Journal of Multidisciplinary Healthcare最新文献

Background: In Norway, individuals who struggle to participate in the workforce can take part in rehabilitation programmes to improve their work ability. The goal is to return to work by working with cognitive, physical, and environmental processes. Virtual Reality (VR) technology has become a popular and common tool in many healthcare and rehabilitation services but has not yet been systematically applied in occupational rehabilitation.

Purpose: This study aimed to investigate the experiences of participants who engaged in a game-based, immersive VR activity as part of an occupational rehabilitation program.

Material and methods: Semi-structured interviews were conducted with individuals on long-term sick leave. In total, 12 women and three men (median age 45 years) from three rehabilitation centres (five individuals from each centre) were interviewed before and after discharge from the inpatient stay. The transcripts were analysed using Systematic Text Condensation.

Results: When analysing the data material, four themes were consistent. The first and the second theme contained information about how the VR headset and the game influenced the participants. The headset forced them to pay full attention and gave a feeling of protection from external stimuli. The game brought joy, energy, and endurance with its music and rhythm. The third and fourth themes provided information about how the gaming activity influenced the participants. They experienced that it provided a free space and symptom relief. In addition, they got carried away, broke barriers, and challenged themselves.

Conclusion: The study results indicated that a gamified VR activity in occupational rehabilitation may be a new and powerful experience, substantially different from other rehabilitation modalities. VR training can facilitate a here-and-now feeling distracted from the outside world. More knowledge is needed on possible synergies between VR and other components of occupational rehabilitation and on how it may influence return to work for individuals on long-term sick leave.

Background: Cancer poses a major global health challenge, and immunotherapy, known as the third revolution in cancer treatment, has brought new hope to patients. The emerging field of immunometabolism has further enhanced the safety and efficacy of immunotherapy. Over the past two decades, this field has rapidly evolved in oncology, leading to numerous significant findings. This review systematically examines the literature on immunometabolism in cancer, visualizing research trends and identifying future directions.

Methods: A comprehensive literature search was conducted in the Web of Science, PubMed, and Scopus databases, covering publications from January 2000 to December 2023. We employed tools like Citespace, VOSviewer, and RStudio for visual analysis of publication trends, regional contributions, institutions, authors, journals, and keywords.

Results: A total of 3320 articles were published by 8090 authors across 1738 institutions, involving 71 countries. Leading contributors were China (n=469), the United States (n=361), and Germany (n=82). Harvard University was the most influential institution, while Frontiers in Immunology had the highest number of publications. The top research areas included glucose, lipid, and amino acid metabolism, the tumor microenvironment, and immune cell regulation.

Conclusion: International collaboration and interdisciplinary efforts are advancing the field of cancer immunometabolism. Future research will likely focus on the interplay between metabolism and immunity, metabolic markers, immune cell reprogramming, and tumor-immune metabolic competition.

Background: Phthalate metabolites are pervasive in the environment and linked to various health issues. This study aimed to investigate the relationship between phthalate metabolites and hearing loss.

Methods: We conducted a cross-sectional study with 1713 participants based on the National Health and Nutrition Examination Survey 2015-2018. Participants were defined as speech-frequency hearing loss (SFHL) or high-frequency hearing loss (HFHL). We analyzed the baseline characteristics of participants and assessed the detection rates of phthalate metabolites in samples. Phthalate metabolites with detection rates of >85% were enrolled. Then, restricted cubic spline and multivariable logistic regression analyses were conducted to explore the association of phthalate metabolites with hearing loss. Multi-model analysis was employed to select an optimal predictive model for HFHL based on phthalate metabolites and clinical factors.

Results: Among participants, 24.518% had SFHL and 41.998% had HFHL, associated with older age, higher BMI, male, non-Hispanic white, lower physical activity levels, higher exposure to work noise, hypertension, and diabetes. Monobenzyl phthalate (MBZP) showed a positive linear association with both SFHL and HFHL. Multivariable logistic regression revealed MBZP as a significant risk factor for HFHL (odds ratio=1.339, 95% confidence interval, 1.053-1.707). According to the area under curve (AUC) values, the logistic regression model had the best diagnostic performance of HFHL, with the highest AUC values of 0.865 in the test set. In the model, gender, diabetes, and MBZP were the top predictors of HFHL.

Conclusion: The study identified a significant association between MBZP exposure and HFHL, highlighting the need to reduce phthalate exposure.

Introduction: In healthcare applications, AI-driven innovations are set to revolutionise patient interactions and care, with the aim of improving patient satisfaction. Recent advancements in Artificial Intelligence have significantly affected nursing, assistive management, medical diagnoses, and other critical medical procedures.

Purpose: Many artificial intelligence (AI) solutions operate online, posing potential risks to patient data security. To address these security concerns and ensure swift operation, this study has developed a chatbot tailored for hospital environments, running on a local server, and utilising TinyML for processing patient data.

Patients and methods: Edge computing technology enables secure on-site data processing. The implementation includes patient identification using a Histogram of Gradient (HOG)-based classification, followed by basic patient care tasks, such as temperature measurement and demographic recording.

Results: The classification accuracy of patient detection was 95.8%. An autonomous temperature-sensing unit equipped with a medical-grade infrared temperature scanner detected and recorded patient temperature. Following the temperature assessment, the tinyML-powered chatbot engaged patients in a series of questions customised by doctors to train the model for diagnostic scenarios. Patients' responses, recorded as "yes" or "no", are stored and printed in their case sheet. The accuracy of the TinyML model is 95.3% and the on-device processing time is 217 ms. The implemented TinyML model uses only 8.8Kb RAM and 50.3Kb Flash memory, with a latency of only 4 ms.

