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- Interdisziplinäre Zentrum für Klinische Forschung (IZKF) (1)
- Medizinische Klinik und Poliklinik 1, Abteilung Kardiologie (1)
- Medizinische Klinik und Poliklinik 1, Abteilung Nephrologie (1)
- Servicezentrum Medizin-Informatik (1)
- Servicezentrum Medizin-Informatik (Universitätsklinikum) (1)
- Universitätsklinikum Würzburg (UKW) (1)
Hintergrund: Circa ein Drittel der Patientinnen und Patienten mit fortgeschrittenen Krebserkrankungen ist von psychischen Komorbiditäten betroffen und circa die Hälfte weist eine psychische Belastung im klinisch signifikanten Bereich auf. Zur psychotherapeutischen Behandlung dieser Patientengruppe stehen unterschiedliche psychotherapeutische Interventionen zur Verfügung. Die CALM-Therapie, eine manualisierte Kurzintervention im Einzelsetting, ist eine dieser Interventionen. Hier bilden vier Module, welche auf den wichtigsten Anliegen und Belastungsfaktoren von Patientinnen und Patienten mit fortgeschrittenen Krebserkrankungen basieren, den inhaltlichen Rahmen.
Ziel: Die Treatment Integrity beschreibt das Maß, inwieweit eine psychotherapeutische Intervention wie vorgesehen umgesetzt wurde. Für eine fundierte Interpretation psychotherapeutischer Interventionseffekte sind Kenntnisse über die Treatment Integrity entscheidend. Die vorliegende Arbeit untersuchte Teilaspekte der Treatment Integrity durchgeführter CALM-Therapien im Vergleich zu durchgeführten konventionellen psychoonkologischen Therapien, um einen Beitrag zu einer fundierten Interpretation von Interventionseffekten der CALM-Therapie zu leisten.
Methoden: Transkriptionen von zwei CALM-Therapien und zwei Therapien einer konventionellen psychoonkologischen Intervention wurden anhand einer qualitativen Inhaltsanalyse nach P. Mayring untersucht. Im Zentrum stand hierbei ein selbst entwickeltes Kategoriensystem zur Analyse des gesamten Textmaterials. Zusätzlich wurden Auffälligkeiten bezüglich Ansprachen von Themenbereichen der CALM-Module unsystematisch beobachtet.
Ergebnisse: Die Inhalte der untersuchten CALM-Therapien bezogen sich durchschnittlich zu 99,54% und die der konventionellen psychoonkologischen Therapien durchschnittlich zu 98,71% auf die Themenbereiche der CALM-Module. Die ermittelten Werte für einzelne Therapiesitzungen lagen für CALM-Sitzungen zwischen 98,12% und 100% und für Sitzungen der konventionellen psychoonkologischen Therapie zwischen 96,20% und 100%. Unsystematisch beobachtete Auffälligkeiten zeigten, dass die Themenbereiche der CALM-Module zum Teil sehr spezifisch durch die CALM-Therapeutinnen und -Therapeuten angesprochen und vernetzt wurden.
Schlussfolgerung: Unter Berücksichtigung von methodischen Grenzen zeigte sich bezüglich des Anteils von Themenbereichen der CALM-Module innerhalb der beiden untersuchten Therapiegruppen kein maßgeblicher Unterschied. Zusätzlich liefert die vorliegende Arbeit Hinweise für einen spezifischen therapeutischen Umgang mit den Themenbereichen der CALM-Module innerhalb der untersuchten CALM-Therapien. Um ermittelte Interventionseffekte der CALM-Therapie fundiert interpretieren zu können, sollten zukünftige Untersuchungen unterschiedliche Umgangsweisen von Therapeutinnen und Therapeuten der beiden Therapiegruppen mit den Themenbereichen der CALM-Module genauer in den Blick nehmen.
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.
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.
Prediction of tinnitus perception based on daily life mHealth data using country origin and season
(2022)
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.
