|
1
|
Breitmayer M, Stach M, Kraft R, Allgaier J, Reichert M, Schlee W, Probst T, Langguth B, Pryss R. Predicting the presence of tinnitus using ecological momentary assessments. Sci Rep 2023; 13:8989. [PMID: 37268689 DOI: 10.1038/s41598-023-36172-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 05/24/2023] [Indexed: 06/04/2023] Open
Abstract
Mobile applications have gained popularity in healthcare in recent years. These applications are an increasingly important pillar of public health care, as they open up new possibilities for data collection and can lead to new insights into various diseases and disorders thanks to modern data analysis approaches. In this context, Ecological Momentary Assessment (EMA) is a commonly used research method that aims to assess phenomena with a focus on ecological validity and to help both the user and the researcher observe these phenomena over time. One phenomenon that benefits from this capability is the chronic condition tinnitus. TrackYourTinnitus (TYT) is an EMA-based mobile crowdsensing platform designed to provide more insight into tinnitus by repeatedly assessing various dimensions of tinnitus, including perception (i.e., perceived presence). Because the presence of tinnitus is the dimension that is of great importance to chronic tinnitus patients and changes over time in many tinnitus patients, we seek to predict the presence of tinnitus based on the not directly related dimensions of mood, stress level, arousal, and concentration level that are captured in TYT. In this work, we analyzed a dataset of 45,935 responses to a harmonized EMA questionnaire using different machine learning techniques. In addition, we considered five different subgroups after consultation with clinicians to further validate our results. Finally, we were able to predict the presence of tinnitus with an accuracy of up to 78% and an AUC of up to 85.7%.
Collapse
Affiliation(s)
- Marius Breitmayer
- Institute of Databases and Information Systems, Ulm University, Ulm, Germany.
| | - Michael Stach
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Robin Kraft
- Institute of Databases and Information Systems, Ulm University, Ulm, Germany
- Department of Clinical Psychology and Psychotherapy, Ulm University, Ulm, Germany
| | - Johannes Allgaier
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Manfred Reichert
- Institute of Databases and Information Systems, Ulm University, Ulm, Germany
| | - Winfried Schlee
- Institute for Information and Process Management, Eastern Switzerland University of Applied Sciences, St. Gallen, Switzerland
- Clinic and Policlinic for Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Thomas Probst
- Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, Krems, Austria
| | - Berthold Langguth
- Clinic and Policlinic for Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| |
Collapse
|
|
2
|
Sun Y, Ding W, Shu L, Li K, Zhang Y, Zhou Z, Han G. On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture: A Vision. IEEE SYSTEMS JOURNAL 2022; 16:132-143. [PMID: 0 DOI: 10.1109/jsyst.2021.3104107] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Affiliation(s)
- Yuanhao Sun
- Nanjing Agricultural University, Nanjing, China
| | - Weimin Ding
- Nanjing Agricultural University, Nanjing, China
| | - Lei Shu
- Nanjing Agricultural University, Nanjing, China
| | - Kailiang Li
- Nanjing Agricultural University, Nanjing, China
| | - Yu Zhang
- Loughborough University, Loughborough, U.K
| | | | - Guangjie Han
- Department of Information and Communication systems, Hohai University, Changzhou, China
| |
Collapse
|
|
3
|
Allgaier J, Schlee W, Langguth B, Probst T, Pryss R. Predicting the gender of individuals with tinnitus based on daily life data of the TrackYourTinnitus mHealth platform. Sci Rep 2021; 11:18375. [PMID: 34526553 PMCID: PMC8443560 DOI: 10.1038/s41598-021-96731-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 08/12/2021] [Indexed: 02/08/2023] Open
Abstract
Tinnitus is an auditory phantom perception in the absence of an external sound stimulation. People with tinnitus often report severe constraints in their daily life. Interestingly, indications exist on gender differences between women and men both in the symptom profile as well as in the response to specific tinnitus treatments. In this paper, data of the TrackYourTinnitus platform (TYT) were analyzed to investigate whether the gender of users can be predicted. In general, the TYT mobile Health crowdsensing platform was developed to demystify the daily and momentary variations of tinnitus symptoms over time. The goal of the presented investigation is a better understanding of gender-related differences in the symptom profiles of users from TYT. Based on two questionnaires of TYT, four machine learning based classifiers were trained and analyzed. With respect to the provided daily answers, the gender of TYT users can be predicted with an accuracy of 81.7%. In this context, worries, difficulties in concentration, and irritability towards the family are the three most important characteristics for predicting the gender. Note that in contrast to existing studies on TYT, daily answers to the worst symptom question were firstly investigated in more detail. It was found that results of this question significantly contribute to the prediction of the gender of TYT users. Overall, our findings indicate gender-related differences in tinnitus and tinnitus-related symptoms. Based on evidence that gender impacts the development of tinnitus, the gathered insights can be considered relevant and justify further investigations in this direction.
