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413
datasets available to search
ShareScore release 0.9.0
Dataset results
413 results for “mobile health”
Mobile Strategies for Women's and Children's Health: Optimizing Adherence and Efficacy of PMTCT/ART
ClinicalTrials.gov study NCT02400671. IPD Sharing: Not stated. Countries: 1. Publications: 6.
LetSync: Pilot Test of Mobile Health (mHealth) Intervention
ClinicalTrials.gov study NCT04951544. IPD Sharing: NO. Countries: 1. Publications: 1.
Mobile Health Intervention to Improve Neonatal Care Practice
ClinicalTrials.gov study NCT05666050. IPD Sharing: NO. Countries: 1. Publications: 2.
Harnessing Mobile Technology to Reduce Mental Health Disorders in College Populations
ClinicalTrials.gov study NCT04162847. IPD Sharing: YES. Countries: 1. Publications: 2.
Mobile Health Technology for Personalized Tobacco Cessation Support Among Cancer Survivors in Laos
ClinicalTrials.gov study NCT05253573. IPD Sharing: YES. Countries: 1. Publications: 1.
Evaluation of the Trans Women Connected Mobile App for Changes in Sexual Health-related Behavior Among Transgender Women
ClinicalTrials.gov study NCT03897049. IPD Sharing: NO. Countries: 1. Publications: 1.
A Mobile Health Exercise Intervention for Older Patients With Myeloid Neoplasms
ClinicalTrials.gov study NCT04035499. IPD Sharing: YES. Countries: 1. Publications: 5.
A Mobile Application to Improve Procurement and Distribution of Healthful Foods & Beverages in Baltimore City
ClinicalTrials.gov study NCT05010018. IPD Sharing: YES. Countries: 1. Publications: 2.
Mobile Health Study and Enhanced Symptom Monitoring in COVID-19 Cancer Patients
ClinicalTrials.gov study NCT04397614. IPD Sharing: NO. Countries: 1. Publications: 0.
The workload of manual data entry for integration between mobile health applications and eHealth infrastructure
Open the record for dataset details and reuse information.
Attitudes toward seeking mental health services and use of mobile technology survey
Open the record for dataset details and reuse information.
Data from: Ibobbly mobile health intervention for suicide prevention in Australian Indigenous youth: a pilot randomised controlled trial
Objectives: Rates of youth suicide in Australian Indigenous communities are four times the national youth average and demand innovative interventions. Historical and persistent disadvantage is coupled with multiple barriers to help-seeking. Mobile phone applications offer the opportunity to deliver therapeutic interventions directly to individuals in remote communities.The pilot study aimed to evaluate the effectiveness of a self-help mobile app (ibobbly) targeting suicidal ideation, depression, psychological distress, and impulsivity among Indigenous youth in remote Australia. Setting: Remote and very remote communities in the Kimberley region of north Western Australia Participants: Indigenous Australians aged 18-35 years. Interventions: 61 participants were recruited and randomised to receive either an app (ibobbly) which delivered acceptance-based therapy over 6 weeks or were waitlisted for 6 weeks and then received the app for the following 6 weeks. Primary and secondary outcome measures: The primary outcome was the Depressive Symptom Inventory – Suicidality Subscale (DSI-SS) to identify the frequency and intensity of suicidal ideation in the previous weeks. Secondary outcomes were the Patient Health Questionnaire 9 (PHQ-9), the Kessler Psychological Distress Scale (K10), and the Barratt Impulsivity Scale (BIS-11). Results: Although pre- post-intervention change on the (DSI-SS) was significant in the ibobbly arm (t=2•40; df=58•1; p=0•0195), this difference was not significant compared to the waitlist arm (t=1•05; df=57•8; p=0•2962). However, participants in the ibobbly group showed substantial and statistically significant reductions in PHQ-9 and K10 scores compared to waitlist. No differences were observed in impulsivity. Waitlist participants improved after 6 weeks of app use. Conclusions: Apps for suicide prevention reduce distress and depression but do not show significant reductions on suicide ideation or impulsivity. A feasible and acceptable means of lowering symptoms for mental health disorders in remote communities is via appropriately designed self-help apps. Trial Registration: Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12613000104752.
Factors used to Influence Mobile Health Application Rating
<p>Over the last five years, mobile health applications (mHealthapp) have evolved exponentially to assess and support our health and well-being. This paper presents an Artificial Intelligence (AI)-enabledmHealth app rating tool which takes multidimensional measures such as starrating, user's review and features declared by the developer to generate apprating. However, currently, there is very little conceptual understanding onhow users' reviews affect app rating from a multi-dimensional perspective. This study applies artificial intelligence (AI)-based text mining technique to develop more comprehensive understanding of users' feedback based on an array of factors, determining the mHealth app ratings. Based on the literature, six variables were identified that influence the mHealth app rating scale. These factors are user's star rating, user's text review, user interface (UI) design, functionality, security and privacy, and clinical approval. Natural Language Toolkit package is used for interpreting text and to identify the App users' sentiment. Additional considerations were accessibility, protection and privacy, UI design for people living with physical disability. Moreover, the details of clinical approval, if exists, were taken from the developer's statement. Finally, we fused all the inputs using fuzzy logic to calculate the new app rating score. Our proposed model concentrates on heart related apps found in the play store and app gallery. The findings indicate the efficacy of the model as opposed to the current device scale. This study has implications for both app developers and consumers who are using mHealth apps to monitor and track their health. The performance evaluation shows that the proposed mHealth scale has shown excellent reliability as well as internal consistency of the scale, and high inter-rater reliability index. It has been also found that the fuzzy based rating has a high variance compared to the conventional app rating whereas the fuzzy based rating shows high relationship in contrast to scoring based on expert opinion.</p>
FOCUS ADHD Mobile Health App for Adult ADHD Patients
ClinicalTrials.gov study NCT05551689. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Reducing Childhood Hearing Loss in Rural Alaska Through a Preschool Screening and Referral Process Using Mobile Health and Telemedicine
ClinicalTrials.gov study NCT03662256. IPD Sharing: Not stated. Countries: 1. Publications: 5.
MOBILE Health Intervention in IntraCerebral Hemorrhage Survivors
ClinicalTrials.gov study NCT05830305. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Social Convoy Palliative Care (Convoy-Pal) Mobile Health for Older Adults
ClinicalTrials.gov study NCT04779931. IPD Sharing: NO. Countries: 1. Publications: 1.
Artificial Intelligence Mobile Health Trial Of A Digital Platform To Optimize GDMT Using Wearable Sensors
ClinicalTrials.gov study NCT04191330. IPD Sharing: NO. Countries: 1. Publications: 1.
CopeSmart: Using Mobile Technology to Promote Positive Mental Health in Young People
ClinicalTrials.gov study NCT02265978. IPD Sharing: Not stated. Countries: 1. Publications: 1.
The Evaluation of Pulse: A Mobile Health App and Teen Pregnancy Prevention Program
ClinicalTrials.gov study NCT03253783. IPD Sharing: NO. Countries: 1. Publications: 1.
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.