Skip to main content
SpringerLink
Log in

Measurement of Heart Rate and Heart Rate Variability: A Review of NeuroIS Research with a Focus on Applied Methods

  • Conference paper
  • First Online:
Information Systems and Neuroscience (NeuroIS 2022)

Part of the book series: Lecture Notes in Information Systems and Organisation ((LNISO,volume 58))

Included in the following conference series:

Abstract

Neuro-Information-Systems (NeuroIS) research contributes to a better understanding of cognitive and affective processes related to the development, adoption, and use of digital technologies. Among others, heart rate (HR) and heart rate variability (HRV) can be used to measure physiological states—more specifically, autonomic nervous system (ANS) activity. Based on a previous systematic literature review in which we surveyed the existing NeuroIS literature on HR and HRV (Stangl and Riedl, 2022 [1]), in the current paper we review completed empirical studies with a focus on the papers’ methodological aspects. Thus, this review provides methodological insights to advance the research on HR and HRV with a focus on NeuroIS research.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Change history

  • 24 January 2023

    In the original version of the book, the title of Chapter 12 includes a small error in the chapter title. It should be changed from “Resolving the Paradoxical Effect of Human-Like Tying Errors by Conversational Agents” to “Resolving the Paradoxical Effect of Human-Like Typing Errors by Conversational Agents”.

    In the original version of the book, the following belated corrections have been incorporated in chapter “Measurement of Heart Rate and Heart Rate Variability: A Review of NeuroIS Research with a Focus on Applied Methods”.

    The correction chapters and the book have been updated with the changes.

Notes

  1. 1.

    Kubios Oy, https://www.kubios.com (accessed on March 13, 2022).

References

  1. Stangl, F. J., & Riedl, R. (2022). Measurement of heart rate and heart rate variability with wearable devices: A systematic review. In Proceedings of the 17th International Conference on Wirtschaftsinformatik.

    Google Scholar 

  2. Riedl, R., Davis, F. D., Banker, R. D., & Kenning, P. H. (2017). Neuroscience in information systems research: Applying knowledge of brain functionality without neuroscience tools. Springer, Cham. https://doi.org/10.1007/978-3-319-48755-7

  3. Riedl, R., Banker, R. D., Benbasat, I., Davis, F. D., Dennis, A. R., Dimoka, A., Gefen, D., Gupta, A., Ischebeck, A., Kenning, P. H., Müller-Putz, G. R., Pavlou, P. A., Straub, D. W., vom Brocke, J., & Weber, B. (2010). On the foundations of NeuroIS: Reflections on the Gmunden Retreat 2009. Communications of the Association for Information Systems, 27, 243–264. https://doi.org/10.17705/1CAIS.02715

  4. Dimoka, A., Davis, F. D., Gupta, A., Pavlou, P. A., Banker, R. D., Dennis, A. R., Ischebeck, A., Müller-Putz, G. R., Benbasat, I., Gefen, D., Kenning, P. H., Riedl, R., vom Brocke, J., & Weber, B. (2012). On the use of neurophysiological tools in IS research: Developing a research agenda for NeuroIS. MIS Quarterly, 36, 679–702. https://doi.org/10.2307/41703475

    Article  Google Scholar 

  5. Riedl, R., & Léger, P.-M. (2016). Fundamentals of NeuroIS: Information systems and the brain. Springer, Heidelberg. https://doi.org/10.1007/978-3-662-45091-8

  6. Chong, S. W., & Reinders, H. (2021). A methodological review of qualitative research syntheses in CALL: The state-of-the-art. System, 103, 102646. https://doi.org/10.1016/j.system.2021.102646

    Article  Google Scholar 

  7. Litviňuková, M., Talavera-López, C., Maatz, H., Reichart, D., Worth, C. L., Lindberg, E. L., Kanda, M., Polanski, K., Heinig, M., Lee, M., Nadelmann, E. R., Roberts, K., Tuck, L., Fasouli, E. S., DeLaughter, D. M., McDonough, B., Wakimoto, H., Gorham, J. M., Samari, S., … Teichmann, S. A. (2020). Cells of the adult human heart. Nature, 588, 466–472. https://doi.org/10.1038/s41586-020-2797-4