Hintergrund. Die gesetzlich vorgeschriebene Gefährdungsbeurteilung psychischer Belastung gewinnt zunehmend an Bedeutung. Ein Standardinstrument, das in diesem Rahmen seit einigen Jahren zur Anwendung kommt, ist der Kurzfragebogens zur Arbeitsanalyse (KFZA), von Prümper et al. (1995). Dieser Fragebogen wurde ursprünglich für die Beurteilung von Bildschirmarbeitsplätzen konzipiert und für diese Berufsgruppe validiert. Ziel der vorliegenden Arbeit war es, die faktorielle Validität des KFZA bei einem Einsatz im Gesundheitswesen mittels einer explorativen Faktorenanalyse zu überprüfen. Da eine Fragebogenversion zum Einsatz kam, die zusätzlich spezifische Ergänzungsfragen für das Gesundheitswesen enthielt, sollte in einem zweiten Schritt auch dieser erweiterte KFZA einer Faktorenanalyse unterzogen werden.
Methodik. Insgesamt 1731 Datensätze waren über einen Zeitraum von zehn Jahren in verschiedenen norddeutschen Krankenhäusern als Routinedaten erhoben worden. Nach listenweisem Fallausschluss in Folge des Einsatzes unterschiedlicher Fragebogenvarianten standen für den KFZA 1163 Datensätze und davon 1095 Datensätze für den erweiterten KFZA zur faktorenanalytischen Auswertung zur Verfügung. Die 26 Items des KFZA bzw. die 37 Items der erweiterten Version wurden einer explorativen Faktorenanalyse nach der Hauptkomponentenmethode unterzogen. Die Zahl der Faktoren wurde sowohl mittels Kaiser- als auch Scree-Kriterium bestimmt. Für die Interpretation der Faktoren wurden diese sowohl orthogonal nach der Varimax-Methode als auch direct-oblimin rotiert. Zur Abschätzung der Reliabilität wurde die interne Konsistenz anhand des Cronbach-α-Koeffizienten berechnet.
Ergebnisse. Für die 26 Items des KFZA führte das Kaiser-Kriterium zu einer 7-Faktoren-Lösung mit einer Gesamtvarianzaufklärung von 62,0%, der Scree-Plot dagegen deutete auf vier Faktoren hin. Orthogonale und oblique Rotation brachten vergleichbare Ergebnisse. Die inhaltliche Interpretation unterstützte die Anzahl von sieben Faktoren, die wie folgt benannt wurden: „Soziale Beziehungen“, „Handlungsspielraum“, „Partizipations- und Entwicklungs-möglichkeiten“, „Quantitative Arbeitsbelastungen“, „Umgebungsbelastungen“, „Vielseitigkeit“ und „Qualitative Arbeitsbelastungen“. Für diese Skalen, die jeweils 2 bis 6 Items umfassten, konnten Cronbach-α-Koeffizienten zwischen 0,63 und 0,80 ermittelt werden. Die Faktorenanalyse des erweiterten KFZA mit insgesamt 37 Items führte nach Bestimmung des Kaiser-Kriteriums und Betrachtung der inhaltlichen Plausibilität zu einer 9-Faktoren-Lösung mit einer Gesamtvarianzaufklärung von 59,5%. Die beiden zusätzlichen Faktoren wurden mit „Fehlbeanspruchungsfolgen“ und „Emotionale Belastungen“ benannt. Die Werte des Cronbach-α-Koeffizienten lagen für diese Skalen zwischen 0,63 und 0,87.
Diskussion. Statt der von den Autoren des KFZA beschriebenen elf Faktoren wurden bei einem Einsatz im Gesundheitswesen sieben Faktoren ermittelt. Auch wenn sich die Anzahl der Faktoren reduzierte, ließ sich die Struktur inhaltlich relativ gut replizieren. Besonders die Items des KFZA-Faktors „Ganzheitlichkeit“ erwiesen sich jedoch für den Einsatz im Gesundheitswesen als nicht passgenau. Die Ergänzungsitems des erweiterten KFZA bildeten zwei zusätzliche Faktoren bzw. ließen sich den zuvor ermittelten Faktoren sinnvoll zuordnen.
