Purpose: Firstly, to examine whether heart rate variability (HRV) responses can be modelled effectively via the Banister Impulse-Response model (IR) when the session rating of perceived exertion (sRPE) alone, and in combination with subjective well-being measures, are utilised. Secondly, to describe seasonal HRV responses and their associations with changes in critical speed (CS) in competitive swimmers. Methods: Ten highly-trained swimmers collected daily 1-min HRV recordings, sRPE training load, and subjective well-being scores via a novel smartphone application for 15-weeks. The IR model was used to describe chronic Root Mean Square of the Successive Differences (rMSSD) responses to training, with sRPE and subjective well-being measures used as systems inputs. Changes in CS were obtained from a 3-min all-out test completed in Week 1 and 14. Results: The level of agreement between predicted and actual HRV data was R2=0.66±0.25 when sRPE alone was used. Model fits improved in the range of 4-21% when different subjective well-being measures were combined with sRPE, representing trivial-to-moderate improvements. There were no significant differences in weekly group Ln rMSSDMEAN (p=0.34) or HRV coefficient of variation (Ln rMSSDCV) (p=0.12), however, small-to-large changes (d=0.21-1.46) were observed in these parameters throughout the season. Large correlations were observed between seasonal changes in HRV measures and CS (ΔLn rMSSDMEAN: r=0.51, p=0.13; ΔLn rMSSDCV: r=-0.68, p=0.03). Conclusion: The IR model and data collected via a novel smartphone application can be used to model HRV responses to swimming training and non-training related stressors. Large relationships between seasonal changes in measured HRV parameters (especially Ln rMSSDCV) and CS provide further evidence for incorporating a HRV-guided training approach.