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Introduction.
Mobile health (mHealth) integrates mobile devices into healthcare, enabling remote monitoring, data collection, and personalized interventions. Machine Learning (ML), a subfield of Artificial Intelligence (AI), can use mHealth data to confirm or extend domain knowledge by finding associations within the data, i.e., with the goal of improving healthcare decisions. In this work, two data collection techniques were used for mHealth data fed into ML systems: Mobile Crowdsensing (MCS), which is a collaborative data gathering approach, and Ecological Momentary Assessments (EMA), which capture real-time individual experiences within the individual’s common environments using questionnaires and sensors. We collected EMA and MCS data on tinnitus and COVID-19. About 15 % of the world’s population suffers from tinnitus.
Materials & Methods.
This thesis investigates the challenges of ML systems when using MCS and EMA data. It asks: How can ML confirm or broad domain knowledge? Domain knowledge refers to expertise and understanding in a specific field, gained through experience and education. Are ML systems always superior to simple heuristics and if yes, how can one reach explainable AI (XAI) in the presence of mHealth data? An XAI method enables a human to understand why a model makes certain predictions. Finally, which guidelines can be beneficial for the use of ML within the mHealth domain? In tinnitus research, ML discerns gender, temperature, and season-related variations among patients. In the realm of COVID-19, we collaboratively designed a COVID-19 check app for public education, incorporating EMA data to offer informative feedback on COVID-19-related matters. This thesis uses seven EMA datasets with more than 250,000 assessments. Our analyses revealed a set of challenges: App user over-representation, time gaps, identity ambiguity, and operating system specific rounding errors, among others. Our systematic review of 450 medical studies assessed prior utilization of XAI methods.
Results.
ML models predict gender and tinnitus perception, validating gender-linked tinnitus disparities. Using season and temperature to predict tinnitus shows the association of these variables with tinnitus. Multiple assessments of one app user can constitute a group. Neglecting these groups in data sets leads to model overfitting. In select instances, heuristics outperform ML models, highlighting the need for domain expert consultation to unveil hidden groups or find simple heuristics.
Conclusion.
This thesis suggests guidelines for mHealth related data analyses and improves estimates for ML performance. Close communication with medical domain experts to identify latent user subsets and incremental benefits of ML is essential.
Objectives
Although the vast majority of COVID-19 cases are treated in primary care, patients' experiences during home isolation have been little studied. This study aimed to explore the experiences of patients with acute COVID-19 and to identify challenges after the initial adaptation of the German health system to the pandemic (after first infection wave from February to June 2020).
Methods
A mixed-method convergent design was used to gain a holistic insight into patients experience. The study consisted of a cross-sectional survey, open survey answers and semi-structured telephone interviews. Descriptive analysis was performed on quantitative survey answers. Between group differences were calculated to explore changes after the first infection wave. Qualitative thematic analysis was conducted on open survey answers and interviews. The results were then compared within a triangulation protocol.
Results
A total of 1100 participants from all German states were recruited by 145 general practitioners from August 2020 to April 2021, 42 additionally took part in qualitative interviews. Disease onset varied from February 2020 to April 2021. After the first infection wave, more participants were tested positive during the acute disease (88.8%; 95.2%; P < 0.001). Waiting times for tests (mean 4.5 days, SD 4.1; 2.7days, SD 2.6, P < 0.001) and test results (mean 2.4 days, SD 1.9; 1.8 days, SD 1.3, P < 0.001) decreased. Qualitative results indicated that the availability of repeated testing and antigen tests reduced insecurities, transmission and related guilt. Although personal consultations at general practices increased (6.8%; 15.5%, P < 0.001), telephone consultation remained the main mode of consultation (78.5%) and video remained insignificant (1.9%). The course of disease, the living situation and social surroundings during isolation, access to health care, personal resilience, spirituality and feelings of guilt and worries emerged as themes influencing the illness experience. Challenges were contact management and adequate provision of care during home isolation. A constant contact person within the health system helped against feelings of care deprivation, uncertainty and fear.
Conclusions
Our study highlights that home isolation of individuals with COVID-19 requires a holistic approach that considers all aspects of patient care and effective coordination between different care providers.
