@article{BornZinnerDuekingetal.2016,
  author    = {Born, Dennis-Peter and Zinner, Christoph and D{\"u}king, Peter and Sperlich, Billy},
  title     = {Multi-Directional Sprint Training Improves Change-Of-Direction Speed and Reactive Agility in Young Highly Trained Soccer Players},
  series = {Journal of Sports Science and Medicine},
  volume    = {15},
  journal   = {Journal of Sports Science and Medicine},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-146866},
  pages     = {314-319},
  year      = {2016},
  abstract  = {The aim of this study was to evaluate the effect of a repeated sprint training with multi-directional change-of-direction (COD) movements (RSmulti) compared to repeated shuttle sprints (RSS) on variables related to COD speed and reactive agility. Nineteen highly-trained male U15 soccer players were assigned into two groups performing either RSmulti or RSS. For both groups, each training session involved 20 repeated 15 s sprints interspersed with 30 s recovery. With RSmulti the COD movements were randomized and performed in response to a visual stimulus, while the RSS involved predefined 180° COD movements. Before and following the six training sessions, performance in the Illinois agility test (IAT), COD speed in response to a visual stimulus, 20 m linear sprint time and vertical jumping height were assessed. Both groups improved their performance in the IAT (p < 0.01, ES = 1.13; p = 0.01, ES = 0.55). The COD speed in response to a visual stimulus improved with the RSmulti (p < 0.01, ES = 1.03), but not the RSS (p = 0.46, ES = 0.28). No differences were found for 20 m sprint time (P=0.73, ES = 0.07; p = 0.14, ES = 0.28) or vertical jumping height (p = 0.46, ES = 0.11; p = 0.29, ES = 0.12) for the RSmulti and RSS, respectively. In conclusion, performance in the IAT improved with the RSmulti as well as RSS. With the RSmulti however, the COD movements are performed in response to a visual stimulus, which may result in specific adaptations that improve COD speed and reactive agility in young highly trained soccer players.},
  language  = {en}
}
@article{DavidsonDuekingZinneretal.2020,
  author    = {Davidson, Padraig and D{\"u}king, Peter and Zinner, Christoph and Sperlich, Billy and Hotho, Andreas},
  title     = {Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study},
  series = {Sensors},
  volume    = {20},
  journal   = {Sensors},
  number    = {9},
  issn      = {1424-8220},
  doi       = {10.3390/s20092637},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-205686},
  year      = {2020},
  abstract  = {The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 "Somewhat hard to hard" on Borg's 6-20 scale vs. RPE >15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 \% for the whole dataset, 81.8 \% for the trained runners, and 86.1 \% for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions.},
  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{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{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{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{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{DuekingTaflerWallmannSperlichetal.2020,
  author    = {D{\"u}king, Peter and Tafler, Marie and Wallmann-Sperlich, Birgit and Sperlich, Billy and Kleih, Sonja},
  title     = {Behavior Change Techniques in Wrist-Worn Wearables to Promote Physical Activity: Content Analysis},
  series = {JMIR Mhealth and Uhealth},
  volume    = {8},
  journal   = {JMIR Mhealth and Uhealth},
  number    = {11},
  doi       = {10.2196/20820},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-230556},
  year      = {2020},
  abstract  = {Background: Decreasing levels of physical activity (PA) increase the incidences of noncommunicable diseases, obesity, and mortality. To counteract these developments, interventions aiming to increase PA are urgently needed. Mobile health (mHealth) solutions such as wearable sensors (wearables) may assist with an improvement in PA. Objective: The aim of this study is to examine which behavior change techniques (BCTs) are incorporated in currently available commercial high-end wearables that target users' PA behavior. Methods: The BCTs incorporated in 5 different high-end wearables (Apple Watch Series 3, Garmin V{\´i}voactive 3, Fitbit Versa, Xiaomi Amazfit Stratos 2, and Polar M600) were assessed by 2 researchers using the BCT Taxonomy version 1 (BCTTv1). Effectiveness of the incorporated BCTs in promoting PA behavior was assessed by a content analysis of the existing literature. Results: The most common BCTs were goal setting (behavior), action planning, review behavior goal(s), discrepancy between current behavior and goal, feedback on behavior, self-monitoring of behavior, and biofeedback. Fitbit Versa, Garmin V{\´i}voactive 3, Apple Watch Series 3, Polar M600, and Xiaomi Amazfit Stratos 2 incorporated 17, 16, 12, 11, and 11 BCTs, respectively, which are proven to effectively promote PA. Conclusions: Wearables employ different numbers and combinations of BCTs, which might impact their effectiveness in improving PA. To promote PA by employing wearables, we encourage researchers to develop a taxonomy specifically designed to assess BCTs incorporated in wearables. We also encourage manufacturers to customize BCTs based on the targeted populations.},
  language  = {en}
}