Towards Preventing Gaps in Health Care Systems through Smartphone Use: Analysis of ARKit for Accurate Measurement of Facial Distances in Different Angles.

There is a growing consensus in the global health community that the use of communication technologies will be an essential factor in ensuring universal health coverage of the world's population. New technologies can only be used profitably if their accuracy is sufficient. Therefore, we explore the feasibility of using Apple's ARKit technology to accurately measure the distance from the user's eye to their smartphone screen. We developed an iOS application for measuring eyes-to-phone distances in various angles, using the built-in front-facing-camera and TrueDepth sensor. The actual position of the phone is precisely controlled and recorded, by fixing the head position and placing the phone in a robotic arm. Our results indicate that ARKit is capable of producing accurate measurements, with overall errors ranging between 0.88% and 9.07% from the actual distance, across various head positions. The accuracy of ARKit may be impacted by several factors such as head size, position, device model, and temperature. Our findings suggest that ARKit is a useful tool in the development of applications aimed at preventing eye damage caused by smartphone use.

Interrater reliability in the assessment of physiotherapy students.

BACKGROUND: Reliable and objective assessment of psychomotor skills in physiotherapy students' education is essential for direct feedback and skill improvement. The aim of this study is to determine the interrater reliability in the assessment process of physiotherapy students and to analyse the assessment behaviour of the examiners. METHODS: Physiotherapy teachers from two different schools assessed students from two different schools performing proprioceptive neuromuscular facilitation (PNF) patterns. An evaluation sheet with a 6-point rating scale and 20 evaluation criteria including an overall rating was used for assessment. The interrater reliability was determined calculating an intraclass-correlation coefficient (ICC) and Krippendorff's alpha. The assessment behaviour of the examiners was further analysed calculating the location parameters and showing the item response distribution over item in form of a Likert plot. RESULTS: The ICC estimates were mostly below 0.4, indicating poor interrater reliability. This was confirmed by Krippendorff's alpha. The examiners showed a certain central tendency and intergroup bias. DISCUSSION AND CONCLUSION: The interrater reliability in this assessment format was rather low. No difference between the two physiotherapy schools concerning the interrater reliability could be identified. Despite certain limitations of this study, there is a definite need for improvement of the assessment process in physiotherapy education to provide the students with reliable and objective feedback and ensure a certain level of professional competence in the students. TRIAL REGISTRATION: The study was approved by the ethics committee of the Medical Faculty RWTH Aachen University (EK 340/16).

Tracking of Mental Workload with a Mobile EEG Sensor.

The aim of the present investigation was to assess if a mobile electroencephalography (EEG) setup can be used to track mental workload, which is an important aspect of learning performance and motivation and may thus represent a valuable source of information in the evaluation of cognitive training approaches. Twenty five healthy subjects performed a three-level N-back test using a fully mobile setup including tablet-based presentation of the task and EEG data collection with a self-mounted mobile EEG device at two assessment time points. A two-fold analysis approach was chosen including a standard analysis of variance and an artificial neural network to distinguish the levels of cognitive load. Our findings indicate that the setup is feasible for detecting changes in cognitive load, as reflected by alterations across lobes in different frequency bands. In particular, we observed a decrease of occipital alpha and an increase in frontal, parietal and occipital theta with increasing cognitive load. The most distinct levels of cognitive load could be discriminated by the integrated machine learning models with an accuracy of 86%.

Can commercially available wearable EEG devices be used for diagnostic purposes? An explorative pilot study.

Electroencephalography (EEG) is a core element in the diagnosis of epilepsy syndromes and can help to monitor antiseizure treatment. Mobile EEG (mEEG) devices are increasingly available on the consumer market and may offer easier access to EEG recordings especially in rural or resource-poor areas. The usefulness of consumer-grade devices for clinical purposes is still underinvestigated. Here, we compared EEG traces of a commercially available mEEG device (Emotiv EPOC) to a simultaneously recorded clinical video EEG (vEEG). Twenty-two adult patients (11 female, mean age 40.2 years) undergoing noninvasive vEEG monitoring for clinical purposes were prospectively enrolled. The EEG recordings were evaluated by 10 independent raters with unmodifiable view settings. The individual evaluations were compared with respect to the presence of abnormal EEG findings (regional slowing, epileptiform potentials, seizure pattern). Video EEG yielded a sensitivity of 56% and specificity of 88% for abnormal EEG findings, whereas mEEG reached 39% and 85%, respectively. Interrater reliability coefficients were better in vEEG as compared to mEEG (ϰ = 0.50 vs. 0.30), corresponding to a moderate and fair agreement. Intrarater reliability between mEEG and vEEG evaluations of simultaneous recordings of a given participant was moderate (ϰ = 0.48). Given the limitations of our exploratory pilot study, our results suggest that vEEG is superior to mEEG, but that mEEG can be helpful for diagnostic purposes. We present the first quantitative comparison of simultaneously acquired clinical and mobile consumer-grade EEG for a clinical use-case.

