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Comparison Between Different 1RM Prediction Tests in Submaximal Load for the Usage of a Future Smartwatch App

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Comparison Between Different 1RM Prediction Tests in Submaximal Load for the Usage of a Future Smartwatch App Comparison between different 1RM prediction tests in submaximal load for the usage of a future smartwatch app Fulfillment of the requirements for the «Master of Science in Sports with Specialization in Teaching» submitted by: Raphael Erni (11-124-047) at the University of Freiburg, Switzerland Faculty of Mathematics and Natural Sciences Department of Medicine in cooperation with Swiss Federal Institute of Sports Magglingen SFISM supervisor: PD Dr. Silvio Lorenzetti advisor: Fabian Lüthy Bern, January 2019 Acknowledgement This master thesis was conducted at the Institute of Sport and Human Movements Sciences at the University of Fribourg in cooperation with the Swiss Federal Institute of Sports Mag- glingen and supervised by PD Dr. Lorenzetti Silvio. Hereby, I would like to thank him for his exceptional help when it came to his great advice, his quick responses to my questions and for always showing me the right direction whenever I needed it. I would also like to thank my advisor Fabian Lüthy for supporting me with good references and helping me when I needed some extra guidance. Furthermore, I would like to thank Dominik Huber which took his precious time to explain the process of his master thesis to me in depth and showing me how the app and the smart- watch worked. He was always just a phone call away for some really good advice. I would also like to word my appreciation to Ravi Sinnathamby, Niklaus Jud and Marielle Kunz for letting me use their gym facility and equipment and helping me find suitable test subjects. Similarly important for the final touch of this thesis were Dr. Samuel Iff, Samuel Böringer, Pascal Stegmann and Cherelle Oestringer for proofreading my thesis, helping with statistical questions and giving me constructive feedback. Finally, I must also express my very profound gratitude to my parents and siblings for always giving me the needed motivation, support and encouragement throughout this project. I would not have accomplished this thesis, without their help and support. 2 Table of content Acknowledgement ..................................................................................................................... 2 Abstract ...................................................................................................................................... 4 1 Introduction ........................................................................................................................ 5 1.1 1RM predictions ......................................................................................................... 7 1.2 Velocity based training ............................................................................................ 11 1.3 Huber’s strength training app ................................................................................... 13 1.4 The objective of the thesis ....................................................................................... 15 2 Methods ............................................................................................................................ 16 2.1 Experimental Approach to the Problem ................................................................... 16 2.2 Subjects .................................................................................................................... 16 2.3 Equipment and Resources ........................................................................................ 16 2.4 Procedures ................................................................................................................ 18 2.5 Data Analysis ........................................................................................................... 19 2.6 Statistical Analyses .................................................................................................. 19 3 Results .............................................................................................................................. 21 3.1 Subjects .................................................................................................................... 21 3.2 1RM predictions ....................................................................................................... 21 3.3 Detection accuracy of the app .................................................................................. 27 4 Discussion ........................................................................................................................ 30 4.1 1RM predictions ....................................................................................................... 30 4.2 Detection accuracy of the app .................................................................................. 31 4.3 Limitations ............................................................................................................... 34 4.4 Implications .............................................................................................................. 34 5 Conclusion ....................................................................................................................... 36 References ................................................................................................................................ 37 List of figures and tables .......................................................................................................... 45 AppendiX .................................................................................................................................. 48 List of abbreviations ............................................................................................................. 48 Subject group ....................................................................................................................... 49 Overview of 1RM predictions .............................................................................................. 50 3 Abstract Overview Research has shown that there is a fundamental importance of quantification of the maximum load that can be lifted through a full range of motion, when it comes to the design of re- sistance training programs. Furthermore, workout assistants that can track your resistance training through wrist worn devices have shown to be quite accurate when it came to exercise recognition and repetition counting but needed improvement on the integration of a one repe- tition maximum (1RM) estimation. Research Question The primary aim for this master thesis was to find out which parameters of the load-velocity or reps-to-fatigue relationship formula showed the best accuracy for the 1RM estimation of the exercises barbell bench press, barbell back squat and barbell deadlift. The secondary aim focused on validating the accuracy of a previously developed strength training app and on its functions of exercise recognition, repetition counting and 1RM estimation. Methods A subject group consisting of recreationally trained athletes (N = 30) performed 4 sets of up to 10 repetitions with an increasing submaximal load (60 – 80 % of predicted 1RM) for each exercise, wearing an Apple watch with an integrated strength training app and having the GymAware Power Tool attached to the bar to collect data. Results The adapted formula of Jovanović and Flanagan (2014) and Epley (1985) showed the best correlations in all three exercises. Regarding the app’s functions, there was an 88.4 % overall correct exercise recognition and an insignificant correlation (p = 0.06) between the true and the recognized repetition counting. Conclusion The adapted velocity parameters VPeak and MVT used by Jovanović and Flanagan (2014) showed the best relationship compared to Epley's (1985) formula and, therefore, propose to be integrated into the strength training app. Moreover, the application showed high accuracy with the exercise recognition and decently reliable results with the repetition counting. Future work has to be put into improving the detection of weight displacement of the Apple Watch and the overall user experience. 4 1 Introduction In the 21st century chronic diseases, such as the four main non-communicable diseases (NCDs), namely cardiovascular diseases, cancer, respiratory diseases and diabetes, are epi- demic worldwide (World Health Organization, 2018). Even children are now more than ever suffering from obesity, diabetes and metabolic syndromes (Federal Office of Public Health (FOPH), 2016; Robert Koch-institut, 2018; World Health Organization, 2017). Furthermore, the World Health Organization (WHO) even stated that «physical inactivity, now recognized as an increasingly important determinant of health, is the result of a progressive shift of life- style towards more sedentary patterns» (World Health Organization, 2003, p. 6). The WHO also noted that, even though, the global risk of «dying from any one of the four main NCDs between ages 30 and 70 decreases from 22 % in 2000 to 18 % in 2016» (World Health Organization, 2018, p. 7), an acceleration of progress towards reducing key risk factors is required, in order to meet the 2015 adopted Sustainable Development Goals (SDG) number 3: Good Health and Well-being. Even in Switzerland the four main NCDs cancer (women 45.1 %, men 29.3 %), cardiovascular diseases (women 10.5 %, men 16.6 %), chronic respiratory diseases (women 3.2 %, men 2.8 %) and diabetes (women 0.9 %, men 1.2 %) accounted for two-thirds of all deaths (Federal Office of Public Health (FOPH), 2016) and for 80 % of healthcare costs (CHF 700 billion in 2013). It
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