Wound Image Quality From a Mobile Health Tool for Home-Based Chronic Wound Management With Real-Time Quality Feedback: Randomized Feasibility Study

BACKGROUND Travel to clinics for chronic wound management is burdensome to patients. Remote assessment and management of wounds using mobile and telehealth approaches can reduce this burden and improve patient outcomes. An essential step in wound documentation is the capture of wound images, but poor image quality can have a negative influence on the reliability of the assessment. To date, no study has investigated the quality of remotely acquired wound images and whether these are suitable for wound self-management and telemedical interpretation of wound status. OBJECTIVE Our goal was to develop a mobile health (mHealth) tool for the remote self-assessment of digital ulcers (DUs) in patients with systemic sclerosis (SSc). We aimed to define and validate objective measures for assessing the image quality, evaluate whether an automated feedback feature based on real-time assessment of image quality improves the overall quality of acquired wound images, and evaluate the feasibility of deploying the mHealth tool for home-based chronic wound self-monitoring by patients with SSc. METHODS We developed an mHealth tool composed of a wound imaging and management app, a custom color reference sticker, and a smartphone holder. We introduced 2 objective image quality parameters based on the sharpness and presence of the color checker to assess the quality of the image during acquisition and enable a quality feedback mechanism in an advanced version of the app. We randomly assigned patients with SSc and DU to the 2 device groups (basic and feedback) to self-document their DU at home over 8 weeks. The color checker detection ratio (CCDR) and color checker sharpness (CCS) were compared between the 2 groups. We evaluated the feasibility of the mHealth tool by analyzing the usability feedback from questionnaires, user behavior and timings, and the overall quality of the wound images. RESULTS A total of 21 patients were enrolled, of which 15 patients were included in the image quality analysis. The average CCDR was 0.96 (191/199) in the feedback group and 0.86 (158/183) in the basic group. The feedback group showed significantly higher (P<.001) CCS compared to the basic group. The usability questionnaire results showed that the majority of patients were satisfied with the tool, but could benefit from disease-specific adaptations. The median assessment duration was <50 seconds in all patients, indicating the mHealth tool was efficient to use and could be integrated into the daily routine of patients. CONCLUSIONS We developed an mHealth tool that enables patients with SSc to acquire good-quality DU images and demonstrated that it is feasible to deploy such an app in this patient group. The feedback mechanism improved the overall image quality. The introduced technical solutions consist of a further step towards reliable and trustworthy digital health for home-based self-management of wounds

Sleep Spindles Are Related to Schizotypal Personality Traits and Thalamic Glutamine/Glutamate in Healthy Subjects

Background: Schizophrenia is a severe mental disorder affecting approximately 1% of the worldwide population. Yet, schizophrenia-like experiences (schizotypy) are very common in the healthy population, indicating a continuum between normal mental functioning and the psychosis found in schizophrenic patients. A continuum between schizotypy and schizophrenia would be supported if they share the same neurobiological origin. Two such neurobiological markers of schizophrenia are: (1) a reduction of sleep spindles (12-15 Hz oscillations during nonrapid eye movement sleep), likely reflecting deficits in thalamo-cortical circuits and (2) increased glutamine and glutamate (Glx) levels in the thalamus. Thus, this study aimed to investigate whether sleep spindles and Glx levels are related to schizotypal personality traits in healthy subjects. Methods: Twenty young male subjects underwent 2 all-night sleep electroencephalography recordings (128 electrodes). Sleep spindles were detected automatically. After those 2 nights, thalamic Glx levels were measured by magnetic resonance spectroscopy. Subjects completed a magical ideation scale to assess schizotypy. Results: Sleep spindle density was negatively correlated with magical ideation (r = −.64, P .1). Conclusions: The common relationship of sleep spindle density with schizotypy and thalamic Glx levels indicates a neurobiological overlap between nonclinical schizotypy and schizophrenia. Thus, sleep spindle density and magical ideation may reflect the anatomy and efficiency of the thalamo-cortical system that shows pronounced impairment in patients with schizophreni

Data integrity based methodology and checklist for identifying implementation risks of physiological sensing in mHealth projects