360-degree Delphi: addressing sociotechnical challenges of healthcare IT.

BACKGROUND: IT systems in the healthcare field can have a marked sociotechnical impact: they modify communication habits, alter clinical processes and may have serious ethical implications. The introduction of such systems involves very different groups of stakeholders because of the inherent multi-professionalism in medicine and the role of patients and their relatives that are often underrepresented. Each group contributes distinct perspectives and particular needs, which create specific requirements for IT systems and may strongly influence their acceptance and success. In the past, needs analysis, challenges and requirements for medical IT systems have often been addressed using consensus techniques such as the Delphi technique. Facing the heterogeneous spectrum of stakeholders there is a need to develop these techniques further to control the (strong) influence of the composition of the expert panel on the outcome and to deal systematically with potentially incompatible needs of stakeholder groups. This approach uses the strong advantages a Delphi study has, identifies the disadvantages of traditional Delphi techniques and aims to introduce and evaluate a modified approach called 360-Degree Delphi. Key aspects of 360-Degree Delphi are tested by applying the approach to the needs and requirements analysis of a system for managing patients' advance directives and living wills. METHODS: 360-Degree Delphi (short 360°D), as a modified Delphi process, is specified as a structured workflow with the optional use of stakeholder groups. The approach redefines the composition of the expert panel by setting up groups of different stakeholders. Consensus is created within individual stakeholder groups, but is also communicated between groups, while the iterative structure of the Delphi process remains unchanged. We hypothesize that (1) 360-Degree Delphi yields complementary statements from different stakeholders, which would be lost in classical Delphi; while (2) the variation of statements within individual stakeholder groups is lower than within the total collective. A user study is performed that addresses five stakeholder groups (patients, relatives, medical doctors, nurses and software developers) on the topic of living will communication in an emergency context. Qualitative open questions are used in a Delphi round 0. Answer texts are coded by independent raters who carry out systematic bottom-up qualitative text analysis. Inter-rater reliability is calculated and the resulting codes are used to test the hypotheses. Qualitative results are transferred into quantitative questions and then surveyed in round 1. The study took place in Germany. RESULTS: About 25% of the invited experts (stakeholders) agreed to take part in the Delphi round 0 (three patients, two relatives, three medical doctors, two qualified nurses and three developers), forming a structured panel of the five stakeholder groups. Two raters created a bottom-up coding, and 238 thematic codes were identified by the qualitative text analysis. The inter-rater reliability showed that 44.95% of the codes were semantically similar and coded for the same parts of the raw textual replies. Based on a consented coding list, a quantitative online-questionnaire was developed and send to different stakeholder groups. With respect to the hypotheses, Delphi round 0 had the following results: (1) doctors had a completely different focus from all the other stakeholder groups on possible channels of communications with the patient; (2) the dispersion of codes within individual stakeholder groups and within the total collective - visualized by box plots - was approximately 28% higher in the total collective than in the sub-collectives, but without a marked effect size. With respect to the hypotheses, Delphi round 1 had the following results: different stakeholder groups had highly diverging opinions with respect to central questions on IT-development. For example, when asked to rate the importance of access control against high availability of data (likert scale, 1 meaning restrictive data access, 6 easy access to all data), patients (mean 4.862, Stdev +/- 1.866) and caregivers (mean 5.667, Stdev: +/- 0.816) highly favored data availability, while relatives would restrict data access (mean 2.778, stdev +/- 1.093). In comparison, the total group would not be representative of either of these individual stakeholder needs (mean 4.344, stdev +/- 1.870). CONCLUSION: 360-Degree Delphi is feasible and allows different stakeholder groups within an expert panel to reach agreement individually. Thus, it generates a more detailed consensus which pays more tribute to individual stakeholders needs. This has the potential to improve the time to consensus as well as to produce a more representative and precise needs and requirements analysis. However, the method may create new challenges for the IT development process, which will have to deal with complementary or even contradictory statements from different stakeholder groups.

Sensor Failure Detection in Ambient Assisted Living Using Association Rule Mining.

