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Peer-reviewedCC BY-NC-ND 4.0Journal of Sport and Health Science

Guiding functional near-infrared spectroscopy research on sedentary behavior and brain health

  • Jinming Li1
  • Yanxia Chen2
  • Yiming Tao1
  • Zhihao Zhang1
  • Qian Yu1
  • Haijing Niu3
  • Matthew Heath4,5,6
  • Wei-Peng Teo7,8
  • Cassandra J. Lowe9
  • Sebastian Ludyga10
  • Benjamin Tari11
  • Mats Hallgren12
12 more authors
  • Neville Owen13,14
  • Boris Cheval15,16
  • Stephane Perrey17
  • Myrto Mavilidi18
  • Fred Paas19,20
  • Mark J. Campbell21,22,23,24
  • Adam Toth21,22,23,24
  • Felix Scholkmann25,26,27
  • Uzair Hakim11
  • Flaminia Ronca11
  • Fabian Herold28
  • Liye Zou1
28 affiliations
  1. 1Body-Brain-Mind Laboratory, School of Physical Education & Sports Science, South China Normal University, Guangzhou 510006, China
  2. 2Department of Physical Education, Shanghai Jiao Tong University, Shanghai 200240, China
  3. 3International Data Group (IDG)/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
  4. 4School of Kinesiology, Faculty of Health Sciences, University of Western Ontario, London, ON N6A 3K7, Canada
  5. 5Graduate Program in Neuroscience, University of Western Ontario, London, ON N6A 3K7, Canada
  6. 6Canadian Centre for Activity and Aging, University of Western Ontario, London, ON N6G 1H1, Canada
  7. 7Physical Education and Sport Science (PESS) Department, National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore
  8. 8Science of Learning in Education Centre (SoLEC), National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore
  9. 9Department of Psychology, University of Exeter, Exeter EX4 4QG, UK
  10. 10Department of Sport, Exercise & Health, University of Basel, Basel 4052, Switzerland
  11. 11Institute of Sport, Exercise and Health, Division of Surgery and Interventional Sciences, University College London, London W1T 7HA, UK
  12. 12Epidemiology of Psychiatric Conditions, Substance Use and Social Environment (EPiCSS), Department of Public Health Sciences, Karolinska Institutet, Solna 17177, Sweden
  13. 13Physical Activity Laboratory, Baker Heart & Diabetes Institute, Melbourne, VIC 3004, Australia
  14. 14Centre for Urban Transitions, Swinburne University of Technology, Melbourne, VIC 3122, Australia
  15. 15Department of Sport Sciences and Physical Education, École Normale Supérieure de Rennes, Bruz 35170, France
  16. 16Violences, Innovations, Politiques, Socialisations et Sports (VIPS2) Laboratory, University of Rennes, Rennes 35000, France
  17. 17EuroMov Digital Health in Motion, University of Montpellier, Institut Mines-Télécom (IMT Mines Ales), Montpellier 34090, France
  18. 18Faculty of Education, Southern Cross University, Lismore, NSW 2480, Australia
  19. 19Department of Psychology Education and Child Studies, Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, 3062 PA, The Netherlands
  20. 20School of Education, University of New South Wales, Sydney, NSW 2052, Australia
  21. 21Lero, The Research Ireland Centre for Software, University of Limerick, Limerick V94 T9PX, Ireland
  22. 22Department of Physical Education & Sport Science, University of Limerick, Limerick V94 T9PX, Ireland
  23. 23Health Research Institute, University of Limerick, Limerick V94 T9PX, Ireland
  24. 24Centre for Sport Leadership, Stellenbosch University, Stellenbosch 7602, South Africa
  25. 25Neurophotonics and Biosignal Processing Research Group, Biomedical Optics Research Laboratory, Department of Neonatology, University Hospital Zurich, University of Zurich, Zurich 8091, Switzerland
  26. 26Institute of Complementary and Integrative Medicine, University of Bern, Bern 3012, Switzerland
  27. 27Neuroscience Center Zurich, University of Zurich and Swiss Federal Institute of Technology (ETH) Zurich, Zurich 8057, Switzerland
  28. 28Department of Physiology, Faculty of Medicine, HMU Health and Medical University Erfurt, Erfurt 99089, Germany
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Abstract

Background: Excessive exposure to sedentary behavior (SB) is associated with cognitive decline and impaired brain health; however, the neurobiological mechanisms underlying this relationship remain unclear. Functional near-infrared spectroscopy (fNIRS) enables real-time monitoring of cerebral hemodynamics in naturalistic settings and offers unique technical advantages for understanding the effects of SB on brain health. Notably, methodological standardization specific to SB research is lacking, limiting cross-study comparisons and evidence synthesis despite established general fNIRS best practices.

Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched PubMed, Scopus, Web of Science, and PsycINFO through February 2026 for studies using fNIRS to assess SB’s effects on brain function. Two independent reviewers conducted study selection, data extraction, and bias risk assessment. We systematically extracted and analyzed instrumentation specifications, spatial positioning, data processing pipelines, statistical methods, and quality control measures to identify methodological variations and best practice patterns in current research.

Results: From 36,477 initial records, 43 studies encompassing 2751 participants were included. Study types comprised acute (25 studies) and chronic interventions (8 studies) manipulating or monitoring SB patterns as well as cross-sectional studies (10 studies) analyzing the association between SB patterns and fNIRS-related parameters. Substantial heterogeneity was evident for specific methodological aspects, such as the use of diverse fNIRS systems and sampling rates (e.g., spanning 1.81–50.00 Hz), whereas others such as source-detector separation (i.e., 3.0 cm) were relatively homogenous across studies. Critical methodological issues included inadequate reporting of spatial registration and data quality control procedures, inconsistent implementation of multiple comparison corrections, and neglected monitoring of relevant environmental factors and physiological confounds.