We assume that a specific health constraint, e.g., a certain aspect of bodily function or quality of life that is measured by a variable X, is absent (or irrelevant) in a healthy reference population (Ref0), and it is materially present and precisely measured in a diseased reference population (Ref1). We further assume that some amount of this constraint of interest is suspected to be present in a population under study (SP). In order to quantify this issue, we propose the introduction of an intuitive measure, the population comparison index (PCI), that relates the mean value of X in population SP to the mean values of X in populations Ref0 and Ref1. This measure is defined as PCI[X] = (mean[X|SP] − mean[X|Ref0])/(mean[X|Ref1] − mean[X|Ref0]) × 100[%], where mean[X|.] is the average value of X in the respective group of individuals. For interpretation, PCI[X] ≈ 0 indicates that the values of X in the population SP are similar to those in population Ref0, and hence, the impairment measured by X is not materially present in the individuals in population SP. On the other hand, PCI[X] ≈ 100 means that the individuals in SP exhibit values of X comparable to those occurring in Ref1, i.e., the constraint of interest is equally present in populations SP and Ref1. A value of 0 < PCI[X] < 100 indicates that a certain percentage of the constraint is present in SP, and it is more than in Ref0 but less than in Ref1. A value of PCI[X] > 100 means that population SP is even more affected by the constraint than population Ref1.
Now that mechanical thrombectomy has substantially improved outcomes after large-vessel occlusion stroke in up to every second patient, futile reperfusion wherein successful recanalization is not followed by a favorable outcome is moving into focus. Unfortunately, blood-based biomarkers, which identify critical stages of hemodynamically compromised yet reperfused tissue, are lacking. We recently reported that hypoxia induces the expression of endoglin, a TGF-β co-receptor, in human brain endothelium in vitro. Subsequent reoxygenation resulted in shedding. Our cell model suggests that soluble endoglin compromises the brain endothelial barrier function. To evaluate soluble endoglin as a potential biomarker of reperfusion (-injury) we analyzed its concentration in 148 blood samples of patients with acute stroke due to large-vessel occlusion. In line with our in vitro data, systemic soluble endoglin concentrations were significantly higher in patients with successful recanalization, whereas hypoxia alone did not induce local endoglin shedding, as analyzed by intra-arterial samples from hypoxic vasculature. In patients with reperfusion, higher concentrations of soluble endoglin additionally indicated larger infarct volumes at admission. In summary, we give translational evidence that the sequence of hypoxia and subsequent reoxygenation triggers the release of vasoactive soluble endoglin in large-vessel occlusion stroke and can serve as a biomarker for severe ischemia with ensuing recanalization/reperfusion.
(1) Background: The health-related quality of life (HRQOL) of colorectal cancer (CRC) survivors >10 years post-diagnosis is understudied. We aimed to compare the HRQOL of CRC survivors 14–24 years post-diagnosis to that of age- and sex-matched non-cancer controls, stratified by demographic and clinical factors. (2) Methods: We used data from 506 long-term CRC survivors and 1489 controls recruited from German population-based multi-regional studies. HRQOL was assessed with the European Organization for Research and Treatment of Cancer Quality of Life Core-30 (EORTC QLQ-C30) questionnaire. We estimated differences in the HRQOL of CRC survivors and controls with multiple regression, adjusted for age at survey, sex, and education, where appropriate. (3) Results: CRC survivors reported poorer social functioning but better health status/QOL than controls. CRC survivors, in general, had higher levels of symptom burden, and in particular diarrhea and constipation, regardless of demographic or clinical factors. In stratified analyses, HRQOL differed by age, sex, cancer type, and having a permanent stoma. (4) Conclusions: Although CRC survivors may have a comparable health status/QOL to controls 14–24 years after diagnosis, they still live with persistent bowel dysfunction that can negatively impact aspects of functioning. Healthcare providers should provide timely and adapted follow-up care to ameliorate potential long-term suffering.
