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The presence of fatigue has been shown to modify running biomechanics. Overall in terms of gender, women are at lower risk than men for sustaining running-related injuries, although it depends on the factors taken into account. One possible reason for these differences in the injury rate and location might be the dissimilar running patterns between men and women. The purpose of this study was to determine the effect of fatigue and gender on the kinematic and ground reaction forces (GRF) parameters in recreational runners. Fifty-seven participants (28 males and 29 females) had kinematic and GRF variables measured while running at speed of 3.3 m s−1 before and after a fatigue test protocol. The fatigue protocol included (1) a running Course-Navette test, (2) running up and down a flight of stairs for 5 min, and (3) performance of alternating jumps on a step (five sets of 1 minute each with 30 resting seconds between the sets). Fatigue decreased dorsiflexion (14.24 ± 4.98° in pre-fatigue and 12.65 ± 6.21° in fatigue condition, p < 0.05) at foot strike phase in females, and plantar flexion (−19.23 ± 4.12° in pre-fatigue and −18.26 ± 5.31° in fatigue condition, p < 0.05) at toe-off phase in males. These changes led to a decreased loading rate (88.14 ± 25.82 BW/s in pre-fatigue and 83.97 ± 18.83 BW/s in fatigue condition, p < 0.05) and the impact peak in females (1.95 ± 0.31 BW in pre-fatigue and 1.90 ± 0.31 BW in fatigue condition, p < 0.05), and higher peak propulsive forces in males (−0.26 ± 0.04 BW in pre-fatigue and −0.27 ± 0.05 BW in fatigue condition, p < 0.05) in the fatigue condition. It seems that better responses to impact under a fatigue condition are observed among women. Further studies should confirm whether these changes represent a strategy to optimize shock attenuation, prevent running injuries and improve running economy.

IntroductionFrailty increases the risk of poor health outcomes, disability, hospitalization, and death in older adults and affects 7%–12% of the aging population. Secondary impacts of frailty on psychological health and socialization are significant negative contributors to poor outcomes for frail older adults.MethodThe My Active and Healthy Aging (My-AHA) consortium has developed an information and communications technology–based platform to support active and healthy aging through early detection of prefrailty and provision of individually tailored interventions, targeting multidomain risks for frailty across physical activity, cognitive activity, diet and nutrition, sleep, and psychosocial activities. Six hundred adults aged 60 years and older will be recruited to participate in a multinational, multisite 18-month randomized controlled trial to test the efficacy of the My-AHA platform to detect prefrailty and the efficacy of individually tailored interventions to prevent development of clinical frailty in this cohort. A total of 10 centers from Italy, Germany, Austria, Spain, United Kingdom, Belgium, Sweden, Japan, South Korea, and Australia will participate in the randomized controlled trial.ResultsPilot testing (Alpha Wave) of the My-AHA platform and all ancillary systems has been completed with a small group of older adults in Europe with the full randomized controlled trial scheduled to commence in 2018.DiscussionThe My-AHA study will expand the understanding of antecedent risk factors for clinical frailty so as to deliver targeted interventions to adults with prefrailty. Through the use of an information and communications technology platform that can connect with multiple devices within the older adult's own home, the My-AHA platform is designed to measure an individual's risk factors for frailty across multiple domains and then deliver personalized domain-specific interventions to the individual. The My-AHA platform is technology-agnostic, enabling the integration of new devices and sensor platforms as they emerge.

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Abstract. Data warehouses, multidimensional databases, and OLAP tools are based on the multidimensional (MD) modeling. Lately, several approaches have been proposed to easily capture main MD properties at the conceptual level. These conceptual MD models, together with a precise management of metadata, are the core of any related tool implementation. However, the broad diversity of MD models and management of metadata justifies the necessity of a universally understood standard definition for metadata, thereby allowing different tools to share information in an easy form. In this paper, we make use of the Common Warehouse Metamodel (CWM) to represent the main MD properties at the conceptual level in terms of CWM metadata. Then, CWM-compliant tools could interoperate by exchanging their CWM-based metadata in a commonly understood format and benefit of the expressiveness of the MD model at the conceptual level.