DIGITAL HEALTH最新文献

Objective: Digital health ecosystems may be the next revolution in improving citizens' well-being, health delivery, data management, and health system processes, but solutions have not yet been broadly established. Reasons could be that health service-organizations have misaligned interests or lack capabilities. This study investigates reasons from a multi-health-service-organization perspective, differentiating between payers, insurers, healthcare providers, and innovators, detailing the expected value-adds, preferred participation roles, and required capabilities including a rating assessment.

Methods: Findings are based on a taxonomy development methodology, which combines a literature review with semi-structured qualitative expert interviews, conducted using a modified Delphi approach. Interviews were thematically analysed.

Results: In total, 21 experts across the four health service-organization groups were interviewed. The capability taxonomy includes a total of 16 capabilities, categorized in three themes: 'Health market', 'organizational', and 'technology and informatic'. Providers expect a value-add from strengthening their health process economics through efficiency gains but reveal the largest capability gaps, especially in 'interoperability' and 'platform'. Innovators' 'technology and informatic' capabilities complement well with those of payers for the 'health market'.

Conclusions: We present a health service-organization-specific three-stage approach for establishing digital health ecosystems. Payers and insurers should address their 'technology and informatic' capability gaps, using technical enablers or forming new entities to reduce dependencies from legacy information technology systems. Innovators should clarify their monetization models and create positive awareness for their services, possibly entering the market directly. Providers must address interoperability issues and may require incentives to encourage their participation. Findings suggest governmental policymakers to prioritize three health policy initiatives.

Objective: Urinary tract infection is one of the most prevalent bacterial infectious diseases in outpatient treatment, and 50-80% of women experience it more than once, with a recurrence rate of 40-50% within a year; consequently, preventing re-hospitalization of patients is critical. However, in the field of urology, no research on the analysis of the re-hospitalization status for urinary tract infections using machine learning algorithms has been reported to date. Therefore, this study uses various machine learning algorithms to analyze the clinical and nonclinical factors related to patients who were re-hospitalized within 30 days of urinary tract infection.

Methods: Data were collected from 497 patients re-hospitalized for urinary tract infections within 30 days and 496 patients who did not require re-hospitalization. The re-hospitalization factors were analyzed using four machine learning algorithms: gradient boosting classifier, random forest, naive Bayes, and logistic regression.

Results: The best-performing gradient boosting classifier identified respiratory rate, days of hospitalization, albumin, diastolic blood pressure, blood urea nitrogen, body mass index, systolic blood pressure, body temperature, total bilirubin, and pulse as the top-10 factors that affect re-hospitalization because of urinary tract infections. The 993 patients whose data were collected were divided into risk groups based on these factors, and the re-hospitalization rate, days of hospitalization, and medical expenses were observed to decrease from the high- to low-risk group.

Conclusions: This study showed new possibilities in analyzing the status of urinary tract infection-related re-hospitalization using machine learning. Identifying factors affecting re-hospitalization and incorporating preventable and reinforcement-based treatment programs can aid in reducing the re-hospitalization rate and average number of days of hospitalization, thereby reducing medical expenses.

Objective: Hyoid bone movement is potentially related to aspiration risk in post-stroke dysphagia (PSD) patients but is difficult to assess quantitatively. This study aimed to measure the distance of hyoid bone movement more efficiently and accurately using a deep learning model and determine the clinical usefulness of the model in PSD patients.

Methods: This study included 85 patients with PSD within 6 months from onset. Patients were grouped into an aspiration group (n = 35) and a non-aspiration group (n = 50) according to the results of a videofluoroscopic swallowing study. Hyoid bone movement was tracked using a deep learning model constructed with the BiFPN-U-Net(T) architecture. The maximum distance of hyoid bone movement was measured horizontally (H _max), vertically (V _max), and diagonally (D _max).

Results: Compared with the non-aspiration group, the aspiration group showed significant decreases in hyoid bone movement in all directions. The area under the curve of V _max was highest at 0.715 with a sensitivity of 0.680 and specificity of 0.743. The V _max cutoff value for predicting aspiration risk was 1.61 cm. The success of oral feeding at the time of discharge was significantly more frequent when hyoid movement was equal to or larger than the cutoff value although no significant relationship was found between hyoid movement and other clinical characteristics.

Conclusion: Hyoid bone movement of PSD patients can be measured quantitatively and efficiently using a deep learning model. Deep learning model-based analysis of hyoid bone movement seems to be useful for predicting aspiration risk and the possibility of resuming oral feeding.

Background: Currently, there is an increased interest in providing mental health care through digital devices and services, and the demand for these services is growing.

