DIGITAL HEALTH最新文献

Background: Regular rehabilitation during or after cancer treatment can bring numerous benefits to colorectal cancer survivors. However, there is a lack of convenient and mobile rehabilitation support systems tailored specifically for this group. The metaverse, as a virtual reality environment, offers an innovative platform for implementing rehabilitation. Hence, our study aims to develop a metaverse-based multimodal rehabilitation program and assess its effects on enhancing outcome measures such as quality of life in colorectal cancer patients.

Methods and analysis: This study was designed as a randomized, single-blind controlled trial design featuring two arms: a rehabilitation group and a conventional care group. Sixty colorectal cancer survivors who have undergone curative surgery followed by adjuvant chemotherapy will be recruited for this study. The intervention will take place within the metaverse over a 4-week period. Assessments will be conducted at baseline and after 4 weeks. The intervention is grounded in the behavior change wheel framework and encompasses dietary intervention, exercise intervention, psychological support, and behavior management. Through the implementation of diverse strategies such as training, education, and motivation, our objective is to enhance patients' capacity, opportunities, and motivation, ultimately fostering healthy behaviors. Outcome measures will encompass quality of life, fear of recurrence, and lifestyle.

Results: The analysis includes statistical description and inference. Quantitative data will be summarized using mean ± standard deviation for normally distributed data and medians with percentiles for non-normally distributed data. Categorical data will be presented as frequencies and percentages. Statistical tests will detect significant differences between pre- and post-intervention periods. Subgroup analysis will explore CRC stage, age, and gender in relation to outcome measures to identify factors affecting intervention efficacy.

Conclusions: The findings from this research will offer valuable insights and practical implications for the implementation of remote interventions and family-based interventions in the context of colorectal cancer survivorship.

Trial registration: NCT05956990 (Registered 21 July 2023).

Introduction: Workload and stress in excess can lead to work disability. The aim of our study was to determine whether commercially available "activity trackers" can be used to make statements about the work - or stress load of different occupational groups.

Material and methods: The study was conducted at the University Hospital Freiburg, Germany. Four occupational groups with a total of 32 subjects were studied: senior physicians (SP, 4), assistant physicians (AP, 11), nursing staff (NS, 12) and administrative staff (AS, 5). The activity trackers were worn on five working days and one day off. Step frequency, distance and heart rate (HR) were measured, and workload was assessed using a visual analog scale.

Results: The highest workload was reported by SP, the lowest by AS. Male employees feel higher workload than female employees (p = 0.009). NS covered the greatest daily distance, AP the least (p = 0.001). There was a significant difference in average HF between AP and NS (p = 0.008). AS showed higher daily distance and maximum HF on days off compared to work days, and NS showed the opposite behavior. With increasing patient volume for ambulatory care, the average HF increased (p = 0.037) in NSs.

Conclusion: "Activity trackers" reliably provide body data during work. In our small sample, interesting differences and results on workload emerged. More data would require more subjects and more study variables.

Background: African Americans have a higher prevalence of cardiovascular risk factors, leading to higher cardiovascular disease mortality than White adults. Our culturally tailored mobile health (mHealth) lifestyle intervention (FAITH! App) has previously demonstrated efficacy in promoting ideal cardiovascular health in African Americans.

Methods: We conducted a secondary analysis from a cluster randomized controlled trial among African-Americans from 16 churches in Minnesota that compared the FAITH! App to a delayed intervention control group. A subgroup of participants with ≥ 1 diagnosis of overweight/obesity, hyperlipidemia, hypertension, or diabetes was examined. The primary outcome was a change in LS7 score-a measure of cardiovascular health ranging from poor to ideal (range 0-14 points)-at 6-months post-intervention.

Results: The analysis included 49 participants (intervention group: n = 20; mean age 58.8 years, 75% female; control group: n = 29, mean age 52.5 years, 76% female) with no significant baseline differences in cardiovascular risk factors. Compared to the control group, the intervention group showed a greater increase in LS7 score across all cardiovascular risk factors at 6-months post-intervention, with statistically significant differences among those with overweight/obesity (intervention effect 1.77, p < 0.0001) and 2+ or 3+ cardiovascular risk factors (1.00, p = 0.03; 1.09, p = 0.04). The intervention group demonstrated a higher increase in the percentage of participants with intermediate or ideal LS7 scores than the control group, although these differences were not statistically significant.

