseudo-periodic signal with a negative phase corresponding to inspiration and a positive phase corresponding to expiration (see Figure 2 C); ϕ signal was integrated to obtain a volume signal (V), in accordance with previous studies [ 44 , 45 ]. V has an increasing trend during exhalation and a decreasing trend during inhalation, comparable to the signal acquired with our device.…”

Section: Validation Of the Smart Facemask And Algorithms: Experimenta...mentioning

confidence: 99%

Design and Testing of a Smart Facemask for Respiratory Monitoring during Cycling Exercise

et al. 2023

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Given the importance of respiratory frequency (fR) as a valid marker of physical effort, there is a growing interest in developing wearable devices measuring fR in applied exercise settings. Biosensors measuring chest wall movements are attracting attention as they can be integrated into textiles, but their susceptibility to motion artefacts may limit their use in some sporting activities. Hence, there is a need to exploit sensors with signals minimally affected by motion artefacts. We present the design and testing of a smart facemask embedding a temperature biosensor for fR monitoring during cycling exercise. After laboratory bench tests, the proposed solution was tested on cyclists during a ramp incremental frequency test (RIFT) and high-intensity interval training (HIIT), both indoors and outdoors. A reference flowmeter was used to validate the fR extracted from the temperature respiratory signal. The smart facemask showed good performance, both at a breath-by-breath level (MAPE = 2.56% and 1.64% during RIFT and HIIT, respectively) and on 30 s average fR values (MAPE = 0.37% and 0.23% during RIFT and HIIT, respectively). Both accuracy and precision (MOD ± LOAs) were generally superior to those of other devices validated during exercise. These findings have important implications for exercise testing and management in different populations.

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“…Related studies have also shown that the auditory stimulation of music at different exercise times positively impacts human physiology and psychology. Music improves personal affect and reduces subjective fatigue during moderate-intensity exercise ( Innocenti et al, 2022 ). The benefits of combining movement and music are not only observed during training, but music as a therapeutic aid also positively contributes to recovery from post-exercise fatigue ( Karageorghis et al, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

The difference of affect improvement effect of music intervention in aerobic exercise at different time periods

Lu,

Tao,

Gao

et al. 2024

Front. Physiol.

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Objectives: A randomized controlled experimental design that combines exercise and music intervention was adopted in this study to verify whether this approach could help improve human affect. The differences in the effect of music listening on affective improvement were compared in four different periods: before, during, and after aerobic power cycling exercise and the whole exercise course.Method: A total of 140 subjects aged 19–30 years (average age: 23.6 years) were recruited and randomly divided into four music intervention groups, namely, the pre-exercise, during-exercise, post-exercise, and the whole-course groups. The subjects’ demographic and sociological variables and daily physical activities were collected using questionnaires. Individual factors, such as the subjects’ noise sensitivity, personality traits, and degree of learning burnout, were collected via scale scoring. A laboratory in Zhejiang Normal University was selected as the experimental site. The testing procedure can be summarized as follows. In a quiet environment, the subjects were asked to sit quietly for 5 min after completing a preparation work, and then they were informed to take a pre-test. The four subject groups wore headphones and completed 20 min of aerobic cycling (i.e., 7 min of moderate-intensity cycling [50%*HRR + RHR] + 6 min of low-intensity interval cycling [30%*HRR + RHR] + 7 min of moderate-intensity cycling [50%*HRR + RHR] after returning to a calm state (no less than 20 min) for post-testing. The affect improvement indicators (dependent variables) collected in the field included blood pressure (BP), positive/negative affect, and heart rate variability indicators (RMSSD, SDNN, and LF/HF).Results: 1) Significant differences were found in the participants’ systolic BP (SBP) indices and the effect of improvement of the positive affect during the exercise–music intervention among the four groups at different durations for the same exercise intensity (F = 2.379, p = 0.030, ɳp2 = 0.058; F = 2.451, p = 0.043, ɳp2 = 0.091). 2) Music intervention for individuals during exercise contribute more to the reduction of SBP than the other three time periods (F = 3.170, p = 0.047, ɳp2 = 0.068). Improvement in the participants’ negativity affective score was also better during exercise, and it was significantly different than the other three time periods (F = 5.516, p = 0.006, ɳp2 = 0.113). No significant differences were found in the improvement effects of the other effective indicators for the four periods.Conclusion: Exercise combined with music intervention has a facilitative effect on human affect improvement, and listening to music during exercise has a better impact on affective improvement than music interventions at the other periods. When people perform physical activities, listening to music during exercise positively affects the progress effect among them.

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How to Investigate the Effect of Music on Breathing during Exercise: Methodology and Tools

Cited by 4 publications

References 48 publications

Respiratory Rate Estimation during Walking and Running Using Breathing Sounds Recorded with a Microphone

Respiratory Rate Estimation during Walking and Running Using Breathing Sounds Recorded with a Microphone

Design and Testing of a Smart Facemask for Respiratory Monitoring during Cycling Exercise

The difference of affect improvement effect of music intervention in aerobic exercise at different time periods

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