@article{DuekingHothoHolmbergetal.2016,
  author    = {D{\"u}king, Peter and Hotho, Andreas and Holmberg, Hans-Christer and Fuss, Franz Konstantin and Sperlich, Billy},
  title     = {Comparison of Non-Invasive Individual Monitoring of the Training and Health of Athletes with Commercially Available Wearable Technologies},
  series = {Frontiers in Physiology},
  volume    = {7},
  journal   = {Frontiers in Physiology},
  number    = {71},
  doi       = {10.3389/fphys.2016.00071},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-165516},
  year      = {2016},
  abstract  = {Athletes adapt their training daily to optimize performance, as well as avoid fatigue, overtraining and other undesirable effects on their health. To optimize training load, each athlete must take his/her own personal objective and subjective characteristics into consideration and an increasing number of wearable technologies (wearables) provide convenient monitoring of various parameters. Accordingly, it is important to help athletes decide which parameters are of primary interest and which wearables can monitor these parameters most effectively. Here, we discuss the wearable technologies available for non-invasive monitoring of various parameters concerning an athlete's training and health. On the basis of these considerations, we suggest directions for future development. Furthermore, we propose that a combination of several wearables is most effective for accessing all relevant parameters, disturbing the athlete as little as possible, and optimizing performance and promoting health.},
  language  = {en}
}
@article{DuekingHolmbergSperlich2017,
  author    = {D{\"u}king, Peter and Holmberg, Hans-Christer and Sperlich, Billy},
  title     = {Instant Biofeedback Provided by Wearable Sensor Technology Can Help to Optimize Exercise and Prevent Injury and Overuse},
  series = {Frontiers in Physiology},
  volume    = {8},
  journal   = {Frontiers in Physiology},
  number    = {167},
  doi       = {10.3389/fphys.2017.00167},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-158044},
  year      = {2017},
  language  = {en}
}
@article{SperlichDuekingHolmberg2017,
  author    = {Sperlich, Billy and D{\"u}king, Peter and Holmberg, Hans-Christer},
  title     = {A SWOT analysis of the use and potential misuse of implantable monitoring devices by athletes},
  series = {Frontiers in Physiology},
  volume    = {8},
  journal   = {Frontiers in Physiology},
  number    = {629},
  doi       = {10.3389/fphys.2017.00629},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-158742},
  year      = {2017},
  abstract  = {Kein Abstract vorhanden.},
  language  = {en}
}
@article{DuekingAchtzehnHolmbergetal.2018,
  author    = {D{\"u}king, Peter and Achtzehn, Silvia and Holmberg, Hans-Christer and Sperlich, Billy},
  title     = {Integrated framework of load monitoring by a combination of smartphone applications, wearables and point-of-care testing provides feedback that allows individual responsive adjustments to activities of daily living},
  series = {Sensors},
  volume    = {18},
  journal   = {Sensors},
  number    = {5},
  doi       = {10.3390/s18051632},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176506},
  pages     = {1632},
  year      = {2018},
  abstract  = {Athletes schedule their training and recovery in periods, often utilizing a pre-defined strategy. To avoid underperformance and/or compromised health, the external load during training should take into account the individual's physiological and perceptual responses. No single variable provides an adequate basis for planning, but continuous monitoring of a combination of several indicators of internal and external load during training, recovery and off-training as well may allow individual responsive adjustments of a training program in an effective manner. From a practical perspective, including that of coaches, monitoring of potential changes in health and performance should ideally be valid, reliable and sensitive, as well as time-efficient, easily applicable, non-fatiguing and as non-invasive as possible. Accordingly, smartphone applications, wearable sensors and point-of-care testing appear to offer a suitable monitoring framework allowing responsive adjustments to exercise prescription. Here, we outline 24-h monitoring of selected parameters by these technologies that (i) allows responsive adjustments of exercise programs, (ii) enhances performance and/or (iii) reduces the risk for overuse, injury and/or illness.},
  language  = {en}
}
@article{DuekingFussHolmbergetal.2018,
  author    = {D{\"u}king, Peter and Fuss, Franz Konstantin and Holmberg, Hans-Christer and Sperlich, Billy},
  title     = {Recommendations for assessment of the reliability, sensitivity, and validity of data provided by wearable sensors designed for monitoring physical activity},
  series = {JMIR Mhealth and Uhealth},
  volume    = {6},
  journal   = {JMIR Mhealth and Uhealth},
  number    = {4},
  doi       = {10.2196/mhealth.9341},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176202},
  pages     = {e102},
  year      = {2018},
  abstract  = {Although it is becoming increasingly popular to monitor parameters related to training, recovery, and health with wearable sensor technology (wearables), scientific evaluation of the reliability, sensitivity, and validity of such data is limited and, where available, has involved a wide variety of approaches. To improve the trustworthiness of data collected by wearables and facilitate comparisons, we have outlined recommendations for standardized evaluation. We discuss the wearable devices themselves, as well as experimental and statistical considerations. Adherence to these recommendations should be beneficial not only for the individual, but also for regulatory organizations and insurance companies.},