Collapse
Affiliation(s)
- Johannes Allgaier
- Institute of Clinical Epidemiology and Biometry, University of Wuerzburg, Wuerzburg, Germany.
| | - Winfried Schlee
- Department for Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Berthold Langguth
- Department for Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Thomas Probst
- Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, Krems an der Donau , Austria
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Wuerzburg, Wuerzburg, Germany
| |
Collapse
|
|
4
|
Cunha BCR, Rodrigues KRDH, Zaine I, da Silva EAN, Viel CC, Pimentel MDGC. Experience Sampling and Programmed Intervention Method and System for Planning, Authoring, and Deploying Mobile Health Interventions: Design and Case Reports. J Med Internet Res 2021; 23:e24278. [PMID: 34255652 PMCID: PMC8314159 DOI: 10.2196/24278] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 10/09/2020] [Accepted: 02/25/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Health professionals initiating mobile health (mHealth) interventions may choose to adapt apps designed for other activities (eg, peer-to-peer communication) or to employ purpose-built apps specialized in the required intervention, or to exploit apps based on methods such as the experience sampling method (ESM). An alternative approach for professionals would be to create their own apps. While ESM-based methods offer important guidance, current systems do not expose their design at a level that promotes replicating, specializing, or extending their contributions. Thus, a twofold solution is required: a method that directs specialists in planning intervention programs themselves, and a model that guides specialists in adopting existing solutions and advises software developers on building new ones. OBJECTIVE The main objectives of this study are to design the Experience Sampling and Programmed Intervention Method (ESPIM), formulated toward supporting specialists in deploying mHealth interventions, and the ESPIM model, which guides health specialists in adopting existing solutions and advises software developers on how to build new ones. Another goal is to conceive and implement a software platform allowing specialists to be users who actually plan, create, and deploy interventions (ESPIM system). METHODS We conducted the design and evaluation of the ESPIM method and model alongside a software system comprising integrated web and mobile apps. A participatory design approach with stakeholders included early software prototype, predesign interviews with 12 health specialists, iterative design sustained by the software as an instance of the method's conceptual model, support to 8 real case studies, and postdesign interviews. RESULTS The ESPIM comprises (1) a list of requirements for mHealth experience sampling and intervention-based methods and systems, (2) a 4-dimension planning framework, (3) a 7-step-based process, and (4) an ontology-based conceptual model. The ESPIM system encompasses web and mobile apps. Eight long-term case studies, involving professionals in psychology, gerontology, computer science, speech therapy, and occupational therapy, show that the method allowed specialists to be actual users who plan, create, and deploy interventions via the associated system. Specialists' target users were parents of children diagnosed with autism spectrum disorder, older persons, graduate and undergraduate students, children (age 8-12), and caregivers of older persons. The specialists reported being able to create and conduct their own studies without modifying their original design. A qualitative evaluation of the ontology-based conceptual model showed its compliance to the functional requirements elicited. CONCLUSIONS The ESPIM method succeeds in supporting specialists in planning, authoring, and deploying mobile-based intervention programs when employed via a software system designed and implemented according to its conceptual model. The ESPIM ontology-based conceptual model exposes the design of systems involving active or passive sampling interventions. Such exposure supports the evaluation, implementation, adaptation, or extension of new or existing systems.
Collapse
Affiliation(s)
| | | | - Isabela Zaine
- Institute of Mathematics and Computer Sciences, University of São Paulo, São Carlos, Brazil
| | | | | | | |
Collapse
|
|
5
|
Beierle F, Schobel J, Vogel C, Allgaier J, Mulansky L, Haug F, Haug J, Schlee W, Holfelder M, Stach M, Schickler M, Baumeister H, Cohrdes C, Deckert J, Deserno L, Edler JS, Eichner FA, Greger H, Hein G, Heuschmann P, John D, Kestler HA, Krefting D, Langguth B, Meybohm P, Probst T, Reichert M, Romanos M, Störk S, Terhorst Y, Weiß M, Pryss R. 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 2021; 18:ijerph18147395. [PMID: 34299846 PMCID: PMC8303497 DOI: 10.3390/ijerph18147395] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/06/2021] [Accepted: 07/06/2021] [Indexed: 01/09/2023]
Abstract
Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.
Collapse
Affiliation(s)
- Felix Beierle
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany; (C.V.); (J.A.); (L.M.); (J.H.); (F.A.E.); (P.H.); (R.P.)
- Correspondence:
| | - Johannes Schobel
- DigiHealth Institute, Neu-Ulm University of Applied Sciences, 89231 Neu-Ulm, Germany;
| | - Carsten Vogel
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany; (C.V.); (J.A.); (L.M.); (J.H.); (F.A.E.); (P.H.); (R.P.)
| | - Johannes Allgaier
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany; (C.V.); (J.A.); (L.M.); (J.H.); (F.A.E.); (P.H.); (R.P.)
| | - Lena Mulansky
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany; (C.V.); (J.A.); (L.M.); (J.H.); (F.A.E.); (P.H.); (R.P.)
| | - Fabian Haug
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany; (F.H.); (M.S.); (M.S.); (M.R.)
| | - Julian Haug
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany; (C.V.); (J.A.); (L.M.); (J.H.); (F.A.E.); (P.H.); (R.P.)
