Modeling Energy Expenditure Estimation in Occupational Context by Actigraphy: A Multi Regression Mixed-Effects Model

The accurate prediction of energy requirements for healthy individuals has many useful applications. The occupational perspective has also been proven to be of great utility for improving workers' ergonomics, safety, and health. This work proposes a statistical regression model based on actigraphy and personal characteristics to estimate energy expenditure and cross-validate the results with reference standardized methods. The model was developed by hierarchical mixed-effects regression modeling based on the multitask protocol data. Measurements combined actigraphy, indirect calorimetry, and other personal and lifestyle information from healthy individuals (n = 50) within the age of 29.8 ± 5 years old. Results showed a significant influence of the variables related to movements, heart rate and anthropometric variables of body composition for energy expenditure estimation. Overall, the proposed model showed good agreement with energy expenditure measured by indirect calorimetry and evidenced a better performance than the methods presented in the international guidelines for metabolic rate assessment proving to be a reliable alternative to normative guidelines. Furthermore, a statistically significant relationship was found between daily activity and energy expenditure, which raised the possibility of further studies including other variables, namely those related to the subject's lifestyle.

THE "EVERYTHING'S DIFFERENT, EVERY TIME" INNOVATION MANAGEMENT PROBLEM: A PROMISING MODEL DEVELOPMENT

This study reports on the testing of a promising approach for aiding decision-making during innovation. By focusing on the effects of risk/action dyads on success (the Risk/Action/Success (R/A/S) framework), and because perceived risks do appear repeatedly even though they emanate from differing contexts, the model offers an opportunity to learn from what worked best before. Using Artificial Neural Networks, this novel approach allows for generalisation and applicability of specific innovation management actions that are context specific. For academics, the proposed approach contributes to the risk-management literature by proposing a new paradigm for understanding and analysing innovation processes and identification of the most frequently occurring risks as seen by managers directly involved in continuous innovation. In addition, the model offers the capacity to use quantitative techniques to model the overlapping risks and actions during innovation-related decision-making. For practitioners, it can provide specific recommendations in the form of success-sorted lists of actions taken by other innovation managers that faced similar risks. This paper presents the theoretical and practical rationales underpinning this R/A/S framework and reports on the viability of this approach using pilot data.

Advances in physical activity monitoring and lifestyle interventions in obesity: a review

Obesity represents a strong risk factor for developing chronic diseases. Strategies for disease prevention often promote lifestyle changes encouraging participation in physical activity. However, determining what amount of physical activity is necessary for achieving specific health benefits has been hampered by the lack of accurate instruments for monitoring physical activity and the related physiological outcomes. This review aims at presenting recent advances in activity-monitoring technology and their application to support interventions for health promotion. Activity monitors have evolved from step counters and measuring devices of physical activity duration and intensity to more advanced systems providing quantitative and qualitative information on the individuals' activity behavior. Correspondingly, methods to predict activity-related energy expenditure using bodily acceleration and subjects characteristics have advanced from linear regression to innovative algorithms capable of determining physical activity types and the related metabolic costs. These novel techniques can monitor modes of sedentary behavior as well as the engagement in specific activity types that helps to evaluate the effectiveness of lifestyle interventions. In conclusion, advances in activity monitoring have the potential to support the design of response-dependent physical activity recommendations that are needed to generate effective and personalized lifestyle interventions for health promotion.