The Internet of Things (IoT) enables a variety of smart applications, including smart home, smart manufacturing, and smart city. By enhancing Business Process Management Systems with IoT capabilities, the execution and monitoring of business processes can be significantly improved. Providing a holistic support for modeling, executing and monitoring IoT-driven processes, however, constitutes a challenge. Existing process modeling and process execution languages, such as BPMN 2.0, are unable to fully meet the IoT characteristics (e.g., asynchronicity and parallelism) of IoT-driven processes. In this article, we present BPMNE4IoT—A holistic framework for modeling, executing and monitoring IoT-driven processes. We introduce various artifacts and events based on the BPMN 2.0 metamodel that allow realizing the desired IoT awareness of business processes. The framework is evaluated along two real-world scenarios from two different domains. Moreover, we present a user study for comparing BPMNE4IoT and BPMN 2.0. In particular, this study has confirmed that the BPMNE4IoT framework facilitates the support of IoT-driven processes.
Background
Patients with coronary heart disease (CHD) with and without diabetes mellitus have an increased risk of recurrent events requiring multifactorial secondary prevention of cardiovascular risk factors. We compared prevalences of cardiovascular risk factors and its determinants including lifestyle, pharmacotherapy and diabetes mellitus among patients with chronic CHD examined within the fourth and fifth EUROASPIRE surveys (EA-IV, 2012–13; and EA-V, 2016–17) in Germany.
Methods
The EA initiative iteratively conducts European-wide multicenter surveys investigating the quality of secondary prevention in chronic CHD patients aged 18 to 79 years. The data collection in Germany was performed during a comprehensive baseline visit at study centers in Würzburg (EA-IV, EA-V), Halle (EA-V), and Tübingen (EA-V).
Results
384 EA-V participants (median age 69.0 years, 81.3% male) and 536 EA-IV participants (median age 68.7 years, 82.3% male) were examined. Comparing EA-IV and EA-V, no relevant differences in risk factor prevalence and lifestyle changes were observed with the exception of lower LDL cholesterol levels in EA-V. Prevalence of unrecognized diabetes was significantly lower in EA-V as compared to EA-IV (11.8% vs. 19.6%) while the proportion of prediabetes was similarly high in the remaining population (62.1% vs. 61.0%).
Conclusion
Between 2012 and 2017, a modest decrease in LDL cholesterol levels was observed, while no differences in blood pressure control and body weight were apparent in chronic CHD patients in Germany. Although the prevalence of unrecognized diabetes decreased in the later study period, the proportion of normoglycemic patients was low. As pharmacotherapy appeared fairly well implemented, stronger efforts towards lifestyle interventions, mental health programs and cardiac rehabilitation might help to improve risk factor profiles in chronic CHD patients.
Summary
Blood oxygen saturation is an important clinical parameter, especially in postoperative hospitalized patients, monitored in clinical practice by arterial blood gas (ABG) and/or pulse oximetry that both are not suitable for a long-term continuous monitoring of patients during the entire hospital stay, or beyond. Technological advances developed recently for consumer-grade fitness trackers could—at least in theory—help to fill in this gap, but benchmarks on the applicability and accuracy of these technologies in hospitalized patients are currently lacking. We therefore conducted at the postanaesthesia care unit under controlled settings a prospective clinical trial with 201 patients, comparing in total >1,000 oxygen blood saturation measurements by fitness trackers of three brands with the ABG gold standard and with pulse oximetry. Our results suggest that, despite of an overall still tolerable measuring accuracy, comparatively high dropout rates severely limit the possibilities of employing fitness trackers, particularly during the immediate postoperative period of hospitalized patients.
Highlights
•The accuracy of O2 measurements by fitness trackers is tolerable (RMSE ≲4%)
•Correlation with arterial blood gas measurements is fair to moderate (PCC = [0.46; 0.64])
•Dropout rates of fitness trackers during O2 monitoring are high (∼1/3 values missing)
•Fitness trackers cannot be recommended for O2 measuring during critical monitoring
Objectives
To evaluate whether a multimodal intervention in general practice reduces the proportion of second line antibiotic prescriptions and the overall proportion of antibiotic prescriptions for uncomplicated urinary tract infections in women.
Design
Parallel, cluster randomised, controlled trial.
Setting
General practices in five regions in Germany. Data were collected between 1 April 2021 and 31 March 2022.
Participants
General practitioners from 128 randomly assigned practices.
Interventions
Multimodal intervention consisting of guideline recommendations for general practitioners and patients, provision of regional data for antibiotic resistance, and quarterly feedback, which included individual first line and second line proportions of antibiotic prescribing, benchmarking with regional or supra-regional practices, and telephone counselling. Participants in the control group received no information on the intervention.
