Understanding health inequalities is essential for improving social justice. Intersectionality refers to a theoretical framework for studying the intersection of multiple social categorizations that create unique experiences and related social inequalities. Currently, the majority of the intersectional studies in the physical activity field have a qualitative design; thus, there is a need for quantitative intersectional studies. This commentary aims to explore primary obstacles impeding intersectional quantitative research and provide recommendations for overcoming these obstacles in physical activity research. In the commentary, we discuss that the lack of accessibility of large-scale and diverse data sets, and suboptimal social categorizations and intersectionality-related questions may contribute to the scarcity of intersectional quantitative research in the field. To facilitate intersectional quantitative analyses, we advocate for making large-scale data sets accessible for intersectional secondary analyses, diverse sampling, standardizing questions and categories related to intersectionality, promoting inclusive research designs and methods, and using the appropriate questions and social categorization that reflect the distinct experiences of each subgroup. By addressing these challenges, researchers may gain new insights into health disparities, making physical activity research more inclusive and contributing to more equitable health outcomes.
Physical activity (PA) decreases from childhood to adolescence, with girls being less active than boys. The timing of these differences remains unknown. Using accelerometer data from three cross-sectional studies in Norway and Belgium (n = 2507, age = 3-17years), we assessed sex differences in sedentary behaviour (SB) and PA levels (light, moderate, vigorous) throughout the day and across the full spectrum of activity intensity distribution on weekdays and weekend days, using linear regression and functional data analyses. Across all age groups (preschoolers (3-5y), children (6-10y), adolescents (11-17y)), girls were less active than boys, particularly on weekdays (e.g., vigorous PA (> 1111 counts/15s) difference:-16.9 min/day (95% Confidence interval:-19.3,-14.4; p-value < 0.001) in children). It was the case throughout the day, particularly during school hours (8h30-15h29) in all age groups. Analysis of the full spectrum of activity intensity distribution (0 to 3000 counts) added to these findings that on weekend days, girls spent less time in zero-count SB than boys (difference=-21.0 min/day (-28.7,-13.4; p-value < 0.001) in children), but higher (17.3 min/day (13.2,21.4; p-value < 0.001)) in the “other SB”, 1-180 counts/15s. The sex differences in PA during school hours suggest the need for targeted interventions promoting activities engaging girls. Additionally, the time spent in zero-count, particularly evident in boys on weekend days, deserves further investigation.
Importance Chronic low back pain (LBP) is a prevalent and costly condition, and regular physical activity may reduce its risk. Walking is a common and accessible form of physical activity, but its association with the risk of chronic LBP is unclear.
Objective To examine whether accelerometer-derived daily walking volume and walking intensity are associated with the risk of chronic LBP.
Design, Setting, and Participants This prospective population-based cohort study used data from the Trøndelag Health (HUNT) Study in Norway, with a baseline in 2017 to 2019 and follow-up in 2021 to 2023. The study included individuals without chronic LBP at baseline and with at least 1 valid day of device-measured walking.
Exposure Daily walking volume (minutes per day) and walking intensity, expressed as metabolic equivalent of task (MET) per minute.
Main Outcomes and Measures The primary outcome was self-reported chronic LBP at follow-up, defined as pain lasting 3 months or longer in the past 12 months. Poisson regression was used to estimate adjusted risk ratios (RRs) with 95% CIs of chronic LBP according to daily walking volume and mean walking intensity.
Results A total of 11 194 participants aged 20 years or older (mean [SD] age, 55.3 [15.1] years; 6564 women [58.6%]) were included in the analysis. At follow-up (mean [SD] follow-up time, 4.2 [0.3] years), 1659 participants (14.8%) reported chronic LBP. Continuous measures of both walking volume and walking intensity were inversely associated with the risk of chronic LBP using restricted cubic splines models. Compared with participants walking less than 78 minutes per day, those walking 78 to 100 minutes per day had an RR for chronic LBP of 0.87 (95% CI, 0.77-0.98), those walking 101 to 124 minutes per day had an RR of 0.77 (95% CI, 0.68-0.87), and those walking 125 minutes or more per day had an RR of 0.76 (95% CI, 0.67-0.87). Compared with a mean walking intensity of less than 3.00 MET per minute, participants with walking intensity of 3.00 to 3.11 MET per minute had an RR for chronic LBP of 0.85 (95% CI, 0.75-0.96), those with walking intensity of 3.12 to 3.26 MET per minute had an RR of 0.82 (95% CI, 0.72-0.93), and those with walking intensity greater than or equal to 3.27 MET per minute had an RR of 0.82 (95% CI, 0.72-0.93). After mutual adjustment, the association remained largely similar for walking volume but was attenuated for walking intensity.
