Laurent Malisoux

Over the past decade, running shoes have undergone substantial innovation with the emergence of Advanced Footwear Technology (AFT). Shoes with AFT are typically characterized by highly compliant (deformable) and resilient (high energy return) midsole foams, embedded curved stiffening elements, low mass, and a rockered longitudinal geometry. This expert statement aims to synthesize established knowledge, remaining uncertainties and key directions for future research on AFT in middle‐ and long‐distance running. Current literature demonstrates robust improvements in running economy and performance with AFT, with reported metabolic savings of approximately 2%–4%, and corresponding performance improvements of 1%–3%. These benefits likely arise from a combination of “acute” and possibly “long‐term” mechanisms. Proposed acute mechanisms include decreased muscle force production due to higher positive work from energy‐returning foams, lower muscle force production to accommodate mechanical running demands due to compliant midsoles, and improved mechanical efficiency associated with greater longitudinal bending stiffness and curved geometry. Importantly, AFT features have a synergistic effect, with the relative importance of each mechanism differing depending on the context. Emerging evidence also suggests potential reductions in muscle damage/inflammation and soreness, which could support better physiological resilience, and higher training volumes/quality. From a biomechanical injury risk perspective, AFT can redistribute loading, potentially increasing metatarsal and tarsal stress while reducing tibia and possibly Achilles tendon loading. However, injury risk associated with AFT use remains equivocal, with limited high‐quality data. Notably, substantial inter‐individual variability has been observed, influencing both running economy and potential injury risk although the determinants of this variability remain unclear.

Background: Accurate monitoring of activities of daily living (ADLs) in real‑world environments is essential for preventive care and rehabilitation, yet it remains difficult to achieve outside controlled laboratory settings. Instrumented insoles provide a promising, unobtrusive solution for continuous monitoring. However, the use of multimodal systems integrating plantar pressure and inertial data in naturalistic conditions and larger cohorts is still limited. This study therefore aims to evaluate the accuracy of a wireless instrumented insole (WalkinSense) that fuses pressure and inertial sensor data to classify ADLs within a semi free-living setting. Methods: A total of 99 participants performed a broad set of indoor and outdoor activities. Frame-by-frame performance was compared to ground truth (direct observation) using overall accuracy, Cohen’s Kappa, precision, recall, F1-scores and a normalised confusion matrix. Agreement on total activity duration was assessed using mean absolute percentage error (MAPE) scores and Bland-Altman plots. Results: Activity classification showed almost perfect agreement (mean overall accuracy 0.87, mean Cohen’s Kappa 0.84). Excellent performance (F1 > 0.90) was achieved for sitting, walking with crutches and cycling, while standing, level and non-level walking showed good performance (F1 > 0.80). Most misclassifications occurred between level walking, hill walking and stairs. Duration-based analysis confirmed high accuracy for sitting, walking with crutches and cycling (MAPE ≤ 10%). Bland-Altman plots indicated overestimation of level walking and underestimation of hill and stair walking. Step count was highly accurate (MAPE < 5%), whereas stair count showed only reasonable accuracy (MAPE ≈ 26%). Conclusion: These findings demonstrate the system’s strong potential for real-world monitoring and classification of ADLs while also highlighting the need for improved detection of non-level walking activities.

Despite all efforts, obesity remains a major health concern worldwide, with continuously increasing rates, affecting approx. 14% of the total world population, being as high as 43% in some countries. As obesity is related to numerous comorbidities, including type 2 diabetes, cardiovascular diseases, and some types of cancer, the consequences for the individual and society at large are drastic. Therefore, it is crucial to understand and predict who is at risk of developing obesity in order to implement early prevention strategies. Many individual risk factors have been emphasized. However, as obesity is a rather multifactorial complication, single markers/biomarkers have thus far not allowed for an efficient prediction of obesity. In addition to less modifiable parameters, such as environment and socio‐demographics, studies have revealed that obesity is related to several interacting aspects, including host factors such as genetics/epigenetics or gut microbiota, and more readily modifiable factors, including nutrition and physical activity, as well as sleep patterns and psychological well‐being. It is likely that combining markers from across different domains, that is, multimodal markers, allows for better predictability for the risk of developing obesity. However, it must be considered that not all markers are fully predictive; many are rather reactive or even both. In this review and position paper, we emphasize the state‐of‐the‐art regarding (bio)markers that have successfully been employed for predicting obesity risk. An emphasis will rest on novel, emerging biomarkers, and the need for a more integrative, multimodal assessment of obesity risk for a combined assessment aimed at improving risk prediction.

