Interactive Journal of Medical Research最新文献

The COVID-19 pandemic led to behavioral exacerbations in people with dementia. Increased hospitalizations and lack of bed availability in specialized dementia wards at a tertiary psychiatric hospital in Singapore resulted in lodging people with dementia in the High Dependency Psychiatric Unit (HDPCU). Customizations to create a dementia-friendly environment at the HDPCU included: (1) environmental modifications to facilitate orientation and engender familiarity; (2) person-centered care to promote attachment, inclusion, identity, occupation, and comfort; (3) risk management for delirium; and (4) training core competencies. Such practical solutions can also be implemented elsewhere to help overcome resource constraints and repurpose services to accommodate increasing populations of people living with dementia.

Background: Students' mental health crisis was recognized before the COVID-19 pandemic. Mindfulness virtual community (MVC), an 8-week web-based mindfulness and cognitive behavioral therapy program, has proven to be an effective web-based program to reduce symptoms of depression, anxiety, and stress. Predicting the success of MVC before a student enrolls in the program is essential to advise students accordingly.

Objective: The objectives of this study were to investigate (1) whether we can predict MVC's effectiveness using sociodemographic and self-reported features and (2) whether exposure to mindfulness videos is highly predictive of the intervention's success.

Methods: Machine learning models were developed to predict MVC's effectiveness, defined as success in reducing symptoms of depression, anxiety, and stress as measured using the Patient Health Questionnaire-9 (PHQ-9), the Beck Anxiety Inventory (BAI), and the Perceived Stress Scale (PSS), to at least the minimal clinically important difference. A data set representing a sample of undergraduate students (N=209) who took the MVC intervention between fall 2017 and fall 2018 was used for this secondary analysis. Random forest was used to measure the features' importance.

Results: Gradient boosting achieved the best performance both in terms of area under the curve (AUC) and accuracy for predicting PHQ-9 (AUC=0.85 and accuracy=0.83) and PSS (AUC=1 and accuracy=1), and random forest had the best performance for predicting BAI (AUC=0.93 and accuracy=0.93). Exposure to online mindfulness videos was the most important predictor for the intervention's effectiveness for PHQ-9, BAI, and PSS, followed by the number of working hours per week.

Conclusions: The performance of the models to predict MVC intervention effectiveness for depression, anxiety, and stress is high. These models might be helpful for professionals to advise students early enough on taking the intervention or choosing other alternatives. The students' exposure to online mindfulness videos is the most important predictor for the effectiveness of the MVC intervention.

Trial registration: ISRCTN Registry ISRCTN12249616; https://www.isrctn.com/ISRCTN12249616.

The collection of sexual orientation in routine data, generated either from contacts with health services or in infrastructure data resources designed and collected for policy and research, has improved substantially in the United Kingdom in the last decade. Inclusive measures of gender and transgender status are now also beginning to be collected. This viewpoint considers current data collections, and their strengths and limitations, including accessing data, sample size, measures of sexual orientation and gender, measures of health outcomes, and longitudinal follow-up. The available data are considered within both sociopolitical and biomedical models of health for individuals who are lesbian, gay, bisexual, transgender, queer, or of other identities including nonbinary (LGBTQ+). Although most individual data sets have some methodological limitations, when put together, there is now a real depth of routine data for LGBTQ+ health research. This paper aims to provide a framework for how these data can be used to improve health and health care outcomes. Four practical analysis approaches are introduced-descriptive epidemiology, risk prediction, intervention development, and impact evaluation-and are discussed as frameworks for translating data into research with the potential to improve health.

Background: Mobile health (mHealth) technologies can be used for disease-specific self-management, and these technologies are experiencing rapid growth in the health care industry. They use mobile devices, specifically smartphone apps, to enhance and support medical and public health practices. In chronic disease management, the use of apps in the realm of mHealth holds the potential to improve health outcomes. This is also true for mHealth apps on osteoporosis, but the usage and patients' experiences with these apps are underexplored.

Objective: This prospective survey study aimed to investigate the eHealth literacy of Danish patients with osteoporosis, as well as the usability and acceptability of the app "My Bones."

Methods: Data on patient characteristics, disease knowledge, eHealth literacy, usability, and acceptability were collected using self-administered questionnaires at baseline, 2 months, and 6 months. The following validated questionnaires were used: eHealth Literacy Questionnaire, System Usability Scale, and Service User Technology Acceptability Questionnaire.

Results: Mean scores for eHealth literacy ranged from 2.6 to 3.1, with SD ranging from 0.5 to 0.6 across the 7 domains. The mean (SD) System Usability Scale score was 74.7 (14.4), and the mean (SD) scores for domains 1, 2, and 6 of the Service User Technology Acceptability Questionnaire were 3.4 (1.2), 4.5 (1.1), 4.1 (1.2), respectively.

Conclusions: Danish patients with osteoporosis are both motivated and capable of using digital health services. The app's usability was acceptable, and it has the potential to reduce visits to general practitioner clinics, enhance health outcomes, and serve as a valuable addition to regular health or social care services.

Maintaining user engagement with mobile health (mHealth) apps can be a challenge. Previously, we developed a conceptual model to optimize patient engagement in mHealth apps by incorporating multiple evidence-based methods, including increasing health literacy, enhancing technical competence, and improving feelings about participation in clinical trials. This viewpoint aims to report on a series of exploratory mini-experiments demonstrating the feasibility of testing our previously published engagement conceptual model. We collected data from 6 participants using an app that showed a series of educational videos and obtained additional data via questionnaires to illustrate and pilot the approach. The videos addressed 3 elements shown to relate to engagement in health care app use: increasing health literacy, enhancing technical competence, and improving positive feelings about participation in clinical trials. We measured changes in participants' knowledge and feelings, collected feedback on the videos and content, made revisions based on this feedback, and conducted participant reassessments. The findings support the feasibility of an iterative approach to creating and refining engagement enhancements in mHealth apps. Systematically identifying the key evidence-based elements intended to be included in an app's design and then systematically testing the implantation of each element separately until a satisfactory level of positive impact is achieved is feasible and should be incorporated into standard app design. While mHealth apps have shown promise, participants are more likely to drop out than to be retained. This viewpoint highlights the potential for mHealth researchers to test and refine mHealth apps using approaches to better engage users.