| | - Winfried Schlee
- Department of Psychiatry and Psychotherapy, University Regensburg, 93053 Regensburg, Germany; (W.S.); (B.L.)
| | | | - Michael Stach
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany; (F.H.); (M.S.); (M.S.); (M.R.)
| | - Marc Schickler
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany; (F.H.); (M.S.); (M.S.); (M.R.)
| | - Harald Baumeister
- Department of Clinical Psychology and Psychotherapy, Ulm University, 89081 Ulm, Germany; (H.B.); (Y.T.)
| | - Caroline Cohrdes
- Mental Health Research Unit, Department of Epidemiology and Health Monitoring, Robert Koch Institute, 12101 Berlin, Germany; (C.C.); (J.-S.E.)
| | - Jürgen Deckert
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital Würzburg, 97080 Würzburg, Germany; (J.D.); (G.H.); (M.W.)
| | - Lorenz Deserno
- Department of Child and Adolescent Psychiatry, University Hospital Würzburg, 97080 Würzburg, Germany; (L.D.); (M.R.)
| | - Johanna-Sophie Edler
- Mental Health Research Unit, Department of Epidemiology and Health Monitoring, Robert Koch Institute, 12101 Berlin, Germany; (C.C.); (J.-S.E.)
| | - Felizitas A. Eichner
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany; (C.V.); (J.A.); (L.M.); (J.H.); (F.A.E.); (P.H.); (R.P.)
| | - Helmut Greger
- Service Center Medical Informatics, University Hospital Würzburg, 97080 Würzburg, Germany;
| | - Grit Hein
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital Würzburg, 97080 Würzburg, Germany; (J.D.); (G.H.); (M.W.)
| | - Peter Heuschmann
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany; (C.V.); (J.A.); (L.M.); (J.H.); (F.A.E.); (P.H.); (R.P.)
| | - Dennis John
- Lutheran University of Applied Sciences Nürnberg, 90429 Nürnberg, Germany;
| | - Hans A. Kestler
- Institute of Medical Systems Biology, Ulm University, 89081 Ulm, Germany;
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, 37075 Göttingen, Germany;
| | - Berthold Langguth
- Department of Psychiatry and Psychotherapy, University Regensburg, 93053 Regensburg, Germany; (W.S.); (B.L.)
| | - Patrick Meybohm
- Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, University Hospital Würzburg, 97080 Würzburg, Germany;
| | - Thomas Probst
- Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, 3500 Krems, Austria;
| | - Manfred Reichert
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany; (F.H.); (M.S.); (M.S.); (M.R.)
| | - Marcel Romanos
- Department of Child and Adolescent Psychiatry, University Hospital Würzburg, 97080 Würzburg, Germany; (L.D.); (M.R.)
| | - Stefan Störk
- Comprehensive Heart Failure Center, University and University Hospital Würzburg, 97080 Würzburg, Germany;
- Department of Internal Medicine I, University Hospital Würzburg, 97080 Würzburg, Germany
| | - Yannik Terhorst
- Department of Clinical Psychology and Psychotherapy, Ulm University, 89081 Ulm, Germany; (H.B.); (Y.T.)
| | - Martin Weiß
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital Würzburg, 97080 Würzburg, Germany; (J.D.); (G.H.); (M.W.)
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany; (C.V.); (J.A.); (L.M.); (J.H.); (F.A.E.); (P.H.); (R.P.)
| |
Collapse
|
|
6
|
Winter M, Pryss R, Probst T, Reichert M. Towards the Applicability of Measuring the Electrodermal Activity in the Context of Process Model Comprehension: Feasibility Study. SENSORS 2020; 20:s20164561. [PMID: 32823891 PMCID: PMC7472239 DOI: 10.3390/s20164561] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 08/05/2020] [Accepted: 08/09/2020] [Indexed: 11/16/2022]
Abstract
Process model comprehension is essential in order to understand the five Ws (i.e., who, what, where, when, and why) pertaining to the processes of organizations. However, research in this context showed that a proper comprehension of process models often poses a challenge in practice. For this reason, a vast body of research exists studying the factors having an influence on process model comprehension. In order to point research towards a neuro-centric perspective in this context, the paper at hand evaluates the appropriateness of measuring the electrodermal activity (EDA) during the comprehension of process models. Therefore, a preliminary test run and a feasibility study were conducted relying on an EDA and physical activity sensor to record the EDA during process model comprehension. The insights obtained from the feasibility study demonstrated that process model comprehension leads to an increased activity in the EDA. Furthermore, EDA-related results indicated significantly that participants were confronted with a higher cognitive load during the comprehension of complex process models. In addition, the experiences and limitations we learned in measuring the EDA during the comprehension of process models are discussed in this paper. In conclusion, the feasibility study demonstrated that the measurement of the EDA could be an appropriate method to obtain new insights into process model comprehension.
Collapse
Affiliation(s)
- Michael Winter
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany;
- Correspondence:
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97080 Würzburg, Germany;
| | - Thomas Probst
- Department for Psychotherapy and Biopsychological Health, Danube University Krems, 3500 Krems, Austria;
| | - Manfred Reichert
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany;
| |
Collapse
|