Objective: In this study, we considered the phenomenon of trust in online consultations, and the factors affecting this trust, within a Russian context.

Methods: An online survey was conducted using Google Forms in May 2023 and the data were analyzed using SPSS. All the participants were students from Moscow universities aged from 18 to 35 years. The final sample consisted of 203 students, of which 154 (75.9%) were women, 44 (21.7%) were men, and five (2.5%) preferred not to specify their gender.

Results: We found that students had a high level of trust, which depended on personal factors, such as experience, socio-economic status, and age, and contextual factors, such as geographical and temporal independence, price of the session, availability of recommendations, popularity of the platform, and the level of technical equipment.

Background: Gestational diabetes mellitus (GDM) can increase the risk of adverse outcomes for both mothers and infants. Preventive interventions can effectively assist pregnant women suffering from GDM. At present, pregnant women are unaware of the importance of preventing GDM, and they possess a low level of self-management ability. Recently, mHealth technology has been used worldwide. Therefore, developing a mobile health app for GDM prevention could potentially help pregnant women reduce the risk of GDM.

Objective: To design and develop a mobile application, evaluate its acceptance, and understand the users'using experience and suggestions, thus providing a valid tool to assist pregnant women at risk of GDM in enhancing their self-management ability and preventing GDM.

Methods: An evidence-based GDM prevent app (Better pregnancy) was developed using user-centered design methods, following the health belief model, and incorporating GDM risk prediction. A convenient sampling method was employed from June to August 2022 to select 102 pregnant women at risk of GDM for the pilot study. After a week, the app's acceptability was evaluated using an application acceptance questionnaire, and we updated the app based on the feedback from the women. We used SPSS 26.0 for data analysis.

Results: The application offers various functionalities, including GDM risk prediction, health management plan, behavior management, health information, personalized guidance and consultation, peer support, family support, and other functions. In total, 102 pregnant women consented to participate in the study, achieving a retention rate of 98%; however, 2% (n = 2) withdrew. The Better pregnancy app's average acceptability score is 4.07 out of 5. Additionally, participants offered several suggestions aimed at enhancing the application.

Conclusions: The Better pregnancy app developed in this study can serve as an auxiliary management tool for the prevention of GDM, providing a foundation for subsequent randomized controlled trials.

Purpose: As a global health concern, the diagnosis of obstructive sleep apnea hypopnea syndrome (OSAHS), characterized by partial reductions and complete pauses in ventilation, has garnered significant scientific and public attention. With the advancement of digital technology, the utilization of three-dimensional (3D) optical devices demonstrates unparalleled potential in diagnosing OSAHS. This study aimed to review the current literature to assess the accuracy of 3D optical devices in identifying the prevalence and severity of OSAHS.

Methods: A systematic literature search was conducted in the Web of Science, Scopus, PubMed/MEDLINE, and Cochrane Library databases for English studies published up to April 2024. Peer-reviewed researches assessing the diagnostic utility of 3D optical devices for OSAHS were included. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) guideline was employed to appraise the risk of bias.

Results: The search yielded 3216 results, with 10 articles meeting the inclusion criteria for this study. Selected studies utilized structured light scanners, stereophotogrammetry, and red, green, blue-depth (RGB-D) cameras. Stereophotogrammetry-based 3D optical devices exhibited promising potential in OSAHS prediction.

Conclusions: The utilization of 3D optical devices holds considerable promise for OSAHS diagnosis, offering potential improvements in accuracy, cost reduction, and time efficiency. However, further clinical data are essential to assist clinicians in the early detection of OSAHS using 3D optical devices.

Objective: With obesity a major health concern and call on healthcare resources in China, we explored the preferences and willingness to pay (WTP) for obesity OMC, including the influencing factors behind WTP and preferences.

Method: We recruited 400 obese participants to undertake a discrete choice experiment (DCE) and the contingent value method (CVM) survey. We used CVM to measure obese participants' WTP for one-click services (OCS) and used DCE to estimate obesity participants' preferences and WTP for OMC with different attributes.

Results: Obese participants were willing to pay more than RMB80 on average for OCS, and more than 50% of participants had a WTP over RMB50 and 5% had a WTP over RMB300, reflecting the strong willingness of Chinese obese patients to pay for OMC. Educational background, income, ethnicity, previous OMC experience and accessibility to offline hospitals with different levels impacted WTP. The relative importance score of attributes in descending order was cost, doctors' hospital level, doctors' level, online waiting time, consultation time and consultation form. Obese patients preferred lower cost, doctors from higher-level hospitals, doctors with higher expertise levels, shorter waiting time and consultation duration, and telephone consultation were preferred. 30-min waiting time, 15-min consultation duration and telephone consultation were the most economically efficient set we found.