    Article  Google Scholar 

  8. Chialvo, D. R. (2002). Unhealthy surprises. Nature, 419, 263–263. https://doi.org/10.1038/419263a

    Article  Google Scholar 

  9. Appel, M. L., Berger, R. D., Saul, J. P., Smith, J. M., & Cohen, R. J. (1989). Beat to beat variability in cardiovascular variables: Noise or music? Journal of the American College of Cardiology, 14, 1139–1148. https://doi.org/10.1016/0735-1097(89)90408-7

    Article  Google Scholar 

  10. Park, H.-D., Correia, S., Ducorps, A., & Tallon-Baudry, C. (2014). Spontaneous fluctuations in neural responses to heartbeats predict visual detection. Nature Neuroscience, 17, 612–618. https://doi.org/10.1038/nn.3671

    Article  Google Scholar 

  11. Riedl, R. (2013). On the biology of technostress: Literature review and research agenda. ACM SIGMIS Database: The DATA BASE for Advances in Information Systems, 44, 18–55. https://doi.org/10.1145/2436239.2436242

  12. Watanabe, T., Hoshide, S., & Kario, K. (2022). Noninvasive method to validate the variability of blood pressure during arrhythmias. Hypertension Research, 45, 530–532. https://doi.org/10.1038/s41440-021-00835-7

    Article  Google Scholar 

  13. Lim, G. B. (2022). Pacing with respiratory sinus arrhythmia improves outcomes in heart failure. Nature Reviews Cardiology, 19, 209–209. https://doi.org/10.1038/s41569-022-00681-1

    Article  Google Scholar 

  14. Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93, 1043–1065. https://doi.org/10.1161/01.CIR.93.5.1043

  15. Cowan, M. J. (1995). Measurement of heart rate variability. Western Journal of Nursing Research, 17, 32–48. https://doi.org/10.1177/019394599501700104

    Article  Google Scholar 

  16. Alabdulgader, A., McCraty, R., Atkinson, M., Dobyns, Y., Vainoras, A., Ragulskis, M., & Stolc, V. (2018). Long-term study of heart rate variability responses to changes in the solar and geomagnetic environment. Science and Reports, 8, 2663. https://doi.org/10.1038/s41598-018-20932-x

    Article  Google Scholar 

  17. Shi, K., Steigleder, T., Schellenberger, S., Michler, F., Malessa, A., Lurz, F., Rohleder, N., Ostgathe, C., Weigel, R., & Koelpin, A. (2021). Contactless analysis of heart rate variability during cold pressor test using radar interferometry and bidirectional LSTM networks. Science and Reports, 11, 3025. https://doi.org/10.1038/s41598-021-81101-1

    Article  Google Scholar 

  18. Pizzoli, S. F. M., Marzorati, C., Gatti, D., Monzani, D., Mazzocco, K., & Pravettoni, G. (2021). A meta-analysis on heart rate variability biofeedback and depressive symptoms. Science and Reports, 11, 6650. https://doi.org/10.1038/s41598-021-86149-7

    Article  Google Scholar 

  19. Quintana, D. S., Alvares, G. A., & Heathers, J. A. J. (2016). Guidelines for reporting articles on psychiatry and heart rate variability (GRAPH): Recommendations to advance research communication. Translational Psychiatry, 6, e803–e803. https://doi.org/10.1038/tp.2016.73

    Article  Google Scholar 

  20. Chemla, D., Young, J., Badilini, F., Maison-Blanche, P., Affres, H., Lecarpentier, Y., & Chanson, P. (2005). Comparison of fast Fourier transform and autoregressive spectral analysis for the study of heart rate variability in diabetic patients. International Journal of Cardiology, 104, 307–313. https://doi.org/10.1016/j.ijcard.2004.12.018

