@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{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}
}
@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}
}