Decentralised detection and monitoring of hearing loss can be supported by new mHealth technologies using automated testing, which can be facilitated by minimally trained persons. These technologies may prove particularly useful in an infectious disease (ID) clinic setting where patients are at high risk for hearing loss. The current study aimed to evaluate the clinical utility of mobile and automated audiometry hearing health technology in an ID clinic setting. The current study was exploratory as it aimed to determine whether smartphone automated audiometry and South African English Digits-In-Noise (SA Eng DIN) smartphone applications could be utilised in an infectious disease clinic setting to monitor an HIV-related hearing loss in a feasible and time efficient way. Smartphone automated audiometry (hearTest™) and speech-in-noise testing (SA English Digits-In-Noise (DIN) test) were compared with manual audiometry at 2, 4, and 8 kHz. Smartphone automated audiometry and the DIN test were repeated to determine the test re-test reliability. Two hundred subjects (73% female and 27% male) were enrolled. Fifty participants were re-tested with the smartphone applications. Participants’ ages ranged from 18 to 55 years with a mean age of 44.4 (8.7 SD). Threshold comparisons were made between smartphone audiometry testing and manual audiometry. Smartphone automated audiometry, manual audiometry, and test re-test measures were compared to determine the statistical significance of any differences observed using the Wilcoxon signed-ranked test. Spearman rank correlation test was used to determine the relationship between the smartphone applications and manual audiometry, as well as for test re-test measurements. For all participants, 88.2% of thresholds corresponded within 10 dB or less between smartphone audiometry and manual audiometry. There was a significant difference (p>0.05) between smartphone and manual audiometry for the right ear at 4 and 8 kHz and the left ear at 2 and 4 kHz respectively. No significant difference was noted (p>0.05) between test and re-test measures of smartphone technology except at 4kHz in the right ear in smartphone automated audiometry. The absolute average difference between the initial and re-test of DIN testing was 1.2 dB (1.5 SD). No significant difference was noted in the test re-test measures of the DIN test (p < vii 0.05). A correlation coefficient of 0.56 was present in the DIN test re-test measures when the Spearman rank correlation test was administered. Smartphone audiometry with calibrated headphones provides reliable results and can be used as a baseline and monitoring tool at ID clinics. / Dissertation (MA)--University of Pretoria, 2017. / Speech-Language Pathology and Audiology / MA / Unrestricted

Background: Tobacco use is one of the key problems that public health has to face. Tobacco smoking is among the main causes of morbidity and preventable mortality that can be effectively avoided. The eHealth approach uses information and communication technologies to improve the quality of health and healthcare. EHealth interventions delivered through technologies and the Internet are an effective therapeutic tool which contributes to behaviour change, including smoking cessation. This paper presents the results of continuous research on fully automated online eHealth intervention for smoking cessation. Aims: The main objective of this study was to identify the effect of the form of reminders (SMS vs. email) in eHealth smoking cessation intervention using the Endre eHealth program in the population of Czech tobacco smokers. Materials and methods: This research consists of two studies. First, a pilot study on user- acceptance of eHealth intervention was performed in a sample involving 30 respondents. This was followed by a randomized two-arm controlled study comparing the effect of a predictor in the form of eHealth intervention reminders for smoking cessation. Adult tobacco smokers were recruited based on advertising through a variety of online channels. 158 respondents were randomised for the...

