Daniel Theisen

The early diagnosis of rare diseases (RDs) is crucial for timely intervention and effective management. The Screen4Care project seeks to accelerate this process by combining newborn screening with artificial intelligence (AI) and machine-learning (ML) tools. The Screen4Care interdisciplinary approach aims to reduce the lengthy diagnostic journey for individuals with RDs and improve their quality of life. A Code of Ethical Practice (CoEP) was developed to ensure the ethical handling of personal data in AI/ML-based screening. This CoEP outlines standards for how Screen4Care partner organizations can share and access patient data while minimizing the risk of misuse. Developed through the combined efforts of expert groups, European teams, advisory bodies, and patient organizations, the CoEP ensures a secure framework for data handling. This establishes a robust set of ethical principles, ensuring that data collection and sharing are conducted in a safe and responsible manner. This framework supports innovative AI/ML solutions, optimizing the diagnosis, treatment, and management of RDs, while safeguarding the interests of individuals and their families.

Previous work has demonstrated the protective effect of shoe cushioning on injury risk in leisure-time runners, but most models currently available on the market have greater cushioning than those investigated so far. Also, the optimal level of cushioning and the role of cushioning on the forepart of the shoe for injury prevention are still unknown. The main aim of this study is to determine whether (1) current ‘extra soft’ cushioning material at the rear part of the shoe reduces injury risk compared with stiffer material and (2) cushioning under the forepart of the shoe also contributes to injury risk reduction. This randomised trial with a 6-month intervention will involve 1000+ healthy leisure-time runners who will randomly receive one of the three running shoe versions. Study shoe versions will differ in their cushioning properties (ie, stiffness) at the rear or the forepart. Participants will self-report any lower limb or lower back problems on a dedicated electronic system every week, while the system will collect training data from the participant’s sports watch. Time-to-event analyses will be used to compare injury risk between the three study groups and to investigate the association between the runner’s characteristics, cushioning level and position, training and injury risk. The study was approved by the National Ethics Committee for Research (Ref: 202405/02 v2.0), and the protocol has been registered on https://clinicaltrials.gov/ (NCT06384872, 02/08/2024). Outcomes will be disseminated through presentations at international conferences and publications in peer-reviewed journals, popular magazines and specialised websites.

Background: Running biomechanics may play a role in running-related injury development, but to date, only a few modifiable factors have been prospectively associated with injury risk. Purpose: To identify risk factors among spatiotemporal and ground-reaction force characteristics in recreational runners and to investigate whether shoe cushioning modifies the association between running biomechanics and injury risk. Study Design: Case-control study; Level of evidence, 3. Methods: Recreational runners (N = 848) were tested on an instrumented treadmill at their preferred running speed in randomly allocated, standardized running shoes (with either hard or soft cushioning). Typical kinetic and spatiotemporal metrics were derived from ground-reaction force recordings. Participants were subsequently followed up for 6 months regarding running activity and injury. Cox regression models for competing risk were used to investigate the association between biomechanical risk factors and injury risk, including stratified analyses by shoe version. Results: In the crude analysis, greater injury risk was found for greater step length (subhazard rate ratio [SHR], 1.01; 95% CI, 1.00-1.02; P = .038), longer flight time (SHR, 1.00; 95% CI, 1.00-1.01; P = .028), shorter contact time (SHR, 0.99; 95% CI, 0.99-1.00; P = .030), and lower duty factor (defined as the ratio between contact time and stride time; SHR, 0.95; 95% CI, 0.91-0.98; P = .005). In the stratified analyses by shoe version, adjusted for previous injury and running speed, lower duty factor was associated with greater injury risk in those using the soft shoes (SHR, 0.92; 95% CI, 0.85-0.99; P = .042) but not in those using the hard shoes (SHR, 0.97; 95% CI, 0.91-1.04; P = .348). Conclusion: Lower duty factor is an injury risk factor, especially for softer shoe use. Contrary to widespread beliefs, vertical impact peak, loading rate, and step rate were not injury risk factors in recreational runners. Registration: NCT03115437 (ClinicalTrials.gov identifier).

