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A Self-Adaptive Serious Game to Improve Motor Learning Among Older Adults in Immersive Virtual Reality: Short-Term Longitudinal Pre-Post Study on Retention and Transfer

A Self-Adaptive Serious Game to Improve Motor Learning Among Older Adults in Immersive Virtual Reality: Short-Term Longitudinal Pre-Post Study on Retention and Transfer

The x-axis represents the block number of the REAsmash-i VR intervention. The y-axis represents the participants’ motor success rate. The green line represents the median and the blue lines the 1st and 3rd quartiles issued from all participants. i VR: immersive virtual reality; VR: virtual reality. As presented in Table 2 and Figure 5, separate repeated measures ANOVA (F2,38=21.9; P Speed-accuracy trade-off changes over time. a RM: repeated measures. Evolution of SAT over time.

Gauthier Everard, Louise Declerck, Thierry Lejeune, Martin Gareth Edwards, Justine Bogacki, Cléo Reiprich, Kelly Delvigne, Nicolas Legrain, Charles Sebiyo Batcho

JMIR Aging 2025;8:e64004

Impact of a Point-of-Care Ultrasound Training Program on the Management of Patients With Acute Respiratory or Circulatory Failure by In-Training Emergency Department Residents (IMPULSE): Before-and-After Implementation Study

Impact of a Point-of-Care Ultrasound Training Program on the Management of Patients With Acute Respiratory or Circulatory Failure by In-Training Emergency Department Residents (IMPULSE): Before-and-After Implementation Study

During their ED stay, of the 123 patients, 98 (79.7%) had a chest x-ray, 40 (32.5%) had a chest CT scan, and 47 (38.2%) had a POCUS performed by a senior supervisor. Pneumonia was the most frequent diagnosis (n=42, 34.1%), followed by acute heart failure (n=41, 33.3%). Antibiotics (n=64, 52%) and diuretics (n=49, 39.8%) were the most frequently prescribed therapies during ED stay.

Sandra Bieler, Stephan von Düring, Damien Tagan, Olivier Grosgurin, Thierry Fumeaux

JMIRx Med 2025;6:e53276

Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled Trial

Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled Trial

The unmatched characteristics were denoted by a red “X,” while matched ones by a green check. Figure 1 shows a prediction with 4 green checks. The AI histogram displayed the probability distribution from 50 AI predictions, each representing the AI’s estimated likelihood that its predicted NDC matches the expected NDC. These probabilities form the histogram, illustrating the AI software “confidence” in the match status.

Chuan-Ching Tsai, Jin Yong Kim, Qiyuan Chen, Brigid Rowell, X Jessie Yang, Raed Kontar, Megan Whitaker, Corey Lester

J Med Internet Res 2025;27:e59946

Authors’ Response to Peer Reviews of “Impact of Weekly Community-Based Dance Training Over 8 Months on Depression and Blood Oxygen Level–Dependent Signals in the Subcallosal Cingulate Gyrus for People With Parkinson Disease: Observational Study”

Authors’ Response to Peer Reviews of “Impact of Weekly Community-Based Dance Training Over 8 Months on Depression and Blood Oxygen Level–Dependent Signals in the Subcallosal Cingulate Gyrus for People With Parkinson Disease: Observational Study”

Figure 2 C: What is the x-axis (variable and units)? Also, the y-axis should be relative (and not percentage) change—or were your maximum changes smaller than 1%? Response: The x-axis for Figure 2 C is time measured in seconds. 12. Figure 2 D: Same comments as for Figure 2 C. Response: The x-axis for this is months where we conducted MR scanning. 13. Figure 2 A could be decreased and Figure 2 B-E could be increased (I had to set zoom at 400% to be able to see those figures properly). Response: Thanks, done! 14.

Karolina A Bearss, Rebecca E Barnstaple, Rachel J Bar, Joseph F X DeSouza

JMIRx Med 2024;5:e67815