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Analysis of physiological changes related to emotions during a zipline activity

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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.

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References

  1. 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

  2. Jain PC (2011) Wireless body area network for medical healthcare. IETE Tech Rev 28:362–371. https://doi.org/10.4103/0256-4602.83556

    Article  Google Scholar 

  3. 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

    Article  Google Scholar 

  4. 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

    Article  Google Scholar 

  5. 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

    Article  Google Scholar 

  6. 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

    Chapter  Google Scholar 

  7. 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

  8. 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

    Article  Google Scholar 

  9. 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

    Article  Google Scholar 

  10. 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

    Article  Google Scholar 

  11. 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

    Article  Google Scholar 

  12. 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

    Article  Google Scholar 

  13. 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

    Article  Google Scholar 

  14. Hoffman J (2014) Physiological aspects of sport training and performance. Human Kinetics, Champaign

    Google Scholar 

  15. 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

    Article  Google Scholar 

  16. Kreibig SD, Samson AC, Gross JJ (2013) The psychophysiology of mixed emotional states. Psychophysiology 50:799–811. https://doi.org/10.1111/psyp.12064

    Article  Google Scholar 

  17. 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

    Article  Google Scholar 

  18. 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

    Article  Google Scholar 

  19. 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

  20. 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

    Article  Google Scholar 

  21. 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

    Article  Google Scholar 

  22. 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

    Article  Google Scholar 

  23. 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

    Google Scholar 

  24. 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

    Google Scholar 

  25. Panksepp J (2004) Affective neuroscience: the foundations of human and animal emotions. Oxford University Press, New York

    Google Scholar 

  26. 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

    Article  Google Scholar 

  27. 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

    Article  Google Scholar 

  28. 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

    Article  Google Scholar 

  29. 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

    Article  Google Scholar 

  30. 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

  31. 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

  32. Bunting CJ (1995) Physiological measurements of stress during outdoor adventure activities. J Exp Educ 18:5–11. https://doi.org/10.1177/105382599501800103

    Article  Google Scholar 

  33. 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

  34. 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

    Article  Google Scholar 

  35. 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

  36. 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

    Article  Google Scholar 

  37. 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

  38. Apogee Electronics Corp (2017) Apogee metarecorder. https://apps.apple.com/us/app/apogee-metarecorder/id965930387. Accessed 1 Jun 2020

  39. Sensumco Ltd. (2017) SYNC. https://play.google.com/store/apps/details?id=co.sensum.careful. Accessed 1 Jun 2020

  40. Cacioppo JT, Tassinary LG, Berntson G (2007) Handbook of psychophysiology. Cambridge University Press, New York

    Google Scholar 

  41. Barrett KE, Barman SM, Brooks HL, Yuan JX-J (2019) Ganong’s review of medical physiology. McGraw-Hill Education, New Delhi

    Google Scholar 

  42. 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

    Article  Google Scholar 

  43. 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

  44. 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

    Article  Google Scholar 

  45. Nakasone A, Prendinger H, Ishizuka M (2005) Emotion recognition from electromyography and skin conductance. In: International workshop on biosignal interpretation. pp 219–222

  46. Boucsein W (2012) Electrodermal activity. Springer, Boston

    Book  Google Scholar 

  47. 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

  48. Wood SN (2017) Generalized additive models: an introduction with r. CRC, Boca Raton

    Book  Google Scholar 

  49. Zuur A, Ieno EN, Walker N, Saveliev AA, Smith GM (2009) Mixed effects models and extensions in ecology with r. Springer, Boston

