Serious Games in Orofacial Myofunctional Disorder Therapy for Children: An Expert Survey.

Orofacial Myofunctional Disorder (OMD) is believed to affect approximately 30-50% of all children. The various causes of OMD often revolve around an incorrect resting position of the tongue and cause symptoms such as difficulty in speech and swallowing. While these symptoms can persist and lead to jaw deformities, such as overjet and open bite, manual therapy has been shown to be effective, especially in children. However, much of the therapy must be done as home exercises by children without the supervision of a therapist. Since these exercises are often not perceived as exciting by the children, half-hearted performance or complete omission of the exercises is common, rendering the therapy less effective or completely useless. To overcome this limitation, we implemented the LudusMyo platform, a serious game platform for OMD therapy. While children are the main target group, the acceptance (and usability) assessment by experts is the first milestone for the successful implementation of an mHealth application for therapy. For this reason, we conducted an expert survey among OMD therapists to gather their input on the LudusMyo prototype. The results of this expert survey are reported in this manuscript.

Insights into the Quality of Mobile Health Apps: Preliminary Results of an Analysis of MARS Scores.

Many mHelath applications have been developed, and the Mobile App Rating Scale (MARS) is a common tool for assessing them. This study aims to provide mean values for MARS scores found in recent literature. We systematically searched for literature in which MARS was used and analyzed them. MARS values for 5,920 applications from 215 studies were compiled. The mean MARS Quality Score is 3.51. The highest average score was achieved in the Functionality category (3.98), followed by Aesthetics (3.52), Information (3.33), Engagement (3.18) and Subjective (2.72). To the best of our knowledge, this is the first study to calculate average values for the five categories of the MARS and the MARS score based on such an extensive collection of data. The study shows that the overall quality of the applications is above the average value of 2.5.

Comparing nnU-Net and deepflash2 for Histopathological Tumor Segmentation.

Machine Learning (ML) has evolved beyond being a specialized technique exclusively used by computer scientists. Besides the general ease of use, automated pipelines allow for training sophisticated ML models with minimal knowledge of computer science. In recent years, Automated ML (AutoML) frameworks have become serious competitors for specialized ML models and have even been able to outperform the latter for specific tasks. Moreover, this success is not limited to simple tasks but also complex ones, like tumor segmentation in histopathological tissue, a very time-consuming task requiring years of expertise by medical professionals. Regarding medical image segmentation, the leading AutoML frameworks are nnU-Net and deepflash2. In this work, we begin to compare those two frameworks in the area of histopathological image segmentation. This use case proves especially challenging, as tumor and healthy tissue are often not clearly distinguishable by hard borders but rather through heterogeneous transitions. A dataset of 103 whole-slide images from 56 glioblastoma patients was used for the evaluation. Training and evaluation were run on a notebook with consumer hardware, determining the suitability of the frameworks for their application in clinical scenarios rather than high-performance scenarios in research labs.

Mobile Apps for COVID-19: A Systematic Review of Reviews.

BACKGROUND: One measure national governments took to react to the acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic was mobile applications (apps). This study aims to provide a high-level overview of published reviews of mobile apps used in association with coronavirus disease 19 (COVID-19), examine factors that contributed to the success of these apps, and provide data for further research into this topic. METHODS: We conducted a systematic review of reviews (also referred to as an umbrella review) and searched two databases, Medline and Embase, for peer-reviewed reviews of COVID-19 mobile apps that were written in English and published between January 1st 2020 and April 25th 2022. RESULTS: Out of the initial 17,611 studies, 24 studies were eligible for the analysis. Publication dates ranged from May 2020 to January 2022. In total, 54% (n = 13) of the studies were published in 2021, and 33% (n = 8) were published in 2020. Most reviews included in our review of reviews analyzed apps from the USA, the UK, and India. Apps from most of the African and Middle and South American countries were not analyzed in the reviews included in our study. Categorization resulted in four clusters (app overview, privacy and security, MARS rating, and miscellaneous). CONCLUSIONS: Our study provides a high-level overview of 24 reviews of apps for COVID-19, identifies factors that contributed to the success of these apps, and identifies a gap in the current literature. The study provides data for further analyses and further research.

Return to work after Post-COVID: describing affected employees' perceptions of personal resources, organizational offerings and care pathways.