  language  = {en}
}
@article{DuekingHolmbergSperlich2018,
  author    = {D{\"u}king, Peter and Holmberg, Hans-Christer and Sperlich, Billy},
  title     = {The potential usefulness of virtual reality systems for athletes: a short SWOT analysis},
  series = {Frontiers in Physiology},
  volume    = {9},
  journal   = {Frontiers in Physiology},
  number    = {128},
  doi       = {10.3389/fphys.2018.00128},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176178},
  year      = {2018},
  abstract  = {No abstract available.},
  language  = {en}
}
@article{DuekingHolmbergKunzetal.2020,
  author    = {D{\"u}king, Peter and Holmberg, Hans‑Christer and Kunz, Philipp and Leppich, Robert and Sperlich, Billy},
  title     = {Intra-individual physiological response of recreational runners to different training mesocycles: a randomized cross-over study},
  series = {European Journal of Applied Physiology},
  volume    = {120},
  journal   = {European Journal of Applied Physiology},
  issn      = {1439-6319},
  doi       = {10.1007/s00421-020-04477-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-235022},
  pages     = {2705-2713},
  year      = {2020},
  abstract  = {Purpose Pronounced differences in individual physiological adaptation may occur following various training mesocycles in runners. Here we aimed to assess the individual changes in performance and physiological adaptation of recreational runners performing mesocycles with different intensity, duration and frequency. Methods Employing a randomized cross-over design, the intra-individual physiological responses [i.e., peak (\(\dot{VO}_{2peak}\)) and submaximal (\(\dot{VO}_{2submax}\)) oxygen uptake, velocity at lactate thresholds (V\(_2\), V\(_4\))] and performance (time-to-exhaustion (TTE)) of 13 recreational runners who performed three 3-week sessions of high-intensity interval training (HIIT), high-volume low-intensity training (HVLIT) or more but shorter sessions of HVLIT (high-frequency training; HFT) were assessed. Results \(\dot{VO}_{2submax}\), V\(_2\), V\(_4\) and TTE were not altered by HIIT, HVLIT or HFT (p > 0.05). \(\dot{VO}_{2peak}\) improved to the same extent following HVLIT (p = 0.045) and HFT (p = 0.02). The number of moderately negative responders was higher following HIIT (15.4\%); and HFT (15.4\%) than HVLIT (7.6\%). The number of very positive responders was higher following HVLIT (38.5\%) than HFT (23\%) or HIIT (7.7\%). 46\% of the runners responded positively to two mesocycles, while 23\% did not respond to any. Conclusion On a group level, none of the interventions altered \(\dot{VO}_{2submax}\), V\(_2\), V\(_4\) or TTE, while HVLIT and HFT improved \(\dot{VO}_{2peak}\). The mean adaptation index indicated similar numbers of positive, negative and non-responders to HIIT, HVLIT and HFT, but more very positive responders to HVLIT than HFT or HIIT. 46\% responded positively to two mesocycles, while 23\% did not respond to any. These findings indicate that the magnitude of responses to HIIT, HVLIT and HFT is highly individual and no pattern was apparent.},
  language  = {en}
}
@article{DuekingGiessingFrenkeletal.2020,
  author    = {D{\"u}king, Peter and Giessing, Laura and Frenkel, Marie Ottilie and Koehler, Karsten and Holmberg, Hans-Christer and Sperlich, Billy},
  title     = {Wrist-Worn Wearables for Monitoring Heart Rate and Energy Expenditure While Sitting or Performing Light-to-Vigorous Physical Activity: Validation Study},
  series = {JMIR mhealth and uhealth},
  volume    = {8},
  journal   = {JMIR mhealth and uhealth},
  number    = {5},
  doi       = {10.2196/16716},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229413},
  year      = {2020},
  abstract  = {Background: Physical activity reduces the incidences of noncommunicable diseases, obesity, and mortality, but an inactive lifestyle is becoming increasingly common. Innovative approaches to monitor and promote physical activity are warranted. While individual monitoring of physical activity aids in the design of effective interventions to enhance physical activity, a basic prerequisite is that the monitoring devices exhibit high validity. Objective: Our goal was to assess the validity of monitoring heart rate (HR) and energy expenditure (EE) while sitting or performing light-to-vigorous physical activity with 4 popular wrist-worn wearables (Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa). Methods: While wearing the 4 different wearables, 25 individuals performed 5 minutes each of sitting, walking, and running at different velocities (ie, 1.1 m/s, 1.9 m/s, 2.7 m/s, 3.6 m/s, and 4.1 m/s), as well as intermittent sprints. HR and EE were compared to common criterion measures: Polar-H7 chest belt for HR and indirect calorimetry for EE. Results: While monitoring HR at different exercise intensities, the standardized typical errors of the estimates were 0.09-0.62, 0.13-0.88, 0.62-1.24, and 0.47-1.94 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 0.9\%-4.3\%, 2.2\%-6.7\%, 2.9\%-9.2\%, and 4.1\%-19.1\%, respectively, for the 4 wearables. While monitoring EE at different exercise intensities, the standardized typical errors of the estimates were 0.34-1.84, 0.32-1.33, 0.46-4.86, and 0.41-1.65 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 13.5\%-27.1\%, 16.3\%-28.0\%, 15.9\%-34.5\%, and 8.0\%-32.3\%, respectively. Conclusions: The Apple Watch Series 4 provides the highest validity (ie, smallest error rates) when measuring HR while sitting or performing light-to-vigorous physical activity, followed by the Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, in that order. The Apple Watch Series 4 and Polar Vantage V are suitable for valid HR measurements at the intensities tested, but HR data provided by the Garmin Fenix 5 and Fitbit Versa should be interpreted with caution due to higher error rates at certain intensities. None of the 4 wrist-worn wearables should be employed to monitor EE at the intensities and durations tested."},
