1
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Allgaier J, Pryss R. Practical approaches in evaluating validation and biases of machine learning applied to mobile health studies. COMMUNICATIONS MEDICINE 2024; 4:76. [PMID: 38649784 PMCID: PMC11035658 DOI: 10.1038/s43856-024-00468-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 02/27/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Machine learning (ML) models are evaluated in a test set to estimate model performance after deployment. The design of the test set is therefore of importance because if the data distribution after deployment differs too much, the model performance decreases. At the same time, the data often contains undetected groups. For example, multiple assessments from one user may constitute a group, which is usually the case in mHealth scenarios. METHODS In this work, we evaluate a model's performance using several cross-validation train-test-split approaches, in some cases deliberately ignoring the groups. By sorting the groups (in our case: Users) by time, we additionally simulate a concept drift scenario for better external validity. For this evaluation, we use 7 longitudinal mHealth datasets, all containing Ecological Momentary Assessments (EMA). Further, we compared the model performance with baseline heuristics, questioning the essential utility of a complex ML model. RESULTS Hidden groups in the dataset leads to overestimation of ML performance after deployment. For prediction, a user's last completed questionnaire is a reasonable heuristic for the next response, and potentially outperforms a complex ML model. Because we included 7 studies, low variance appears to be a more fundamental phenomenon of mHealth datasets. CONCLUSIONS The way mHealth-based data are generated by EMA leads to questions of user and assessment level and appropriate validation of ML models. Our analysis shows that further research needs to follow to obtain robust ML models. In addition, simple heuristics can be considered as an alternative for ML. Domain experts should be consulted to find potentially hidden groups in the data.
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Affiliation(s)
- Johannes Allgaier
- Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-University Würzburg, Josef-Schneider-Straße 2, Würzburg, Germany.
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, Julius-Maximilians-University Würzburg, Josef-Schneider-Straße 2, Würzburg, Germany
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2
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Stach M, Mulansky L, Reichert M, Pryss R, Beierle F. Call to Action: Investigating Interaction Delay in Smartphone Notifications. SENSORS (BASEL, SWITZERLAND) 2024; 24:2612. [PMID: 38676228 PMCID: PMC11053777 DOI: 10.3390/s24082612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/31/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024]
Abstract
Notifications are an essential part of the user experience on smart mobile devices. While some apps have to notify users immediately after an event occurs, others can schedule notifications strategically to notify them only on opportune moments. This tailoring allows apps to shorten the users' interaction delay. In this paper, we present the results of a comprehensive study that identified the factors that influence users' interaction delay to their smartphone notifications. We analyzed almost 10 million notifications collected in-the-wild from 922 users and computed their response times with regard to their demographics, their Big Five personality trait scores and the device's charging state. Depending on the app category, the following tendencies can be identified over the course of the day: Most notifications were logged in late morning and late afternoon. This number decreases in the evening, between 8 p.m. and 11 p.m., and at the same time exhibits the lowest average interaction delays at daytime. We also found that the user's sex and age is significantly associated with the response time. Based on the results of our study, we encourage developers to incorporate more information on the user and the executing device in their notification strategy to notify users more effectively.
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Affiliation(s)
- Michael Stach
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Josef-Schneider-Straße 2, 97080 Würzburg, Germany; (L.M.); (R.P.)
- Institute for Medical Data Sciences, University Hospital Würzburg, Josef-Schneider-Straße 2, 97080 Würzburg, Germany
| | - Lena Mulansky
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Josef-Schneider-Straße 2, 97080 Würzburg, Germany; (L.M.); (R.P.)
- Institute for Medical Data Sciences, University Hospital Würzburg, Josef-Schneider-Straße 2, 97080 Würzburg, Germany
| | - Manfred Reichert
- Institute of Databases and Information Systems, Ulm University, James-Franck-Ring, 89081 Ulm, Germany;
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Josef-Schneider-Straße 2, 97080 Würzburg, Germany; (L.M.); (R.P.)