Background: Most individuals recover from the acute phase of infection with the SARS-CoV-2 virus, however, some encounter prolonged effects, referred to as the Post-COVID syndrome. Evidence exists that such persistent symptoms can significantly impact patients' ability to return to work. This paper gives a comprehensive overview of different care pathways and resources, both personal and external, that aim to support Post-COVID patients during their work-life reintegration process. By describing the current situation of Post-COVID patients pertaining their transition back to the workplace, this paper provides valuable insights into their needs. Methods: A quantitative research design was applied using an online questionnaire as an instrument. Participants were recruited via Post-COVID outpatients, rehab facilities, general practitioners, support groups, and other healthcare facilities. Results: The analyses of 184 data sets of Post-COVID affected produced three key findings: (1) The evaluation of different types of personal resources that may lead to a successful return to work found that particularly the individuals' ability to cope with their situation (measured with the FERUS questionnaire), produced significant differences between participants that had returned to work and those that had not been able to return so far (F = 4.913, p = 0.001). (2) In terms of organizational provisions to facilitate successful reintegration into work-life, predominantly structural changes (i.e., modification of the workplace, working hours, and task) were rated as helpful or very helpful on average (meanworkplace 2.55/SD = 0.83, meanworking hours 2.44/SD = 0.80; meantasks 2.55/SD = 0.83), while the remaining offerings (i.e., job coaching or health courses) were rated as less helpful or not helpful at all. (3) No significant correlation was found between different care pathways and a successful return to work. Conclusion: The results of the in-depth descriptive analysis allows to suggests that the level of ability to cope with the Post-COVID syndrome and its associated complaints, as well as the structural adaptation of the workplace to meet the needs and demands of patients better, might be important determinants of a successful return. While the latter might be addressed by employers directly, it might be helpful to integrate training on coping behavior early in care pathways and treatment plans for Post-COVID patients to strengthen their coping abilities aiming to support their successful return to work at an early stage.

The statistical analysis plan for the unification of treatments and interventions for tinnitus patients randomized clinical trial (UNITI-RCT).

BACKGROUND: Tinnitus is a leading cause of disease burden globally. Several therapeutic strategies are recommended in guidelines for the reduction of tinnitus distress; however, little is known about the potentially increased effectiveness of a combination of treatments and personalized treatments for each tinnitus patient. METHODS: Within the Unification of Treatments and Interventions for Tinnitus Patients project, a multicenter, randomized clinical trial is conducted with the aim to compare the effectiveness of single treatments and combined treatments on tinnitus distress (UNITI-RCT). Five different tinnitus centers across Europe aim to treat chronic tinnitus patients with either cognitive behavioral therapy, sound therapy, structured counseling, or hearing aids alone, or with a combination of two of these treatments, resulting in four treatment arms with single treatment and six treatment arms with combinational treatment. This statistical analysis plan describes the statistical methods to be deployed in the UNITI-RCT. DISCUSSION: The UNITI-RCT trial will provide important evidence about whether a combination of treatments is superior to a single treatment alone in the management of chronic tinnitus patients. This pre-specified statistical analysis plan details the methodology for the analysis of the UNITI trial results. TRIAL REGISTRATION: ClinicalTrials.gov NCT04663828 . The trial is ongoing. Date of registration: December 11, 2020. All patients that finished their treatment before 19 December 2022 are included in the main RCT analysis.

Prediction meets time series with gaps: User clusters with specific usage behavior patterns.

With mHealth apps, data can be recorded in real life, which makes them useful, for example, as an accompanying tool in treatments. However, such datasets, especially those based on apps with usage on a voluntary basis, are often affected by fluctuating engagement and by high user dropout rates. This makes it difficult to exploit the data using machine learning techniques and raises the question of whether users have stopped using the app. In this extended paper, we present a method to identify phases with varying dropout rates in a dataset and predict for each. We also present an approach to predict what period of inactivity can be expected for a user in the current state. We use change point detection to identify the phases, show how to deal with uneven misaligned time series and predict the user's phase using time series classification. In addition, we examine how the evolution of adherence develops in individual clusters of individuals. We evaluated our method on the data of an mHealth app for tinnitus, and show that our approach is appropriate for the study of adherence in datasets with uneven, unaligned time series of different lengths and with missing values.

Supporting SURgery with GEriatric Co-Management and AI (SURGE-Ahead): A study protocol for the development of a digital geriatrician.