    Article  Google Scholar 

  21. Kleiger, R. E., Stein, P. K., Bosner, M. S., & Rottman, J. N. (1992). Time domain measurements of heart rate variability. Cardiology Clinics, 10, 487–498. https://doi.org/10.1016/S0733-8651(18)30230-3

    Article  Google Scholar 

  22. Malik, M. (1997). Time-domain measurement of heart rate variability. Cardiac Electrophysiology Review, 1, 329–334.

    Article  Google Scholar 

  23. Umetani, K., Singer, D. H., McCraty, R., & Atkinson, M. (1998). Twenty-four hour time domain heart rate variability and heart rate: Relations to age and gender over nine decades. Journal of the American College of Cardiology, 31, 593–601. https://doi.org/10.1016/S0735-1097(97)00554-8

    Article  Google Scholar 

  24. Őri, Z., Monir, G., Weiss, J., Sayhouni, X., & Singer, D. H. (1992). Heart rate variability: Frequency domain analysis. Cardiology Clinics, 10, 499–533. https://doi.org/10.1016/S0733-8651(18)30231-5

    Article  Google Scholar 

  25. Montano, N., Porta, A., Cogliati, C., Costantino, G., Tobaldini, E., Casali, K. R., & Iellamo, F. (2009). Heart rate variability explored in the frequency domain: A tool to investigate the link between heart and behavior. Neuroscience and Biobehavioral Reviews, 33, 71–80. https://doi.org/10.1016/j.neubiorev.2008.07.006

    Article  Google Scholar 

  26. Bozhokin, S. V., & Suslova, I. B. (2014). Analysis of non-stationary HRV as a frequency modulated signal by double continuous wavelet transformation method. Biomedical Signal Processing and Control, 10, 34–40. https://doi.org/10.1016/j.bspc.2013.12.006

    Article  Google Scholar 

  27. Konok, V., Pogány, Á., & Miklósi, Á. (2017). Mobile attachment: Separation from the mobile phone induces physiological and behavioural stress and attentional bias to separation-related stimuli. Computers in Human Behavior, 71, 228–239. https://doi.org/10.1016/j.chb.2017.02.002

    Article  Google Scholar 

  28. Keissar, K., Davrath, L. R., Akselrod, S. (2009). Coherence analysis between respiration and heart rate variability using continuous wavelet transform. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 367, 1393–1406. https://doi.org/10.1098/rsta.2008.0273

  29. Shi, B., Wang, L., Yan, C., Chen, D., Liu, M., & Li, P. (2019). Nonlinear heart rate variability biomarkers for gastric cancer severity: A pilot study. Science and Reports, 9, 13833. https://doi.org/10.1038/s41598-019-50358-y

    Article  Google Scholar 

  30. Voss, A., Kurths, J., Kleiner, H. J., Witt, A., & Wessel, N. (1995). Improved analysis of heart rate variability by methods of nonlinear dynamics. Journal of Electrocardiology, 28, 81–88. https://doi.org/10.1016/S0022-0736(95)80021-2

    Article  Google Scholar 

  31. Gao, R., Yan, H., Duan, J., Gao, Y., Cao, C., Li, L., & Guo, L. (2022). Study on the nonfatigue and fatigue states of orchard workers based on electrocardiogram signal analysis. Science and Reports, 12, 4858. https://doi.org/10.1038/s41598-022-08705-z

    Article  Google Scholar 

  32. Riedl, R., Fischer, T., Léger, P.-M., Davis, F. D. (2020). A decade of NeuroIS research: Progress, challenges, and future directions. ACM SIGMIS Database: The DATA BASE for Advances in Information Systems, 51, 13–54. https://doi.org/10.1145/3410977.3410980

  33. Webster, J., & Watson, R.T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26, xiii–xxiii.

    Google Scholar 

  34. Kitchenham, B., & Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering version 2.3 (EBSE Technical Report EBSE-2007-01). Keele University and University of Durham.