Conclusions: Our culturally tailored mHealth lifestyle intervention was associated with significant increases in LS7 scores among African Americans with preexisting cardiovascular risk factors, suggesting its efficacy in improving cardiovascular health among this population.

Mass media campaigns for public health often rely heavily on digital media and advertising tools that are customarily the domain of marketing professionals and primarily used for commercial purposes. Digital campaigns also generate a myriad of metrics, which can pose both a challenge and opportunity for scientists wishing to leverage these data for research and evaluation.

Objective: The aim of this article is to provide practical guidance for the evaluation of paid media campaigns, with a focus on analyzing digital data generated directly by the campaign.

Methods: Building off the Centers for Disease Control framework for program evaluation, we describe a step-by-step process for evaluation tailored to the unique considerations of digital and paid media campaigns. We contextualize our guidance with our experience evaluating a suicide prevention campaign conducted from 2021 to 2023 that focused on firearms safety in U.S. military veterans.

Results: Key terminology, conceptual models, and selected findings from our evaluation are presented alongside our guidance.

Conclusions: We conclude with key lessons learned and offer recommendations that are broadly applicable to evaluation of other digital campaigns.

Aim: This study aimed to evaluate perceptions and expectations towards artificial intelligence (AI) applications in diagnostic radiology among radiologists across academic, non-academic and private practice settings in the Federal State of Styria, Austria. It also sought to determine how participant's characteristics and AI-specific knowledge might influence these views.

Methods: An online quantitative survey comprising 20 multiple-choice questions in German language was distributed via email to radiologists in outpatient and hospital settings throughout Styria in 2024.

Results: Out of 149 radiologists contacted, 66 responded. Of these, 75.4% reported having basic knowledge of AI, 13.8% indicated good to very good knowledge and only 10.8% had minimal AI-specific knowledge. The majority (84.4%) expressed willingness to use certified AI software in diagnostics. About half of the respondents (50.8%) believed that AI would not fully replace radiologists in the next 10-15 years, although 46.0% anticipated partial replacement. Additionally, 87.7% did not foresee a decrease in professional income due to AI integration. 64.6% anticipated improvement in diagnostic tasks through AI, with this expectation being significantly linked to an academic career (χ² = 8.97, p = 0.01). However, opinions varied on AI's potential to outperform radiologists in diagnostics in the near future. There was no statistically significant relationship between participant's AI-specific knowledge and perceptions and expectations towards AI.

Conclusion: The study reveals a generally positive attitude towards AI among radiologists, with uncertainties about its future performance compared to human radiologists. Although AI is anticipated to positively influence workload without reducing income, there may be a discrepancy between these expectations and actual outcomes.

Objective: This study investigates the influence of psychological factors-specifically affective and cognitive risk perceptions, social distancing attitudes, subjective norms, and cabin fever syndrome-on smartphone usage intensity during the COVID-19 pandemic, with a particular focus on university students.

Methods: Utilizing a cross-sectional survey design, data were collected from 314 university students from South Korea and Vietnam. Structural equation modeling was employed to analyze the relationships between the psychological constructs and their impact on smartphone usage.

Results: The analysis confirms that both affective and cognitive risk perceptions significantly influence attitudes towards social distancing. Furthermore, these social distancing attitudes are found to significantly affect cabin fever syndrome, suggesting that positive attitudes towards social distancing are closely associated with higher reports of cabin fever. Notably, cabin fever syndrome emerges as a significant predictor of increased smartphone usage, underscoring its role as a mediator between prolonged isolation and digital engagement. Additionally, subjective norms are also shown to significantly influence smartphone usage intensity, highlighting the impact of social expectations on digital behaviors during the pandemic.

Conclusion: The study highlights the complex interplay between psychological distress induced by social restrictions and increased reliance on digital technology for social connectivity. These insights suggest that mental health interventions and digital literacy programs tailored to university students' needs can be effective in managing the negative impacts of prolonged social isolation.

The integration of the metaverse in healthcare has been evolving, encompassing various areas such as mental health interventions, neurological treatments, physical therapy, rehabilitation, medical education, and surgical procedure assistance. For the adolescent population, growing in the digital era and witnessing the interaction of technology with daily life has made digitalization a second nature. Despite the potential of this technology in advancing adolescent mental health care and treatment, there is a notable gap in research and development. Thus, this commentary article aims to elucidate the current landscape of emerging technologies for adolescent mental healthcare in the metaverse, identify potential challenges with its implementation in this growing population, as well as provide recommendations to overcome these obstacles.

