@misc{toth_zusammenhang_2025, title = {Zusammenhang zwischen Trainingszeitpunkt und Schlafqualität}, author = {Toth, Yannick and Hollbach, Dario and Hizlan, Arif}, date = {2025-10-26}, year = {2025} } @article{odriscoll_how_2020, title = {How well do activity monitors estimate energy expenditure? A systematic review and meta-analysis of the validity of current technologies}, year = {2020}, volume = {54}, issn = {1473-0480}, doi = {10.1136/bjsports-2018-099643}, shorttitle = {How well do activity monitors estimate energy expenditure?}, abstract = {{OBJECTIVE}: To determine the accuracy of wrist and arm-worn activity monitors' estimates of energy expenditure ({EE}). {DATA} {SOURCES}: {SportDISCUS} ({EBSCOHost}), {PubMed}, {MEDLINE} (Ovid), {PsycINFO} ({EBSCOHost}), Embase (Ovid) and {CINAHL} ({EBSCOHost}). {DESIGN}: A random effects meta-analysis was performed to evaluate the difference in {EE} estimates between activity monitors and criterion measurements. Moderator analyses were conducted to determine the benefit of additional sensors and to compare the accuracy of devices used for research purposes with commercially available devices. {ELIGIBILITY} {CRITERIA}: We included studies validating {EE} estimates from wrist-worn or arm-worn activity monitors against criterion measures (indirect calorimetry, room calorimeters and doubly labelled water) in healthy adult populations. {RESULTS}: 60 studies (104 effect sizes) were included in the meta-analysis. Devices showed variable accuracy depending on activity type. Large and significant heterogeneity was observed for many devices (I2 {\textgreater}75\%). Combining heart rate or heat sensing technology with accelerometry decreased the error in most activity types. Research-grade devices were statistically more accurate for comparisons of total {EE} but less accurate than commercial devices during ambulatory activity and sedentary tasks. {CONCLUSIONS}: {EE} estimates from wrist and arm-worn devices differ in accuracy depending on activity type. Addition of physiological sensors improves estimates of {EE}, and research-grade devices are superior for total {EE}. These data highlight the need to improve estimates of {EE} from wearable devices, and one way this can be achieved is with the addition of heart rate to accelerometry. {PROSPEROREGISTRATION} {NUMBER}: {CRD}42018085016.}, pages = {332--340}, number = {6}, journaltitle = {British Journal of Sports Medicine}, shortjournal = {Br J Sports Med}, author = {O'Driscoll, Ruairi and Turicchi, Jake and Beaulieu, Kristine and Scott, Sarah and Matu, Jamie and Deighton, Kevin and Finlayson, Graham and Stubbs, James}, date = {2020-03}, pmid = {30194221}, keywords = {accelerometer, Accelerometry, Activities of Daily Living, Arm, Bicycling, energy expenditure, Energy Metabolism, Equipment Design, Fitness Trackers, Heart Rate, Humans, meta-analysis, Running, Sedentary Behavior, Stair Climbing, validation, Walking, wrist, Wrist}, } @article{brage_estimation_2015, title = {Estimation of Free-Living Energy Expenditure by Heart Rate and Movement Sensing: A Doubly-Labelled Water Study}, year = {2015}, volume = {10}, issn = {1932-6203}, url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0137206}, doi = {10.1371/journal.pone.0137206}, shorttitle = {Estimation of Free-Living Energy Expenditure by Heart Rate and Movement Sensing}, abstract = {Background Accurate assessment of energy expenditure ({EE}) is important for the study of energy balance and metabolic disorders. Combined heart rate ({HR}) and acceleration ({ACC}) sensing may increase precision of physical activity {EE} ({PAEE}) which is the most variable component of total {EE} ({TEE}). Objective To evaluate estimates of {EE} using {ACC} and {HR} data with or without individual calibration against doubly-labelled water ({DLW}) estimates of {EE}. Design 23 women and 23 men (22–55 yrs, 48–104 kg, 8–46\%body fat) underwent 45-min resting {EE} ({REE}) measurement and completed a 20-min treadmill test, an 8-min step test, and a 3-min walk test for individual calibration. {ACC} and {HR} were monitored and {TEE} measured over 14 days using {DLW}. Diet-induced thermogenesis ({DIT}) was calculated from food-frequency questionnaire. {PAEE} ({TEE} ÷ {REE} ÷ {DIT}) and {TEE} were compared to estimates from {ACC} and {HR} using bias, root mean square error ({RMSE}), and correlation statistics. Results Mean({SD}) measured {PAEE} and {TEE} were 66(25) {kJ}·day-1·kg-1, and 12(2.6) {MJ}·day-1, respectively. Estimated {PAEE} from {ACC} was 54(15) {kJ}·day-1·kg-1 (p{\textless}0.001), with {RMSE} 24 {kJ}·day-1·kg-1 and correlation r = 0.52. {PAEE} estimated from {HR} and {ACC}+{HR} with treadmill calibration were 67(42) and 69(25) {kJ}·day-1·kg-1 (bias non-significant), with {RMSE} 34 and 20 {kJ}·day-1·kg-1 and correlations r = 0.58 and r = 0.67, respectively. Similar results were obtained with step-calibrated and walk-calibrated models, whereas non-calibrated models were less precise ({RMSE}: 37 and 24 {kJ}·day-1·kg-1, r = 0.40 and r = 0.55). {TEE} models also had high validity, with biases {\textless}5\%, and correlations r = 0.71 ({ACC}), r = 0.66–0.76 ({HR}), and r = 0.76–0.83 ({ACC}+{HR}). Conclusions Both accelerometry and heart rate may be used to estimate {EE} in adult European men and women, with improved precision if combined and if heart rate is individually calibrated.