INTRODUCTION: Geriatric co-management is known to improve treatment of older adults in various clinical settings, however, widespread application of the concept is limited due to restricted resources. Digitalization may offer options to overcome these shortages by providing structured, relevant information and decision support tools for medical professionals. We present the SURGE-Ahead project (Supporting SURgery with GEriatric co-management and Artificial Intelligence) addressing this challenge. METHODS: A digital application with a dashboard-style user interface will be developed, displaying 1) evidence-based recommendations for geriatric co-management and 2) artificial intelligence-enhanced suggestions for continuity of care (COC) decisions. The development and implementation of the SURGE-Ahead application (SAA) will follow the Medical research council framework for complex medical interventions. In the development phase a minimum geriatric data set (MGDS) will be defined that combines parametrized information from the hospital information system with a concise assessment battery and sensor data. Two literature reviews will be conducted to create an evidence base for co-management and COC suggestions that will be used to display guideline-compliant recommendations. Principles of machine learning will be used for further data processing and COC proposals for the postoperative course. In an observational and AI-development study, data will be collected in three surgical departments of a University Hospital (trauma surgery, general and visceral surgery, urology) for AI-training, feasibility testing of the MGDS and identification of co-management needs. Usability will be tested in a workshop with potential users. During a subsequent project phase, the SAA will be tested and evaluated in clinical routine, allowing its further improvement through an iterative process. DISCUSSION: The outline offers insights into a novel and comprehensive project that combines geriatric co-management with digital support tools to improve inpatient surgical care and continuity of care of older adults. TRIAL REGISTRATION: German clinical trials registry (Deutsches Register für klinische Studien, DRKS00030684), registered on 21st November 2022.

Self-Assessment of Having COVID-19 With the Corona Check mHealth App.

At the beginning of the COVID-19 pandemic, with a lack of knowledge about the novel virus and a lack of widely available tests, getting first feedback about being infected was not easy. To support all citizens in this respect, we developed the mobile health app Corona Check. Based on a self-reported questionnaire about symptoms and contact history, users get first feedback about a possible corona infection and advice on what to do. We developed Corona Check based on our existing software framework and released the app on Google Play and the Apple App Store on April 4, 2020. Until October 30, 2021, we collected 51,323 assessments from 35,118 users with explicit agreement of the users that their anonymized data may be used for research purposes. For 70.6% of the assessments, the users additionally shared their coarse geolocation with us. To the best of our knowledge, we are the first to report about such a large-scale study in this context of COVID-19 mHealth systems. Although users from some countries reported more symptoms on average than users from other countries, we did not find any statistically significant differences between symptom distributions (regarding country, age, and sex). Overall, the Corona Check app provided easily accessible information on corona symptoms and showed the potential to help overburdened corona telephone hotlines, especially during the beginning of the pandemic. Corona Check thus was able to support fighting the spread of the novel coronavirus. mHealth apps further prove to be valuable tools for longitudinal health data collection.

Enhancing mHealth data collection applications with sensing capabilities.

Smart mobile devices such as smartphones or tablets have become an important factor for collecting data in complex health scenarios (e.g., psychological studies, medical trials), and are more and more replacing traditional pen-and-paper instruments. However, simply digitizing such instruments does not yet realize the full potential of mobile devices: most modern smartphones have a variety of different sensor technologies (e.g., microphone, GPS data, camera, ...) that can also provide valuable data and potentially valuable insights for the medical purpose or the researcher. In this context, a significant development effort is required to integrate sensing capabilities into (existing) data collection applications. Developers may have to deal with platform-specific peculiarities (e.g., Android vs. iOS) or proprietary sensor data formats, resulting in unnecessary development effort to support researchers with such digital solutions. Therefore, a cross-platform mobile data collection framework has been developed to extend existing data collection applications with sensor capabilities and address the aforementioned challenges in the process. This framework will enable researchers to collect additional information from participants and environment, increasing the amount of data collected and drawing new insights from existing data.

Apps for Covid-19 in Germany: assessment using the German Mobile App Rating Scale.

Objective: The purpose of this study is to provide an overview of apps to help control the spread of Covid-19 in Germany and rate them according to standardized instruments. Materials and methods: The Apple App Store and Google Play Store were systematically searched to identify apps dealing with Covid-19 in Germany. The German Mobile App Rating Scale (MARS-G) was used to independently assess app quality by 2 trained reviewers. Results: Overall, the quality of the 6 rated apps was good with a mean score of 4.15 (3.88-4.34). The best-rated apps were NINA app (4.34) and Corona Health App (4.29). The best-rated sections were functionality (4.40), aesthetic (4.25), and information (4.25). In contrast, the worst-rated section was engagement (3.63). Even though some of the apps were used by more people than others, there was no correlation between the MARS-G rating and app store rating. In addition, the MARS-G proved to be effective even with rating apps, which have different goals and methods to achieve them. Conclusions: To our knowledge, this is the first study that identified and evaluated German Covid-19 mobile health apps available in the German app stores. The review shows that despite the excellent quality in aspects like information and functionality, there is still a gap in the engagement section. To motivate more people to use the Covid-19 apps, new ideas are needed, besides more information and education about the functionality of the apps, to gain trust in app developers and raise the number of downloads.

