The present paper concerns a new description of changing in metabolism during incremental exercises test that permit an individually tailored program of exercises for obese subjects. We analyzed heart rate variability from RR interval time series (tachogram) with an alternative approach, the recurrence quantification analysis, that allows a description of a time series in terms of its dynamic structure and is able to identify the phase transitions. A transition in cardiac signal dynamics was detected and it perfectly reflects the aerobic threshold, as identified by gas exchange during an incremental exercise test, revealing the coupling from the respiratory system toward the heart. Moreover, our analysis shows that, in the recurrence plot of RR interval, it is possible to identify a specific pattern that allows to identify phase transitions between different dynamic regimes. The perfect match of the occurrence of the phase transitions with changes observed in the VO 2 consumption, the gold standard approach to estimate thresholds, strongly supports the possibility of using our analysis of RR interval to detect metabolic threshold. In conclusion, we propose a novel nonlinear data analysis method that allows for an easy and personalized detection of thresholds both from professional and even from low-cost wearable devices, without the need of expensive gas analyzers.

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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.

The purpose of this study was to validate the Volition in Exercise Questionnaire in Italian language (VEQ-I). The translation and cultural adaptation of the VEQ-I was conducted using the forward-backward translation method. VEQ-I eighteen items correspond to the six-factors structure of the original version. The construct validity was verified by the confirmatory factor analysis (CFA) (CFI = 0.960; TLI = 0.943; RMSEA = 0.039; and SRMR = 0.040). The eighteen items were well distributed in six subscales and the six-factors structure of the questionnaire was supported. Internal Consistency value of the questionnaire was investigated for each subscale of the VEQ-I. Cronbach’s alpha and Omega values of the Reasons, Postponing Training, Unrelated Thoughts, Self-Confidence, Approval from Others and Coping with Failure subscales were 0.76 (α) and 0.76 (ω), 0.76 (α) and 0.76 (ω), 0.87 (α) and 0.88 (ω), 0.85 (α) and 0.85 (ω), 0.70 (α) and 0.72 (ω) and 0.74 (α) and 0.74 (ω), respectively. They were acceptable in all the six subscales. The concurrent validity was assessed using the correlation among the subscales of VEQ-I measures and those contained in two questionnaires: Psychobiosocial States in Physical Education (PBS-SPE) and Exercise Motivations Inventory (EMI-2).