    Google Scholar 

  35. vom Brocke, J., Simons, A., Niehaves, B., Riemer, K., Plattfaut, R., & Cleven, A. (2009). Reconstructing the giant: On the importance of rigour in documenting the literature search process. In S. Newell, E. A. Whitley, N. Pouloudi, J. Wareham & L. Mathiassen (Eds.), Proceedings of the 17th European Conference on Information Systems (pp. 2206–2217).

    Google Scholar 

  36. Gaskin, J., Jenkins, J., Meservy, T., Steffen, J., & Payne, K. (2017). Using wearable devices for non-invasive, inexpensive physiological data collection. In Proceedings of the 50th Hawaii International Conference on System Sciences (pp. 597–605). https://doi.org/10.24251/HICSS.2017.072

  37. Jensen, M., Piercy, C., Elzondo, J., Twyman, N., Valacich, J., Miller, C., Lee, Y.-H., Dunbar, N., Bessarabova, E., Burgoon, J., Adame, B., & Wilson, S. (2016). Exploring failure and engagement in a complex digital training game: A multi-method examination. AIS Transactions on Human-Computer Interaction, 8, 1–20. https://doi.org/10.17705/1thci.08102

  38. Öksüz, N., Biswas, R., Shcherbatyi, I., & Maass, W. (2018). Measuring biosignals of overweight and obese children for real-time feedback and predicting performance. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger & A. B. Randolph (Eds.), Information Systems and Neuroscience: Gmunden Retreat on NeuroIS 2017 (Vol. 25, pp. 185–193). LNISO. Springer, Cham. https://doi.org/10.1007/978-3-319-67431-5_21

  39. Sheng, H., & Joginapelly, T. (2012). Effects of web atmospheric cues on users’ emotional responses in e-commerce. AIS Transactions on Human-Computer Interaction, 4, 1–24. https://doi.org/10.17705/1thci.00036

  40. Fischer, T., & Riedl, R. (2020). Technostress measurement in the field: A case report. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, A. B. Randolph & T. Fischer (Eds.), Information Systems and Neuroscience: NeuroIS Retreat 2020 (Vol. 43, pp. 71–78). LNISO. Springer, Cham. https://doi.org/10.1007/978-3-030-60073-0_9

  41. Adam, M. T. P., Gamer, M., Krämer, J., & Weinhardt, C. (2011). Measuring emotions in electronic markets. In Proceedings of the 32nd International Conference on Information Systems.

    Google Scholar 

  42. Adam, M. T. P., Krämer, J., & Weinhardt, C. (2012). Excitement up! Price down! Measuring emotions in Dutch auctions. International Journal of Electronic Commerce, 17, 7–40. https://doi.org/10.2753/JEC1086-4415170201

    Article  Google Scholar 

  43. Lutz, B., Adam, M. T. P., Feuerriegel, S., Pröllochs, N., & Neumann, D. (2020). Affective information processing of fake news: Evidence from NeuroIS. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, A. B. Randolph & T. Fischer (Eds.), Information Systems and Neuroscience: NeuroIS Retreat 2019 (Vol. 32, pp. 121–128). LNISO. Springer, Cham. https://doi.org/10.1007/978-3-030-28144-1_13

  44. Lutz, B., Adam, M. T. P., Feuerriegel, S., Pröllochs, N., & Neumann, D. (2020). Identifying linguistic cues of fake news associated with cognitive and affective processing: Evidence from NeuroIS. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, A. B. Randolph & T. Fischer (Eds.), Information Systems and Neuroscience: NeuroIS Retreat 2020 (Vol. 43, pp. 16–23). LNISO. Springer, Cham. https://doi.org/10.1007/978-3-030-60073-0_2

  45. Astor, P. J., Adam, M. T. P., Jerčić, P., Schaaff, K., & Weinhardt, C. (2013). Integrating biosignals into information systems: A NeuroIS tool for improving emotion regulation. Journal of Management Information Systems, 30, 247–278. https://doi.org/10.2753/MIS0742-1222300309