Introduction: Osteoarthritis is the second most prevalent musculoskeletal disorder and mostly affects hip and knee joints. Movement behaviours have been associated with the incidence of osteoarthritis. However, previous studies have mainly relied on self-reported questionnaires. Moreover, the inherently compositional nature of 24-hour movement behaviours has not yet been considered. Objective: This prospective study aims to investigate the association between objective device-measured movement behaviours and incident hip and knee osteoarthritis, accounting for the compositional nature of movement behaviours. Methods: Data from the UK Biobank cohort were used, which included individuals aged 40–69 years old. Seven days movement behaviours were measured with a wrist-worn triaxial accelerometer. Incident hip and knee osteoarthritis cases were prospectively ascertained on hospital inpatient data. Compositional Cox proportional hazard models were used. Results: 85 706 individuals were included in the study between 2013 and 2015. During a mean follow-up of 7.6 years, 5308 participants were diagnosed with hip and knee osteoarthritis. Results showed that reallocating 30 min/day to moderate-to-vigorous physical activity from sedentary time (HR=0.98, 95% CI 0.97 to 0.99) or from light physical activity (HR=0.94, 95% CI 0.92 to 0.96) was associated with lower incidence of hip and knee osteoarthritis. Similarly, reallocating 30 min/day to sleep from sedentary time (HR=0.97, 95% CI 0.96 to 0.99) or from light physical activity (HR=0.93, 95% CI 0.92 to 0.95) was associated with lower incidence hip and knee osteoarthritis. Conclusion: Using objective measures of movement behaviours, this study provides evidence that moderate-to-vigorous physical activity and sleep may play important roles in hip and knee osteoarthritis prevention, while accounting for the other movement behaviours. Further research is needed to understand whether the associations are causal.

Background: Recommender systems are pivotal in organizing information to enhance noticeability, reduce overload, and streamline decision-making. They can be even more effective if combined with digital nudges. Digital nudging is a subtle approach that combines design, information, and interaction elements to create a choice architecture that can guide user behavior in digital environments. While promising in many fields, there is a notable gap in health promotion, particularly because digital nudges and recommender systems can encourage and support sustained healthier choices in nutrition, physical activity (PA), and sedentary behavior reduction to prevent overweight and obesity. Objective: This scoping review addresses these gaps by exploring how digital nudges and recommender systems are used in obesity prevention. Methods: We prospectively published the scoping review protocol and adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Eligibility was defined using the PCC (Population, Concept, Context) framework. We searched 7 bibliographic databases (MEDLINE and PsycINFO via Ovid, Web of Science, CINAHL via EBSCO, Scopus, ACM Digital Library, and IEEE Xplore) up to October 2023. Following a 2-stage screening by independent reviewers, we selected 68 articles that included 94 user evaluations. Results: Most articles (36/68, 53%) report on recommender systems focused on nutrition, with fewer (16/68, 23%) aiming to promote PA. Most studies on digital nudges (11/68, 16%) targeted nutrition-related nudges for shopping and meal selection (8/68, 2%). Articles address PA and sedentary behavior less frequently (3/68, 4%). Three out of 68 (4%) articles report on recommender systems in combination with games, and 2 out of 68 (3%) articles report on recommender systems and digital nudges. Approaches to item retrieval vary widely, with 31 out of 68 (46%) articles failing to describe their methods. In the scoping review, we found a discrepancy between the target group for which the system was developed and the group with which the evaluation was conducted. Sixty-eight evaluations report positive results, while 26 studies report mixed, negative, or no-difference results. Conclusions: Integrating digital nudges and recommender systems might hold potential in overweight and obesity prevention by subtly encouraging healthier lifestyle choices. However, the heterogeneity in study designs, outcome measures, and reporting quality limits the comparability of findings and makes it difficult to draw robust conclusions about effectiveness. Future work should include detailed definitions, mechanism descriptions, broader geographic representation, and rigorous intervention testing and user evaluations to fully leverage these systems for improved health outcomes and to support sustainability and well-being objectives.

