Guiding functional near-infrared spectroscopy research on sedentary behavior and brain health
Year: 2026
Type: review
Study design: Systematic Review
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.
Authors: Jinming Li, Yanxia Chen, Yiming Tao, Zhihao Zhang, Qian Yu, Haijing Niu, Matthew Heath, Wei-Peng Teo, Cassandra J. Lowe, Sebastian Ludyga, Benjamin Tari, Mats Hallgren, Neville Owen, Boris Cheval, Stephane Perrey, Myrto Mavilidi, Fred Paas, Mark J. Campbell, Adam Toth, Felix Scholkmann, Uzair Hakim, Flaminia Ronca, Fabian Herold, Liye Zou
Institutions: South China Normal University, Shanghai Jiao Tong University, Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning, Western University, Canadian Centre for Activity and Aging, Nanyang Technological University, National Institute of Education, University of Exeter, University of Basel, University College London, Karolinska Institutet, Baker Heart and Diabetes Institute, Swinburne University of Technology, École Normale Supérieure de Rennes, Valeurs, Innovations, Politiques, Socialisations et Sports, Université de Rennes, Université de Montpellier, Institut Mines-Télécom, IMT Mines Alès, EuroMov Digital Health in Motion, Southern Cross University, UNSW Sydney, Erasmus University Rotterdam, University of Limerick, Stellenbosch University, Lero, University of Bern, University of Zurich, ETH Zurich, University Hospital Zurich, HMU Health and Medical University Erfurt
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7170135141