Smartphone-based behaviour analysis for challenging behaviour in intellectual and developmental disabilities and autism spectrum disorder - Study protocol for the ProVIA trial.

Background: Challenging behaviour (CB) comprises various forms of aggressive and problematic behaviours frequently occurring in children with intellectual and developmental disability (IDD) or autism spectrum disorder (ASD). CB often arises from impaired communication or problem solving skills. It is often met with coercive measure due to a lack of alternative strategies on the part of the caregiver, while it also impacts on the caregivers due to the exposure to physical harm and high levels of stress. Within the ProVIA project we developed a smartphone-based tool for caregivers of children with IDD and/or ASD to prevent and modify CB. The ProVIA app systematically helps caregivers to identify specific causes of CB and provides individualised practical guidance to prevent CB and consecutive coercive measures, thus aiming to improve the health and well-being of the children and caregivers. Methods: In this uncontrolled open trial we will enrol N = 25 caregivers of children aged 3-11 years with a diagnosis of IDD and/or ASD. Participants will use the ProVIA-Kids app for 8 weeks. During the intervention phase, participants will conduct behaviour analyses after each instance of CB. The app will summarise the identified putative causes for the CB in each situation, and provide recommendations regarding the handling and prevention of CB. Furthermore, the app will aggregate data from all available behaviour analyses and identify the most relevant (i.e., most frequently reported) risk factors. Measurement points are at baseline (T0), after the intervention (T1) and 12 weeks after the end of the intervention (follow-up; T2). The primary outcome is the absolute change in parental stress (EBI total scale) between T0 and T1. Further aspects of interest are changes in CB severity and frequency, caregiver mood, satisfaction with the parenting role (EFB-K total scale) and experienced parenting competence (FKE total scale). Pre-post comparisons will be analysed with paired sample t-tests. Discussion: ProVIA is pioneering structured behaviour analysis via smartphone, assessing predefined causes of CB and providing feedback and recommendations. If this approach proves successful, the ProVIA-Kids app will be a valuable tool for caregivers to prevent CB and improve their own as well as the children's quality of life. Trial registration: The study is registered at https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_IDDRKS00029039 (registered May 31, 2022).

PeterPandemic: A Serious Game for Pandemic Management.

The COVID-19 situation shows that a deep understanding of how pandemics spread and how they can be managed is important in a multitude of domains. Various studies have shown that students benefit from game-based learning approaches. Therefore, we introduce the concept of a web-based serious game to teach students important aspects when dealing with pandemic situations.

Collecting Data from Senior Citizens Using Serious Games.

Training cognitive skills of people suffering from dementia by using serious games has been found to be a successful approach. But there is still no approach which takes advantage of performance measurements to evaluate the treatment of the patient. The literature describes several games which aim to improve cognitive skills, but none of them collects and analyses data while the patient is playing the latter. In this work we present a prototypical mobile application which applies concepts from serious games to collect performance data of patients and thereby evaluate the success of their treatment in the long run. We expect this approach to be a fast and cheap way of keeping track of the success of specific treatments.

The Impact of Coping Styles and Gender on Situational Coping: An Ecological Momentary Assessment Study With the mHealth Application TrackYourStress.

The aim of this study was to investigate the impact of different coping styles on situational coping in everyday life situations and gender differences. An ecological momentary assessment study with the mobile health app TrackYourStress was conducted with 113 participants. The coping styles Positive Thinking, Active Stress Coping, Social Support, Support in Faith, and Alcohol and Cigarette Consumption of the Stress and Coping Inventory were measured at baseline. Situational coping was assessed by the question "How well can you cope with your momentary stress level" over 4 weeks. Multilevel models were conducted to test the effects of the coping styles on situational coping. Additionally, gender differences were evaluated. Positive Thinking (p = 0.03) and Active Stress Coping (p = 0.04) had significant positive impacts on situational coping in the total sample. For women, Social Support had a significant positive effect on situational coping (p = 0.046). For men, Active Stress Coping had a significant positive effect on situational coping (p = 0.001). Women had higher scores on the SCI scale Social Support than men (p = 0.007). These results suggest that different coping styles could be more effective in daily life for women than for men. Taking this into account, interventions tailored to users' coping styles might lead to better coping outcomes than generalized interventions.

Predicting Ecological Momentary Assessments in an App for Tinnitus by Learning From Each User's Stream With a Contextual Multi-Armed Bandit.