Conclusion: This review provides the first SB-specific standardized methodological framework for fNIRS applications, building on established fNIRS best practices and extending them to address unique challenges of SB research, encompassing 6 core domains: hardware configuration, extended monitoring protocols, data processing, statistical analysis, quality assessment, and SB-specific reporting. Recommendations are systematically classified by evidence level, distinguishing minimum reporting standards applicable across all laboratories from best practices and aspirational approaches for well-resourced settings. Minimum recommendations include reporting optode geometry and placement with sufficient detail for replication and, where feasible, using short-separation regression (or a clearly justified alternative) to mitigate systemic physiological confounds. These recommendations aim to enhance methodological rigor, promote cross-study comparability, and support the long-term goal of identifying SB-related neurobiological biomarkers.

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Cite

BibTeXRIS
BibTeX
@article{li2026guiding,
  title = {{Guiding functional near-infrared spectroscopy research on sedentary behavior and brain health}},
  author = {Jinming Li and Yanxia Chen and Yiming Tao and Zhihao Zhang and Qian Yu and Haijing Niu and Matthew Heath and Wei-Peng Teo and Cassandra J. Lowe and Sebastian Ludyga and Benjamin Tari and Mats Hallgren and Neville Owen and Boris Cheval and Stephane Perrey and Myrto Mavilidi and Fred Paas and Mark J. Campbell and Adam Toth and Felix Scholkmann and Uzair Hakim and Flaminia Ronca and Fabian Herold and Liye Zou},
  journal = {Journal of Sport and Health Science},
  year = {2026},
  month = jul,
  doi = {10.1016/j.jshs.2026.101161},
  url = {https://doi.org/10.1016/j.jshs.2026.101161},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Guiding functional near-infrared spectroscopy research on sedentary behavior and brain health
AU  - Jinming Li
AU  - Yanxia Chen
AU  - Yiming Tao
AU  - Zhihao Zhang
AU  - Qian Yu
AU  - Haijing Niu
AU  - Matthew Heath
AU  - Wei-Peng Teo
AU  - Cassandra J. Lowe
AU  - Sebastian Ludyga
AU  - Benjamin Tari
AU  - Mats Hallgren
AU  - Neville Owen
AU  - Boris Cheval
AU  - Stephane Perrey
AU  - Myrto Mavilidi
AU  - Fred Paas
AU  - Mark J. Campbell
AU  - Adam Toth
AU  - Felix Scholkmann
AU  - Uzair Hakim
AU  - Flaminia Ronca
AU  - Fabian Herold
AU  - Liye Zou
PY  - 2026
DA  - 2026/07/23
JO  - Journal of Sport and Health Science
DO  - 10.1016/j.jshs.2026.101161
UR  - https://doi.org/10.1016/j.jshs.2026.101161
AB  - Background: Excessive exposure to sedentary behavior (SB) is associated with cognitive decline and impaired brain health; however, the neurobiological mechanisms underlying this relationship remain unclear. Functional near-infrared spectroscopy (fNIRS) enables real-time monitoring of cerebral hemodynamics in naturalistic settings and offers unique technical advantages for understanding the effects of SB on brain health. Notably, methodological standardization specific to SB research is lacking, limiting cross-study comparisons and evidence synthesis despite established general fNIRS best practices.  Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched PubMed, Scopus, Web of Science, and PsycINFO through February 2026 for studies using fNIRS to assess SB’s effects on brain function. Two independent reviewers conducted study selection, data extraction, and bias risk assessment. We systematically extracted and analyzed instrumentation specifications, spatial positioning, data processing pipelines, statistical methods, and quality control measures to identify methodological variations and best practice patterns in current research.  Results: From 36,477 initial records, 43 studies encompassing 2751 participants were included. Study types comprised acute (25 studies) and chronic interventions (8 studies) manipulating or monitoring SB patterns as well as cross-sectional studies (10 studies) analyzing the association between SB patterns and fNIRS-related parameters. Substantial heterogeneity was evident for specific methodological aspects, such as the use of diverse fNIRS systems and sampling rates (e.g., spanning 1.81–50.00 Hz), whereas others such as source-detector separation (i.e., 3.0 cm) were relatively homogenous across studies. Critical methodological issues included inadequate reporting of spatial registration and data quality control procedures, inconsistent implementation of multiple comparison corrections, and neglected monitoring of relevant environmental factors and physiological confounds.  Conclusion: This review provides the first SB-specific standardized methodological framework for fNIRS applications, building on established fNIRS best practices and extending them to address unique challenges of SB research, encompassing 6 core domains: hardware configuration, extended monitoring protocols, data processing, statistical analysis, quality assessment, and SB-specific reporting. Recommendations are systematically classified by evidence level, distinguishing minimum reporting standards applicable across all laboratories from best practices and aspirational approaches for well-resourced settings. Minimum recommendations include reporting optode geometry and placement with sufficient detail for replication and, where feasible, using short-separation regression (or a clearly justified alternative) to mitigate systemic physiological confounds. These recommendations aim to enhance methodological rigor, promote cross-study comparability, and support the long-term goal of identifying SB-related neurobiological biomarkers.
SN  - 2095-2546
SN  - 2213-2961
ER  -