Abstract
Despite the popularity of physiological wearable sensors in sport activities to provide feedback on athletes’ performance, understanding the factors influencing changes in athletes’ physiological rhythms remains a challenge. Changes in physiological rhythms such as heart rate, breathing rate, or galvanic skin response can be due to both physical exertion and psycho-emotional states. Separating the influence of physical exertion and psycho-emotional states in activities that involves both is complicated. As a result, the influence of psycho-emotional states is usually underestimated. To identify the specific influence of psycho-emotional states in physiological rhythm changes, 28 participants were asked to participate in a zipline activity, which involve little or no physical exertion while stimulating psycho-emotional states. Using nonlinear analyses, results show that specific changes in physiological rhythms can be associated with phases in ziplining, after which they can be related to emotional states felt during the activity. Regarding data analysis of wearable sensors, this paper presents a workflow to identify significant physiological patterns across multiple athletes performing the same outdoor activity.
Similar content being viewed by others
References
Poh M-Z, Loddenkemper T, Swenson NC, Goyal S, Madsen JR, Picard RW (2010) Continuous monitoring of electrodermal activity during epileptic seizures using a wearable sensor. In: International conference of the engineering in medicine and biology society. pp 4415–4418
Jain PC (2011) Wireless body area network for medical healthcare. IETE Tech Rev 28:362–371. https://doi.org/10.4103/0256-4602.83556
Picard RW, Fedor S, Ayzenberg Y (2016) Multiple arousal theory and daily-life electrodermal activity asymmetry. Emot Rev 8:62–75. https://doi.org/10.1177/1754073914565517
Picard RW (2009) Future affective technology for autism and emotion communication. Philos T Roy Soc B 364:3575–3584. https://doi.org/10.1098/rstb.2009.0143
Etemadi M, Inan OT, Heller JA, Hersek S, Klein L, Roy S (2016) A wearable patch to enable long-term monitoring of environmental, activity and hemodynamics variables. IEEE Trans Biomed Circ Syst 10:280–288. https://doi.org/10.1109/tbcas.2015.2405480
Szczesna A, Nowak A, Grabiec P, Paszkuta M, Tajstra M, Wojciechowska M (2017) Survey of wearable multi-modal vital parameters measurement systems. In: Gzik M, Tkacz E, Paszenda Z, Pietka E (eds) Innovations in biomedical engineering. Springer, Boston, pp 323–329
Aroganam G, Manivannan N, Harrison D (2019) Review on wearable technology sensors used in consumer sport applications. Ah S Sens 19:1983. https://doi.org/10.3390/s19091983. Accessed 23 July 2020
Wahl Y, Duking P, Droszez A, Wahl P, Mester J (2017) Criterion-validity of commercially available physical activity tracker to estimate step count, covered distance and energy expenditure during sports conditions. Front Physiol 8:725. https://doi.org/10.3389/fphys.2017.00725
Li RT, Kling SR, Salata MJ, Cupp SA, Sheehan J, Voos JE (2016) Wearable performance devices in sports medicine. Sports Health 8:74–78. https://doi.org/10.1177/1941738115616917
Silva P, Dos Santos E, Grishin M, Rocha JM (2018) Validity of heart rate-based indices to measure training load and intensity in elite football players. J Strength Conditi Res 32:2340–2347. https://doi.org/10.1519/jsc.0000000000002057
Flatt AA, Howells D (2019) Effects of varying training load on heart rate variability and running performance among an olympic rugby sevens team. J Sci Med Sport 22:222–226. https://doi.org/10.1016/j.jsams.2018.07.014
Barrett S (2017) Monitoring elite soccer players’ external loads using real-time data. Int J Sport Physiol 12:1285–1287. https://doi.org/10.1123/ijspp.2016-0516
Foster C, Daniels JT, Yarbrough RA (1977) Physiological and training correlates of marathon running performance. Aust J Sports Med 9:58–61. https://doi.org/10.2147/oajsm.s141657
Hoffman J (2014) Physiological aspects of sport training and performance. Human Kinetics, Champaign
Courneya KS, Carron AV (1992) The home advantage in sport competitions: a literature review. J Sport Exercise Psy 14:13–27. https://doi.org/10.1123/jsep.14.1.13
Kreibig SD, Samson AC, Gross JJ (2013) The psychophysiology of mixed emotional states. Psychophysiology 50:799–811. https://doi.org/10.1111/psyp.12064