Ecological Momentary Assessments (EMA) deliver insights on how patients perceive tinnitus at different times and how they are affected by it. Moving to the next level, an mHealth app can support users more directly by predicting a user's next EMA and recommending personalized services based on these predictions. In this study, we analyzed the data of 21 users who were exposed to an mHealth app with non-personalized recommendations, and we investigate ways of predicting the next vector of EMA answers. We studied the potential of entity-centric predictors that learn for each user separately and neighborhood-based predictors that learn for each user separately but take also similar users into account, and we compared them to a predictor that learns from all past EMA indiscriminately, without considering which user delivered which data, i.e., to a "global model." Since users were exposed to two versions of the non-personalized recommendations app, we employed a Contextual Multi-Armed Bandit (CMAB), which chooses the best predictor for each user at each time point, taking each user's group into account. Our analysis showed that the combination of predictors into a CMAB achieves good performance throughout, since the global model was chosen at early time points and for users with few data, while the entity-centric, i.e., user-specific, predictors were used whenever the user had delivered enough data-the CMAB chose itself when the data were "enough." This flexible setting delivered insights on how user behavior can be predicted for personalization, as well as insights on the specific mHealth data. Our main findings are that for EMA prediction the entity-centric predictors should be preferred over a user-insensitive global model and that the choice of EMA items should be further investigated because some items are answered more rarely than others. Albeit our CMAB-based prediction workflow is robust to differences in exposition and interaction intensity, experimentators that design studies with mHealth apps should be prepared to quantify and closely monitor differences in the intensity of user-app interaction, since users with many interactions may have a disproportionate influence on global models.

Smartphone-Guided Educational Counseling and Self-Help for Chronic Tinnitus.

Tinnitus is an auditory phantom perception in the ears or head in the absence of a corresponding external stimulus. There is currently no effective treatment available that reliably reduces tinnitus. Educational counseling is a treatment approach that aims to educate patients and inform them about possible coping strategies. For this feasibility study, we implemented educational material and self-help advice in a smartphone app. Participants used the educational smartphone app unsupervised during their daily routine over a period of four months. Comparing the tinnitus outcome measures before and after smartphone-guided treatment, we measured changes in tinnitus-related distress, but not in tinnitus loudness. Improvements on the Tinnitus Severity numeric rating scale reached an effect size of 0.408, while the improvements on the Tinnitus Handicap Inventory (THI) were much smaller with an effect size of 0.168. An analysis of user behavior showed that frequent and intensive use of the app is a crucial factor for treatment success: participants that used the app more often and interacted with the app intensively reported a stronger improvement in the tinnitus. Between study allocation and final assessment, 26 of 52 participants dropped out of the study. Reasons for the dropouts and lessons for future studies are discussed in this paper.

Quantifying Heterogeneity in Tumors: Proposing a New Method Utilizing Convolutional Neuronal Networks.

Heterogeneity is a hallmark of glioblastoma (GBM), the most common malignant brain tumor, and a key reason for the poor survival rate of patients. However, establishing a clinically applicable, cost-efficient tool to measure and quantify heterogeneity is challenging. We present a novel method in an ongoing study utilizing two convolutional neuronal networks (CNN). After digitizing tumor samples, the first CNN delimitates GBM from normal tissue, the second quantifies heterogeneity within the tumor. Since neuronal networks can detect and interpret underlying and hidden information within images and have the ability to incorporate different information sets (i.e. clinical data and mutational status), this approach might venture towards a next level of integrated diagnosis. It may be applicable to other tumors as well and lead to a more precision-based medicine.

Methods Used to Evaluate mHealth Applications for Cardiovascular Disease: A Quasi-Systematic Scoping Review.

In the face of demographic change and constantly increasing health care costs, health care system decision-makers face ever greater challenges. Mobile health applications (mHealth apps) have the potential to combat this trend. However, in order to integrate mHealth apps into care structures, an evaluation of such apps is needed. In this paper, we focus on the criteria and methods of evaluating mHealth apps for cardiovascular disease and the implications for developing a widely applicable evaluation framework for mHealth interventions. Our aim is to derive substantiated patterns and starting points for future research by conducting a quasi-systematic scoping review of relevant peer-reviewed literature published in English or German between 2000 and 2021. We screened 4066 articles and identified n = 38 studies that met our inclusion criteria. The results of the data derived from these studies show that usability, motivation, and user experience were evaluated primarily using standardized questionnaires. Usage protocols and clinical outcomes were assessed primarily via laboratory diagnostics and quality-of-life questionnaires, and cost effectiveness was tested primarily based on economic measures. Based on these findings, we propose important considerations and elements for the development of a common evaluation framework for professional mHealth apps, including study designs, data collection tools, and perspectives.