IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0209475.html
   My bibliography  Save this article

Intensive longitudinal modelling predicts diurnal activity of salivary alpha-amylase

Author

Listed:
  • Jesús F Rosel
  • Pilar Jara
  • Francisco H Machancoses
  • Jacinto Pallarés
  • Pedro Torrente
  • Sara Puchol
  • Juan J Canales

Abstract

Salivary alpha-amylase (sAA) activity has been widely used in psychological and medical research as a surrogate marker of sympathetic nervous system activation, though its utility remains controversial. The aim of this work was to compare alternative intensive longitudinal models of sAA data: (a) a traditional model, where sAA is a function of hour (hr) and hr squared (sAAj,t = f(hr, hr2), and (b) an autoregressive model, where values of sAA are a function of previous values (sAAj,t = f(sAA j,t-1, sAA j,t-2, …, sAA j,t-p). Nineteen normal subjects (9 males and 10 females) participated in the experiments and measurements were performed every hr between 9:00 and 21:00 hr. Thus, a total of 13 measurements were obtained per participant. The Napierian logarithm of the enzymatic activity of sAA was analysed. Data showed that a second-order autoregressive (AR(2)) model was more parsimonious and fitted better than the traditional multilevel quadratic model. Therefore, sAA follows a process whereby, to forecast its value at any given time, sAA values one and two hr prior to that time (sAA j,t = f(SAAj,t-1, SAAj,t-2) are most predictive, thus indicating that sAA has its own inertia, with a “memory” of the two previous hr. These novel findings highlight the relevance of intensive longitudinal models in physiological data analysis and have considerable implications for physiological and biobehavioural research involving sAA measurements and other stress-related biomarkers.

Suggested Citation

  • Jesús F Rosel & Pilar Jara & Francisco H Machancoses & Jacinto Pallarés & Pedro Torrente & Sara Puchol & Juan J Canales, 2019. "Intensive longitudinal modelling predicts diurnal activity of salivary alpha-amylase," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-17, January.
    • Handle: RePEc:plo:pone00:0209475
    DOI: 10.1371/journal.pone.0209475
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0209475
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0209475&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0209475?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Frees,Edward W., 2004. "Longitudinal and Panel Data," Cambridge Books, Cambridge University Press, number 9780521828284, September.
    2. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    3. Frees,Edward W., 2004. "Longitudinal and Panel Data," Cambridge Books, Cambridge University Press, number 9780521535380, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Naima Chrid & Sami Saafi & Mohamed Chakroun, 2021. "Export Upgrading and Economic Growth: a Panel Cointegration and Causality Analysis," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 12(2), pages 811-841, June.
    2. Adolfo Maza & Paula Gutiérrez-Portilla, 2022. "Outward FDI and exports relation: A heterogeneous panel approach dealing with cross-sectional dependence," International Economics, CEPII research center, issue 170, pages 174-189.
    3. Xie, Bofeng & Rehman, Mubeen Abdur & Zhang, Junyan & Yang, Runze, 2022. "Does the financialization of natural resources lead toward sustainability? An application of advance panel Granger non-causality," Resources Policy, Elsevier, vol. 79(C).
    4. Saidi, Hichem & El Montasser, Ghassen & Ajmi, Noomen, 2018. "Renewable Energy, Quality of Institutions and Economic Growth in MENA Countries: a Panel Cointegration Approach," MPRA Paper 84055, University Library of Munich, Germany.
    5. Mehdi Ben Jebli & Montassar Kahia, 2020. "The interdependence between CO2 emissions, economic growth, renewable and non-renewable energies, and service development: evidence from 65 countries," Climatic Change, Springer, vol. 162(2), pages 193-212, September.
    6. Vo, Duc, 2019. "The Impact of Foreign Direct Investment on Environment Degradation: Evidence from Emerging Markets in Asia," MPRA Paper 103292, University Library of Munich, Germany.
    7. Anh Hoang To & Dao Thi-Thieu Ha & Ha Minh Nguyen & Duc Hong Vo, 2019. "The Impact of Foreign Direct Investment on Environment Degradation: Evidence from Emerging Markets in Asia," IJERPH, MDPI, vol. 16(9), pages 1-24, May.
    8. Kerstens, Kristiaan & Van de Woestyne, Ignace, 2014. "Comparing Malmquist and Hicks–Moorsteen productivity indices: Exploring the impact of unbalanced vs. balanced panel data," European Journal of Operational Research, Elsevier, vol. 233(3), pages 749-758.
    9. Patricio Aroca & Juan Gabriel Brida & Juan Sebastián Pereyra & Serena Volo, 2014. "Tourism statistics: correcting data inadequacy using coarsened exact matching," BEMPS - Bozen Economics & Management Paper Series BEMPS22, Faculty of Economics and Management at the Free University of Bozen.
    10. Fulvia Pennoni & Beata Bal-Domańska, 2022. "NEETs and Youth Unemployment: A Longitudinal Comparison Across European Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(2), pages 739-761, July.
    11. Marco Angrisani & Kevin Foster & Marcin Hitczenko, 2015. "The 2013 Survey of Consumer Payment Choice: technical appendix," Research Data Report 15-5, Federal Reserve Bank of Boston.
    12. Rodrigo V. Ventura & Manoela Cabo & Rafael Caixeta & Elton Fernandes & Vicente Aprigliano Fernandes, 2020. "Air Transportation Income and Price Elasticities in Remote Areas: The Case of the Brazilian Amazon Region," Sustainability, MDPI, vol. 12(15), pages 1-18, July.
    13. Lynn, Peter & Bosch, Oriol, 2021. "Methodological lessons from the pilot longitudinal survey on debt advice," ISER Working Paper Series 2021-03, Institute for Social and Economic Research.
    14. Caudill, Jonathan W. & Getty, Ryan & Smith, Rick & Patten, Ryan & Trulson, Chad R., 2013. "Discouraging window breakers: The lagged effects of police activity on crime," Journal of Criminal Justice, Elsevier, vol. 41(1), pages 18-23.
    15. Abayomi Oredegbe, 2022. "Competition and Banking Industry Stability: How Do BRICS and G7 Compare?," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 21(1), pages 7-31, March.
    16. Maliyamu Abudureheman & Qingzhe Jiang & Xiucheng Dong & Cong Dong, 2022. "CO 2 Emissions in China: Does the Energy Rebound Matter?," Energies, MDPI, vol. 15(12), pages 1-25, June.
    17. Edward Frees & Jee-Seon Kim, 2006. "Multilevel Model Prediction," Psychometrika, Springer;The Psychometric Society, vol. 71(1), pages 79-104, March.
    18. Jun Zhao & Xiucheng Dong & Kangyin Dong, 2021. "Can agglomeration of producer services reduce urban–rural income inequality? The case of China," Australian Economic Papers, Wiley Blackwell, vol. 60(4), pages 736-762, December.
    19. LU, Yi & Zhao, Jianting & Wu, Xueying & Lo, Siu Ming, 2020. "Escaping to nature in pandemic: a natural experiment of COVID-19 in Asian cities," SocArXiv rq8sn, Center for Open Science.
    20. Kaixing Huang, 2015. "The Economic Impacts of Global Warming on Agriculture: the Role of Adaptation," School of Economics and Public Policy Working Papers 2015-20, University of Adelaide, School of Economics and Public Policy.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0209475. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.