Cavalade M, Papadopoulou V, Theunissen S, Balestra C (2015) Heart rate variability and critical flicker fusion frequency changes during and after parachute jumping in experienced skydivers. Eur J Appl Physiol 115:1533–1545. https://doi.org/10.1007/s00421-015-3137-5
Bourgois JG, Boone J, Callewaert M, Tipton MJ, Tallir IB (2014) Biomechanical and physiological demands of kitesurfing and epidemiology of injury among kitesurfers. Sports Med 44:55–66. https://doi.org/10.1007/s40279-013-0103-4
Dupre D, Bland B, Bolster A, Morrison G, McKeown G (2017) Dynamic model of athletes’ emotions based on wearable devices. In: International conference on applied human factors and ergonomics. pp 42–50
Ulrich RS, Dimberg U, Driver BL (1990) Psychophysiological indicators of leisure consequences. J Leisure Res 22:154–166. https://doi.org/10.1080/00222216.1990.11969822
Siegel E, Geuss M, Stefanucci J (2008) Studying the relationship between emotion and height perception in naturalistic settings. J Vis 8:757–757. https://doi.org/10.1167/8.6.757
Hanstock HG, Edwards JP, Roberts R, Walsh NP (2018) High heart rate reactors display greater decreases in tear siga concentration following a novel acute stressor. Biol Psychol 133:85–88. https://doi.org/10.1016/j.biopsycho.2018.02.00
Kim J (2019) Effects of immersive technology exposure on stress level changes: comparative analysis of zipline rides and immersive technology. Int J Comput Eng Informat Technol 11:229–234
Ellsworth PC, Scherer KR (2003) Appraisal processes in emotion. In: Davidson RJ, Scherer KR, Goldsmith HH (eds) Handbook of affective sciences. Oxford University Press, New York, pp 572–595
Panksepp J (2004) Affective neuroscience: the foundations of human and animal emotions. Oxford University Press, New York
Ekkekakis P, Parfitt G, Petruzzello SJ (2011) The pleasure and displeasure people feel when they exercise at different intensities. Sports Med 41:641–671. https://doi.org/10.2165/11590680-000000000-00000
Russell JA (2003) Core affect and the psychological construction of emotion. Psychol Rev 110:145–172. https://doi.org/10.1037/0033-295x.110.1.145
Samuel O, Walker G, Salmon P, Filtness A, Stevens N, Mulvihill C, Payne S, Stanton N (2019) Riding the emotional roller-coaster: using the circumplex model of affect to model motorcycle riders’ emotional state-changes at intersections. Trans Res Part F Traffic Psychol behav 66:139–150. https://doi.org/10.1016/j.trf.2019.08.018
Hall EE, Ekkekakis P, Petruzzello SJ (2002) The affective beneficence of vigorous exercise revisited. Br J Health Psychol 7:47–66. https://doi.org/10.1348/135910702169358
Niedermeier M, Einwanger J, Hartl A, Kopp M (2017) Affective responses in mountain hiking—a randomized crossover trial focusing on differences between indoor and outdoor activity. PLoS One 12: https://doi.org/10.1371/journal.pone.0177719. Accessed 23 July 2020
Oliveira BR, Slama FA, Deslandes AC, Furtado ES, Santos TM (2013) Continuous and high-intensity interval training: Which promotes higher pleasure? PloS one 8: https://doi.org/10.1371/journal.pone.0079965. Accessed 23 July 2020
Bunting CJ (1995) Physiological measurements of stress during outdoor adventure activities. J Exp Educ 18:5–11. https://doi.org/10.1177/105382599501800103
Liu Y, Zhu SH, Wang GH, Ye F, Li PZ (2013) Validity and reliability of multiparameter physiological measurements recorded by the equivital lifemonitor during activities of various intensities. J Occup Environ Hyg 10:78–85. https://doi.org/10.1080/15459624.2012.747404
Akintola AA, van de Pol V, Bimmel D, Maan AC, van Heemst D (2016) Comparative analysis of the equivital eq02 lifemonitor with holter ambulatory ecg device for continuous measurement of ecg, heart rate, and heart rate variability: A validation study for precision and accuracy. Front Physiol 7:391. https://doi.org/10.3389/fphys.2016.00391
Burns A, Greene BR, McGrath MJ, O’Shea TJ, Kuris B, Ayer SM, Stroiescu F, Cionca V (2010) SHIMMER\(^\text{TM}\)–a wireless sensor platform for noninvasive biomedical research. IEEE Sens J 10:1527–1534. https://doi.org/10.1109/jsen.2010.2045498. Accessed 23 July 2020
Ray PP, Dash D, De D (2019) Analysis and monitoring of iot-assisted human physiological galvanic skin responsefactor for smart e-healthcare. Sensor Rev 39:525–541. https://doi.org/10.1108/SR-07-2018-0181
