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The influence of metformin on IGF-1 levels in humans: A systematic review and meta-analysis

Yang X, Kord-Varkaneh H, Talaei S, Clark CCT, Zanghelini F, Tan SC, et al.

Pharmacol Res, 2020 01;151:104588.
PMID: 31816435 DOI: 10.1016/j.phrs.2019.104588

BACKGROUND: A meta-analysis is needed to comprehensively consolidate findings from the influence of metformin on IGF-1 levels. The present study was conducted with the objective to accurately evaluate the influence of metformin intake on IGF-1 levels via a meta-analysis of randomized controlled trials.
METHODS: A comprehensive systematic search was carried out in PubMed/MEDLINE, Web of Science, SCOPUS and Embase from inception until June 2019. Weighted mean difference (WMD) with the 95 % CI were applied for estimating the effects of metformin on serum IGF-1 levels.
RESULTS: 11 studies involving a total of 569 individuals reported changes in IGF-1 plasma concentrations as an outcome measure. Pooled results demonstrated an overall non-significant decline in IGF-1 following metformin intake (WMD: -8.292 ng/ml, 95 % CI: -20.248, 3.664, p = 0.174) with heterogeneity among (p = 0.000,I2 = 87.1 %). The subgroup analyses displayed that intervention duration <12 weeks on children (WMD:-55.402 ng/ml, 95 % CI: -79.845, -30.960, I2 = 0.0 %) significantly reduced IGF-1. Moreover, in age 18 < years older metformin intake (WMD: 15.125 ng/ml, 95 % CI: 5.522, 24.729, I2 = 92.5 %) significantly increased IGF-1 than 18 ≤ years older (WMD:-1.038 ng/ml, 95 % CI: -3.578,1.502,I2 = 78.0 %). Following dose-response evaluation, metformin intake reduced IGF-1 (coefficient for dose-response analysis= -13.14, P = 0.041 and coefficient for liner analysis= -0.066, P = 0.038) significantly based on treatment duration.
CONCLUSION: We found in children, intervention duration <12 weeks yielded significant reductions in IGF-1, whilst paradoxically, in participants >18 years old, metformin intake significantly increased IGF-1. We suggest that caution be taken when interpreting the findings of this review, particularly given the discordant supplementation practices between children and adults.
Fulltext Using artificial intelligence for exercise prescription in personalised health promotion: A critical evaluation of OpenAI's GPT-4 model

Dergaa I, Saad HB, El Omri A, Glenn JM, Clark CCT, Washif JA, et al.

Biol Sport, 2024 Mar;41(2):221-241.
PMID: 38524814 DOI: 10.5114/biolsport.2024.133661

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.
Fulltext Globally altered sleep patterns and physical activity levels by confinement in 5056 individuals: ECLB COVID-19 international online survey

Trabelsi K, Ammar A, Masmoudi L, Boukhris O, Chtourou H, Bouaziz B, et al.

Biol Sport, 2021 Oct;38(4):495-506.
PMID: 34937958 DOI: 10.5114/biolsport.2021.101605

Symptoms of psychological distress and disorder have been widely reported in people under quarantine during the COVID-19 pandemic; in addition to severe disruption of peoples' daily activity and sleep patterns. This study investigates the association between physical-activity levels and sleep patterns in quarantined individuals. An international Google online survey was launched in April 6th, 2020 for 12-weeks. Forty-one research organizations from Europe, North-Africa, Western-Asia, and the Americas promoted the survey through their networks to the general society, which was made available in 14 languages. The survey was presented in a differential format with questions related to responses "before" and "during" the confinement period. Participants responded to the Pittsburgh Sleep Quality Index (PSQI) questionnaire and the short form of the International Physical Activity Questionnaire. 5056 replies (59.4% female), from Europe (46.4%), Western-Asia (25.4%), America (14.8%) and North-Africa (13.3%) were analysed. The COVID-19 home confinement led to impaired sleep quality, as evidenced by the increase in the global PSQI score (4.37 ± 2.71 before home confinement vs. 5.32 ± 3.23 during home confinement) (p < 0.001). The frequency of individuals experiencing a good sleep decreased from 61% (n = 3063) before home confinement to 48% (n = 2405) during home confinement with highly active individuals experienced better sleep quality (p < 0.001) in both conditions. Time spent engaged in all physical-activity and the metabolic equivalent of task in each physical-activity category (i.e., vigorous, moderate, walking) decreased significantly during COVID-19 home confinement (p < 0.001). The number of hours of daily-sitting increased by ~2 hours/days during home confinement (p < 0.001). COVID-19 home confinement resulted in significantly negative alterations in sleep patterns and physical-activity levels. To maintain health during home confinement, physical-activity promotion and sleep hygiene education and support are strongly warranted.
Fulltext Sleep Quality and Physical Activity as Predictors of Mental Wellbeing Variance in Older Adults during COVID-19 Lockdown: ECLB COVID-19 International Online Survey

Trabelsi K, Ammar A, Masmoudi L, Boukhris O, Chtourou H, Bouaziz B, et al.

Int J Environ Res Public Health, 2021 Apr 19;18(8).
PMID: 33921852 DOI: 10.3390/ijerph18084329

BACKGROUND: The COVID-19 lockdown could engender disruption to lifestyle behaviors, thus impairing mental wellbeing in the general population. This study investigated whether sociodemographic variables, changes in physical activity, and sleep quality from pre- to during lockdown were predictors of change in mental wellbeing in quarantined older adults.
METHODS: A 12-week international online survey was launched in 14 languages on 6 April 2020. Forty-one research institutions from Europe, Western-Asia, North-Africa, and the Americas, promoted the survey. The survey was presented in a differential format with questions related to responses "pre" and "during" the lockdown period. Participants responded to the Short Warwick-Edinburgh Mental Wellbeing Scale, the Pittsburgh Sleep Quality Index (PSQI) questionnaire, and the short form of the International Physical Activity Questionnaire.
RESULTS: Replies from older adults (aged >55 years, n = 517), mainly from Europe (50.1%), Western-Asia (6.8%), America (30%), and North-Africa (9.3%) were analyzed. The COVID-19 lockdown led to significantly decreased mental wellbeing, sleep quality, and total physical activity energy expenditure levels (all p < 0.001). Regression analysis showed that the change in total PSQI score and total physical activity energy expenditure (F(2, 514) = 66.41 p < 0.001) were significant predictors of the decrease in mental wellbeing from pre- to during lockdown (p < 0.001, R2: 0.20).
CONCLUSION: COVID-19 lockdown deleteriously affected physical activity and sleep patterns. Furthermore, change in the total PSQI score and total physical activity energy expenditure were significant predictors for the decrease in mental wellbeing.