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314 results for “digital health”
Respondents' perspectives on the impact of digital data-based health services on disaster risk management in Indonesia.
<p>This data contains respondents' perspectives on the impact of digital data-based health services on disaster risk management. Digital health services are the implementation of digital, information, and communication technologies in the context of health services. Digital health services include: mHealth, Health Information Technology, Wearable Devices, Telehealth and Telemedicine, and Personalized Medicine. </p> <p>Data was collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (Project ID: HORIZON MSCA-SE 101086381) would be advisable.</p>
OMOP2OBO: Semantic Integration of Standardized Clinical Terminologies to Power Translational Digital Medicine Across Health Systems (Recorded Introduction)
<p>This entry contains the recorded introduction that was presented at the 2020 Observational Health Data Science Initiative Symposium (<a href="https://www.ohdsi.org/events/2020-ohdsi-symposium/">https://www.ohdsi.org/events/2020-ohdsi-symposium/</a>).</p>
Dataset: First Trust Nasdaq Lux Digital Health Solutions ETF (EKG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Global X Telemedicine & Digital Health ETF (EDOC) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Extended dataset and Coreq Checklist for 'Teaching critical thinking about health information and choices in secondary schools: human-centred design of digital resources"
<p>Individual user test interview guides and group interview guides for multiple stakeholders</p> <p>Coreq checklist</p> <p>Design reporting checklist</p>
Dataset for "Teaching critical thinking about health information and choices in secondary schools: human-centred design of digital resources"
<p>A qualitative dataset for the article: Teaching critical thinking about health information and choices in secondary schools: human-centred design of digital resources</p> <p>We collected this data in Phase 2 of the work described in the article, to inform development of educational resources (<em>Be Smart About Your Health</em>) to support teaching critical thinking about health claims and making informed health choices for use in secondary schools, based on a set of Informed Health Choices Key Concepts. </p> <p>Data collection methods: individual and group interviews, observation of classroom pilots, in Kenya, Rwanda, and Uganda, and via email from an international advisory group. Timeframe for data collection and analysis: 2020-2022</p> <p>This dataset is a part of the research project: <em>Enabling sustainable public engagement in improving health and health equity, </em>2019-2024. Funded by GLOBVAC programme, Research Council of Norway. </p> <p> </p>
The Transformative Storytelling Technique for Developing Guided Digital Narratives in Mental Health Support. Pilot study
<p>In a society where advances and innovations occur on a daily basis, the development of digital mental health tools continues at almost uncontrollable rates. Following trends in the utilization of storytelling across fields and reflecting on the lack of working models and frameworks for the application of storytelling for mental health support, the Transformative Storytelling Technique (TST) is designed and developed as a part of this work. This technique represents a new category for creating hybrid content to guide the experience of audiences, starting with the case of informal caregivers. In this pilot study, a TST caregiver story is assessed pre-post in an interventional study, investigating the potential role of mental health audio stories in supporting informal caregiver wellbeing. The anonymized raw dataset is available.</p>
Optimizing Efficiency and Impact of Digital Health Interventions for Caregivers
ClinicalTrials.gov study NCT04986904. IPD Sharing: YES. Countries: 1. Publications: 3.
Going digital: Added value of electronic data collection in 2018 Afghanistan Health Survey
<p>These data were collected and analyzed to compare measures of cost efficiency, data quality and user acceptability between paper-based and digitally collected data for a nationally representative household survey in Afghanistan. </p>
A Survey on Usability Evaluation in Digital Health and Potential Efficiency Issues
<p><strong>A Survey on Usability Evaluation in Digital Health and Potential Efficiency Issues</strong> is a research study published at HEALTHINF24 that aimed to collect and analyse data from 144 usability experts on their experiences with usability evaluation of digital health applications.</p> <ol> <li>This file, "<strong>Usability Survey Paper Appendix A (Questionnaire)</strong>", contains the comprehensive set of questions and materials used in the usability survey discussed in the main paper. It serves as an essential resource for readers and researchers aiming to gain in-depth insights into the survey's questionnaire design, structure, and content. This appendix offers a detailed overview of the questionnaire, including the questions' wording, order, and categorizations, enabling an in-depth understanding of the survey's methodology and findings. <ul> <li>The survey had three different parts with a total of 19 questions including <ul> <li>1) the introductory and screening part,</li> <li>2) the demographic part,</li> <li>and 3) the main part asking usability-related questions (see survey questionnaires).