    Article  Google Scholar 

  46. Barral, O., Kosunen, I., & Jacucci, G. (2018). No need to laugh out loud: Predicting humor appraisal of comic strips based on physiological signals in a realistic environment. ACM Transactions on Computer-Human Interaction, 24, 1–29. https://doi.org/10.1145/3157730

    Article  Google Scholar 

  47. Clayton, R. B., Leshner, G., & Almond, A. (2015). The extended iSelf: The impact of iPhone separation on cognition, emotion, and physiology. Journal of Computer-Mediated Communication, 20, 119–135. https://doi.org/10.1111/jcc4.12109

    Article  Google Scholar 

  48. Hariharan, A., Dorner, V., & Adam, M. T. P. (2017). Impact of cognitive workload and emotional arousal on performance in cooperative and competitive interactions. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger & A. B. Randolph (Eds.), Information Systems and Neuroscience: Gmunden Retreat on NeuroIS 2016 (Vol. 16, pp. 35–42). LNISO. Springer, Cham. https://doi.org/10.1007/978-3-319-41402-7_5

  49. Ortiz de Guinea, A., & Webster, J. (2013). An investigation of information systems use patterns: Technological events as triggers, the effect of time, and consequences for performance. MIS Quarterly, 37, 1165–1188. https://doi.org/10.25300/MISQ/2013/37.4.08

  50. Shalom, J. G., Israeli, H., Markovitzky, O., & Lipsitz, J. D. (2015). Social anxiety and physiological arousal during computer mediated vs. face to face communication. Computers in Human Behavior, 44, 202–208. https://doi.org/10.1016/j.chb.2014.11.056

  51. Teubner, T., Adam, M. T. P., & Riordan, R. (2015). The impact of computerized agents on immediate emotions, overall arousal and bidding behavior in electronic auctions. Journal of the Association for Information Systems, 16, 838–879. https://doi.org/10.17705/1jais.00412

  52. Walla, P., & Lozovic, S. (2020). The effect of technology on human social perception: A multi-methods NeuroIS pilot investigation. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger, A. B. Randolph & T. Fischer (Eds.), Information Systems and Neuroscience: NeuroIS Retreat 2019 (Vol. 32, pp. 63–71). LNISO. Springer, Cham. https://doi.org/10.1007/978-3-030-28144-1_7

  53. Buettner, R., Bachus, L., Konzmann, L., & Prohaska, S. (2019). Asking both the user’s heart and its owner: Empirical evidence for substance dualism. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger & A. B. Randolph (Eds.), Information Systems and Neuroscience: NeuroIS Retreat 2018 (Vol. 29, pp. 251–257). LNISO. Springer, Cham. https://doi.org/10.1007/978-3-030-01087-4_30

  54. Tozman, T., Magdas, E. S., MacDougall, H. G., & Vollmeyer, R. (2015). Understanding the psychophysiology of flow: A driving simulator experiment to investigate the relationship between flow and heart rate variability. Computers in Human Behavior, 52, 408–418. https://doi.org/10.1016/j.chb.2015.06.023

    Article  Google Scholar 

  55. Cipresso, P., Serino, S., Gaggioli, A., Albani, G., Mauro, A., & Riva, G. (2015). Psychometric modeling of the pervasive use of Facebook through psychophysiological measures: Stress or optimal experience? Computers in Human Behavior, 49, 576–587. https://doi.org/10.1016/j.chb.2015.03.068

    Article  Google Scholar 

  56. Kothgassner, O. D., Felnhofer, A., Hlavacs, H., Beutl, L., Palme, R., Kryspin-Exner, I., & Glenk, L. M. (2016). Salivary cortisol and cardiovascular reactivity to a public speaking task in a virtual and real-life environment. Computers in Human Behavior, 62, 124–135. https://doi.org/10.1016/j.chb.2016.03.081