In a recent randomised trial investigating running shoe cushioning, injury risk was greater in recreational runners who trained in the shoe version with greater cushioning stiffness (Stiff) compared to those using the Soft version. However, vertical impact peak force (VIPF) was lower in the Stiff version. To investigate further the mechanisms involved in the protective effect of greater cushioning, the present study used an intra‐subject design and analysed the differences in running kinematics and kinetics between the Stiff and Soft shoe versions on a subsample of 41 runners from the previous trial. Data were recorded in the two shoe conditions using an instrumented treadmill at 10 km.h⁻¹. VIPF was confirmed to be lower in the Stiff version compared to the Soft version (1.39 ± 0.25 vs. 1.50 ± 0.25 BW, respectively; p = 0.009, d = 0.42), but not difference was observed in vertical loading rate (p = 0.255 and 0.897 for vertical average and instantaneous loading rate, respectively). Ankle eversion maximal velocity was not different (p = 0.099), but the Stiff version induced greater ankle negative work (−0.55 ± 0.09 vs. −0.52 ± 0.10 J.kg⁻¹; p = 0.009, d = 0.32), maximal ankle negative power (−7.21 ± 1.90 vs. −6.96 ± 1.92 W.kg⁻¹; p = 0.037, d = 0.13) and maximal hip extension moment (1.25 ± 0.32 vs.1.18 ± 0.30 N.m.kg⁻¹; p = 0.009, d = 0.22). Our results suggest that the Stiff shoe version is related to increased mechanical burden for the musculoskeletal system, especially around the ankle joint.Trial registration: ClinicalTrials.gov identifier: NCT03115437.

Background: Several reviews of mobile health (mHealth) physical activity (PA) interventions suggest their beneficial effects on behavior change in adolescents and adults. Owing to the ubiquitous presence of smartphones, their use in mHealth PA interventions seems obvious; nevertheless, there are gaps in the literature on the evaluation reporting processes and best practices of such interventions. Objective: The primary objective of this review is to analyze the development and evaluation trajectory of smartphone-based mHealth PA interventions and to review systematic theory- and evidence-based practices and methods that are implemented along this trajectory. The secondary objective is to identify the range of evidence (both quantitative and qualitative) available on smartphone-based mHealth PA interventions to provide a comprehensive tabular and narrative review of the available literature in terms of its nature, features, and volume. Methods: We conducted a scoping review of qualitative and quantitative studies examining smartphone-based PA interventions published between 2008 and 2018. In line with scoping review guidelines, studies were not rejected based on their research design or quality. This review, therefore, includes experimental and descriptive studies, as well as reviews addressing smartphone-based mHealth interventions aimed at promoting PA in all age groups (with a subanalysis conducted for adolescents). Two groups of studies were additionally included: reviews or content analyses of PA trackers and meta-analyses exploring behavior change techniques and their efficacy. Results: Included articles (N=148) were categorized into 10 groups: commercial smartphone app content analyses, smartphone-based intervention review studies, activity tracker content analyses, activity tracker review studies, meta-analyses of PA intervention studies, smartphone-based intervention studies, qualitative formative studies, app development descriptive studies, qualitative follow-up studies, and other related articles. Only 24 articles targeted children or adolescents (age range: 5-19 years). There is no agreed evaluation framework or taxonomy to code or report smartphone-based PA interventions. Researchers did not state the coding method, used various evaluation frameworks, or used different versions of behavior change technique taxonomies. In addition, there is no consensus on the best behavior change theory or model that should be used in smartphone-based interventions for PA promotion. Commonly reported systematic practices and methods have been successfully identified. They include PA recommendations, trial designs (randomized controlled trials, experimental trials, and rapid design trials), mixed methods data collection (surveys, questionnaires, interviews, and focus group discussions), scales to assess app quality, and industry-recognized reporting guidelines. Conclusions: Smartphone-based mHealth interventions aimed at promoting PA showed promising results for behavior change. Although there is a plethora of published studies on the adult target group, the number of studies and consequently the evidence base for adolescents is limited. Overall, the efficacy of smartphone-based mHealth PA interventions can be considerably improved through a more systematic approach of developing, reporting, and coding of the interventions.