Ambient Assisted Living (AAL) is becoming crucial to help governments face the consequences of the emerging ageing population. It aims to motivate independent living of older adults at their place of residence by monitoring their activities in an unobtrusive way. However, challenges are still faced to develop a practical AAL system. One of those challenges is detecting failures in non-intrusive sensors in the presence of the non-deterministic human behaviour. This paper proposes sensor failure detection and isolation system in the AAL environments equipped with event-driven, ambient binary sensors. Association Rule mining is used to extract fault-free correlations between sensors during the nominal behaviour of the resident. Pruning is then applied to obtain a non-redundant set of rules that captures the strongest correlations between sensors. The pruned rules are then monitored in real-time to update the health status of each sensor according to the satisfaction and/or unsatisfaction of rules. A sensor is flagged as faulty when its health status falls below a certain threshold. The results show that detection and isolation of sensors using the proposed method could be achieved using unlabelled datasets and without prior knowledge of the sensors' topology.

Privacy-Preserving Record Grouping and Consent Management Based on a Public-Private Key Signature Scheme: Theoretical Analysis and Feasibility Study.

BACKGROUND: Clinical and social trials create evidence that enables medical progress. However, the gathering of personal and patient data requires high security and privacy standards. Direct linking of personal information and medical data is commonly hidden through pseudonymization. While this makes unauthorized access to personal medical data more difficult, a centralized pseudonymization list can still pose a security risk. In addition, medical data linked via pseudonyms can still be used for data-driven reidentification. OBJECTIVE: Our objective was to propose a novel approach to pseudonymization based on public-private key cryptography that allows (1) decentralized patient-driven creation and maintenance of pseudonyms, (2) 1-time pseudonymization of each data record, and (3) grouping of patient data records even without knowing the pseudonymization key. METHODS: Based on public-private key cryptography, we set up a signing mechanism for patient data records and detailed the workflows for (1) user registration, (2) user log-in, (3) record storing, and (4) record grouping. We evaluated the proposed mechanism for performance, examined the potential risks based on cryptographic collision, and carried out a threat analysis. RESULTS: The performance analysis showed that all workflows could be performed with an average runtime of 0.057 to 42.320 ms (user registration), 0.083 to 0.606 ms (record creation), and 0.005 to 0.198 ms (record grouping) depending on the chosen cryptographic tools. We expected no realistic risk of cryptographic collision in the proposed system, and the threat analysis revealed that 3 distinct server systems of the proposed setup had to be compromised to allow access to combined medical data and private data. However, this would still allow only for data-driven deidentification. For a full reidentification, all 3 trial servers and all study participants would have to be compromised. In addition, the approach supports consent management, automatically anonymizes the data after trial closure, and provides basic mechanisms against data forging. CONCLUSIONS: The proposed approach has a high security and privacy level in comparison with traditional centralized pseudonymization approaches and does not require a trusted third party. The only drawback in comparison with central pseudonymization is the directed feedback of accidental findings to individual participants, as this is not possible with a quasi-anonymous storage of patient data.

Recursive neural networks in hospital bed occupancy forecasting.

BACKGROUND: Efficient planning of hospital bed usage is a necessary condition to minimize the hospital costs. In the presented work we deal with the problem of occupancy forecasting in the scale of several months, with a focus on personnel's holiday planning. METHODS: We construct a model based on a set of recursive neural networks, which performs an occupancy prediction using historical admission and release data combined with external factors such as public and school holidays. The model requires no personal information on patients or staff. It is optimized for a 60 days forecast during the summer season (May-September). RESULTS: An average mean absolute percentage error (MAPE) of 6.24% was computed on 8 validation sets. CONCLUSIONS: The proposed machine learning model has shown to be competitive to standard time-series forecasting models and can be recommended for incorporation in medium-size hospitals automatized scheduling and decision making.

Correction: Kutafina, E.; Laukamp, D.; Bettermann, R.; Schroeder, U.; Jonas, S.M. Wearable Sensors for eLearning of Manual Tasks: Using Forearm EMG in Hand Hygiene Training. Sensors 2016, 16, 1221.

The authors wish to make the following erratum to this paper [...].

Case study in major quotation errors: a critical commentary on the Newcastle-Ottawa scale.