</li> </ul> </li> <li>The questionnaire used in the study included both fixed-alternative questions (such as true/false, multiple choice, checkbox, and rating scale) and open-ended free-text responses. It incorporates an initial set of response options for the fixed-alternative questions, which were primarily derived from the relevant literature and grey literature. Almost every question included open-text fields to accommodate a broader range of responses, allowing participants to share answers not listed in the fixed alternative questions.</li> <li>The introductory and screening part includes survey information and screening them based on a non-leading mandatory screening question to determine whether volunteers meet the eligibility criteria. </li> <li>The demographic part consists of eight questions designed to know participants better regarding their demographics and previous experiences in assessing and ensuring the usability of digital health applications (see Part II of survey questionnaire). The questions asked participants about their gender, age, job position or title, and their experiences regarding evaluating the usability of applications in the digital health sector. They were also asked about the types of digital health systems/services/technologies they had evaluated for usability.</li> <li>The main part contains ten usability evaluation related questions, with one attention check question placed in the middle of the survey. This section focuses on usability evaluation tools, methods, and approaches to understand how usability experts assess and ensure the usability of digital healthcare software. Participants were also asked about the usability characteristics that are covered during the usability evaluation in digital healthcare. Furthermore, another sub-part of this section explores the benefits of using tools during usability evaluation, as well as the overall perceived benefits and challenges encountered during conducting usability evaluation of digital health apps.</li> </ul> </li> <li><strong>Appendix B</strong>, titled "<strong>Usability Survey Paper Appendix B (Detailed Results in Tabular Form)</strong>," provides an exhaustive compilation of the results obtained from the usability survey detailed in the main paper. The file contains organized, tabulated data offering insights into participants' responses, practices, and experiences. Each table is carefully structured to ensure clarity, making the data accessible and interpretable for subsequent analysis, review, and comparison.</li> <li>The <strong>quantitative dataset </strong>(filename: <strong>Usability Survey Paper Dataset.xlsx</strong>) includes the responses of usability experts to questionnaires about their demographics, experience with usability testing, and the frequency of use of different usability evaluation methods. This dataset also includes quantitative data on the ranking of the importance of different aspects of usability evaluation, such as ease of use, efficiency, and satisfaction. The qualitative dataset also includes the responses of usability experts to open-ended field of survey questions.</li> </ol>
Replication package - Potential Effectiveness and Efficiency Issues in Usability Evaluation within Digital Health: A Systematic Literature Review
<p>1. File: <strong>Maqbool_SLR_2023_JSS_Inclusion_610.xlsm.</strong></p> <p>There are two sheets in file. A. <strong>Final_Selected_papers</strong>, (sheet) aims to provide a comprehensive list of articles (n=610) selected for our SLR, whose process and data items specified and detailed in the article. </p> <p>B. <strong>Rejected_After_Full_Review</strong>, (sheet) aims to provide a comprehensive list of articles (n=153) rejected for our SLR based on inclusion or exclusion criteria after full article review process, whose process and data items specified and detailed in the article. </p> <p>2. File: <strong>Maqbool_SLR_2023_JSS_Data_Extraction_Form.pdf</strong></p> <p>This file aims to provide a comprehensive data extraction form, whose process and data items specified and detailed in the article. The form was used to elicit data relevant to answer the postulated research questions. This form served as the foundation for the additional information presented in the final paper.</p> <p>3. File: <strong>Bilal_SLR_JSS_Primary_Studies_References.pdf</strong></p> <p>This file contains the primary selected studies (n=610) for the systematic literature review. The systematic review aims to explore and analyse research literature related to usability evaluation methods and their effectiveness and efficiency in the context of digital health applications. This file will help to identity reference of the primary selected study that is cited in the paper using a prefix (S, e.g. S137). This file can be used for peer review, ensuring the reliability and correctness of findings.</p> <p>4. File: <strong>SLR_Analysis_updated_2023.nvp</strong></p> <p>The data extracted from each article was recorded in a worksheet (Excel) and then coded in NVivo 12/14 to categorise (classify) and compare extracted facets. Each data item's category and related paper id are coded in the given Excel file. Papers were not included in the NVivo project due to copyright concerns. Relevant papers can be tracked using the provided spreadsheet file (see Paper ID cell).<br>The file(s) are cleaned as much as reasonable and other raw data is removed. This file does not include the matrix tables or codes, which were produced and analysed run-time during the analysis phase. Although the given package allows for re-generation.</p> <p>-------- UPDATE: --------</p> <p>5. File: <strong>SLR_Analysis_updated_2023_for_MAC.nvpx</strong></p> <p>This is an extra copy of NVivo project, created for the MAC user.</p> <p> </p> <p>This replication package is produced and published here. Research conducted by Karlstad University researchers. We publish data sets to improve coverage and accessibility. For more info or concerns, contact us.</p> <p> </p> <p>Linked paper published at: Maqbool, Bilal, and Sebastian Herold. "Potential effectiveness and efficiency issues in usability evaluation within digital health: A systematic literature review." <em>Journal of Systems and Software</em> (2023): 111881.</p> <p>DOI: <a href="https://doi.org/10.1016/j.jss.2023.111881">https://doi.org/10.1016/j.jss.2023.111881</a><br> </p> <p>This work was funded, in parts, by Region Värmland through the DHINO project, Sweden (Grant: RUN/220266) and Vinnova through the DigitalWell Arena (DWA) project, Sweden (Grant: 2018-03025).</p>