- Institute for Medical Data Sciences, University Hospital Würzburg, Josef-Schneider-Straße 2, 97080 Würzburg, Germany
| | - Felix Beierle
- National Institute of Informatics, Tokyo 101-8430, Japan;
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Kraft R, Reichert M, Pryss R. Mobile Crowdsensing in Ecological Momentary Assessment mHealth Studies: A Systematic Review and Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:472. [PMID: 38257567 PMCID: PMC10820952 DOI: 10.3390/s24020472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/13/2023] [Accepted: 01/09/2024] [Indexed: 01/24/2024]
Abstract
As mobile devices have become a central part of our daily lives, they are also becoming increasingly important in research. In the medical context, for example, smartphones are used to collect ecologically valid and longitudinal data using Ecological Momentary Assessment (EMA), which is mostly implemented through questionnaires delivered via smart notifications. This type of data collection is intended to capture a patient's condition on a moment-to-moment and longer-term basis. To collect more objective and contextual data and to understand patients even better, researchers can not only use patients' input via EMA, but also use sensors as part of the Mobile Crowdsensing (MCS) approach. In this paper, we examine how researchers have embraced the topic of MCS in the context of EMA through a systematic literature review. This PRISMA-guided review is based on the databases PubMed, Web of Science, and EBSCOhost. It is shown through the results that both EMA research in general and the use of sensors in EMA research are steadily increasing. In addition, most of the studies reviewed used mobile apps to deliver EMA to participants, used a fixed-time prompting strategy, and used signal-contingent or interval-contingent self-assessment as sampling/assessment strategies. The most commonly used sensors in EMA studies are the accelerometer and GPS. In most studies, these sensors are used for simple data collection, but sensor data are also commonly used to verify study participant responses and, less commonly, to trigger EMA prompts. Security and privacy aspects are addressed in only a subset of mHealth EMA publications. Moreover, we found that EMA adherence was negatively correlated with the total number of prompts and was higher in studies using a microinteraction-based EMA (μEMA) approach as well as in studies utilizing sensors. Overall, we envision that the potential of the technological capabilities of smartphones and sensors could be better exploited in future, more automated approaches.
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Affiliation(s)
- Robin Kraft
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany
- Department of Clinical Psychology and Psychotherapy, Ulm University, 89081 Ulm, Germany
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97070 Würzburg, Germany
| | - Manfred Reichert
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97070 Würzburg, Germany
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4
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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%.
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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
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Marcrum SC, Engelke M, Goedhart H, Langguth B, Schlee W, Vesala M, Simoes JP. The Influence of Diet on Tinnitus Severity: Results of a Large-Scale, Online Survey. Nutrients 2022; 14:nu14245356. [PMID: 36558515 PMCID: PMC9784733 DOI: 10.3390/nu14245356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/30/2022] [Accepted: 12/03/2022] [Indexed: 12/24/2022] Open
Abstract
Optimization of dietary intake is an essential component in the multidimensional effort to prevent and manage chronic disease. Recently, demand has increased for nutrition-focused management strategies for chronic tinnitus. The primary aim of this study was to evaluate 10 dietary items for their association with changes in subjective tinnitus severity. A secondary aim was to develop an algorithm to better identify those individuals who might benefit from dietary modification strategies. A total of 5017 anonymous users of the TinnitusTalk forum completed an online survey regarding how various dietary items affected the severity of their tinnitus. Results suggest that, while intake of caffeine [positive effect (PE): 0.4%; negative effect (NE): 16.2%], alcohol (PE: 2.7%; NE: 13.3%, and salt (PE: 0.1%; NE: 9.9%) was most likely to influence tinnitus severity, it did so only for a small proportion of participants and reported effects were most commonly mild. Further, though a classification algorithm was able to leverage participant demographic, comorbidity, and tinnitus characteristics to identify those individuals most likely to benefit from dietary modification above chance levels, further efforts are required to achieve significant clinical utility. Taken together, these results do not support dietary modification as a primary treatment strategy for chronic tinnitus in the general population, though clinically meaningful effects might be observable in certain individuals.