    Book  Google Scholar 

  50. 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

    Article  MathSciNet  MATH  Google Scholar 

  51. 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

    Article  MathSciNet  Google Scholar 

  52. 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

    Article  MathSciNet  MATH  Google Scholar 

  53. Wood SN (2003) Thin-plate regression splines. J R Stat Soc Ser B 65:95–114. https://doi.org/10.1111/1467-9868.00374

    Article  MathSciNet  MATH  Google Scholar 

  54. 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

    Article  MathSciNet  MATH  Google Scholar 

  55. Loader C (2006) Local regression and likelihood. Springer, Boston

    MATH  Google Scholar 

  56. 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

    Article  Google Scholar 

  57. 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

    Article  MathSciNet  MATH  Google Scholar 

  58. 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

    Article  MathSciNet  MATH  Google Scholar 

  59. 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

    Article  MathSciNet  Google Scholar 

  60. 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

    Article  Google Scholar 

  61. 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

    Chapter  Google Scholar 

  62. 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

    Article  Google Scholar 

  63. Fanselow MS (2018) Emotion, motivation and function. Curr Opin Behav Sci 19:105–109. https://doi.org/10.1016/j.cobeha.2017.12.013

    Article  Google Scholar 

  64. 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

  65. 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

    Article  Google Scholar 

  66. 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

    Article  Google Scholar 

  67. R Core Team (2019) R: a language and environment for statistical computing. https://www.R-project.org/

  68. Robinson D, Hayes A, Couch S (2020) broom: convert statistical objects into tidy tibbles. https://CRAN.R-project.org/package=broom

  69. Alathea L (2015) captioner: numbers figures and creates simple captions. https://CRAN.R-project.org/package=captioner

  70. 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

  71. Oller Moreno S (2019). http://github.com/zeehio/facetscales. Accessed 23 July 2020

  72. Wickham H (2019) Forcats: tools for working with categorical variables (factors). https://CRAN.R-project.org/package=forcats. Accessed 23 July 2020

  73. Wickham H (2016) ggplot2: elegant graphics for data analysis. Springer-Verlag, New York

    Book  Google Scholar 

  74. Kassambara A (2020) ggpubr: ’Ggplot2’ based publication ready plots. https://CRAN.R-project.org/package=ggpubr

  75. Auguie B (2017) GridExtra: miscellaneous functions for ”grid” graphics. https://CRAN.R-project.org/package=gridExtra

  76. Warnes GR, Bolker B, Lumley T (2020) Gtools: various r programming tools. https://CRAN.R-project.org/package=gtools

  77. Müller K (2017) Here: a simpler way to find your files. https://CRAN.R-project.org/package=here. Accessed 23 July 2020

  78. Zhu H (2019) KableExtra: construct complex table with ’kable’ and pipe syntax. https://CRAN.R-project.org/package=kableExtra. Accessed 23 July 2020

  79. Bache SM, Wickham H (2014) Magrittr: a forward-pipe operator for r. https://CRAN.R-project.org/package=magrittr. Accessed 23 July 2020

  80. 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

  81. Aust F (2020) papaja: create APA manuscripts with R Markdown. https://github.com/crsh/papaja

  82. Wickham H (2011) The split-apply-combine strategy for data analysis. J Stat Softw 40:1–29

    Google Scholar 

  83. Urbanek S (2013) Png: read and write png images. https://CRAN.R-project.org/package=png

  84. Henry L, Wickham H (2019) Purrr: functional programming tools. https://CRAN.R-project.org/package=purrr. Accessed 23 July 2020

  85. Wickham H, Hester J, Francois R (2018) Readr: read rectangular text data. https://CRAN.R-project.org/package=readr. Accessed 23 July 2020

  86. Wickham H (2019) Stringr: simple, consistent wrappers for common string operations. https://CRAN.R-project.org/package=stringr. Accessed 23 July 2020

  87. Müller K, Wickham H (2019) Tibble: simple data frames. https://CRAN.R-project.org/package=tibble. Accessed 23 July 2020

  88. Wickham H, Henry L (2020) Tidyr: tidy messy data. https://CRAN.R-project.org/package=tidyr. Accessed 23 July 2020

  89. 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

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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].

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This research was supported and led by Sensum Ltd. in collaboration with Queen’s University Belfast.

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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

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