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Exploring the Design of Upper Limb Strength Training Through High-Intensity Interval Training Combined With Exergaming: Usability Study

Exploring the Design of Upper Limb Strength Training Through High-Intensity Interval Training Combined With Exergaming: Usability Study

In this study, we recruited 15 healthy male participants. They had an average age of 24.4 (SD 10.4) years, stood at an average height of 174 cm with a minimal variance of 0.05 cm, and weighed an average of 71.9 (SD 13) kg. Their BMI averaged at 23.5 (SD 3.57) kg/m2. All participants maintained a regular exercise routine, engaging in physical activity 3 times per week over the past year, and had experience in performing push-ups correctly.

Shu-Cheng Lin, Jing-Yu Lee, Yong Yang, Chu-Chun Fang, Hsiao-Lin Fang, Tien-Hung Hou

JMIR Serious Games 2024;12:e51730

Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation

Classifying COVID-19 Patients From Chest X-ray Images Using Hybrid Machine Learning Techniques: Development and Evaluation

In [28], a CNN-based architecture was proposed to delineate CXR images into 3 categories: healthy, pneumonia, and COVID-19. The data sets were collected from 6 public databases, including 10,451 healthy, 573 COVID-19, and 11,673 pneumonia images. The proposed model achieved an accuracy of 0.912 in the prediction of the 3 classes (healthy, pneumonia, and COVID-19) and an accuracy of 0.982 in the prediction of the 2 classes (COVID-19 or pneumonia).

Thanakorn Phumkuea, Thakerng Wongsirichot, Kasikrit Damkliang, Asma Navasakulpong

JMIR Form Res 2023;7:e42324

Evaluating the Relationship Between Fitbit Sleep Data and Self-Reported Mood, Sleep, and Environmental Contextual Factors in Healthy Adults: Pilot Observational Cohort Study

Evaluating the Relationship Between Fitbit Sleep Data and Self-Reported Mood, Sleep, and Environmental Contextual Factors in Healthy Adults: Pilot Observational Cohort Study

Fitbit has an intradevice reliability of 96.5%-99.1% for measuring sleep efficiency and total sleep time compared with the gold standard of PSG in a study of 24 healthy volunteers [10]. Another study compared the Fitbit Flex, the same device used in this study, against PSG and found a correlation of 97.4% for total sleep time in good sleepers and 88.6% for participants with insomnia [14].

Darshan Thota

JMIR Form Res 2020;4(9):e18086