The Newcastle-Ottawa scale (NOS) is one of many scales used to judge the quality of observational studies in systematic reviews. It was criticized for its arbitrary definitions of quality items in a commentary in 2010 in this journal. That commentary was cited 1,250 times through December 2016. We examined the citation history of this commentary in a random sample of 100 full papers citing it, according to the Web of Science. Of these, 96 were systematic reviews, none of which quoted the commentary directly. All but 2 of the 96 indirect quotations (98%) portrayed the commentary as supporting use of the NOS in systematic reviews when, in fact, the opposite was the case. It appears that the vast majority of systematic review authors who cited this commentary did not read it. Journal reviewers and editors did not recognize and correct these major quotation errors. Authors should read each source they cite to make sure their direct and indirect quotations are accurate. Reviewers and editors should do a better job of checking citations and quotations for accuracy. It might help somewhat for commentaries to include abstracts, so that the basic content can be conveyed by PubMed and other bibliographic resources.

Wearable Sensors for eLearning of Manual Tasks: Using Forearm EMG in Hand Hygiene Training.

In this paper, we propose a novel approach to eLearning that makes use of smart wearable sensors. Traditional eLearning supports the remote and mobile learning of mostly theoretical knowledge. Here we discuss the possibilities of eLearning to support the training of manual skills. We employ forearm armbands with inertial measurement units and surface electromyography sensors to detect and analyse the user's hand motions and evaluate their performance. Hand hygiene is chosen as the example activity, as it is a highly standardized manual task that is often not properly executed. The World Health Organization guidelines on hand hygiene are taken as a model of the optimal hygiene procedure, due to their algorithmic structure. Gesture recognition procedures based on artificial neural networks and hidden Markov modeling were developed, achieving recognition rates of 98 . 30 % ( ± 1 . 26 % ) for individual gestures. Our approach is shown to be promising for further research and application in the mobile eLearning of manual skills.

Tumor volume as a prognostic factor in resectable malignant melanoma.

BACKGROUND: Vertical tumor thickness according to Breslow and histological ulceration are still the most powerful predictors for the clinical outcome of resectable cutaneous malignant melanoma (MM) without lymph node infiltration. It has been proposed that tumor volume in MM may also be of prognostic relevance. METHODS: We retrospectively analyzed the prognostic impact of tumor volume and other established risk factors in 122 MM patients with a median follow-up period of 39.7 months. RESULTS: We found the logarithmic tumor volume to be a better prognostic factor compared to Breslow tumor thickness in multivariate analysis. MM with a tumor volume below a threshold of 140 mm(3) had a significantly higher relapse-free survival after 5 years of 98% compared to 47% in larger MMs (p < 0.0001). CONCLUSION: In some melanomas with a low tumor thickness, a higher tumor volume appeared to be linked to a higher risk of disease recurrence. Inclusion of tumor volume into the risk assessment of resectable MM may be of benefit in the future.

Simplifying electronic data capture in clinical trials: workflow embedded image and biosignal file integration and analysis via web services.

To improve data quality and save cost, clinical trials are nowadays performed using electronic data capture systems (EDCS) providing electronic case report forms (eCRF) instead of paper-based CRFs. However, such EDCS are insufficiently integrated into the medical workflow and lack in interfacing with other study-related systems. In addition, most EDCS are unable to handle image and biosignal data, although electrocardiography (EGC, as example for one-dimensional (1D) data), ultrasound (2D data), or magnetic resonance imaging (3D data) have been established as surrogate endpoints in clinical trials. In this paper, an integrated workflow based on OpenClinica, one of the world's largest EDCS, is presented. Our approach consists of three components for (i) sharing of study metadata, (ii) integration of large volume data into eCRFs, and (iii) automatic image and biosignal analysis. In all components, metadata is transferred between systems using web services and JavaScript, and binary large objects (BLOBs) are sent via the secure file transfer protocol and hypertext transfer protocol. We applied the close-looped workflow in a multicenter study, where long term (7 days/24 h) Holter ECG monitoring is acquired on subjects with diabetes. Study metadata is automatically transferred into OpenClinica, the 4 GB BLOBs are seamlessly integrated into the eCRF, automatically processed, and the results of signal analysis are written back into the eCRF immediately.

KIJANI: Designing a Physical Activity Promoting Collaborative Augmented Reality Game.

BACKGROUND: There is an increased need for physical activity among children and adolescents. KIJANI, a mobile augmented reality game, is designed to increase physical activity through gamified exercises. OBJECTIVES: The primary aim of this study is to get feedback on the design and implementation of potentially physical activity-increasing features in KIJANI. METHODS: A mixed-method study (n=13) evaluates newly implemented game design features quantitatively through measuring physical activity and qualitatively through participant feedback. RESULTS: Preliminary results are limited and need further studies. Participants' feedback shows a positive trend and highlights the game's potential effectiveness. CONCLUSION: KIJANI shows potential for increasing physical activity among children and adolescents through gamified exercise. Future work will refine the game based on user feedback and findings presented in related work. The game's long-term impact is to be explored.