Objective: Telemedicine is a digital substitute for in-person healthcare service delivery systems that has gained popularity amid the global COVID-19 pandemic. The objective of this study was to evaluate telemedicine compatibility from the perspective of healthcare practitioners to enhance the effectiveness and spectrum of the Model for Assessment of Telemedicine.

Method: Primary and Secondary Healthcare and King Edward Medical University extended their respective telemedicine services in 2020 where 24,516 patients were benefited from the telemedicine services provided by 1273 doctors from different specializations. A cross-sectional survey via online questionnaire was conducted among purposively sampled 248 healthcare practitioners designated at telemedicine portals in the public sector; further analysed by descriptive analysis and Monte Carlo Feature Selection.

Results: Healthcare practitioner perception was analysed explicitly and found significant in addition to the existing domains under multidisciplinary assessment in the Model for Assessment of Telemedicine model. The variables of subdomains integration with healthcare system, patient facilitation, technology ease, capacity building, ethical integrity, outcome assessment and communication gap under proposed healthcare practitioner perception domain were found interdependent. The variables of patient satisfaction, resource preservation, healthcare practitioner satisfaction, digital connectivity, user-friendliness, and patient safety were found to be of higher importance (RI values). However, the compatibility of telemedicine with the healthcare system was also influenced by interdependencies (RI plot) and multifaceted interactions of variables derived from the healthcare practitioner perception.

Conclusion: The variables of healthcare practitioner perception were exhibiting various weightages of importance and interdependencies in determining the compatibility of telemedicine within the healthcare system and recommended to be considered in the Model for Assessment of Telemedicine framework.

Objective: Australia is committed to establishing a digitally enabled healthcare system that fosters innovation, strengthens data capabilities, and establishes a foundation for future digital health reform. This study provides a comprehensive overview of digital health implementation research in Australia, employing scientometric analysis and data visualization. We assess the existing knowledge base, identify key research areas and frontier trends, and explore their implications for healthcare delivery in rural and remote settings.

Methods: A systematic search of the Web of Science Core Collection database was conducted for relevant documents up to December 31, 2023. Analysis of annual growth patterns, journals, institutional and authorship contributions, reference co-citation patterns, and keyword co-occurrence was conducted using scientometrics to create outputs in the form of graphs and tables. Evolutionary analyses were undertaken to delineate the current knowledge base, predominant research themes, and frontier trends in the field.

Results: A total of 196 documents related to digital health implementation in Australia were identified, demonstrating sustained growth since 2019. The evolution of the field is characterized by four distinct phases, with a pronounced focus on telehealth, particularly in the context of the COVID-19 pandemic. 'Remote health' emerged as a significant area of contemporary interest.

Conclusions: This scientometric study contributes to our understanding of digital health implementation research in Australia. Despite a considerable body of research, there remains a relative paucity of studies focused on implementation in underserved rural and remote areas which arguably stand to benefit the most from digital health advancements. Continued research in this field is crucial to ensure equitable access to the benefits offered by digital health innovations.

Objective: Early detection of Parkinson's disease (PD) is essential for halting its progression, yet challenges remain in leveraging deep learning for accurate identification. This study aims to overcome these obstacles by introducing a hybrid deep learning approach that enhances PD detection through a combination of autoencoder (AE) and radial basis function neural network (RBFNN).

Methods: The proposed method analyzes the power spectral density (PSD) of preprocessed electroencephalography (EEG) signals, with artifacts removed, to assess energy distribution across EEG sub-bands. AEs are employed to extract features from reconstructed signals, which are subsequently classified by an RBFNN. The approach is validated on UC SanDiego's EEG dataset, consisting of 31 subjects and 93 minutes of recordings.

Results: The hybrid model demonstrates promising performance, achieving a classification accuracy of 99%. The improved accuracy is attributed to advanced feature selection techniques, robust data preprocessing, and the integration of AEs with RBFNN, setting a new benchmark in PD detection frameworks.

Conclusion: This study highlights the efficacy of the hybrid deep learning framework in detecting PD, particularly emphasizing the importance of using multiple EEG channels and advanced preprocessing techniques. The results underscore the potential of this approach for practical clinical applications, offering a reliable solution for early and accurate PD detection.