Leisure-time running is one of the most popular forms of physical activity around the world. It can be practiced almost everywhere and requires mainly a pair of “appropriate” running shoes. However, the term appropriate is ambiguous, and the properties of running footwear have always generated hot debates among clinicians, coaches, and athletes, whatever the level of practice. As the main interface between the runner's foot and the ground, the shoe potentially plays an important role in managing repetitive external mechanical loads applied to the musculoskeletal system and, thus, in injury prevention. Consequently, over the last decades, running shoes have been prescribed based on matching shoe features to foot morphology. This strategy aligns with the popular belief that footwear is one of the main extrinsic factors influencing running-related injury risk. Despite a seemingly sound strategy for shoe prescription and constant progress in running-footwear technology, the injury rate remains high. Therefore, our aim in this narrative literature review is to clarify whether the prescription of appropriate footwear to prevent injury in running is evidence based, the result of logical fallacy, or just a myth. The literature presented in this review is based on a nonsystematic search of the MEDLINE database and focuses on work investigating the effect of shoe features on injury risk in runners. In addition, key elements for a proper understanding of the literature on running footwear and injury risk are addressed. In this literature review, we outline (1) the main risk factors and the mechanisms underlying the occurrence of running-related injury, (2) important methodologic considerations for generating high-level evidence, (3) the evidence regarding the influence of running-shoe features on injury risk, (4) future directions for research, and (5) final general recommendations.

BACKGROUND Several reviews of mobile health (mHealth) physical activity (PA) interventions suggest their beneficial effects on behavior change in adolescents and adults. Owing to the ubiquitous presence of smartphones, their use in mHealth PA interventions seems obvious; nevertheless, there are gaps in the literature on the evaluation reporting processes and best practices of such interventions. OBJECTIVE The primary objective of this review is to analyze the development and evaluation trajectory of smartphone-based mHealth PA interventions and to review systematic theory- and evidence-based practices and methods that are implemented along this trajectory. The secondary objective is to identify the range of evidence (both quantitative and qualitative) available on smartphone-based mHealth PA interventions to provide a comprehensive tabular and narrative review of the available literature in terms of its nature, features, and volume. METHODS We conducted a scoping review of qualitative and quantitative studies examining smartphone-based PA interventions published between 2008 and 2018. In line with scoping review guidelines, studies were not rejected based on their research design or quality. This review, therefore, includes experimental and descriptive studies, as well as reviews addressing smartphone-based mHealth interventions aimed at promoting PA in all age groups (with a subanalysis conducted for adolescents). Two groups of studies were additionally included: reviews or content analyses of PA trackers and meta-analyses exploring behavior change techniques and their efficacy. RESULTS Included articles (N=148) were categorized into 10 groups: commercial smartphone app content analyses, smartphone-based intervention review studies, activity tracker content analyses, activity tracker review studies, meta-analyses of PA intervention studies, smartphone-based intervention studies, qualitative formative studies, app development descriptive studies, qualitative follow-up studies, and other related articles. Only 24 articles targeted children or adolescents (age range: 5-19 years). There is no agreed evaluation framework or taxonomy to code or report smartphone-based PA interventions. Researchers did not state the coding method, used various evaluation frameworks, or used different versions of behavior change technique taxonomies. In addition, there is no consensus on the best behavior change theory or model that should be used in smartphone-based interventions for PA promotion. Commonly reported systematic practices and methods have been successfully identified. They include PA recommendations, trial designs (randomized controlled trials, experimental trials, and rapid design trials), mixed methods data collection (surveys, questionnaires, interviews, and focus group discussions), scales to assess app quality, and industry-recognized reporting guidelines. CONCLUSIONS Smartphone-based mHealth interventions aimed at promoting PA showed promising results for behavior change. Although there is a plethora of published studies on the adult target group, the number of studies and consequently the evidence base for adolescents is limited. Overall, the efficacy of smartphone-based mHealth PA interventions can be considerably improved through a more systematic approach of developing, reporting, and coding of the interventions.

