Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI’s GPT-4 model

The rise of artificial intelligence (AI) applications in healthcare provides new possibilities for personalized health management. AI-based fitness applications are becoming more common, facilitating the opportunity for individualised exercise prescription. However, the use of AI carries the risk of inadequate expert supervision, and the efficacy and validity of such applications have not been thoroughly investigated, particularly in the context of diverse health conditions. The aim of the study was to critically assess the efficacy of exercise prescriptions generated by OpenAI’s Generative Pre-Trained Transformer 4 (GPT-4) model for five example patient profiles with diverse health conditions and fitness goals. Our focus was to assess the model’s ability to generate exercise prescriptions based on a singular, initial interaction, akin to a typical user experience. The evaluation was conducted by leading experts in the field of exercise prescription. Five distinct scenarios were formulated, each representing a hypothetical individual with a specific health condition and fitness objective. Upon receiving details of each individual, the GPT-4 model was tasked with generating a 30-day exercise program. These AI-derived exercise programs were subsequently subjected to a thorough evaluation by experts in exercise prescription. The evaluation encompassed adherence to established principles of frequency, intensity, time, and exercise type; integration of perceived exertion levels; consideration for medication intake and the respective medical condition; and the extent of program individualization tailored to each hypothetical profile. The AI model could create general safety-conscious exercise programs for various scenarios. However, the AI-generated exercise prescriptions lacked precision in addressing individual health conditions and goals, often prioritizing excessive safety over the effectiveness of training. The AI-based approach aimed to ensure patient improvement through gradual increases in training load and intensity, but the model’s potential to fine-tune its recommendations through ongoing interaction was not fully satisfying. AI technologies, in their current state, can serve as supplemental tools in exercise prescription, particularly in enhancing accessibility for individuals unable to access, often costly, professional advice. However, AI technologies are not yet recommended as a substitute for personalized, progressive, and health condition specific prescriptions provided by healthcare and fitness professionals. Further research is needed to explore more interactive use of AI models and integration of real-time physiological feedback

Chemical and physical characteristics of dune sand filters after operation

International audienceWastewater flow through a sand filter causes variations in the physical and chemical properties of the filter media. The objective of this study was to investigate and characterize the physical and chemical properties of dune sand filters after a certain period of operation. Lab-scale studies were performed using four dune sand filter columns for treating wastewater. The column was split into ten layers; each of which was through physical and chemical analyses after a certain operational period. It was observed that 34%–51% of the organic matter in the column was found in the top layer. The flow of wastewater caused a deposition of limestone and fine particles onto the filter. There was a noticeably low pH in the upper layers of the columns due to the high amounts of organic matter. Lastly, the virgin sands were more saturated in salts than the used sand after operation. © 2018 Desalination Publications. All rights reserved

Geochemical inverse modeling of chemical and isotopic data from groundwaters in Sahara (Ouargla basin, Algeria)

Abstract. New samples were collected in the three major Saharan aquifers namely, the “Continental Intercalaire” (CI), the “Complexe Terminal” (CT) and the Phreatic aquifer (Phr) and completed with unpublished more ancient chemical and isotopic data. Instead of classical Debye-Hückel extended law, Specific Interaction Theory (SIT) model, recently incorporated in Phreeqc 3.0 was used. Inverse modeling of hydro chemical data constrained by isotopic data was used here to quantitatively assess the influence of geochemical processes: at depth, the dissolution of salts from the geological formations during upward leakage without evaporation explains the tran sitions from CI to CT and to a first pole of Phr (pole I); near the surface, the dissolution of salts from sebkhas by rainwater explains another pole of Phr (pole II). In every case, secondary precipitation of calcite occurs during dissolution. All Phr waters result from the mixing of these two poles together with calcite precipitation and ion exchange processes. These processes are quantitatively assessed by Phreeqc model. Globally, gypsum dissolution and calcite precipitation were found to act as a carbon sink. </jats:p

Characterization of Alkaliphilus hydrothermalis sp. nov., a novel alkaliphilic anaerobic bacterium, isolated from a carbonaceous chimney of the Prony hydrothermal field, New Caledonia,

International audienceA novel anaerobic, alkaliphilic, Gram-positive staining bacterium was isolated from a hydrothermal chimney in the Prony Bay, New Caledonia. This strain designated FatMR1T grew at temperatures from 20 to 55 °C (optimum 37 °C) and at pH between 7.5 and 10.5 (optimum 8.8–9). NaCl is not required for growth (optimum 0.2–0.5 %), but is tolerated up to 3 %. Sulfate, thiosulfate, elemental sulfur, sulfite, nitrate and nitrite are not used as terminal electron acceptors. Strain FatMR1T fermented pyruvate, yeast extract, peptone and biotrypcase and used fructose as the only sugar. The main fermentation products from fructose and proteinaceous compounds (e.g. peptone and biotrypcase) were acetate, H2 and CO2. Crotonate was disproportionated to acetate and butyrate. The predominant cellular fatty acids were C14:0 and C16:0. The G + C content of the genomic DNA was 37.1 mol %. On the basis of phylogenetic, genetic, and physiological properties, strain FatMR1T (=DSM 25890T, =JCM 18390T) belonging to the phylum Firmicutes, class Clostridia, order Clostridiales, is proposed as a novel species of the genus Alkaliphilus, A. hydrothermalis sp. nov

Using artificial intelligence for exercise prescription in personalised health promotion:A critical evaluation of OpenAI’s GPT-4 model

The rise of artificial intelligence (AI) applications in healthcare provides new possibilities for personalized health management. AI-based fitness applications are becoming more common, facilitating the opportunity for individualised exercise prescription. However, the use of AI carries the risk of inadequate expert supervision, and the efficacy and validity of such applications have not been thoroughly investigated, particularly in the context of diverse health conditions. The aim of the study was to critically assess the efficacy of exercise prescriptions generated by OpenAI’s Generative Pre-Trained Transformer 4 (GPT-4) model for five example patient profiles with diverse health conditions and fitness goals. Our focus was to assess the model’s ability to generate exercise prescriptions based on a singular, initial interaction, akin to a typical user experience. The evaluation was conducted by leading experts in the field of exercise prescription. Five distinct scenarios were formulated, each representing a hypothetical individual with a specific health condition and fitness objective. Upon receiving details of each individual, the GPT-4 model was tasked with generating a 30-day exercise program. These AI-derived exercise programs were subsequently subjected to a thorough evaluation by experts in exercise prescription. The evaluation encompassed adherence to established principles of frequency, intensity, time, and exercise type; integration of perceived exertion levels; consideration for medication intake and the respective medical condition; and the extent of program individualization tailored to each hypothetical profile. The AI model could create general safety-conscious exercise programs for various scenarios. However, the AI-generated exercise prescriptions lacked precision in addressing individual health conditions and goals, often prioritizing excessive safety over the effectiveness of training. The AI-based approach aimed to ensure patient improvement through gradual increases in training load and intensity, but the model’s potential to fine-tune its recommendations through ongoing interaction was not fully satisfying. AI technologies, in their current state, can serve as supplemental tools in exercise prescription, particularly in enhancing accessibility for individuals unable to access, often costly, professional advice. However, AI technologies are not yet recommended as a substitute for personalized, progressive, and health condition-specific prescriptions provided by healthcare and fitness professionals. Further research is needed to explore more interactive use of AI models and integration of real-time physiological feedback.</p