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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
University of Würzburg, Würzburg, Germany.
German Sport University, Cologne, Germany.
Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences. UiT The Arctic University of Norway, Tromso, Norway.ORCID iD: 0000-0002-3814-6246
University of Würzburg, Würzburg, Germany.
2018 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 5, article id 1632Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2018. Vol. 18, no 5, article id 1632
Keywords [en]
Biofeedback, eHealth, Individualized training, Injury prevention, IoT, Load management, Periodization
National Category
Health Sciences
Identifiers
URN: urn:nbn:se:miun:diva-34112DOI: 10.3390/s18051632ISI: 000435580300331PubMedID: 29783763Scopus ID: 2-s2.0-85047263268OAI: oai:DiVA.org:miun-34112DiVA, id: diva2:1230738
Available from: 2018-07-04 Created: 2018-07-04 Last updated: 2018-09-26

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