Design and implementation of a brief digital mindfulness and compassion training app for health care professionals: cluster randomized controlled trial
<p><strong>Background: </strong>Several studies show that intense work schedules make health care professionals particularly vulnerable to emotional exhaustion and burnout.</p> <p><strong>Objective:</strong> In this scenario, promoting self-compassion and mindfulness may be beneficial for well-being. Notably, scalable, digital app–based methods may have the potential to enhance self-compassion and mindfulness in health care professionals.</p> <p><strong>Methods: </strong>In this study, we designed and implemented a scalable, digital app–based, brief mindfulness and compassion training program called "WellMind" for health care professionals. A total of 22 adult participants completed up to 60 sessions of WellMind training, 5-10 minutes in duration each, over 3 months. Participants completed behavioral assessments measuring self-compassion and mindfulness at baseline (preintervention), 3 months (postintervention), and 6 months (follow-up). In order to control for practice effects on the repeat assessments and calculate effect sizes, we also studied a no-contact control group of 21 health care professionals who only completed the repeated assessments but were not provided any training. Additionally, we evaluated preand postintervention neural activity in core brain networks using electroencephalography source imaging as an objective<br>neurophysiological training outcome.</p> <p><strong>Results:</strong> Findings showed a post- versus preintervention increase in self-compassion (Cohen d=0.57; P=.007) and state-mindfulness (d=0.52; P=.02) only in the WellMind training group, with improvements in self-compassion sustained at follow-up (d=0.8; P=.01). Additionally, WellMind training durations correlated with the magnitude of improvement in self-compassion across human participants (ρ=0.52; P=.01). Training-related neurophysiological results revealed plasticity specific to the default mode network (DMN) that is implicated in mind-wandering and rumination, with DMN network suppression selectively observed at the postintervention time point in the WellMind group (d=–0.87; P=.03). We also found that improvement in self-compassion was directly related to the extent of DMN suppression (ρ=–0.368; P=.04).</p> <p><strong>Conclusions:</strong> Overall, promising behavioral and neurophysiological findings from this first study demonstrate the benefits of brief digital mindfulness and compassion training for health care professionals and compel the scale-up of the digital intervention.</p>
Trust in Digital Health dataset
<p>The Trust in Digital Health project was conducted by the Centre for Social Research in Health, UNSW Sydney in collaboration with community organisations to assess views of digital health systems in Australia, particularly among communities affected by bloodborne viruses and sexually transmissible infections. We conducted a national, online survey of Australians' attitudes to digital health in April–June 2020. The sample (N=2,240) was recruited from the general population and four priority populations affected by HIV and other sexually transmissible infections: gay and bisexual men, people living with HIV, sex workers, and trans and gender-diverse people. The deidentified dataset and syntax provided here were used for an analysis of factors associated with greater knowledge of My Health Record and the likelihood of opting out of the system. My Health Record is Australia's national, digital, personal health record system. </p>
Harnessing Digital Health to Understand Clinical Trajectories of Opioid Use Disorder
ClinicalTrials.gov study NCT04535583. IPD Sharing: YES. Countries: 1. Publications: 1.
The Impact of 8 Weeks of Digital Meditation Application and Healthy Eating Program on Work Stress and Health Outcomes
ClinicalTrials.gov study NCT03945214. IPD Sharing: NO. Countries: 1. Publications: 1.
Spark: Finding the Optimal Tracking Strategy for Weight Loss in a Digital Health Intervention
ClinicalTrials.gov study NCT05249465. IPD Sharing: NO. Countries: 1. Publications: 1.
Developing a Digital Intervention to Prevent Risky Health Behaviors
ClinicalTrials.gov study NCT06538922. IPD Sharing: NO. Countries: 1. Publications: 13.
Digital Health and Exercise for Autonomous Longevity Program
ClinicalTrials.gov study NCT06722976. IPD Sharing: YES. Countries: 1. Publications: 3.
Implementing a Digitally-enabled Community Health Worker Intervention for Patients With Heart Failure
ClinicalTrials.gov study NCT05130008. IPD Sharing: YES. Countries: 1. Publications: 6.
Use of Aptar Digital Health's Respiratory Disease Management Platform for Asthma
ClinicalTrials.gov study NCT06364527. IPD Sharing: YES. Countries: 1. Publications: 15.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.