The German version of the mHealth App Usability Questionnaire (GER-MAUQ): Translation and validation study in patients with cardiovascular disease.

Objective: In Germany, only a few standardized evaluation tools for assessing the usability of mobile Health apps exist so far. This study aimed to translate and validate the English patient version for standalone apps of the mHealth App Usability Questionnaire (MAUQ) into a German version. Methods: Following scientific guidelines for translation and cross-cultural adaptation, the patient version for standalone apps was forward and back-translated from English into German by an expert panel. In total, 53 participants who were recruited as part of the beta testing process of the recently developed mHealth app HerzFit, answered the questions of the German version of the MAUQ (GER-MAUQ) and the System Usability Scale. Subsequently, a descriptive as well as a psychometric analysis was performed to test validity and reliability. Results: After conducting three cognitive interviews, five items were modified. The values for Cronbach alpha for the entire questionnaire and the three subscales (0.966, 0.814, 0.910, and 0.909) indicate strong internal consistency. The correlation analysis revealed that the scores of the GER-MAUQ, the subscales and the SUS were strongly correlated with each other. The correlation coefficient of the SUS and the GER-MAUQ overall score was r = 0.854, P < 0.001 and the coefficients of the subscales and the SUS were r = 0.642, P < 0.001; r = 0.866, P < 0.001 and r = 0.643, P < 0.001. Conclusions: We have developed a novel German version of the MAUQ and demonstrated it as a reliable and valid measurement tool for assessing the usability of standalone mHealth apps from the patients' perspective. The GER-MAUQ allows a new form of standardized assessment of usability of mHealth apps for patients with cardiovascular disease in Germany. Further research with a larger sample and other samples is recommended.

KIJANI App to Promote Physical Activity in Children and Adolescents: Protocol for a Mixed Method Evaluation.

BACKGROUND: The prevalence of physical inactivity among children and adolescents is alarmingly high despite the well-documented and comprehensive benefits of regular physical activity (PA). Therefore, PA promotion should start early in childhood and adolescence. Although reducing recreational screen time in children and adolescents is an urgent concern, digital approaches have the potential to make activity promotion attractive and age appropriate for the target group. KIJANI is a mobile app approach to promote PA in children and adolescents via gamification and augmented reality. OBJECTIVE: This study protocol aims to describe the KIJANI intervention in detail, as well as the evaluation approach. METHODS: KIJANI is based on the concept that virtual coins can be earned through PA, for example, in the form of a collected step count. With these coins, in turn, blocks can be bought, which can be used to create virtual buildings and integrate them into the player's real-world environment via augmented reality. PA of users is detected via accelerometers integrated into the smartphones. KIJANI can be played at predefined play locations that were comprehensively identified as safe, child-friendly, and attractive for PA by the target group in a partner project. The evaluation process will be divided into 2 different stages. The phase-I evaluation will be a mixed methods approach with one-on-one semistructured interviews and questionnaires to evaluate the user experience and receive feedback from the target group. After the implementation of results and feedback from the target group, the phase-II evaluation will proceed in the form of a 2-arm randomized controlled trial, in which the effectiveness of KIJANI will be assessed via objectively measured PA as well as questionnaires. RESULTS: The study received ethical approval from the ethical board of the Technical University of Munich. Participants for the phase-I evaluation are currently being recruited. CONCLUSIONS: The study will help to determine the efficacy, applicability, and user experience of a gamified activity promotion application in children and adolescents. Overall, digital health approaches provide easy and wide reachability at low cost and are age appropriate and attractive for the target group of adolescents. Strategies have to be developed to apply digital health approaches in the best possible way for activity promotion. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55156.

Evaluation of the gamified application KIJANI to promote physical activity in children and adolescents: A multimethod study.