Background : Movement behavior is inherently complex and heterogeneous, making it difficult to capture using a single indicator. To better identify meaningful movement behavior phenotypes, robust dimensionality reduction techniques are needed that integrate multiple behavioral dimensions while preserving their continuous nature. This study aimed to identify distinct movement behavior phenotypes and to investigate how variations in movement behavior phenotypes relate to cardiometabolic health biomarkers. Methods : This cross-sectional study included 1009 Luxembourg residents aged 25–79 years, each with at least 4 valid days of triaxial accelerometry data. A reversed graph embedding method was used to reduce the complexity of movement behaviors into a 2-dimensional tree structure, enabling the identification of distinct phenotypes. Separate linear regression models were used to assess associations between the derived tree dimensions and both movement behavior indicators and cardiometabolic health biomarkers. Results : Four distinct movement behavior phenotypes were identified: inactive , irregularly active , regularly active , and regularly low active . Both tree dimensions were favorably associated with cardiometabolic health outcomes, suggesting that higher overall activity volume, regardless of its regularity, may offer health benefits. Average 24-hour acceleration and total step count emerged as the most influential indicators contributing to phenotypic variation. Conclusion : This study demonstrates that a novel dimensionality reduction approach effectively captures the complexity of movement behaviors, identifies key movement behavior indicators, and distinguishes between meaningful phenotypes. The findings provide new insights into behavioral heterogeneity and highlight key movement dimensions linked to cardiometabolic health.

Introduction: Obesity prevalence has increased globally, imposing a significant burden on individuals and societies. Innovative solutions are, therefore, essential to mitigate its impact. Methods and analysis: This protocol outlines the framework of seven independent European pilot studies conducted in Bulgaria, Germany, Luxembourg and Spain, under the EU-Horizon HealthyW8 project. These studies aim to evaluate as primary outcomes feasibility, usage patterns (adherence) and user satisfaction of the Healthy Lifestyle Recommender System (HLRS), a personalised digital tool designed to promote healthy lifestyles through tailored physical activity and meal recommendations, considering emotional aspects. The seven pilot trials will collectively include 240 (around 30 participants/trial) older adults (≥65 years) with overweight (body mass index (BMI) 25.0–29.9 kg/m ² ) over a 3-month period. As a recruitment mitigation strategy, we will extend the age range to include individuals aged ≥55 years and those with normal weight (BMI 18.5–24.9 kg/m ² ). Other parameters collected include anthropometric measurements, questionnaires to survey lifestyle (alcohol and tobacco consumption, sleep quality), dietary patterns (food frequency questionnaire and 24-hour recall) and emotional well-being, as well as data collected from wearable devices (smartwatch, accelerometer) to track 24-hour activity patterns. Additionally, two pilot studies will collect blood, urine, saliva (only one partner) and stool samples to explore biomarkers of inflammation, oxidative stress, gut microbiome and circulating miRNAs. Expected outcomes: It is hypothesised that participants will use the HLRS consistently enough to assess its feasibility and impact. The findings will contribute to planning and executing long-term trials focused on health outcomes and enhance understanding of the multimodal nature of obesity risk and its comorbidities. This protocol facilitates comparisons across studies in diverse cultural and contextual settings, offering insights into how personal and environmental factors influence the implementation and effectiveness of the HLRS. Ethics and dissemination: Ethical approval has been obtained in each country independently. Dissemination efforts will prioritise high-impact journal publications. Trial registration number: NCT07011368 .

Purpose: Reliable gait analysis is essential for clinical assessment and research. Wearable technologies such as the WalkinSense system (WSS), a wireless instrumented insole system equipped with an inertial measurement unit, enable the measurement of spatiotemporal and kinematic gait parameters in real‐world settings. The accuracy of the WSS has been previously validated. This study aims to evaluate the test–retest reliability of the WSS in healthy adults walking and running under various speed and slope conditions. Methods: Forty‐nine healthy adults completed two sessions one week apart, walking and running at 3, 4.5 (−3°, −6°, +3°, +6°), 6 and 9 km/h on a treadmill. Spatiotemporal variables, including stance time, swing time, stride time, stride length, single and double support time, as well as kinematic variables such as foot ground angle at initial contact and toe‐off, were recorded using the WSS. Relative reliability was assessed using intraclass correlation coefficients (ICC _2,1 ), while absolute reliability was evaluated using the standard error of measurement (SEM), minimal detectable change (MDC) and their percentage values (SEM%, MDC%). Bland‐Altman plots were used to detect systematic bias and visualise agreement. Results: Results demonstrated good to excellent reliability for most spatiotemporal parameters across all conditions, with ICC values ranging from 0.76–0.95, while the foot ground angles exhibited lower reliability (ICC: 0.71–0.86). SEM% and MDC% were generally below 10% for spatiotemporal measures (SEM%: 1.63–6.63; MDC%: 3.44–18.36), reflecting both low measurement error and high sensitivity to detect real changes beyond random variation. Bland‐Altman analyses revealed no relevant heteroscedasticity. Conclusion: These findings support the WSS as a reliable tool for assessing spatiotemporal variables in healthy adults across diverse walking and running conditions, underscoring its potential for use in both clinical and research environments. However, clinical studies are needed to fully establish its utility in patient assessment. Level of Evidence: Level II, diagnostic studies.