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Affiliation(s)
- Steven C. Marcrum
- Department of Otolaryngology, University Hospital Regensburg, 93053 Regensburg, Germany
- Correspondence: ; Tel.: +49-941-944-9462
| | - Milena Engelke
- Department of Psychiatry and Psychotherapy, University of Regensburg, Bezirksklinikum Regensburg, 93053 Regensburg, Germany
| | | | - Berthold Langguth
- Department of Psychiatry and Psychotherapy, University of Regensburg, Bezirksklinikum Regensburg, 93053 Regensburg, Germany
| | - Winfried Schlee
- Department of Psychiatry and Psychotherapy, University of Regensburg, Bezirksklinikum Regensburg, 93053 Regensburg, Germany
- Institute for Information and Process Management, Eastern University of Applied Sciences, 9000 St. Gallen, Switzerland
| | | | - Jorge P. Simoes
- Department of Psychiatry and Psychotherapy, University of Regensburg, Bezirksklinikum Regensburg, 93053 Regensburg, Germany
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Allgaier J, Schlee W, Probst T, Pryss R. Prediction of Tinnitus Perception Based on Daily Life MHealth Data Using Country Origin and Season. J Clin Med 2022; 11:jcm11154270. [PMID: 35893370 PMCID: PMC9331976 DOI: 10.3390/jcm11154270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 07/01/2022] [Accepted: 07/08/2022] [Indexed: 02/01/2023] Open
Abstract
Tinnitus is an auditory phantom perception without external sound stimuli. This chronic perception can severely affect quality of life. Because tinnitus symptoms are highly heterogeneous, multimodal data analyses are increasingly used to gain new insights. MHealth data sources, with their particular focus on country- and season-specific differences, can provide a promising avenue for new insights. Therefore, we examined data from the TrackYourTinnitus (TYT) mHealth platform to create symptom profiles of TYT users. We used gradient boosting engines to classify momentary tinnitus and regress tinnitus loudness, using country of origin and season as features. At the daily assessment level, tinnitus loudness can be regressed with a mean absolute error rate of 7.9% points. In turn, momentary tinnitus can be classified with an F1 score of 93.79%. Both results indicate differences in the tinnitus of TYT users with respect to season and country of origin. The significance of the features was evaluated using statistical and explainable machine learning methods. It was further shown that tinnitus varies with temperature in certain countries. The results presented show that season and country of origin appear to be valuable features when combined with longitudinal mHealth data at the level of daily assessment.
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Affiliation(s)
- Johannes Allgaier
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97070 Würzburg, Germany;
- Correspondence:
| | - Winfried Schlee
- Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany;
| | - Thomas Probst
- Department for Psychotherapy and Biopsychosocial Health, University for Continuing Education Krems, 3500 Krems, Austria;
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97070 Würzburg, Germany;
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Berger SE, Baria AT. Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches. FRONTIERS IN PAIN RESEARCH 2022; 3:896276. [PMID: 35721658 PMCID: PMC9201034 DOI: 10.3389/fpain.2022.896276] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 05/12/2022] [Indexed: 11/13/2022] Open
Abstract
Pain research traverses many disciplines and methodologies. Yet, despite our understanding and field-wide acceptance of the multifactorial essence of pain as a sensory perception, emotional experience, and biopsychosocial condition, pain scientists and practitioners often remain siloed within their domain expertise and associated techniques. The context in which the field finds itself today-with increasing reliance on digital technologies, an on-going pandemic, and continued disparities in pain care-requires new collaborations and different approaches to measuring pain. Here, we review the state-of-the-art in human pain research, summarizing emerging practices and cutting-edge techniques across multiple methods and technologies. For each, we outline foreseeable technosocial considerations, reflecting on implications for standards of care, pain management, research, and societal impact. Through overviewing alternative data sources and varied ways of measuring pain and by reflecting on the concerns, limitations, and challenges facing the field, we hope to create critical dialogues, inspire more collaborations, and foster new ideas for future pain research methods.
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Affiliation(s)
- Sara E. Berger
- Responsible and Inclusive Technologies Research, Exploratory Sciences Division, IBM Thomas J. Watson Research Center, Yorktown Heights, NY, United States
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Skeptical Learning—An Algorithm and a Platform for Dealing with Mislabeling in Personal Context Recognition. ALGORITHMS 2022. [DOI: 10.3390/a15040109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Mobile Crowd Sensing (MCS) is a novel IoT paradigm where sensor data, as collected by the user’s mobile devices, are integrated with user-generated content, e.g., annotations, self-reports, or images. While providing many advantages, the human involvement also brings big challenges, where the most critical is possibly the poor quality of human-provided content, most often due to the inaccurate input from non-expert users. In this paper, we propose Skeptical Learning, an interactive machine learning algorithm where the machine checks the quality of the user feedback and tries to fix it when a problem arises. In this context, the user feedback consists of answers to machine generated questions, at times defined by the machine. The main idea is to integrate three core elements, which are (i) sensor data, (ii) user answers, and (iii) existing prior knowledge of the world, and to enable a second round of validation with the user any time these three types of information jointly generate an inconsistency. The proposed solution is evaluated in a project focusing on a university student life scenario. The main goal of the project is to recognize the locations and transportation modes of the students. The results highlight an unexpectedly high pervasiveness of user mistakes in the university students life project. The results also shows the advantages provided by Skeptical Learning in dealing with the mislabeling issues in an interactive way and improving the prediction performance.