}, pages = {e0137206}, page = {2}, number = {9}, journaltitle = {{PLOS} {ONE}}, shortjournal = {{PLOS} {ONE}}, author = {Brage, Søren and Westgate, Kate and Franks, Paul W. and Stegle, Oliver and Wright, Antony and Ekelund, Ulf and Wareham, Nicholas J.}, urldate = {2025-11-23}, date = {2015-09-08}, langid = {english}, note = {Publisher: Public Library of Science}, keywords = {Accelerometers, Adults, Bioenergetics, Diet, Energy metabolism, Heart rate, Physical activity, Urine}, } @article{noauthor_using_2015, title = {Using Smartphone Sensors for Improving Energy Expenditure Estimation}, volume = {3}, year = {2015}, issn = {2168-2372}, url = {https://pmc.ncbi.nlm.nih.gov/articles/PMC4848104/}, doi = {10.1109/JTEHM.2015.2480082}, abstract = {Energy expenditure ({EE}) estimation is an important factor in tracking personal activity and preventing chronic diseases, such as obesity and diabetes. Accurate and real-time {EE} estimation utilizing small wearable sensors is a difficult task, primarily because the most existing schemes work offline or use heuristics. In this paper, we focus on accurate {EE} estimation for tracking ambulatory activities (walking, standing, climbing upstairs, or downstairs) of a typical smartphone user. We used built-in smartphone sensors (accelerometer and barometer sensor), sampled at low frequency, to accurately estimate {EE}. Using a barometer sensor, in addition to an accelerometer sensor, greatly increases the accuracy of {EE} estimation. Using bagged regression trees, a machine learning technique, we developed a generic regression model for {EE} estimation that yields upto 96\% correlation with actual {EE}. We compare our results against the state-of-the-art calorimetry equations and consumer electronics devices (Fitbit and Nike+ {FuelBand}). The newly developed {EE} estimation algorithm demonstrated superior accuracy compared with currently available methods. The results were calibrated against {COSMED} K4b2 calorimeter readings.}, pages = {2700212}, journaltitle = {{IEEE} Journal of Translational Engineering in Health and Medicine}, shortjournal = {{IEEE} J Transl Eng Health Med}, urldate = {2025-11-23}, date = {2015-09-18}, pmid = {27170901}, pmcid = {PMC4848104}, } @article{edwards_evidence_2025, title = {Evidence of a circadian variation in 10-km laboratory running time-trial performance, where a standardised approach has been employed}, volume = {42}, year = {2025}, issn = {0742-0528}, url = {https://doi.org/10.1080/07420528.2025.2459668}, doi = {10.1080/07420528.2025.2459668}, abstract = {Diurnal variations in time-trial performance have been shown in people living normally, where a “standardised protocol” has been employed to reduce bias. We tested the hypothesis that a circadian variation exists for a 10-km running laboratory-based time-trial, where such a standardised approach is used. Twelve recreationally active adult males were recruited. The participants completed three familiarisation time-trials to the best of their ability at a self-selected pace and six 10-km time-trials at 06:00, 10:00, 14:00, 18:00, 22:00 and 02:00 h. Each session was separated by 7-days. Participants were allocated into 6 groups due to finish times ({FT}); sessions were counterbalanced in order of administration. A cosine fit for resting intra-aural temperature and {FT} both showed a significant circadian rhythm (p {\textless} 0.05) with mesor, amplitude and acrophases of 36.61°C vs 2994 s, 0.34°C vs 149 s; and 17:29 vs 18:44 h:min, respectively. The parallelism of temperature and {FT} agrees with previously published research. The finding of a 24-h rhythm in 10-km {FT} (5.0\%, d = 0.80; power = 100\%) concurs with that of a shorter distance where “standardised protocols” have been employed (4-km, 2.6\%, d = 0.34). This finding has implications for scheduling of competition and training. Whether this variation is apparent in other populations, however, is unclear.}, pages = {244--258}, number = {2}, journaltitle = {Chronobiology International}, author = {Edwards, Ben J. and Boyle, Brenda B. and Burniston, Jatin G. and Doran, Dominic A. and Doran, Dave and Giacomoni, Magali and Mahon, Elizabeth and Pullinger, Samuel A.}, urldate = {2025-11-23}, date = {2025-02-01}, pmid = {39908409}, note = {Publisher: Taylor \& Francis \_eprint: https://doi.org/10.1080/07420528.2025.2459668}, keywords = {core body temperature, Daily-variation, design-checklist, time-of-day}, } @book{geron_praxiseinstieg_ml_2023, author = {Géron, Aurélien}, title = {Praxiseinstieg Machine Learning mit Scikit‑Learn, Keras und TensorFlow}, subtitle = {Konzepte, Tools und Techniken für intelligente Systeme}, translator = {Rother, Kristian and Demmig, Thomas}, edition = {3. Auflage}, year = {2023}, publisher = {O'Reilly Verlag / dpunkt.Verlag}, address = {Heidelberg}, isbn = {978‑3‑96009‑212‑4}, pages = {878}, } @misc{WasWirdZur, title = {Was Wird Zur {{Berechnung}} Der {{Kalorien}} Auf Einer {{Multisport-Uhr}} Verwendet? \textbar{} {{Garmin Support-Center}}}, journal = {Garmin}, urldate = {2025-11-23}, howpublished = {https://support.garmin.com/de-DE/?faq=Le7blBDFA95R8zNntMRMe5} }