Kleckner I, Feldman M, Goodwin MS, Quigley KS (2019) Framework for benchmarking mobile sensors in psychophysiological research. https://doi.org/10.31234/osf.io/a9ju4. Accessed 23 July 2020
Apogee Electronics Corp (2017) Apogee metarecorder. https://apps.apple.com/us/app/apogee-metarecorder/id965930387. Accessed 1 Jun 2020
Sensumco Ltd. (2017) SYNC. https://play.google.com/store/apps/details?id=co.sensum.careful. Accessed 1 Jun 2020
Cacioppo JT, Tassinary LG, Berntson G (2007) Handbook of psychophysiology. Cambridge University Press, New York
Barrett KE, Barman SM, Brooks HL, Yuan JX-J (2019) Ganong’s review of medical physiology. McGraw-Hill Education, New Delhi
Malik M, Bigger JT, Camm AJ, Kleiger RE, Malliani A, Moss AJ, Schwartz PJ (1996) Heart rate variability: standards of measurement, physiological interpretation, and clinical use. Eur Heart J 17:354–381. https://doi.org/10.1161/01.cir.93.5.1043
Godin C, Prost-Boucle F, Campagne A, Charbonnier S, Bonnet S, Vidal A (2015) Selection of the most relevant physiological features for classifying emotion. In: International conference on physiological computing systems. pp 17–25
Lane RD, McRae K, Reiman EM, Chen K, Ahern GL, Thayer JF (2009) Neural correlates of heart rate variability during emotion. Neuroimage 44:213–222. https://doi.org/10.1016/j.neuroimage.2008.07.056
Nakasone A, Prendinger H, Ishizuka M (2005) Emotion recognition from electromyography and skin conductance. In: International workshop on biosignal interpretation. pp 219–222
Boucsein W (2012) Electrodermal activity. Springer, Boston
Ohman A, Esteves F, Flykt A, Soares JJF (1993) Gateways to consciousness: Emotion, attention, and electrodermal activity. In: Progress in electrodermal research. Springer, Boston, MA, pp 137–157
Wood SN (2017) Generalized additive models: an introduction with r. CRC, Boca Raton
Zuur A, Ieno EN, Walker N, Saveliev AA, Smith GM (2009) Mixed effects models and extensions in ecology with r. Springer, Boston
Wood SN (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc Ser B 73:3–36. https://doi.org/10.1111/j.1467-9868.2010.00749.x
Wood SN, Pya N, Säfken B (2016) Smoothing parameter and model selection for general smooth models. J Am Stat Assoc 111:1548–1575. https://doi.org/10.1080/01621459.2016.1180986
Wood SN (2004) Stable and efficient multiple smoothing parameter estimation for generalized additive models. J Am Stat Assoc 99:673–686. https://doi.org/10.1198/016214504000000980
Wood SN (2003) Thin-plate regression splines. J R Stat Soc Ser B 65:95–114. https://doi.org/10.1111/1467-9868.00374
Lin X, Zhang D (1999) Inference in generalized additive mixed modelsby using smoothing splines. Journal of the Royal Statistical Society: Series B 61:381–400. https://doi.org/10.1111/1467-9868.00183
Loader C (2006) Local regression and likelihood. Springer, Boston
Ventura F, Granadeiro JP, Padget O, Catry P (2020) Gadfly petrels use knowledge of the windscape, not memorized foraging patches, to optimize foraging trips on ocean-wide scales. Proc R Soc B 287:20191775. https://doi.org/10.1098/rspb.2019.1775
Rondonotti V, Marron JS, Park C (2007) SiZer for time series: a new approach to the analysis of trends. Electron J Stat 1:268–289. https://doi.org/10.1214/07-ejs006
Chaudhuri P, Marron JS (1999) SiZer for exploration of structures in curves. J Am Stat Assoc 94:807–823. https://doi.org/10.1080/01621459.1999.10474186
Gamiz ML, Martinez-Miranda MD, Raya-Miranda R (2018) Graphical goodness-of-fit test for mortality models. Math Popul Stud 25:123–142. https://doi.org/10.1080/08898480.2018.1477381
Sonderegger DL, Wang H, Clements WH, Noon BR (2009) Using sizer to detect thresholds in ecological data. Front Ecol Environ 7:190–195. https://doi.org/10.1890/070179
Lazarus RS, Folkman S (1986) Cognitive theories of stress and the issue of circularity. In: Appley MH, Trumbull R (eds) Dyn Stress. Springer, Boston, pp 63–80
Zilio D (2016) On the autonomy of psychology from neuroscience: a case study of skinner’s radical behaviorism and behavior analysis. Rev Gen Psychol 20:155–170. https://doi.org/10.1037/gpr0000067
Fanselow MS (2018) Emotion, motivation and function. Curr Opin Behav Sci 19:105–109. https://doi.org/10.1016/j.cobeha.2017.12.013