Die vorliegende Arbeit liefert somit einen Beitrag zur Einschätzung der Validität dieses in der Praxis häufig eingesetzten Instruments. Die psychometrische Prüfung kann jedoch noch nicht als vollständig erachtet werden und sollte in nachfolgenden Studien fortgeführt werden.
Toxic trace elements in maternal and cord blood and social determinants in a Bolivian mining city
(2016)
This study assessed lead, arsenic, and antimony in maternal and cord blood, and associations between maternal concentrations and social determinants in the Bolivian mining city of Oruro using the baseline assessment of the ToxBol/Mine-Niño birth cohort. We recruited 467 pregnant women, collecting venous blood and sociodemographic information as well as placental cord blood at birth. Metallic/semimetallic trace elements were measured using inductively coupled plasma mass spectrometry. Lead medians in maternal and cord blood were significantly correlated (Spearman coefficient = 0.59; p < 0.001; 19.35 and 13.50 μg/L, respectively). Arsenic concentrations were above detection limit (3.30 μg/L) in 17.9 % of maternal and 34.6 % of cord blood samples. They were not associated (Fischer’s p = 0.72). Antimony medians in maternal and cord blood were weakly correlated (Spearman coefficient = 0.15; p < 0.03; 9.00 and 8.62 μg/L, respectively). Higher concentrations of toxic elements in maternal blood were associated with maternal smoking, low educational level, and partner involved in mining.
Toxic trace elements in maternal and cord blood and social determinants in a Bolivian mining city
(2016)
This study assessed lead, arsenic, and antimony in maternal and cord blood, and associations between maternal concentrations and social determinants in the Bolivian mining city of Oruro using the baseline assessment of the ToxBol/Mine-Nino birth cohort. We recruited 467 pregnant women, collecting venous blood and sociodemographic information as well as placental cord blood at birth. Metallic/semimetallic trace elements were measured using inductively coupled plasma mass spectrometry. Lead medians in maternal and cord blood were significantly correlated (Spearman coefficient=0.59; p<0.001; 19.35 and 13.50 μg/L, respectively). Arsenic concentrations were above detection limit (3.30 μg/L) in 17.9% of maternal and 34.6% of cord blood samples. They were not associated (Fischer's p=0.72). Antimony medians in maternal and cord blood were weakly correlated (Spearman coefficient=0.15; p<0.03; 9.00 and 8.62 μg/L, respectively). Higher concentrations of toxic elements in maternal blood were associated with maternal smoking, low educational level, and partner involved in mining.
Objectives
Liver biopsies are the current gold standard in non-alcoholic steatohepatitis (NASH) diagnosis. Their invasive nature, however, still carries an increased risk for patients' health. The development of non-invasive diagnostic tools to differentiate between bland steatosis (NAFL) and NASH remains crucial. The aim of this study is the evaluation of investigated circulating microRNAs in combination with new targets in order to optimize the discrimination of NASH patients by non-invasive serum biomarkers.
Methods
Serum profiles of four microRNAs were evaluated in two cohorts consisting of 137 NAFLD patients and 61 healthy controls. In a binary logistic regression model microRNAs of relevance were detected. Correlation of microRNA appearance with known biomarkers like ALT and CK18-Asp396 was evaluated. A simplified scoring model was developed, combining the levels of microRNA in circulation and CK18-Asp396 fragments. Receiver operating characteristics were used to evaluate the potential of discriminating NASH.