Depressive Störungen sind Erkrankungen mit hoher Prävalenz und weitreichenden Beeinträchtigungen für das Leben Betroffener, sie bergen Risiken für Rezidive und Chronifizierung. Eine Vielzahl diagnostischer und therapeutischer Verfahren und Methoden steht zur Verfügung, um depressiven Patienten zu helfen, doch erreicht diese Hilfe längst nicht alle. Die digitale Revolution und der Einzug mobiler Geräte wie Smartphones oder tragbarer Sensorgeräte in den Alltag eröffnen neue Möglichkeiten und Wege sich diesen Herausforderungen für die Behandlung depressiv Erkrankter zu stellen. Der Markt an Apps für das Selbst-Monitoring und das Krankheitsmanagement bei Depression wächst beständig, doch ob und in welchem Umfang Depressive solche Angebote wahrnehmen ist wenig erforscht. Die vorliegende Arbeit zu Präferenzen und Wünschen potentieller Nutzer versucht hier Antworten zu finden. Mobile Systeme aus Smartphones, Apps und tragbaren Sensoren können einen Beitrag zum Krankheitsmanagement bei Depression leisten und so die Versorgung Erkrankter verbessern. Mit Hilfe solcher Geräte und Apps lassen sich subjektive oder objektive Daten messen, verarbeiten, evaluieren und für Selbstmanagement, Diagnose und Therapie nutzbar machen. Kontinuierlich im Lebensalltag erhobene objektive Daten wie Bewegung, Aktivitäten, Schlaf als auch physiologische Parameter wie Hautleitfähigkeit oder Herzaktivität sind von unschätzbarem Wert, um Patienten frühzeitig auf Symptome, Wahrnehmungs- und Verhaltensveränderungen aufmerksam zu machen. Die Untersuchung der Veränderungen in der Herzfrequenz (Heart Rate Varibitly, HRV) und depressiven Symptomen stellt einen wichtigen Ansatz für die Suche nach reliablen Bio-Markern für Depression dar. Um objektiven Bioparametern wie HRV in Zukunft in mobilen Systemen zum Selbst-Monitoring und individuellen Krankheitsmanagement bei Depression einsetzen zu können ist aber ein besseres Verständnis der Zusammenhänge zwischen beiden nötig. Die vorliegende Arbeit versucht, weitere Einsicht in den Zusammenhang zwischen Veränderungen in der HRV und dem Krankheitszustand bei Depression zu liefern.:Einführung S5 Publikationsmanuskript 1 S15 Publikationsmanuskript 2 S23 Zusammenfassung S31 Literaturverzeichnis S32 Appendix S43 Darstellung des eigenen Beitrags S44 Selbstständigkeitserklärung S46 Publikationen und Vorträge S47 Danksagung S48

Die vorliegende Arbeit befasst sich mit der Generierung einer Methode, welche als Schablone zur Entwicklung von Patienten-Monitoringsystemen herangezogen wird. Das primäre Ziel dieser Methode besteht darin, dass während des Entwicklungsprozesses die Aspekte technischer, organisatorischer, datenschutzrechtlicher sowie ethischer Natur mit einfließen und Beachtung finden, sodass ein weitgehend reibungsfreier und unproblematischer Entwicklungsablauf eingehalten werden kann. Infolgedessen können auch vielerlei potentielle Probleme der soeben genannten Aspekte präventiv verhindert werden, wodurch unnötige Entwicklungsiterationen verhindert werden können und folglich eine Ressourceneinsparung erzielt wird. Schwerpunktmäßig liegt der Fokus auf dem innerhalb der Methode definierten Vorgehensmodell, wobei hier eine grobe sequentielle Einteilung zwischen den initialen Analysephasen sowie den darauffolgenden Umsetzungsphasen definiert ist. Diese sequentielle Abgrenzung stellt einen Kompromiss dar, um einerseits restriktive Bestimmungen der anfangs genannten Problematiken und Herausforderungen Rechnung tragen zu können und andererseits innerhalb der Sequenzen der Methode genügend Flexibilität für agile Fragmente zuzulassen. Die Beschreibung des Vorgehensmodells fokussiert sich insbesondere auf die ersteren Analysephasen, deren Zielbestimmung neben der Analyse der einfließenden Aspekte auch in der Synchronisation eines einheitlichen Wissensstandes zwischen dem medizinischen und technischen Personal besteht. In dieser Hinsicht spielt das in dieser Dissertation entwickelte Simulationsframework eine essentielle Rolle. Insgesamt werden in dieser Arbeit Verfahren und Werkzeuge zur Anwendung der Methode als Entwicklungsschablone bereitgestellt, wodurch die Entwicklung von Patienten- Monitoringsystemen unter Beachtung der genannten Herausforderungen ausgeführt werden kann.