  language  = {en}
}
@article{DuekingZinnerReedetal.2020,
  author    = {D{\"u}king, Peter and Zinner, Christoph and Reed, Jennifer L. and Holmberg, Hans-Christer and Sperlich, Billy},
  title     = {Predefined vs data-guided training prescription based on autonomic nervous system variation: A systematic review},
  series = {Scandinavian Journal of Medicine \& Science in Sports},
  volume    = {30},
  journal   = {Scandinavian Journal of Medicine \& Science in Sports},
  number    = {12},
  doi       = {10.1111/sms.13802},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-217893},
  pages     = {2291 -- 2304},
  year      = {2020},
  abstract  = {Monitoring variations in the functioning of the autonomic nervous system may help personalize training of runners and provide more pronounced physiological adaptations and performance improvements. We systematically reviewed the scientific literature comparing physiological adaptations and/or improvements in performance following training based on responses of the autonomic nervous system (ie, changes in heart rate variability) and predefined training. PubMed, SPORTDiscus, and Web of Science were searched systematically in July 2019. Keywords related to endurance, running, autonomic nervous system, and training. Studies were included if they (a) involved interventions consisting predominantly of running training; (b) lasted at least 3 weeks; (c) reported pre- and post-intervention assessment of running performance and/or physiological parameters; (d) included an experimental group performing training adjusted continuously on the basis of alterations in HRV and a control group; and (e) involved healthy runners. Five studies involving six interventions and 166 participants fulfilled our inclusion criteria. Four HRV-based interventions reduced the amount of moderate- and/or high-intensity training significantly. In five interventions, improvements in performance parameters (3000 m, 5000 m, Loadmax, Tlim) were more pronounced following HRV-based training. Peak oxygen uptake (VO\(_{2peak}\)) and submaximal running parameters (eg, LT1, LT2) improved following both HRV-based and predefined training, with no clear difference in the extent of improvement in VO\(_{2peak}\). Submaximal running parameters tended to improve more following HRV-based training. Research findings to date have been limited and inconsistent. Both HRV-based and predefined training improve running performance and certain submaximal physiological adaptations, with effects of the former training tending to be greater.},
  language  = {en}
}
@article{SperlichDuekingLeppichetal.2023,
  author    = {Sperlich, Billy and D{\"u}king, Peter and Leppich, Robert and Holmberg, Hans-Christer},
  title     = {Strengths, weaknesses, opportunities, and threats associated with the application of artificial intelligence in connection with sport research, coaching, and optimization of athletic performance: a brief SWOT analysis},
  series = {Frontiers in Sports and Active Living},
  volume    = {5},
  journal   = {Frontiers in Sports and Active Living},
  doi       = {10.3389/fspor.2023.1258562},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357973},
  year      = {2023},
  abstract  = {Here, we performed a non-systematic analysis of the strength, weaknesses, opportunities, and threats (SWOT) associated with the application of artificial intelligence to sports research, coaching and optimization of athletic performance. The strength of AI with regards to applied sports research, coaching and athletic performance involve the automation of time-consuming tasks, processing and analysis of large amounts of data, and recognition of complex patterns and relationships. However, it is also essential to be aware of the weaknesses associated with the integration of AI into this field. For instance, it is imperative that the data employed to train the AI system be both diverse and complete, in addition to as unbiased as possible with respect to factors such as the gender, level of performance, and experience of an athlete. Other challenges include e.g., limited adaptability to novel situations and the cost and other resources required. Opportunities include the possibility to monitor athletes both long-term and in real-time, the potential discovery of novel indicators of performance, and prediction of risk for future injury. Leveraging these opportunities can transform athletic development and the practice of sports science in general. Threats include over-dependence on technology, less involvement of human expertise, risks with respect to data privacy, breaching of the integrity and manipulation of data, and resistance to adopting such new technology. Understanding and addressing these SWOT factors is essential for maximizing the benefits of AI while mitigating its risks, thereby paving the way for its successful integration into sport science research, coaching, and optimization of athletic performance.},
  language  = {en}
}