Results
The new finding of our study is the different profile of circulating miR-21 in NASH patients (p<0.0001). Also, it validates recently published results of miR-122 and miR-192 to be differentially regulated in NAFL and NASH. Combined microRNA expression profiles with CK18-Asp396 fragment level scoring model had a higher potential of NASH prediction compared to other risk biomarkers (AUROC = 0.83, 95% CI = 0.754-0.908; p<0.001). Evaluation of score model for NAFL (Score = 0) and NASH (Score = 4) had shown high rates of sensitivity (91%) and specificity (83%).
Conclusions
Our study defines candidates for a combined model of miRNAs and CK18-Asp396 levels relevant as a promising expansion for diagnosis and in turn treatment of NASH.
During the COVID-19 pandemic, the novel coronavirus had an impact not only on public health but also on the mental health of the population. Public sentiment on mental health and depression is often captured only in small, survey-based studies, while work based on Twitter data often only looks at the period during the pandemic and does not make comparisons with the pre-pandemic situation. We collected tweets that included the hashtags #MentalHealth and #Depression from before and during the pandemic (8.5 months each). We used LDA (Latent Dirichlet Allocation) for topic modeling and LIWC, VADER, and NRC for sentiment analysis. We used three machine-learning classifiers to seek evidence regarding an automatically detectable change in tweets before vs. during the pandemic: (1) based on TF-IDF values, (2) based on the values from the sentiment libraries, (3) based on tweet content (deep-learning BERT classifier). Topic modeling revealed that Twitter users who explicitly used the hashtags #Depression and especially #MentalHealth did so to raise awareness. We observed an overall positive sentiment, and in tough times such as during the COVID-19 pandemic, tweets with #MentalHealth were often associated with gratitude. Among the three classification approaches, the BERT classifier showed the best performance, with an accuracy of 81% for #MentalHealth and 79% for #Depression. Although the data may have come from users familiar with mental health, these findings can help gauge public sentiment on the topic. The combination of (1) sentiment analysis, (2) topic modeling, and (3) tweet classification with machine learning proved useful in gaining comprehensive insight into public sentiment and could be applied to other data sources and topics.
Background
Performance anxiety is the most frequently reported anxiety disorder among professional musicians. Typical symptoms are - on a physical level - the consequences of an increase in sympathetic tone with cardiac stress, such as acceleration of heartbeat, increase in blood pressure, increased respiratory rate and tremor up to nausea or flush reactions. These symptoms can cause emotional distress, a reduced musical and artistical performance up to an impaired functioning. While anxiety disorders are preferably treated using cognitive-behavioral therapy with exposure, this approach is rather difficult for treating music performance anxiety since the presence of a public or professional jury is required and not easily available. The use of virtual reality (VR) could therefore display an alternative. So far, no therapy studies on music performance anxiety applying virtual reality exposure therapy have investigated the therapy outcome including cardiovascular changes as outcome parameters.
Methods
This mono-center, prospective, randomized and controlled clinical trial has a pre-post design with a follow-up period of 6 months. 46 professional and semi-professional musicians will be recruited and allocated randomly to an VR exposure group or a control group receiving progressive muscle relaxation training. Both groups will be treated over 4 single sessions. Music performance anxiety will be diagnosed based on a clinical interview using ICD-10 and DSM-5 criteria for specific phobia or social anxiety. A behavioral assessment test is conducted three times (pre, post, follow-up) in VR through an audition in a concert hall. Primary outcomes are the changes in music performance anxiety measured by the German Bühnenangstfragebogen and the cardiovascular reactivity reflected by heart rate variability (HRV). Secondary outcomes are changes in blood pressure, stress parameters such as cortisol in the blood and saliva, neuropeptides, and DNA-methylation.
Discussion
The trial investigates the effect of VR exposure in musicians with performance anxiety compared to a relaxation technique on anxiety symptoms and corresponding cardiovascular parameters. We expect a reduction of anxiety but also a consecutive improvement of HRV with cardiovascular protective effects.
Trial registration
This study was registered on clinicaltrials.gov. (ClinicalTrials.gov Number: NCT05735860)