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Morgiève M, Yasri D, Genty C, Dubois J, Leboyer M, Vaiva G, Berrouiguet S, Azé J, Courtet P. Acceptability and satisfaction with emma, a smartphone application dedicated to suicide ecological assessment and prevention. Front Psychiatry 2022; 13:952865. [PMID: 36032223 PMCID: PMC9403788 DOI: 10.3389/fpsyt.2022.952865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/19/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND As mHealth may contribute to suicide prevention, we developed emma, an application using Ecological Momentary Assessment and Intervention (EMA/EMI). OBJECTIVE This study evaluated emma usage rate and acceptability during the first month and satisfaction after 1 and 6 months of use. METHODS Ninety-nine patients at high risk of suicide used emma for 6 months. The acceptability and usage rate of the EMA and EMI modules were monitored during the first month. Satisfaction was assessed by questions in the monthly EMA (Likert scale from 0 to 10) and the Mobile App Rating Scale (MARS; score: 0-5) completed at month 6. After inclusion, three follow-up visits (months 1, 3, and 6) took place. RESULTS Seventy-five patients completed at least one of the proposed EMAs. Completion rates were lower for the daily than weekly EMAs (60 and 82%, respectively). The daily completion rates varied according to the question position in the questionnaire (lower for the last questions, LRT = 604.26, df = 1, p-value < 0.0001). Completion rates for the daily EMA were higher in patients with suicidal ideation and/or depression than in those without. The most used EMI was the emergency call module (n = 12). Many users said that they would recommend this application (mean satisfaction score of 6.92 ± 2.78) and the MARS score at month 6 was relatively high (overall rating: 3.3 ± 0.87). CONCLUSION Emma can target and involve patients at high risk of suicide. Given the promising users' satisfaction level, emma could rapidly evolve into a complementary tool for suicide prevention.
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Affiliation(s)
- Margot Morgiève
- Université Paris Cité, CNRS, Inserm, Cermes3, Paris, France.,Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France.,ICM - Paris Brain Institute, Hôpital de la Pitié-Salpêtriére, Paris, France.,GEPS - Groupement d'Étude et de Prévention du Suicide, Paris, France
| | - Daniel Yasri
- Université Paris Cité, CNRS, Inserm, Cermes3, Paris, France.,Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France
| | - Catherine Genty
- Université Paris Cité, CNRS, Inserm, Cermes3, Paris, France.,Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France
| | - Jonathan Dubois
- Université Paris Cité, CNRS, Inserm, Cermes3, Paris, France.,Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France
| | - Marion Leboyer
- Fondation Fondamental, Hôpital Albert-Chenevier, Créteil, France.,Faculté de Médicine, Institut National de la Santé et de la Recherche Médicale, Université Paris-Est Créteil, Créteil, France.,Assistance Publique Hôpitaux de Paris, Pôle de Psychiatrie et Addictologie, Hôpitaux Universitaires Henri Mondor, Créteil, France
| | - Guillaume Vaiva
- CHU Lille, Hôpital Fontan, Department of Psychiatry, Lille, France.,Centre National de Resources and Résilience pour les Psychotraumatisme, Université de Lille, Lille, France.,CNRS UMR-9193, SCALab - Sciences Cognitives et Sciences Affectives, Université de Lille, Lille, France
| | - Sofian Berrouiguet
- Laboratoire du Traitement de l'Information Médicale, INSERM UMR1101, CHRU Brest, Brest, France
| | - Jérôme Azé
- LIRMM, CNRS, Univ Montpellier, Montpellier, France
| | - Philippe Courtet
- Université Paris Cité, CNRS, Inserm, Cermes3, Paris, France.,Department of Emergency Psychiatry and Acute Care, Lapeyronie Hospital, CHU Montpellier, Montpellier, France.,Fondation Fondamental, Hôpital Albert-Chenevier, Créteil, France
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Kraft R, Reichert M, Pryss R. Towards the Interpretation of Sound Measurements from Smartphones Collected with Mobile Crowdsensing in the Healthcare Domain: An Experiment with Android Devices. SENSORS (BASEL, SWITZERLAND) 2021; 22:170. [PMID: 35009713 PMCID: PMC8749792 DOI: 10.3390/s22010170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 11/26/2021] [Accepted: 12/23/2021] [Indexed: 06/14/2023]
Abstract
The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users' individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.