In a recent randomized trial including 800+ recreational runners, injury risk was lower in those who received the Soft shoe version compared to those using the Hard version (Hazard ratio = 1.52; 95% Confidence Interval = 1.07–2.16). Here, we investigated the effect of shoe cushioning on ground reaction forces (GRF) and spatiotemporal parameters in the same cohort, with a special focus on Vertical Impact Peak Force (VIPF) and Vertical Instantaneous Loading Rate (VILR). Healthy runners (n = 848) randomly received one of two shoe prototypes that differed only in their cushioning properties (Global stiffness: 61 ± 3 and 95 ± 6 N/mm in the Soft and Hard versions, respectively). Participants were tested on an instrumented treadmill at their preferred running speed. GRF data was recorded over 2 min. VIPF was higher in the Soft shoe group compared to the Hard shoe group (1.53 ± 0.21 vs. 1.44 ± 0.23 BW, respectively; p < 0.001). However, the proportion of steps with detectable VIPF was lower in the Soft shoe group (84 vs. 97%, respectively; p < 0.001) and Time to VIPF was longer (46.9 ± 8.5 vs. 43.4 ± 7.4 milliseconds, respectively; p < 0.001). No significant differences were observed for VILR (60.1 ± 13.8 vs. 58.9 ± 15.6 BW/s for Soft and Hard shoe group, respectively; p = 0.070) or any other kinetic variable. These results show that the beneficial effect of greater shoe cushioning on injury risk in the present cohort is not associated with attenuated VIPF and VILR. These GRF metrics may be inappropriate markers of the shoe cushioning‐injury risk relationship, while delayed VIPF and the proportion of steps displaying a VIPF could be more relevant.Trial registration: ClinicalTrials.gov identifier: NCT03115437.

Objective: This pilot randomized controlled trial (RCT) aimed at evaluating the feasibility and potential efficacy of a motivational interviewing (MI) intervention to increase physical activity (PA) behavior in cancer patients. Methods: Participants were randomly assigned to an experimental group with standard care plus 12 MI sessions within 12 weeks or a control group with standard care only. The number of recruited participants and the modality of recruitment were recorded to describe the reach of the study. The acceptability of the study was estimated using the attrition rate during the intervention phase. The potential efficacy of the intervention was evaluated by analyzing the PA behavior. Results: Twenty-five participants were recruited within the 16-month recruitment period (1.6 participants per month). Five participants (38.5%) from the experimental group (n = 13) and one participant (8.3%) from the control group (n = 12) dropped out of the study before the end of the intervention phase. No group by time interaction effect for PA behavior was observed at the end of the intervention. Conclusion: Due to the low recruitment rate and compliance, no conclusion can be drawn regarding the efficacy of MI to increase PA behavior in cancer patients. Moreover, the current literature cannot provide any evidence on the effectiveness of MI to increase PA in cancer survivors. Future RCTs should consider that the percentage of uninterested patients to join the study may be as high as 60%. Overrecruitment (30% to 40%) is also recommended to accommodate the elevated attrition rate.

Anterior cruciate ligament (ACL) injury is a common problem with consequences ranging from chronic joint instability to early development of osteoarthritis. Recent studies suggest that changes in brain activity (i.e., functional neuroplasticity) may be related to ACL injury. The purpose of this article is to summarize the available evidence of functional brain plasticity after an ACL injury. A scoping review was conducted following the guidelines of the Joanna Briggs Institute and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. The terms “brain,” “activity,” “neuroplasticity,” “ACL,” “injury,” and “reconstruction” were used in an electronic search of articles in PubMed, PEDro, CINAHL, and SPORTDiscus databases. Eligible studies included the following criteria: (a) population with ACL injury, (b) a measure of brain activity, and (c) a comparison to the ACL-injured limb (contralateral leg or healthy controls). The search yielded 184 articles from which 24 were included in this review. The effect size of differences in brain activity ranged from small (0.05, ACL-injured vs. noninjured limbs) to large (4.07, ACL-injured vs. healthy control). Moreover, heterogeneity was observed in the methods used to measure brain activity and in the characteristics of the participants included. In conclusion, the evidence summarized in this scoping review supports the notion of functional neuroplastic changes in people with ACL injury. The techniques used to measure brain activity and the presence of possible confounders, as identified and reported in this review, should be considered in future research to increase the level of evidence for functional neuroplasticity following ACL injury.