2025/07/16 by Yingjian Ye, Jinfang Yang, Junyan Zhang +1 · 1 voice
Medicine · #Body Composition Measurement Techniques #Cardiovascular and exercise physiology #Physical Activity and Health
paper · pdf · doi:10.1111/joim.20120
openalex publication_date 2025/07/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Dear Editor, The recent investigation by Fridolfsson et al. offers valuable insights into the utility of step counting as a practical alternative to accelerometer-derived metrics for assessing physical activity in middle-aged adults. Although the study commendably addresses the challenges of translating laboratory-based intensity thresholds to free-living conditions, several critical considerations warrant further discussion to strengthen the interpretation and application of these findings [1]. Threshold applicability across heterogeneous populations: A central finding—the identification of 80 steps/min as a more relevant cadence threshold for moderate-intensity activity compared to the conventional 100 steps/min—raises important questions about the generalizability of this threshold. The study population, while sizable, represents a relatively healthy subset of middle-aged adults (mean age 57 years) with exclusion criteria favoring individuals without cardiovascular limitations. This homogeneity may inadvertently overlook the variability in functional capacity observed in broader populations, particularly older adults or those with chronic conditions. For instance, individuals with reduced gait stability or cardiopulmonary limitations may achieve health benefits at lower cadences, whereas highly fit individuals might require higher thresholds. A “one-size-fits-all” cadence threshold risks misclassifying activity intensity for substantial demographic subgroups. Future studies could explore stratified thresholds based on functional capacity assessments (e.g., 6-min walk test performance) to better align recommendations with individual capabilities [1, 2]. Temporal dynamics of cadence-health relationships: The cross-sectional design limits causal inference regarding the observed associations between step metrics and cardiometabolic health. Although higher cadence likely contributes to improved health outcomes, reverse causality remains plausible: Individuals with better cardiometabolic profiles may inherently engage in higher intensity activities. This bidirectional relationship complicates the interpretation of optimal thresholds. Longitudinal analyses tracking cadence patterns alongside changes in health markers could clarify whether sustained adherence to specific cadence thresholds predicts risk reduction. Additionally, investigating bout duration (e.g., cumulative minutes at ≥80 steps/min in continuous 10-min intervals) might reveal whether prolonged moderate-intensity activity—rather than sporadic accumulation—drives health benefits, aligning with current physical activity guidelines emphasizing sustained effort. Technical considerations in threshold calibration: The discordance between laboratory-derived cadence thresholds (100–120 steps/min) and the free-living threshold identified (80 steps/min) highlights methodological challenges in translating controlled experiments to real-world settings. Laboratory studies typically measure cadence during structured walking, whereas free-living cadence encompasses diverse activities (e.g., stair climbing and load carrying) that alter energy expenditure independent of stepping rate. This discrepancy suggests that accelerometer-based MET estimates in free-living conditions may systematically differ from laboratory measurements due to variations in movement patterns. To address this, future calibration studies could employ portable metabolic analyzers during unstructured daily activities, capturing the relationship among cadence, acceleration, and energy expenditure across common real-world scenarios [1, 3, 4]. Ethical implications of simplified metrics: Although step counting offers accessibility advantages, overreliance on simplified metrics risks oversimplifying physical activity prescription. The finding that step data captured 88% of accelerometer-derived health information might inadvertently discourage clinicians from considering non-ambulatory activities (e.g., resistance training and cycling) that contribute to cardiometabolic health but are poorly captured by step counts. Integrating hybrid metrics—combining step cadence with accelerometer-derived non-ambulatory movement data—could provide a more holistic representation of activity patterns without sacrificing practicality. Recommendations for future research: (1) threshold personalization: Develop algorithms incorporating individual fitness levels, anthropometrics, and health status to generate personalized cadence thresholds. (2) Activity contextualization: Employ machine learning techniques to classify stepping data by activity type (e.g., walking vs. household tasks) using accelerometer raw signals. (3) Longitudinal validation: Initiate cohort studies examining how cadence thresholds predict incident cardiometabolic events rather than cross-sectional associations. (4) Diverse population inclusion: Prioritize enrollment of underrepresented groups, including older adults, individuals with mobility impairments, and ethnically diverse populations [5-7]. In conclusion, although Fridolfsson et al. provide compelling evidence supporting step-based metrics, their work also underscores the complexity of physical activity assessment in heterogeneous populations. Refining intensity thresholds to account for individual variability and activity context will be essential for translating these findings into effective public health strategies. Yingjian Ye, Jinfang Yang, Junyan Zhang, and Peng An performed initial manuscript drafting and finalized scientific narratives. The corresponding authors, Peng An and Junyan Zhang, revised it and approved submission. All authors contributed to intellectual content refinement and endorsed the published version. The authors declare no conflicts of interest. This research was supported by the “323” Major Chronic Disease Project of the Hubei Provincial Health Commission and Xiangyang No.1 People's Hospital (Project Number: XYY2022-323); General Program of Hubei Provincial Natural Science Foundation of China (2025AFB885); Innovative Research Program of Xiangyang No.1 People's Hospital (XYY2025SD17). Data sharing is not applicable to this article as no new data were created or analyzed in this study.