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Affiliation(s)
- Robin Kraft
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany;
- Department of Clinical Psychology and Psychotherapy, Ulm University, 89081 Ulm, Germany
| | - Manfred Reichert
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany;
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97078 Würzburg, Germany;
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Prakash S, Unnikrishnan V, Pryss R, Kraft R, Schobel J, Hannemann R, Langguth B, Schlee W, Spiliopoulou M. Interactive System for Similarity-Based Inspection and Assessment of the Well-Being of mHealth Users. ENTROPY 2021; 23:e23121695. [PMID: 34946001 PMCID: PMC8700569 DOI: 10.3390/e23121695] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/13/2021] [Accepted: 12/13/2021] [Indexed: 12/21/2022]
Abstract
Recent digitization technologies empower mHealth users to conveniently record their Ecological Momentary Assessments (EMA) through web applications, smartphones, and wearable devices. These recordings can help clinicians understand how the users’ condition changes, but appropriate learning and visualization mechanisms are required for this purpose. We propose a web-based visual analytics tool, which processes clinical data as well as EMAs that were recorded through a mHealth application. The goals we pursue are (1) to predict the condition of the user in the near and the far future, while also identifying the clinical data that mostly contribute to EMA predictions, (2) to identify users with outlier EMA, and (3) to show to what extent the EMAs of a user are in line with or diverge from those users similar to him/her. We report our findings based on a pilot study on patient empowerment, involving tinnitus patients who recorded EMAs with the mHealth app TinnitusTips. To validate our method, we also derived synthetic data from the same pilot study. Based on this setting, results for different use cases are reported.
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Affiliation(s)
- Subash Prakash
- Knowledge Management and Discovery Lab, Otto-von-Guericke University, 39106 Magdeburg, Germany
- Correspondence: (S.P.); (V.U.); (M.S.)
| | - Vishnu Unnikrishnan
- Knowledge Management and Discovery Lab, Otto-von-Guericke University, 39106 Magdeburg, Germany
- Correspondence: (S.P.); (V.U.); (M.S.)
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, 97078 Würzburg, Germany;
| | - Robin Kraft
- Institute of Databases and Information Systems, Ulm University, 89081 Ulm, Germany;
| | - Johannes Schobel
- Institute DigiHealth, Neu-Ulm University of Applied Sciences, 89231 Neu-Ulm, Germany;
| | | | - Berthold Langguth
- Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany; (B.L.); (W.S.)
| | - Winfried Schlee
- Department of Psychiatry and Psychotherapy, University of Regensburg, 93053 Regensburg, Germany; (B.L.); (W.S.)
| | - Myra Spiliopoulou
- Knowledge Management and Discovery Lab, Otto-von-Guericke University, 39106 Magdeburg, Germany
- Correspondence: (S.P.); (V.U.); (M.S.)
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Sahandi Far M, Stolz M, Fischer JM, Eickhoff SB, Dukart J. JTrack: A Digital Biomarker Platform for Remote Monitoring of Daily-Life Behaviour in Health and Disease. Front Public Health 2021; 9:763621. [PMID: 34869177 PMCID: PMC8639579 DOI: 10.3389/fpubh.2021.763621] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 10/28/2021] [Indexed: 11/13/2022] Open
Abstract
Health-related data being collected by smartphones offer a promising complementary approach to in-clinic assessments. Despite recent contributions, the trade-off between privacy, optimization, stability and research-grade data quality is not well met by existing platforms. Here we introduce the JTrack platform as a secure, reliable and extendable open-source solution for remote monitoring in daily-life and digital-phenotyping. JTrack is an open-source (released under open-source Apache 2.0 licenses) platform for remote assessment of digital biomarkers (DB) in neurological, psychiatric and other indications. JTrack is developed and maintained to comply with security, privacy and the General Data Protection Regulation (GDPR) requirements. A wide range of anonymized measurements from motion-sensors, social and physical activities and geolocation information can be collected in either active or passive modes by using JTrack Android-based smartphone application. JTrack also provides an online study management dashboard to monitor data collection across studies. To facilitate scaling, reproducibility, data management and sharing we integrated DataLad as a data management infrastructure. Smartphone-based Digital Biomarker data may provide valuable insight into daily-life behaviour in health and disease. As illustrated using sample data, JTrack provides as an easy and reliable open-source solution for collection of such information.
