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Abstract Purpose Blood-flow restriction (BFR) endurance training may increase endurance performance and muscle strength similar to traditional endurance training while requiring a lower training intensity. We aimed to compare acute cardiorespiratory responses to low-intensity interval exercise under BFR with moderate-intensity traditional interval exercise (TRA). Methods We conducted a randomized crossover study. The protocol involved three cycling intervals interspersed with 1 min resting periods. With a 48-h washout period, individuals performed the protocol twice in random order: once as BFR-50 (i.e., 50% incremental peak power output [IPPO] and 50% limb occlusion pressure [LOP]) and once as TRA-65 (65% IPPO without occlusion). TRA-65 intervals lasted 2 min, and time-matched BFR-50 lasted 2 min and 18 s. Respiratory parameters were collected by breath-by-breath analysis. The ratings of perceived breathing and leg exertion (RPE, 0 to 10) were assessed. Linear mixed models were used for analysis. Results Out of the 28 participants initially enrolled in the study, 24 healthy individuals (18 males and 6 females) completed both measurements. Compared with TRA-65, BFR-50 elicited lower minute ventilation (VE, primary outcome) (-3.1 l/min [-4.4 to -1.7]), oxygen consumption (-0.22 l/min [-0.28 to -0.16]), carbon dioxide production (-0.25 l/min [-0.29 to -0.20]) and RPE breathing (-0.9 [-1.2 to -0.6]). RPE leg was significantly greater in the BFR-50 group (1.3 [1.0 to 1.7]). Conclusion BFR endurance exercise at 50% IPPO and 50% LOP resulted in lower cardiorespiratory work and perceived breathing effort compared to TRA at 65% IPPO. BFR-50 could be an attractive alternative for TRA-65, eliciting less respiratory work and perceived breathing effort while augmenting perceived leg muscle effort. Trial registration NCT05163600; December 20, 2021.

The Probabilistic Reward Task (PRT) is widely used to investigate the impact of Major Depressive Disorder (MDD) on reinforcement learning (RL), and recent studies have used it to provide insight into decision-making mechanisms affected by MDD. The current project used PRT data from unmedicated, treatment-seeking adults with MDD to extend these efforts by: (1) providing a more detailed analysis of standard PRT metrics—response bias and discriminability—to better understand how the task is performed; (2) analyzing the data with two computational models and providing psychometric analyses of both; and (3) determining whether response bias, discriminability, or model parameters predicted responses to treatment with placebo or the atypical antidepressant bupropion. Analysis of standard metrics replicated recent work by demonstrating a dependency between response bias and response time (RT), and by showing that reward totals in the PRT are governed by discriminability. Behavior was well-captured by the Hierarchical Drift Diffusion Model (HDDM), which models decision-making processes; the HDDM showed excellent internal consistency and acceptable retest reliability. A separate “belief” model reproduced the evolution of response bias over time better than the HDDM, but its psychometric properties were weaker. Finally, the predictive utility of the PRT was limited by small samples; nevertheless, depressed adults who responded to bupropion showed larger pre-treatment starting point biases in the HDDM than non-responders, indicating greater sensitivity to the PRT’s asymmetric reinforcement contingencies. Together, these findings enhance our understanding of reward and decision-making mechanisms that are implicated in MDD and probed by the PRT.

The Orthelligent Pro sensor is a practicable, portable measuring instrument. This study assessed the validity and reliability of this sensor in measuring single-leg countermovement jumps. Fifty healthy athletic adults participated in two measurement sessions a week apart in time. They performed single-leg countermovement jumps on the force plate while wearing the Orthelligent Pro sensor on their lower leg. During the first measurement session, Tester 1 invited the participants to make three single-leg countermovement jumps; subsequently, Tester 2 did the same. For assessing the sensor’s intratester reliability, Tester 1 again invited the participants to make three single-leg countermovement jumps during the second measurement session. The sensor’s validity was assessed by using the force plate results as the gold standard. To determinate the agreement between two measurements, Bland–Altman plots were created. The intertester reliability (ICC = 0.99; 0.97) and intratester reliability (ICC = 0.96; 0.82) were both excellent. The validity calculated (i) on the basis of the mean value of three jumps and (ii) on the basis of the maximum value of three jumps was very high, but it showed a systematic error. Taking this error into account, physiotherapists can use the Orthelligent Pro sensor as a valid and reliable instrument for measuring the jump height of countermovement jumps.

Abstract Aim of the study Artificial intelligence (AI) chatbots are established as tools for answering medical questions worldwide. Healthcare trainees are increasingly using this cutting-edge technology, although its reliability and accuracy in the context of healthcare remain uncertain. This study evaluated the suitability of Chat-GPT versions 3.5 and 4 for healthcare professionals seeking up-to-date evidence and recommendations for resuscitation by comparing the key messages of the resuscitation guidelines, which methodically set the gold standard of current evidence and recommendations, with the statements of the AI chatbots on this topic. Methods This prospective comparative content analysis was conducted between the 2021 European Resuscitation Council (ERC) guidelines and the responses of two freely available ChatGPT versions (ChatGPT-3.5 and the Bing version of the ChatGPT-4) to questions about the key messages of clinically relevant ERC guideline chapters for adults. (1) The content analysis was performed bidirectionally by independent raters. The completeness and actuality of the AI output were assessed by comparing the key message with the AI-generated statements. (2) The conformity of the AI output was evaluated by comparing the statements of the two ChatGPT versions with the content of the ERC guidelines. Results In response to inquiries about the five chapters, ChatGPT-3.5 generated a total of 60 statements, whereas ChatGPT-4 produced 32 statements. ChatGPT-3.5 did not address 123 key messages, and ChatGPT-4 did not address 132 of the 172 key messages of the ERC guideline chapters. A total of 77% of the ChatGPT-3.5 statements and 84% of the ChatGPT-4 statements were fully in line with the ERC guidelines. The main reason for nonconformity was superficial and incorrect AI statements. The interrater reliability between the two raters, measured by Cohen’s kappa, was greater for ChatGPT-4 (0.56 for completeness and 0.76 for conformity analysis) than for ChatGPT-3.5 (0.48 for completeness and 0.36 for conformity). Conclusion We advise healthcare professionals not to rely solely on the tested AI-based chatbots to keep up to date with the latest evidence, as the relevant texts for the task were not part of the training texts of the underlying LLMs, and the lack of conceptual understanding of AI carries a high risk of spreading misconceptions. Original publications should always be considered for comprehensive understanding.