Conclusion: Each patient was assigned a unique ID, and their data were securely stored for further consultation and diagnosis via hospital management. This research demonstrates faster patient data recording and increased security compared to existing AI-based healthcare solutions, as all processes occur within the local host.

Purpose: Exercise is a vital adjunct therapy for patients with hypertension comorbidities. However, medical personnel and patients face significant obstacles in implementing exercise prescription recommendations. AI has been developed as a beneficial tool in the healthcare field. The performance of intelligent tools such as ChatGPT 4.0 and Intelligent Health Promotion Systems (IHPS) in issuing exercise prescriptions for patients with hypertension comorbidities remains to be verified.

Patients and methods: After collecting patient information through IHPS hardware and questionnaire systems, the data were input into the software terminals of ChatGPT 4.0 and IHPS according to the five stages of the Transtheoretical Model, resulting in exercise prescriptions. Subsequently, experts from various fields scored the accuracy, comprehensiveness, and applicability of each prescription, along with providing professional recommendations based on their expertise. By comparing the performance of both systems, their capability to serve this specific group was evaluated.

Results: In most cases, ChatGPT scored significantly higher than IHPS in terms of accuracy, comprehensiveness, and applicability. However, when patients exhibited certain functional movement disorders, GPT's exercise prescriptions involved higher health risks, whereas the more conservative approach of IHPS was advantageous.

Conclusion: The path of generating exercise prescriptions using artificial intelligence, whether via ChatGPT or IHPS, cannot achieve a completely satisfactory state.But can serve as a supplementary tool for professionals issuing exercise prescriptions to patients with hypertension comorbidities, especially in alleviating the financial burden of consulting costs. Future research could further explore the performance of AI in issuing exercise prescriptions, harmonize it with physiological indicators and phased feedback, and develop an interactive user experience.

Introduction: Contact tracing has been a cornerstone of non-pharmaceutical interventions to control the COVID-19 epidemic, with highly mixed effectiveness internationally. In Jordan, the Ministry of Health (MOH) collaborated with the Jordan Nurses and Midwives Council and the USAID Local Health System Sustainability Project to set up a call center for contact tracing of COVID-19.

Objective: This study described the operation and assessed the effectiveness of Jordan's COVID-19 call center activities in reaching COVID-19 cases and their contacts.

Methods: A retrospective observational design was conducted using data from all calls made by the COVID-19 call center cases between November 2020 and April 2022. Data were collected from initial and follow-up calls to PCR-confirmed COVID-19 cases and their contacts. Data on socio-demographics, symptoms, and contact tracing activities were recorded. The study focused on key outcomes, including call success rates, the number of cases and contacts reached, and the role of different detection modes in identifying cases.

Results: During the study period, the call center attempted to contact 1,027,911 COVID-19 cases, successfully reaching 802,525 cases (78.1%). Follow-up calls were made to 1,126,334 cases, with a success rate of 74%. The call center appeared particularly valuable during the initial period of the pandemic until it was overwhelmed by the significantly more transmissible Omicron variant of the virus. Two weaknesses were identified: gaps in reaching non-Jordanian citizen cases and difficulty in keeping up with case volume during the Omicron wave of February-March 2022, when reported cases peaked at over 20,000 per day. One-third of all reached cases said that they had been referred for testing through contact tracing.

Conclusion: Contact tracing activities led by the MOH were instrumental in identifying new cases, optimizing resource allocation, improving surveillance and data systems, targeting vulnerable population, and supporting mitigation strategies to combat the COVID-19 pandemic in Jordan.

Purpose: To evaluate the accuracy of vision screening and detection of ocular abnormalities conducted by teachers in school children in Jordan compared with vision testing by optometrists and ocular disease identification by ophthalmologists.

Methods: A non-random, purposive sampling strategy where 6-year-old and 15-year-old school children from three schools in Amman, Jordan were included. Twenty-two teachers were trained to conduct visual acuity screening using the 0.2 logMAR line of the distance visual acuity (DVA) chart at 10 feet and near visual acuity (NVA) chart at 16 inches, in addition to detecting ocular abnormalities. An optometrist assessed the visual acuity and conducted objective and subjective refraction, while an ophthalmologist examined the ocular health of all children.

Results: A total of 542 children (51% female) were included in the study, of which 47% were 6 years old and 53% were 15 years old. Teacher screening had sensitivities of 76.92% for DVA, 68% for NVA, and 37.50% for abnormality detection. The specificities of DVA, NVA, and ocular abnormalities were 98.22%, 98.48%, and 99.24%, respectively. The positive predictive value for DVA, NVA, and ocular abnormalities were 83.33%, 99.00%, and 60%, respectively. The negative predictive value for each procedure was 98.22% for DVA, 98.48% for NVA, and 98.12% for ocular abnormalities.

Conclusion: Teachers were able to conduct vision screening with a high level of accuracy compared to the gold standard of testing by optometrists, which would be useful for the early detection and referral of refractive errors in school children. However, they were unable to detect ocular abnormalities compared with the gold standard for disease identification by ophthalmologists. Further training and monitoring, or different training approaches should be implemented to enable teachers to identify ocular abnormalities at acceptable levels.