Objective: Digital approaches have the potential to make activity promotion attractive and age-appropriate for children and adolescents. KIJANI is a mobile application aiming to increase physical activity (PA) in youth via gamification and augmented reality. This study investigates the user experience with KIJANI through a multimethod approach. Approaches: KIJANI is based on the concept that virtual coins can be earned through PA, for example, in the form of collected step counts. With these coins, blocks can be bought, which can be used to create virtual buildings and landscapes and integrate these into the player's real-world environment via augmented reality. To evaluate the user experience, participants played KIJANI in groups of three for 25 min. Afterwards KIJANI was evaluated qualitatively with one-on-one semi-structured interviews as well as quantitatively with standardized questionnaires. Results: Overall, 22 participants (12.6 ± 1.7 years, 6 girls) were included in the study. The overall game concept and realization were well received by the target group. Study participants did have various and creative ideas for the further development of KIJANI. The majority (n = 16) thought that using KIJANI would increase their PA level. User experience based on the UEQ scale was (mean ± SD): attractiveness (1.78 ± 1.82), perspicuity (2.15 ± 0.680), efficiency (0.67 ± 1.25), dependability, (1.21 ± 0.93), stimulation (1.24 ± 1.78), and novelty (1.27 ± 1.34). Conclusion: With these insights, a further step has been taken in the participatory development of KIJANI. Apps like KIJANI appear to be suitable for PA promotion in children and adolescents.

User-centered development of an mHealth app for cardiovascular prevention.

Background: Mobile health (mHealth) apps can be used for cardiovascular disease (CVD) prevention. User-centered design, evidence-based content and user testing can be applied to ensure a high level of usability and adequate app access. Objective: To develop and evaluate an mHealth app (HerzFit) for CVD prevention. Methods: HerzFit´s development included a user-centered design approach and guideline-based content creation based on the identified requirements of the target group. Beta testing and a preliminary usability evaluation of the HerzFit prototype were performed. For evaluation, German versions of the System Usability Scale (SUS) and the mHealth App Usability Questionnaire (GER-MAUQ) as well as free text feedback were applied. Results: User-centered design thinking led to the definition of four personas. Based on their requirements, HerzFit enables users to individually assess, monitor, and optimize their cardiovascular risk profile. Users are also provided with a variety of evidence-based information on CVD and their risk factors. The user interface and system design followed the identified functional requirements. Beta-testers provided feedback on the structure and functionality and rated the usability of HerzFit´s prototype as slightly above average both in SUS and GER-MAUQ rating. Participants positively noted the variety of functions and information presented in HerzFit, while negative feedback mostly concerned wearable synchronization. Conclusions: The present study demonstrates the user-centered development of a guideline-based mHealth app for CVD prevention. Beta-testing and a preliminary usability study were used to further improve the HerzFit app until its official release.

QRS detection in single-lead, telehealth electrocardiogram signals: Benchmarking open-source algorithms.

BACKGROUND AND OBJECTIVES: A key step in electrocardiogram (ECG) analysis is the detection of QRS complexes, particularly for arrhythmia detection. Telehealth ECGs present a new challenge for automated analysis as they are noisier than traditional clinical ECGs. The aim of this study was to identify the best-performing open-source QRS detector for use with telehealth ECGs. METHODS: The performance of 18 open-source QRS detectors was assessed on six datasets. These included four datasets of ECGs collected under supervision, and two datasets of telehealth ECGs collected without clinical supervision. The telehealth ECGs, consisting of single-lead ECGs recorded between the hands, included a novel dataset of 479 ECGs collected in the SAFER study of screening for atrial fibrillation (AF). Performance was assessed against manual annotations. RESULTS: A total of 12 QRS detectors performed well on ECGs collected under clinical supervision (F1 score ≥0.96). However, fewer performed well on telehealth ECGs: five performed well on the TELE ECG Database; six performed well on high-quality SAFER data; and performance was poorer on low-quality SAFER data (three QRS detectors achieved F1 of 0.78-0.84). The presence of AF had little impact on performance. CONCLUSIONS: The Neurokit and University of New South Wales QRS detectors performed best in this study. These performed sufficiently well on high-quality telehealth ECGs, but not on low-quality ECGs. This demonstrates the need to handle low-quality ECGs appropriately to ensure only ECGs which can be accurately analysed are used for clinical decision making.

TrustNShare: Development of a Blockchain-Based Data Trust Model for Secure and Controlled Health Data Sharing Grounded on Empirical Research.

Ensuring data quality and protecting data are key requirements when working with health-related data. Re-identification risks of feature-rich data sets have led to the dissolution of the hard boundary between data protected by data protection laws (GDPR) and anonymized data sets. To solve this problem, the TrustNShare project is creating a transparent data trust that acts as a trusted intermediary. This allows for secure and controlled data exchange, while offering flexible datasharing options, considering trustworthiness, risk tolerance, and healthcare interoperability. Empirical studies and participatory research will be conducted to develop a trustworthy and effective data trust model.