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Affiliation(s)
- Mehran Sahandi Far
- Research Centre Jülich, Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Jülich, Germany.,Medical Faculty, Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Michael Stolz
- Research Centre Jülich, Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Jülich, Germany
| | - Jona M Fischer
- Research Centre Jülich, Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Jülich, Germany
| | - Simon B Eickhoff
- Research Centre Jülich, Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Jülich, Germany.,Medical Faculty, Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Juergen Dukart
- Research Centre Jülich, Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Jülich, Germany.,Medical Faculty, Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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13
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Kraft R, Idrees AR, Stenzel L, Nguyen T, Reichert M, Pryss R, Baumeister H. eSano - An eHealth Platform for Internet- and Mobile-based Interventions. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1997-2002. [PMID: 34891679 DOI: 10.1109/embc46164.2021.9629534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The prevention and treatment of mental disorders and chronic somatic diseases is a core challenge for health care systems of the 21th century. Mental- and behavioral health interventions provide the means for lowering the public health burden. However, structural deficits, reluctance to use existing services, perceived stigma and further personal and environmental reasons restrict the uptake of these evidence-based approaches. Internet- and mobile-based interventions (IMIs) might overcome some of the limitations of on-site interventions by providing an anonymous, scalable, time- and location-independent, yet evidence-based approach. In order to implement digital mental and behavioral health concepts across the life-span into practice, a technical solution to support the design, creation, and execution of IMIs is needed. However, there are various conceptual, technical as well as legal challenges to implementing a corresponding software solution in the healthcare domain. Therefore, the work at hand (1) identifies these challenges and derives a number of respective requirements, (2) introduces the eHealth platform eSano, a software project developed by an interdisciplinary team of computer scientists, psychologists, therapists, and other domain experts, with the aim to serve as a flexible basis for mental and behavioral research and health care, and (3) provides technical insights into the developed platform and its approach to address the aforementioned requirements.
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14
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Vogel C, Schobel J, Schlee W, Engelke M, Pryss R. UNITI Mobile-EMI-Apps for a Large-Scale European Study on Tinnitus. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2358-2362. [PMID: 34891756 DOI: 10.1109/embc46164.2021.9630482] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
More and more observational studies exploit the achievements of mobile technology to ease the overall implementation procedure. Many strategies like digital phenotyping, ecological momentary assessments or mobile crowdsensing are used in this context. Recently, an increasing number of intervention studies makes use of mobile technology as well. For the chronic disorder tinnitus, only few long-running intervention studies exist, which use mobile technology in a larger setting. Tinnitus is characterized by its heterogeneous patient's symptom profiles, which complicates the development of general treatments. In the UNITI project, researchers from different European countries try to unify existing treatments and interventions to cope with this heterogeneity. One study arm (UNITI Mobile) exploits mobile technology to investigate newly implemented interventions types, especially within the pan-European setting. The goals are to learn more about the validity and usefulness of mobile technology in this context. Furthermore, differences among the countries shall be investigated. Practically, two native intervention apps have been developed for UNITI and the mobile study arm, which pose features not presented so far in other apps of the authors. Along the implementation procedure, it is discussed whether these features might leverage similar types of studies in future. Since instruments like the mHealth evidence reporting and assessment checklist (mERA), developed by the WHO mHealth technical evidence review group, indicate that aspects shown for UNITI Mobile are important in the context of health interventions using mobile phones, our findings may be of a more general interest and are therefore being discussed in the work at hand.
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15
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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.
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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
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16
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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.
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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.)
