TY - JOUR A1 - Sperlich, Billy A1 - Matzka, Manuel A1 - Holmberg, Hans-Christer T1 - The proportional distribution of training by elite endurance athletes at different intensities during different phases of the season JF - Frontiers in Sports and Active Living N2 - The present review examines retrospective analyses of training intensity distribution (TID), i.e., the proportion of training at moderate (Zone 1, Z1), heavy (Z2) and severe (Z3) intensity by elite-to-world-class endurance athletes during different phases of the season. In addition, we discuss potential implications of our findings for research in this field, as well as for training by these athletes. Altogether, we included 175 TIDs, of which 120 quantified exercise intensity on the basis of heart rate and measured time-in-zone or employed variations of the session goal approach, with demarcation of zones of exercise intensity based on physiological parameters. Notably, 49% of the TIDs were single-case studies, predominantly concerning cross-country skiing and/or the biathlon. Eighty-nine TIDs were pyramidal (Z1 > Z2 > Z3), 65 polarized (Z1 > Z3 > Z2) and 8 “threshold” (Z2 > Z1 = Z3). However, these relative numbers varied between sports and the particular phases of the season. In 91% (n = 160) of the TIDs >60% of the endurance exercise was of low intensity. Regardless of the approach to quantification or phase of the season, cyclists and swimmers were found to perform a lower proportion of exercise in Z1 (<72%) and higher proportion in Z2 (>16%) than athletes involved in the triathlon, speed skating, rowing, running, cross-country skiing or biathlon (>80% in Z1 and <12% in Z2 in all these cases). For most of the athletes their proportion of heavy-to-severe exercise was higher during the period of competition than during the preparatory phase, although with considerable variability between sports. In conclusion, the existing literature in this area does not allow general conclusions to be drawn. The methods utilized for quantification vary widely and, moreover, contextual information concerning the mode of exercise, environmental conditions, and biomechanical aspects of the exercise is often lacking. Therefore, we recommend a more comprehensive approach in connection with future investigations on the TIDs of athletes involved in different endurance sports. KW - training intensity distribution KW - exercise intensity KW - HIIT (High intensity interval training) KW - endurance KW - elite athlete KW - endurance training Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-357988 VL - 5 ER - TY - JOUR A1 - Sperlich, Billy A1 - Düking, Peter A1 - Leppich, Robert A1 - Holmberg, Hans-Christer T1 - 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 JF - Frontiers in Sports and Active Living N2 - 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. KW - XAI and explainable artificial intelligence KW - XAI KW - elite sport KW - performance KW - exercise science KW - SWOT KW - artifical inteligence Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-357973 VL - 5 ER - TY - JOUR A1 - Düking, Peter A1 - Tafler, Marie A1 - Wallmann-Sperlich, Birgit A1 - Sperlich, Billy A1 - Kleih, Sonja T1 - Behavior Change Techniques in Wrist-Worn Wearables to Promote Physical Activity: Content Analysis JF - JMIR Mhealth and Uhealth N2 - 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 Vívoactive 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 Vívoactive 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. KW - cardiorespiratory fitness KW - innovation KW - smartwatch KW - technology KW - wearable KW - eHealth KW - mHealth Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-230556 VL - 8 IS - 11 ER - TY - JOUR A1 - Wallmann-Sperlich, Birgit A1 - Hoffmann, Sophie A1 - Salditt, Anne A1 - Bipp, Tanja A1 - Froboese, Ingo T1 - Moving to an “active” biophilic designed office workplace: a pilot study about the effects on sitting time and sitting habits of office-based workers JF - International Journal of Environmental Research and Public Health N2 - Promising initial insights show that offices designed to permit physical activity (PA) may reduce workplace sitting time. Biophilic approaches are intended to introduce natural surroundings into the workplace, and preliminary data show positive effects on stress reduction and elevated productivity within the workplace. The primary aim of this pilot study was to analyze changes in workplace sitting time and self-reported habit strength concerning uninterrupted sitting and PA during work, when relocating from a traditional office setting to “active” biophilic-designed surroundings. The secondary aim was to assess possible changes in work-associated factors such as satisfaction with the office environment, work engagement, and work performance, among office staff. In a pre-post designed field study, we collected data through an online survey on health behavior at work. Twelve participants completed the survey before (one-month pre-relocation, T1) and twice after the office relocation (three months (T2) and seven months post-relocation (T3)). Standing time per day during office hours increased from T1 to T3 by about 40 min per day (p < 0.01). Other outcomes remained unaltered. The results suggest that changing office surroundings to an active-permissive biophilic design increased standing time during working hours. Future larger-scale controlled studies are warranted to investigate the influence of office design on sitting time and work-associated factors during working hours in depth. KW - desk-based KW - office-workers KW - standing KW - online survey KW - walking KW - work engagement KW - habit strength KW - work performance KW - office environment Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-197371 SN - 1660-4601 VL - 16 IS - 9 ER -