    Article  Google Scholar 

  57. Léger, P.-M., Davis, F. D., Cronan, T. P., & Perret, J. (2014). Neurophysiological correlates of cognitive absorption in an enactive training context. Computers in Human Behavior, 34, 273–283. https://doi.org/10.1016/j.chb.2014.02.011

    Article  Google Scholar 

  58. Smith, A.-L., Owen, H., & Reynolds, K. J. (2013). Heart rate variability indices for very short-term (30 beat) analysis. Part 1: Survey and toolbox. Journal of Clinical Monitoring and Computing, 27, 569–576. https://doi.org/10.1007/s10877-013-9471-4

  59. Bravi, A., Longtin, A., & Seely, A. J. E. (2011). Review and classification of variability analysis techniques with clinical applications. Biomedical Engineering Online, 10, 90. https://doi.org/10.1186/1475-925X-10-90

    Article  Google Scholar 

  60. Tarvainen, M. P., Niskanen, J.-P., Lipponen, J. A., Ranta-aho, P. O., & Karjalainen, P. A. (2014). Kubios HRV—Heart rate variability analysis software. Computer Methods and Programs in Biomedicine, 113, 210–220. https://doi.org/10.1016/j.cmpb.2013.07.024

    Article  Google Scholar 

  61. Lipponen, J. A., & Tarvainen, M. P. (2019). A robust algorithm for heart rate variability time series artefact correction using novel beat classification. Journal of Medical Engineering & Technology, 43, 173–181. https://doi.org/10.1080/03091902.2019.1640306

    Article  Google Scholar 

  62. Niskanen, J.-P., Tarvainen, M. P., Ranta-aho, P. O., & Karjalainen, P. A. (2004). Software for advanced HRV analysis. Computer Methods and Programs in Biomedicine, 76, 73–81. https://doi.org/10.1016/j.cmpb.2004.03.004

    Article  Google Scholar 

  63. Tarvainen, M. P., Ranta-aho, P. O., & Karjalainen, P. A. (2002). An advanced detrending method with application to HRV analysis. IEEE Transactions on Biomedical Engineering, 49, 172–175. https://doi.org/10.1109/10.979357

    Article  Google Scholar 

  64. Baumgartner, D., Fischer, T., Riedl, R., & Dreiseitl, S. (2019). Analysis of heart rate variability (HRV) feature robustness for measuring technostress. In F. D. Davis, R. Riedl, J. vom Brocke, P.-M. Léger & A. B. Randolph (Eds.), Information Systems and Neuroscience: NeuroIS Retreat 2018 (Vol. 29, pp. 221–228). LNISO. Springer, Cham.

    Google Scholar 

  65. Machado, A. V., Pereira, M. G., Souza, G. G. L., Xavier, M., Aguiar, C., de Oliveira, L., & Mocaiber, I. (2021). Association between distinct coping styles and heart rate variability changes to an acute psychosocial stress task. Science and Reports, 11, 24025. https://doi.org/10.1038/s41598-021-03386-6

    Article  Google Scholar 

  66. Baevsky, R. M. (2002). Analysis of heart rate variability in space medicine. Human Physiology, 28, 202–213.

    Article  Google Scholar 

  67. Baevsky, R. M., & Chernikova, A. G. (2017). Heart rate variability analysis: Physiological foundations and main methods. Cardiometry, 66–76. https://doi.org/10.12710/cardiometry.2017.10.6676

  68. Fiske, D. W., & Fiske, S. T. (2005). Laboratory studies. In K. Kempf-Leonard (Ed.), Encyclopedia of Social Measurement (pp. 435–439). Elsevier. https://doi.org/10.1016/B0-12-369398-5/00407-2