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17
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Medical Device Regulation Efforts for mHealth Apps during the COVID-19 Pandemic—An Experience Report of Corona Check and Corona Health. J 2021. [DOI: 10.3390/j4020017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Within the healthcare environment, mobile health (mHealth) applications (apps) are becoming more and more important. The number of new mHealth apps has risen steadily in the last years. Especially the COVID-19 pandemic has led to an enormous amount of app releases. In most countries, mHealth applications have to be compliant with several regulatory aspects to be declared a “medical app”. However, the latest applicable medical device regulation (MDR) does not provide more details on the requirements for mHealth applications. When developing a medical app, it is essential that all contributors in an interdisciplinary team—especially software engineers—are aware of the specific regulatory requirements beforehand. The development process, however, should not be stalled due to integration of the MDR. Therefore, a developing framework that includes these aspects is required to facilitate a reliable and quick development process. The paper at hand introduces the creation of such a framework on the basis of the Corona Health and Corona Check apps. The relevant regulatory guidelines are listed and summarized as a guidance for medical app developments during the pandemic and beyond. In particular, the important stages and challenges faced that emerged during the entire development process are highlighted.
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18
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Schleicher M, Unnikrishnan V, Neff P, Simoes J, Probst T, Pryss R, Schlee W, Spiliopoulou M. Understanding adherence to the recording of ecological momentary assessments in the example of tinnitus monitoring. Sci Rep 2020; 10:22459. [PMID: 33384428 PMCID: PMC7775469 DOI: 10.1038/s41598-020-79527-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 12/09/2020] [Indexed: 01/07/2023] Open
Abstract
The recording of Ecological Momentary Assessments (EMA) can assist people with chronic diseases in monitoring their health state. However, many users quickly lose interest in their respective EMA platforms. Therefore, we studied the adherence of users of the mHealth app TrackYourTinnitus (TYT). The app is used to record EMA in people with tinnitus. 1292 users, who interacted with the app between April 2014 and February 2017, were analyzed in this work. We defined “adherence” based on the dimensions of interaction duration and interaction continuity. We propose methods that are able to predict the (dis)continuation of interaction with the app and identify user segments that are characterized by similar patterns of adherence. For the prediction task we used the data of the questionnaires MiniTF and TSCHQ, which are filled in when the users enter TYT for the first time. Additionally, time series of the eight items of the daily EMA questionnaire were used. The distribution of user activity pertaining to the adherence dimension of interaction duration revealed a very skewed distribution, with most users giving up after only 1 day of interaction. However, many users returned after interrupting for some time. Some of the MiniTF items indicated that the worries of users might have lead to an increased likelihood of returning back to the app. The MiniTF score itself was not predictive, though. The answers to the TSCHQ items, in turn, pointed to user strata (more than 65 years of age at registration), which tended towards higher interaction continuity. As the registration questionnaires predicted adherence only to a limited extent, it is promising to study the activities of the users in the very first days of interaction more deeply. It turned out in this context that the effects of interaction stimulants like personalized and non-personalized tips, pointers to information sources, and mechanisms used in online treatments for tinnitus (e.g., in iCBT) should be further investigated.
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Affiliation(s)
- Miro Schleicher
- Institute of Technical and Business Information Systems, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany.
| | - Vishnu Unnikrishnan
- Institute of Technical and Business Information Systems, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Patrick Neff
- Department of Psychiatry and Psychotherapy of Regensburg University, Regensburg, Germany.,University Research Priority Program 'Dynamics of Healthy Aging', University of Zurich, Zurich, Switzerland
| | - Jorge Simoes
- Department of Psychiatry and Psychotherapy of Regensburg University, Regensburg, Germany
| | - Thomas Probst
- Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, Krems, Austria
| | - Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Winfried Schlee
- Department of Psychiatry and Psychotherapy of Regensburg University, Regensburg, Germany
| | - Myra Spiliopoulou
- Institute of Technical and Business Information Systems, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
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19
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The Effect of Non-Personalised Tips on the Continued Use of Self-Monitoring mHealth Applications. Brain Sci 2020; 10:brainsci10120924. [PMID: 33266315 PMCID: PMC7760069 DOI: 10.3390/brainsci10120924] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 11/20/2020] [Accepted: 11/22/2020] [Indexed: 11/17/2022] Open
Abstract
Chronic tinnitus, the perception of a phantom sound in the absence of corresponding stimulus, is a condition known to affect patients’ quality of life. Recent advances in mHealth have enabled patients to maintain a ‘disease journal’ of ecologically-valid momentary assessments, improving patients’ own awareness of their disease while also providing clinicians valuable data for research. In this study, we investigate the effect of non-personalised tips on patients’ perception of tinnitus, and on their continued use of the application. The data collected from the study involved three groups of patients that used the app for 16 weeks. Groups A & Y were exposed to feedback from the start of the study, while group B only received tips for the second half of the study. Groups A and Y were run by different supervisors and also differed in the number of hospital visits during the study. Users of Group A and B underwent assessment at baseline, mid-study, post-study and follow-up, while users of group Y were only assessed at baseline and post-study. It is seen that the users in group B use the app for longer, and also more often during the day. The answers of the users to the Ecological Momentary Assessments are seen to form clusters where the degree to which the tinnitus distress depends on tinnitus loudness varies. Additionally, cluster-level models were able to predict new unseen data with better accuracy than a single global model. This strengthens the argument that the discovered clusters really do reflect underlying patterns in disease expression.