Peter C, Ebert E, Beikirch H (2005) A wearable multi-sensor system for mobile acquisition of emotion-related physiological data. In: International conference on affective computing and intelligent interaction. pp 691–698
Woodman T, Hardy L (2003) The relative impact of cognitive anxiety and self-confidence upon sport performance: a meta-analysis. J Sport Sci 21:443–457. https://doi.org/10.1080/0264041031000101809
Beedie CJ, Terry PC, Lane AM (2000) The profile of mood states and athletic performance: two meta-analyses. J Appl Sport Psychol 12:49–68. https://doi.org/10.1080/10413200008404213
R Core Team (2019) R: a language and environment for statistical computing. https://www.R-project.org/
Robinson D, Hayes A, Couch S (2020) broom: convert statistical objects into tidy tibbles. https://CRAN.R-project.org/package=broom
Alathea L (2015) captioner: numbers figures and creates simple captions. https://CRAN.R-project.org/package=captioner
Wickham H, François R, Henry L, Müller K (2020) Dplyr: a grammar of data manipulation. https://CRAN.R-project.org/package=dplyr. Accessed 23 July 2020
Oller Moreno S (2019). http://github.com/zeehio/facetscales. Accessed 23 July 2020
Wickham H (2019) Forcats: tools for working with categorical variables (factors). https://CRAN.R-project.org/package=forcats. Accessed 23 July 2020
Wickham H (2016) ggplot2: elegant graphics for data analysis. Springer-Verlag, New York
Kassambara A (2020) ggpubr: ’Ggplot2’ based publication ready plots. https://CRAN.R-project.org/package=ggpubr
Auguie B (2017) GridExtra: miscellaneous functions for ”grid” graphics. https://CRAN.R-project.org/package=gridExtra
Warnes GR, Bolker B, Lumley T (2020) Gtools: various r programming tools. https://CRAN.R-project.org/package=gtools
Müller K (2017) Here: a simpler way to find your files. https://CRAN.R-project.org/package=here. Accessed 23 July 2020
Zhu H (2019) KableExtra: construct complex table with ’kable’ and pipe syntax. https://CRAN.R-project.org/package=kableExtra. Accessed 23 July 2020
Bache SM, Wickham H (2014) Magrittr: a forward-pipe operator for r. https://CRAN.R-project.org/package=magrittr. Accessed 23 July 2020
Pinheiro J, Bates D, DebRoy S, Sarkar D, R Core Team (2020) nlme: linear and nonlinear mixed effects models. https://CRAN.Rproject.org/package=nlme
Aust F (2020) papaja: create APA manuscripts with R Markdown. https://github.com/crsh/papaja
Wickham H (2011) The split-apply-combine strategy for data analysis. J Stat Softw 40:1–29
Urbanek S (2013) Png: read and write png images. https://CRAN.R-project.org/package=png
Henry L, Wickham H (2019) Purrr: functional programming tools. https://CRAN.R-project.org/package=purrr. Accessed 23 July 2020
Wickham H, Hester J, Francois R (2018) Readr: read rectangular text data. https://CRAN.R-project.org/package=readr. Accessed 23 July 2020
Wickham H (2019) Stringr: simple, consistent wrappers for common string operations. https://CRAN.R-project.org/package=stringr. Accessed 23 July 2020
Müller K, Wickham H (2019) Tibble: simple data frames. https://CRAN.R-project.org/package=tibble. Accessed 23 July 2020
Wickham H, Henry L (2020) Tidyr: tidy messy data. https://CRAN.R-project.org/package=tidyr. Accessed 23 July 2020
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019) Welcome to the tidyverse. J Open Sour Softw 4:1686 https://doi.org/10.21105/joss.01686. Accessed 23 July 2020
Acknowledgements
The authors would like to thank the anonymous reviewers for their insightful comments and suggestions. The authors also would like to thank the developers of the following R packages used to process, analyze, display, and report data: R [67] and the R packages broom [68], captioner [69], dplyr [70], facetscales [71], forcats [72], ggplot2 [73], ggpubr [74], gridExtra [75], gtools [76], here [77], kableExtra [78], magrittr [79], mgcv [51,52,53], nlme [80], papaja [81], plyr [70, 82], png [83], purrr [84], readr [85], stringr [86], tibble [87], tidyr [88], and tidyverse [89].
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This research was supported and led by Sensum Ltd. in collaboration with Queen’s University Belfast.
Rights and permissions
About this article
Cite this article
Dupré, D., Andelic, N., Moore, D.S. et al. Analysis of physiological changes related to emotions during a zipline activity. Sports Eng 23, 15 (2020). https://doi.org/10.1007/s12283-020-00328-9
Published:
DOI: https://doi.org/10.1007/s12283-020-00328-9