(1) Telerehabilitation (TR) is a part of telemedicine involved in providing rehabilitation services to people in remote locations. TR in physical therapy in the kingdom of Saudi Arabia is still in its infancy and its implementation may pose different challenges in the physical therapy settings. The purpose of this nation-wide survey is to explore physiotherapists (PTs) knowledge, attitudes, and barriers towards implementation of TR in physical therapy settings; (2) Methods: A 14 item questionnaire was developed and mailed to PTs working in hospitals and rehabilitation centers across 13 provinces in Saudi Arabia; (3) Results: 347 PTs responded. Results are as follows: 58.8% (n = 204) of PTs reported that they had sufficient knowledge about TR. About31.7% (n = 110) of PTs reported that their hospital and rehabilitation center had installed TR, yet only 19.9% (n = 69) utilized the TR facility. Image-based TR was more frequently used (n = 33) as compared to sensor-based TR (n = 29) and virtual reality TR (n = 10).The main barriers were technical issues and cost related to implement TR in physical therapy settings; and (4) Conclusions: There is a relatively high number of PTs with self-reported knowledge about TR, however facilities and usage were limited. The main barriers were technical issues, staff skills, and the high cost involved in the introduction of TR in the PT-based health care settings.

Health informatics (HI) has become a significant research area due to the massive generation of digital health and medical data by biomedical and health research organizations. The health data sources are available in different forms namely electronic health records (EHRs), biomedical imaging, bio-signals, sensor data, genomic data, medical history, social media data, and so on. The structured health data can be utilized for HI and effective predictive modeling of health data assists in the decision-making process. The recently developed artificial intelligence (Al), machine learning (ML), and deep learning (DL) techniques pave a way for effective predictive modeling on health data. Numerous existing works have been presented in the literature depending upon the ML and DL based HI for various applications. With this motivation, this study aims to review the recent state of art ML and DL based predictive models for health sector. This survey primarily identifies the difference between the ML and DL architectures with their significance in health sector. In addition, the existing works are extensively reviewed and compared in terms of different aspects such as objectives, underlying methodology, input source, dataset used, performance validation, metrics, and so on. Finally, the open challenges and future scope of the HI are examined in detail. At the end of the survey, the readers find it useful to identify the present research and possible future scope of the ML and DL based predictive models for HI.

Comment on: The association between the timing of energy intake and the risk of overweight and obesity among Saudi female university students

The current study aimed to determine the extent to which electronic transactions affect the quality of healthcare services. To achieve the study's goal, a descriptive and analytical approach was adopted to evaluate healthcare services, and a questionnaire was distributed to administrative staff in Saudi hospitals. The data was processed, classified, hypotheses tested, and results analyzed, and conclusions and appropriate recommendations were drawn. A total of 120 questionnaires were distributed, and it was found that all were valid for statistical analysis. The research found that the level of electronic transactions in all its requirements (administrative, human, financial, technical, and security) in Saudi hospitals is high, and there is a high level of quality in the provision of healthcare services in Saudi hospitals. There is a direct effect of electronic transactions in all their requirements (administrative, human, financial, technical, and security) on the quality of healthcare services in Saudi hospitals.