Conclusions and Relevance In this cohort study, daily walking volume and walking intensity were inversely associated with the risk of chronic LBP. The findings suggest that walking volume may have a more pronounced benefit than walking intensity.
The intensity gradient (IG) quantifies the distribution of time spent across accelerometer-assessed physical activity intensity and is positively associated with health. It was developed using the Euclidean norm minus one (ENMO) acceleration measure. This study aimed to enable generation of comparable IGs across other measures (mean amplitude deviation [MAD], monitor independent movement summary [MIMS], and counts) by addressing a key step in the IG algorithm of dividing physical activity intensity into incremental intensity bins. Two methods of creating analogous bins for MAD, MIMS, and counts were explored: (a) linear scaling (“naïve”) and (b) nonlinear modeling (“modeled”). Generated IGs were compared with the original IG (IG_ENMO) using limits of agreement and intraclass correlation. Forty-three adults (age: median [interquartile range]: 23 [21, 26], 61% female) were included. Relative to IG_ENMO, the naïve approach led to higher IGs (+0.27, +0.39, +0.54 for MAD, MIMS, and counts, respectively). In contrast, the modeled approach led to lower IGs (bias: −0.43, −1.23, and −0.91, respectively). For MAD and counts, limits of agreement were slightly wider for naïve bins (95% limits of agreement: ±0.26 and ±0.34) versus modeled bins (±0.21 and ±0.28) but for MIMS were slightly wider for modeled bins (modeled: ±0.35, naïve: ±0.31). For the naïve approach, IG_MAD was most consistent (intraclass correlation 95% CI: 0.49–0.82) and IG_Counts least consistent (0.09–0.61). Intraclass correlations were higher for the modeled approach with IG_MAD most consistent (0.72–0.91) and IG_MIMS least consistent (0.59–0.86). Results indicate that consistency of the IG between measures is improved with appropriate scaling to create analogous intensity bins, but agreement is limited.
Background
Regular Physical Activity (PA) is important for disease prevention and health promotion. PA has been assessed through surveys, questionnaires, and devices such as accelerometers. Alongside PA, Sedentary Behaviour (SB) and sleep are the main components of 24/7 movement behaviours, and their adequate measurement is important for assessing health outcomes. Many different metrics to summarise 24/7 movement behaviours are used; however, little attention has been paid to visualising these metrics. Data visualisation is likely to impact the way results are communicated and understood by different audiences. This study systematically reviews 24/7 movement behaviour metrics, presents an overview of their visualisations, and develops a framework to guide context-specific visualisation choices.
Methods
An umbrella review was conducted in February 2025 in Scopus and Web of Science. Included papers were reviews of any type, with any human population and study design, having at least one of the three 24/7 movement behaviours as exposure or outcome measured through accelerometers, and clearly reporting the outcome metrics. Data extraction and an adapted thematic data analysis were performed in April 2025. The overview of the visualisations used for the metrics identified in the review and thematic analysis was created through non-systematic web searches and use of Microsoft Copilot. Finally, a framework was created based on the sender-receiver model for effective communication.
Results
In total, 93 reviews were included, with a total of 5667 articles reporting on 134 unique output metrics based on accelerometer data. The most common metrics were step counts and time spent in Moderate-to-Vigorous Physical Activity (MVPA). The non-systematic web searches showed that most researchers use bar charts, line graphs, or pie graphs to visualise 24/7 movement behaviour data, while Copilot input provided more options of visualisations. The resulting framework was the product of an iterative process aggregating the previous results, providing clear guidance for organising metrics and their corresponding visualisations.
Conclusions
This study structures and summarises types of visualisations of accelerometer-derived metrics to describe 24/7 human movement behaviour data. Future research is needed to apply the framework in practical contexts and investigate how the visualisations are perceived by different audiences.