This study investigates meditation apps from a user perspective. While focusing on the user, interviews with psychologists and an auto-ethnographic study of three different meditation apps were used to inform the research, enrich the findings and create an as wholesome as possible picture. The research aims to explore user’s motivations and experiences as well as the possible paradox of meditation through a smartphone in light of digital stress. Taking a user-centered approach, the theories informing this work include the Instrumental Theory of Technology; Theories of the self, including Foucault’s Practices of Selfhood and Lipton’s self-tracking practices; Existential Media Theory; and theories of the public and the private including the Publicization of the Private. This study shows that high achieving young adults use meditation apps as a convenient, accessible and cost-effective tool for self-improvement. However, users mainly see the apps as a stepping block and have the goal to eventually establish a meditation practice without using the phone. While users think that it would be better to meditate without an app, their meditation app allows them to fit the meditation practice into the context of their busy everyday life.

In the epoch of advanced mHealth (mobile health) use in ophthalmology, there is a scientific call for regulating the validity and reliability of eye-related apps. For a positive health outcome that works towards enhancing mobile-application guided diagnosis in joint decision-making between eye specialists and individuals, the aging population should be provided with a reliable and valid tool for assessment of their eye status outside the physician office. This interdisciplinary study aims to determine through hypothesis testing validity and reliability of a limited set of five mHealth apps (mHAs ) and through binary logistic regression the prediction possibilities of investigated apps to exclude the four major eye diseases in the particular demographic population. The study showed that 189 aging adults (45- 86 years old) who did complete the mHAs’ tests were able to produce reliable results of selected eye function tests through four out of five mHAs measuring visual acuity, contrast sensitivity, red desaturation, visual field and Amsler grid in comparison with a “gold standard” - comprehensive eye examination. Also, part of the participants was surveyed for assessing the Quality of Experience on mobile apps. Understanding of current reliability of existing eye-related mHAs will lead to the creation of ideal mobile application’ self-assessment protocol predicting the timely need for clinical assessment and treatment of age-related macular degeneration, diabetic retinopathy, glaucoma and cataract. Detecting the level of eye function impairments by mHAs is cost-effective and can contribute to research methodology in eye diseases’ prediction by expanding the system of clear criteria specially created for mobile applications and provide returning significant value in preventive ophthalmology.

Men who have sex with men continue to be at highest risk of HIV infection, with Black and Latino men who have sex with men [BLMSM] disproportionately at risk. The impact of alcohol consumption on condomless anal intercourse [CAI] is compounded for BLMSM by unique risk factors like internalized homophobia and racial stigma, reinforcing barriers to treatment. The traditional formats of existing HIV interventions fail to address heightened confidentiality concerns of BLMSM and few target both CAI and alcohol use. Existing interventions may be modified with mobile health [mhealth] technologies to improve outcomes for BLMSM. The current study examined the feasibility and acceptability of a novel mhealth intervention to reduce heavy drinking episodes [HDE], reduce CAI, and increase intentions to use pre-exposure prophylaxis medication [PrEP]. METHODS: Enrollment criteria included (1) Black and/or Latino man, (2) at least one episode of CAI with another man in the past six months, (3) at least one HDE in the past month and (4) no current PrEP use. Twelve participants completed a brief videoconferencing session then four weeks of interactive mobile messages. Outcome assessment was completed 8-weeks post-baseline. To assess feasibility and acceptability (primary outcomes), message response rates, ratings of intervention satisfaction (Client Satisfaction Questionnaire-8, CSQ), and ratings from a 10-item acceptability measure were used. RESULTS: Message response rates (M= 96%, SD = 0.04, Mdn = 98%) indicated high engagement. Ratings at follow-up indicated high acceptability (item rating M = 1.77, SD = 0.73, Mdn = 1.45; scores range from 1-5, lower ratings indicate higher acceptability) and high satisfaction (CSQ M = 26.7, SD = 4.08, Mdn = 27.5; scores range from 8-32, higher scores indicate higher satisfaction). Descriptive statistics were used to characterize post-intervention outcomes. HDEs decreased by 45% from baseline while PrEP use intentions remained largely unchanged (decreased by 5%). Only three of twelve participants reported CAI at post-intervention. DISCUSSION: Results show high engagement, acceptability, and satisfaction with the mhealth modality and support the feasibility of this approach to address HDE among BLMSM. Future efficacy testing of this novel mhealth intervention via randomized controlled trial is warranted.