Main outcome measures
Primary outcome was the proportion of second line antibiotics prescribed by general practices, in relation to all antibiotics prescribed, for uncomplicated urinary tract infections after one year between the intervention and control group. General practices were randomly assigned in blocks (1:1), with a block size of four, into the intervention or control group using SAS version 9.4; randomisation was stratified by region. The secondary outcome was the prescription proportion of all antibiotics, relative within all cases (instances of UTI diagnosis), for the treatment of urinary tract infections after one year between the groups. Adverse events were assessed as exploratory outcomes.
Results
110 practices with full datasets identified 10 323 cases during five quarters (ie, 15 months). The mean proportion of second line antibiotics prescribed was 0.19 (standard deviation 0.20) in the intervention group and 0.35 (0.25) in the control group after 12 months. After adjustment for preintervention proportions, the mean difference was −0.13 (95% confidence interval −0.21 to −0.06, P<0.001). The overall proportion of all antibiotic prescriptions for urinary tract infections over 12 months was 0.74 (standard deviation 0.22) in the intervention and 0.80 (0.15) in the control group with a mean difference of −0.08 (95% confidence interval −0.15 to −0.02, P<0.029). No differences were noted in the number of complications (ie, pyelonephritis, admission to hospital, or fever) between the groups.
Conclusions
The multimodal intervention in general practice significantly reduced the proportion of second line antibiotics and all antibiotic prescriptions for uncomplicated urinary tract infections in women.
Trial registration
German Clinical Trials Register (DRKS), DRKS00020389
Background
Tobacco smoking is accountable for more than one in ten deaths in patients with cardiovascular disease. Thus, smoking cessation has a high priority in secondary prevention of coronary heart disease (CHD). The present study meant to assess smoking cessation patterns, identify parameters associated with smoking cessation and investigate personal reasons to change or maintain smoking habits in patients with established CHD.
Methods
Quality of CHD care was surveyed in 24 European countries in 2012/13 by the fourth European Survey of Cardiovascular Disease Prevention and Diabetes. Patients 18 to 79 years of age at the date of the CHD index event hospitalized due to first or recurrent diagnosis of coronary artery bypass graft, percutaneous coronary intervention, acute myocardial infarction or acute myocardial ischemia without infarction (troponin negative) were included. Smoking status and clinical parameters were iteratively obtained a) at the cardiovascular disease index event by medical record abstraction, b) during a face-to-face interview 6 to 36 months after the index event (i.e. baseline visit) and c) by telephone-based follow-up interview two years after the baseline visit. Parameters associated with smoking status at the time of follow-up interview were identified by logistic regression analysis. Personal reasons to change or maintain smoking habits were assessed in a qualitative interview and analyzed by qualitative content analysis.
Results
One hundred and four of 469 (22.2%) participants had been classified current smokers at the index event and were available for follow-up interview. After a median observation period of 3.5 years (quartiles 3.0, 4.1), 65 of 104 participants (62.5%) were classified quitters at the time of follow-up interview. There was a tendency of diabetes being more prevalent in quitters vs non-quitters (37.5% vs 20.5%, p=0.07). Higher education level (15.4% vs 33.3%, p=0.03) and depressed mood (17.2% vs 35.9%, p=0.03) were less frequent in quitters vs non-quitters. Quitters more frequently participated in cardiac rehabilitation programs (83.1% vs 48.7%, p<0.001). Cardiac rehabilitation appeared as factor associated with smoking cessation in multivariable logistic regression analysis (OR 5.19, 95%CI 1.87 to 14.46, p=0.002). Persistent smokers at telephone-based follow-up interview reported on addiction as wells as relaxation and pleasure as reasons to continue their habit. Those current and former smokers who relapsed at least once after a quitting attempt, stated future health hazards as their main reason to undertake quitting attempts. Prevalent factors leading to relapse were influence by their social network and stress. Successful quitters at follow-up interview referred to smoking-related harm done to their health having had been their major reason to quit.
Interpretation
Participating in a cardiac rehabilitation program was strongly associated with smoking cessation after a cardiovascular disease index event. Smoking cessation counseling and relapse prophylaxis may include alternatives for the pleasant aspects of smoking and incorporate effective strategies to resist relapse.