  69. Senior, C., Russell, T., & Gazzaniga, M. S. (2009). Methods in mind. The MIT Press, Cambridge.

    Google Scholar 

  70. Li, P., Zhao, L., Jiang, Z., Yu, M., Li, Z., Zhou, X., & Zhao, Y. (2019). A wearable and sensitive graphene-cotton based pressure sensor for human physiological signals monitoring. Science and Reports, 9, 14457. https://doi.org/10.1038/s41598-019-50997-1

    Article  Google Scholar 

  71. Libanori, A., Chen, G., Zhao, X., Zhou, Y., & Chen, J. (2022). Smart textiles for personalized healthcare. Nature Electronics, 5, 142–156. https://doi.org/10.1038/s41928-022-00723-z

    Article  Google Scholar 

  72. Wang, A., Nguyen, D., Sridhar, A. R., & Gollakota, S. (2021). Using smart speakers to contactlessly monitor heart rhythms. Communications Biology, 4, 319. https://doi.org/10.1038/s42003-021-01824-9

    Article  Google Scholar 

  73. Goverdovsky, V., von Rosenberg, W., Nakamura, T., Looney, D., Sharp, D. J., Papavassiliou, C., Morrell, M. J., & Mandic, D. P. (2017). Hearables: Multimodal physiological in-ear sensing. Science and Reports, 7, 6948. https://doi.org/10.1038/s41598-017-06925-2

    Article  Google Scholar 

  74. Riedl, R., Davis, F. D., & Hevner, A. R. (2014). Towards a NeuroIS research methodology: Intensifying the discussion on methods, tools, and measurement. Journal of the Association for Information Systems, 15, I–XXXV. https://doi.org/10.17705/1jais.00377

  75. Pashler, H., & Harris, C. R. (2012). Is the replicability crisis overblown? Three arguments examined. Perspectives on Psychological Science, 7, 531–536. https://doi.org/10.1177/1745691612463401

    Article  Google Scholar 

  76. Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149–1160. https://doi.org/10.3758/BRM.41.4.1149

  77. Maxwell, S. E., & Kelley, K. (2011). Ethics and sample size planning. In A. T. Panter & S. K. Sterba (Eds.), Handbook of Ethics in Quantitative Methodology (pp. 159–184). Routledge, New York.

    Google Scholar 

Download references

Acknowledgements

This research was funded by the Austrian Science Fund (FWF) as part of the project “Technostress in Organizations” (project number: P 30865) at the University of Applied Sciences Upper Austria.

Author information

Authors and Affiliations

  1. University of Applied Sciences Upper Austria, Steyr, Austria

    Fabian J. Stangl & René Riedl

  2. Johannes Kepler University Linz, Linz, Austria

    René Riedl

Authors
  1. Fabian J. Stangl
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. René Riedl
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fabian J. Stangl .

Editor information

Editors and Affiliations

  1. Information Technology Division, Texas Tech University, Lubbock, TX, USA

    Fred D. Davis

  2. Digital Business Institute, University of Applied Sciences Upper Austria, Steyr, Austria

    René Riedl

  3. Department of Information Systems, University of Liechtenstein, Vaduz, Liechtenstein

    Jan vom Brocke

  4. Department of Information Technologies, HEC Montreal, Montréal, QC, Canada

    Pierre-Majorique Léger

  5. Department of Information Systems and Security, Kennesaw State University, Kennesaw, GA, USA

    Adriane B. Randolph

  6. Institute of Neural Engineering, Graz University of Technology, Graz, Austria

    Gernot R. Müller-Putz

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Stangl, F.J., Riedl, R. (2022). Measurement of Heart Rate and Heart Rate Variability: A Review of NeuroIS Research with a Focus on Applied Methods. In: Davis, F.D., Riedl, R., vom Brocke, J., Léger, PM., Randolph, A.B., Müller-Putz, G.R. (eds) Information Systems and Neuroscience. NeuroIS 2022. Lecture Notes in Information Systems and Organisation, vol 58. Springer, Cham. https://doi.org/10.1007/978-3-031-13064-9_28

Download citation

Publish with us

Policies and ethics

Search

Navigation