Conclusion: To maximize health resources, provincial tertiary and municipal hospitals face different paths to developing obesity OMC platforms. We encouraged young doctors to use OMC. OMC regulators should implement consumer protection policies to optimize OMC pricing and address potential 'unfair' pricing.

Objective: The objective of this study was to determine the most effective cut-off point for the Smombie Scale and evaluate its ability to screen for pedestrian safety risks among young adults.

Methods: Data were obtained from an online sample of 396 Korean young adults aged 18-39 years. Latent profile analysis was used to distinguish the risk group as a reference measure for the Smombie Scale. Discriminative power was assessed using sensitivity, specificity, receiver operating characteristic (ROC) curves, and the area under the ROC curve. The cut-off points were estimated from the Youden index and the balanced score.

Results: The latent profile analysis showed two different classes: "risk group" of 17.8% and "others." Based on the latent profile analysis, sensitivity, and specificity analysis showed that an adequate cut-off point of 2.78 of five points or higher was associated with a high risk of distracted walking.

Conclusion: The Smombie Scale is a good predictor of problematic smartphone use on the road and can be used as a screening tool for assessing risk levels among young adult pedestrians.

Background: Memory complaints are the early symptoms of cognitive impairment, and they usually bring anxiety about cognitive deterioration among the elderly population. Musical interventions were demonstrated to relieve dementia symptoms. This pilot study investigated the potential benefits of rhythmic musical intervention, African drumming, on cognitive function and mood status with traditional and digital assessments for elderly participants.

Method: Participants were recruited through social media. The musical intervention was arranged by drumming instructors certified by the Hong Kong Association of African Drumming. Participants joined regular training classes with eight lessons, which covered rhythmic clapping and drumming, power control, and overall performance with songs. The inclusion criteria included the following: (1) age over 50; (2) self-reported complaints of memory loss; (3) the ability to use digital devices, such as a smartphone; and (4) can understand the content of questionnaires and follow the intervention schedules. Those with hearing impairment, failure to use Chinese, and active psychosis or dementia were excluded. Cognitive function was measured by the Hong Kong version of the Montreal Cognitive Assessment (HK-MoCA) and a digital platform, ScreenMat. Anxiety and depression levels were assessed by the State-Trait Anxiety Inventory (STAI) and Geriatric Depression Scale (GDS-15). All assessments were performed before and after the drumming classes. The outcomes were compared using the Wilcoxon signed rank test with 0.05 as the significance level.

Result: Twenty-two participants joined this study with an attendance rate of 90%. The overall cognitive function of the participants was good with an average score of 27 for HK-MoCA. After eight sessions of African drum intervention, the cognitive function did not show a significant improvement, but the response time of answering the digital cognitive questions was significantly faster than before the intervention (-39.9 s, p = 0.03). The response time for the short-term memory function was most significantly reduced (-13.5 s, p = 0.017). The anxiety and depression scores (i.e. STAI and GDS) also significantly improved (p < 0.001) after the intervention.

Conclusion: Rhythmic musical intervention is not only effective in improving emotional status, but also potentially good for improving cognitive symptoms, including the response time of the memory test. Digital behavioral analysis may bring new insights for future research on cognitive assessment.

Objective: This study implemented a 12-week walking program using the mobile app WalkON among college students and evaluated its effects.

Methods: This study used a quasi-experimental, non-equivalent control group, pre/post design. The WalkON program was conducted from September to December 2022, involving 50 participants in the experimental group and 52 participants in the control group.

Results: Significant between-group differences were observed as follows. Sleep quality evaluation score (i.e. higher scores mean poorer sleep quality) decreased more significantly in the experimental group (vs. control group; 1.40 vs. 0.34 points, respectively; p = .027). Anxiety decreased more in the experimental group than in the control group (2.08 vs. 1.75 points, respectively; p = .020). Regarding the mean score of the "health responsibility" domain of health-promoting behaviors, the experimental group saw an increase of 0.25 points compared to 0.15 in the control group (p = .005). The self-efficacy level increased by an average of 0.34 points in the experimental group and 0.03 points in the control group (p = .046).

Conclusions: The WalkON program has the potential to positively influence physical activity engagement and health-promoting behaviors in college students. It could be an effective strategy for promoting the physical and mental health of college students lacking engagement in physical activity. This study is significant in that it provides evidence of a mobile app-based program potentially useful to encourage college students to increase their regular physical activity through walking exercises, something especially pertinent in the new norm of contactless environments post-COVID-19.