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20
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Pryss R, Schlee W, Hoppenstedt B, Reichert M, Spiliopoulou M, Langguth B, Breitmayer M, Probst T. 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. J Med Internet Res 2020; 22:e15547. [PMID: 32602842 PMCID: PMC7367527 DOI: 10.2196/15547] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 12/23/2019] [Accepted: 02/29/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1% to 42.7% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient's quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)-Android and iOS-to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider. OBJECTIVE In this study, we explored whether the mobile OS-Android and iOS-used during user assessments can be predicted by the dynamic daily-life TYT data. METHODS TYT mainly applies the paradigms ecological momentary assessment (EMA) and mobile crowdsensing to collect dynamic EMA (EMA-D) daily-life data. The dynamic daily-life TYT data that were analyzed included eight questions as part of the EMA-D questionnaire. In this study, 518 TYT users were analyzed, who each completed at least 11 EMA-D questionnaires. Out of these, 221 were iOS users and 297 were Android users. The iOS users completed, in total, 14,708 EMA-D questionnaires; the number of EMA-D questionnaires completed by the Android users was randomly reduced to the same number to properly address the research question of the study. Machine learning methods-a feedforward neural network, a decision tree, a random forest classifier, and a support vector machine-were applied to address the research question. RESULTS Machine learning was able to predict the mobile OS used with an accuracy up to 78.94% based on the provided EMA-D questionnaires on the assessment level. In this context, the daily measurements regarding how users concentrate on the actual activity were particularly suitable for the prediction of the mobile OS used. CONCLUSIONS In the work at hand, two particular aspects have been revealed. First, machine learning can contribute to EMA-D data in the medical context. Second, based on the EMA-D data of TYT, we found that the accuracy in predicting the mobile OS used has several implications. Particularly, in clinical studies using mobile devices, the OS should be assessed as a covariate, as it might be a confounder.
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Affiliation(s)
- Rüdiger Pryss
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Winfried Schlee
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | | | - Manfred Reichert
- Institute of Databases and Information Systems, Ulm University, Ulm, Germany
| | - Myra Spiliopoulou
- Faculty of Computer Science, Otto von Guericke University of Magdeburg, Magdeburg, Germany
| | - Berthold Langguth
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Marius Breitmayer
- Institute of Databases and Information Systems, Ulm University, Ulm, Germany
| | - Thomas Probst
- Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, Krems, Austria
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21
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Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform. SENSORS 2020; 20:s20123456. [PMID: 32570953 PMCID: PMC7349608 DOI: 10.3390/s20123456] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 06/07/2020] [Accepted: 06/16/2020] [Indexed: 11/17/2022]
Abstract
Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.
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22
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Predicting the Health Condition of mHealth App Users with Large Differences in the Number of Recorded Observations - Where to Learn from? DISCOVERY SCIENCE 2020. [PMCID: PMC7556387 DOI: 10.1007/978-3-030-61527-7_43] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Abstract
Some mHealth apps record user activity continuously and unobtrusively, while other apps rely by nature on user engagement and self-discipline: users are asked to enter data that cannot be assessed otherwise, e.g., on how they feel and what non-measurable symptoms they have. Over time, this leads to substantial differences in the length of the time series of recordings for the different users. In this study, we propose two algorithms for wellbeing-prediction from such time series, and we compare their performance on the users of a pilot study on diabetic patients - with time series length varying between 8 and 87 recordings.
Our first approach learns a model from the few users, on which many recordings are available, and applies this model to predict the 2nd, 3rd, and so forth recording of users newly joining the mHealth platform. Our second approach rather exploits the similarity among the first few recordings of newly arriving users. Our results for the first approach indicate that the target variable for users who use the app for long are not predictive for users who use the app only for a short time. Our results for the second approach indicate that few initial recordings suffice to inform the predictive model and improve performance considerably.
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