Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describe an approach to support athletes at various fitness levels in their training load analysis using heart rate (HR) and heart rate variability (HRV). A smartphone-based application (HRV4Training) was developed that captures heart activity over one to five minutes using photoplethysmography (PPG) and derives HR and HRV features. HRV4Training integrated a guide for an early morning spot measurement protocol and a questionnaire to capture self-reported training activity. The smartphone application was made publicly available for interested users to quantify training effect. Here  analyze data acquired over a period of 3 eks to 5 months, including 797 users, breaking down results by gender and age group. Our results suggest a strong relation beten HR, HRV and self-reported training load independent of gender and age group. HRV changes due to training re larger than those of HR.  conclude that smartphone-based training monitoring is feasible and a can be used as a practical tool to support large populations outside controlled laboratory environments.
['Marco Altini', 'Oliver Amft']
I am writing a research paper on a idea HRV4Training: Large-scale longitudinal training load analysis in unconstrained free-living settings using a smartphone application
[{"from": "human", "value": "I am writing a research paper on a idea HRV4Training: Large-scale longitudinal training load analysis in unconstrained free-living settings using a smartphone application"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describe an approach to support athletes at various fitness levels in their training load analysis using heart rate (HR) and heart rate variability (HRV). A smartphone-based application (HRV4Training) was developed that captures heart activity over one to five minutes using photoplethysmography (PPG) and derives HR and HRV features. HRV4Training integrated a guide for an early morning spot measurement protocol and a questionnaire to capture self-reported training activity. The smartphone application was made publicly available for interested users to quantify training effect. Here  analyze data acquired over a period of 3 eks to 5 months, including 797 users, breaking down results by gender and age group. Our results suggest a strong relation beten HR, HRV and self-reported training load independent of gender and age group. HRV changes due to training re larger than those of HR.  conclude that smartphone-based training monitoring is feasible and a can be used as a practical tool to support large populations outside controlled laboratory environments."}]