ArticleFrontiers in neuroscience2020
Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.
- Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and Analysis.Sensors (Basel, Switzerland) · 2024Pooled it
- Implementation of a pragmatic pathway to ensure high-quality processing of heterogeneous remote health sensing data.Smart health (Amsterdam, Netherlands) · 2026Article
- Global 10 year ecological momentary assessment and mobile sensing study on tinnitus and environmental sounds.NPJ digital medicine · 2025Article
- Process mining in mHealth data analysis.NPJ digital medicine · 2024Review
- Practical approaches in evaluating validation and biases of machine learning applied to mobile health studies.Communications medicine · 2024Article
- Call to Action: Investigating Interaction Delay in Smartphone Notifications.Sensors (Basel, Switzerland) · 2024Article
- Predicting the presence of tinnitus using ecological momentary assessments.Scientific reports · 2023Article
- The Influence of Diet on Tinnitus Severity: Results of a Large-Scale, Online Survey.Nutrients · 2022Article
- Prediction of Tinnitus Perception Based on Daily Life MHealth Data Using Country Origin and Season.Journal of clinical medicine · 2022Article
- Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches.Frontiers in pain research (Lausanne, Switzerland) · 2022Review
- Acceptability and satisfaction withFrontiers in psychiatry · 2022Article
- Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices.Sensors (Basel, Switzerland) · 2021Article
- Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users.Entropy (Basel, Switzerland) · 2021Article
- Predicting the gender of individuals with tinnitus based on daily life data of the TrackYourTinnitus mHealth platform.Scientific reports · 2021Article
- Corona Health-A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic.International journal of environmental research and public health · 2021Article
- JTrack: A Digital Biomarker Platform for Remote Monitoring of Daily-Life Behaviour in Health and Disease.Frontiers in public health · 2021Article
- Understanding adherence to the recording of ecological momentary assessments in the example of tinnitus monitoring.Scientific reports · 2020Article
- The Effect of Non-Personalised Tips on the Continued Use of Self-Monitoring mHealth Applications.Brain sciences · 2020Article
- Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study.Journal of medical Internet research · 2020Observational
- Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing prevalence of smart mobile devices (e.g., smartphones) enables the combined use of mobile crowdsensing (MCS) and ecological momentary assessments (EMA) in the healthcare domain. By correlating qualitative longitudinal and ecologically valid EMA assessment data sets with sensor measurements in mobile apps, new valuable insights about patients (e.g., humans who suffer from chronic diseases) can be gained. However, there are numerous conceptual, architectural and technical, as well as legal challenges when implementing a respective software solution. Therefore, the work at hand (1) identifies these challenges, (2) derives respective recommendations, and (3) proposes a reference architecture for a MCS-EMA-platform addressing the defined recommendations. The required insights to propose the reference architecture were gained in several large-scale mHealth crowdsensing studies running for many years and different healthcare questions. To mention only two examples, we are running crowdsensing studies on questions for the tinnitus chronic disorder or psychological stress. We consider the proposed reference architecture and the identified challenges and recommendations as a contribution in two respects. First, they enable other researchers to align our practical studies with a baseline setting that can satisfy the variously revealed insights. Second, they are a proper basis to better compare data that was gathered using MCS and EMA. In addition, the combined use of MCS and EMA increasingly requires suitable architectures and associated digital solutions for the healthcare domain.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.