Opioid use disorder (OUD) is one of the most pressing public health problems of our time, with staggering morbidity, social impact, and economic costs. Prescription opioids play a critical role in the opioid crisis as they increase exposure and availability in the general population, making them an attractive target for much needed prevention and risk mitigation strategies. Opioid exposure, including legitimate prescription use, leads to a variety of physiologic adaptations (e.g. dependence) that may be leveraged to understand and identify risk of misuse. Mobile health (mHealth) tools, including wearable sensors have great potential in this space, but have been underutilized. Of specific interest are digital biomarkers, or end-user generated physiologic or behavioral measurements that correlate with events of interest, health, or pathology. Preliminary data support the concept that wearable sensors can detect digital biomarkers of opioid use and may provide clues regarding individual physiologic adaptations to opioid use over time. This dissertation follows a path though the exploration and refinement of these digital biomarkers of opioid use in various clinical use cases. Longitudinal data from individuals treated with opioids for acute pain will be explored through various machine learning models to detect opioid use and to explore patient and treatment factors that impact model performance. Next, a signal processing approach will be undertaken to explore the effects of opioid agonism in a different population of individuals- those presenting with opioid toxicity and precipitated withdrawal. Both approaches will be combined to further refine the digital biomarker capabilities, this time with a focus on the difference between opioid naive and chronic users. And finally, usability, facilitators and barriers to use of a sensor-based monitoring system for opioids will be evaluated through a qualitative lens. Taken together, theses data support the development of a smart technology, driven by empirically derived algorithms which can be used to monitor opioid use, support safe prescribing practices, and reduce OUD and death.

The present study is based on the hypotheses that a better user experience in mobile applications increases the frequency of use among users, and that a higher frequency of use of continuous glucose monitoring systems leads to better health status in patients with diabetes.The purpose of this study is to understand how users perceive and interact with CGM software.The result of this study shows that existing CGM applications and the functionalities they provide are very much appreciated by the participants. Many of the user needs may however not have been met in one application alone, since a large proportion of the participants use more than one CGM application.This study highlights importance of providing options for customization in every aspect offunctionality due to the individuality of each user’s condition, and that the settings of such should consider minimizing the cognitive load for the user.

I denna studie undersöks individers generella attityder till vårdapplikationer som använder maskininlärning. Datainsamlingen har skett genom både kvalitativa och kvantitativa metoder som kompletterar varandra. Metoderna innefattar en enkätundersökning och två fokusgrupper baserade på scenario-based design. Teorin är baserad på forskning inom digitaliseringen av vården, bland annat maskininlärning och mHealth, som ligger till grund och stödjer undersökningen. Även teori om attityder och förtroende till digitaliseringen av vården har underbyggt undersökningen.I slutsatsen framkommer det att det finns en korrelation mellan hög medvetenhet och positiv inställning när det kommer det användandet av vårdapplikationer med maskininlärning. Den generella attityden till att få en diagnos av maskininlärning är negativ då de flesta föredrar att få en diagnos förmedlad av en läkare. Studien indikerar på att detta kan bero på att patienterna söker empati från vården, vilket artificiell intelligens saknar. Förtroendet för en vårdapplikation grundar sig främst i ryktet om den men även i vilket företag eller organisation som ligger bakom. Studien indikerar på att individer är positivt inställda till att bidra med privat hälsodata till en vårdapplikation om det leder till förebyggande av sjukdom. Studien ger även en antydan på att det finns en rädsla kring var privata hälsodata hamnar när den har lämnats ut. / This study aims to research on individuals’ general attitudes towards healthcare applications that use machine learning. The data collection has taken place through both qualitative and quantitative methods as a complement to each other. The methods include a questionnaire survey, two focus groups based on scenario-based design. The theory is based on research in the digitalisation of healthcare, including machine learning and mHealth, which is based and supports the investigation. The theory of attitudes and confidence in the digitalisation of care also forms the basis for the study.The conclusion shows that there is a correlation between high awareness and positive attitude when it comes to the use of healthcare applications with machine learning. The general attitude towards a diagnosis from machine learning is negative since most people prefer to get a diagnosis mediated by a doctor. The study indicates that this may be because the patients seek empathy from the healthcare system, which artificial intelligence lacks of. Trust towards a healthcare application is based primarily on the reputation of it, but also in which company or organization that is behind it. The respondents in the survey are positive about contributing with their personal data to a healthcare application if it leads to a prevention of a disease. The study also gives an indication that there is a fear of what happens with private health data.