Mobile health (mHealth) technologies have the potential to bring health care closer to people with otherwise limited access to adequate health care. However, physiological monitoring using mobile medical sensors is not yet widely used as adding biomedical sensors to mHealth projects inherently introduces new challenges. Thus far, no methodology exists to systematically evaluate these implementation challenges and identify the related risks.; This study aimed to facilitate the implementation of mHealth initiatives with mobile physiological sensing in constrained health systems by developing a methodology to systematically evaluate potential challenges and implementation risks.; We performed a quantitative analysis of physiological data obtained from a randomized household intervention trial that implemented sensor-based mHealth tools (pulse oximetry combined with a respiratory rate assessment app) to monitor health outcomes of 317 children (aged 6-36 months) that were visited weekly by 1 of 9 field workers in a rural Peruvian setting. The analysis focused on data integrity such as data completeness and signal quality. In addition, we performed a qualitative analysis of pretrial usability and semistructured posttrial interviews with a subset of app users (7 field workers and 7 health care center staff members) focusing on data integrity and reasons for loss thereof. Common themes were identified using a content analysis approach. Risk factors of each theme were detailed and then generalized and expanded into a checklist by reviewing 8 mHealth projects from the literature. An expert panel evaluated the checklist during 2 iterations until agreement between the 5 experts was achieved.; Pulse oximetry signals were recorded in 78.36% (12,098/15,439) of subject visits where tablets were used. Signal quality decreased for 1 and increased for 7 field workers over time (1 excluded). Usability issues were addressed and the workflow was improved. Users considered the app easy and logical to use. In the qualitative analysis, we constructed a thematic map with the causes of low data integrity. We sorted them into 5 main challenge categories: environment, technology, user skills, user motivation, and subject engagement. The obtained categories were translated into detailed risk factors and presented in the form of an actionable checklist to evaluate possible implementation risks. By visually inspecting the checklist, open issues and sources for potential risks can be easily identified.; We developed a data integrity-based methodology to assess the potential challenges and risks of sensor-based mHealth projects. Aiming at improving data integrity, implementers can focus on the evaluation of environment, technology, user skills, user motivation, and subject engagement challenges. We provide a checklist to assist mHealth implementers with a structured evaluation protocol when planning and preparing projects

Resisting Sleep Pressure:Impact on Resting State Functional Network Connectivity

In today's 24/7 society, sleep restriction is a common phenomenon which leads to increased levels of sleep pressure in daily life. However, the magnitude and extent of impairment of brain functioning due to increased sleep pressure is still not completely understood. Resting state network (RSN) analyses have become increasingly popular because they allow us to investigate brain activity patterns in the absence of a specific task and to identify changes under different levels of vigilance (e.g. due to increased sleep pressure). RSNs are commonly derived from BOLD fMRI signals but studies progressively also employ cerebral blood flow (CBF) signals. To investigate the impact of sleep pressure on RSNs, we examined RSNs of participants under high (19 h awake) and normal (10 h awake) sleep pressure with three imaging modalities (arterial spin labeling, BOLD, pseudo BOLD) while providing confirmation of vigilance states in most conditions. We demonstrated that CBF and pseudo BOLD signals (measured with arterial spin labeling) are suited to derive independent component analysis based RSNs. The spatial map differences of these RSNs were rather small, suggesting a strong biological substrate underlying these networks. Interestingly, increased sleep pressure, namely longer time awake, specifically changed the functional network connectivity (FNC) between RSNs. In summary, all FNCs of the default mode network with any other network or component showed increasing effects as a function of increased 'time awake'. All other FNCs became more anti-correlated with increased 'time awake'. The sensorimotor networks were the only ones who showed a within network change of FNC, namely decreased connectivity as function of 'time awake'. These specific changes of FNC could reflect both compensatory mechanisms aiming to fight sleep as well as a first reduction of consciousness while becoming drowsy. We think that the specific changes observed in functional network connectivity could imply an impairment of information transfer between the affected RSNs

Multispectral camera fusion increases robustness of ROI detection for biosignal estimation with nearables in real-world scenarios

Thermal cameras enable non-contact estimation of the respiratory rate (RR). Accurate estimation of RR is highly dependent on the reliable detection of the region of interest (ROI), especially when using cameras with low pixel resolution. We present a novel approach for the automatic detection of the human nose ROI, based on facial landmark detection from an RGB camera that is fused with the thermal image after tracking. We evaluated the detection rate and spatial accuracy of the novel algorithm on recordings obtained from 16 subjects under challenging detection scenarios. Results show a high detection rate (median: 100 %, 5th - 95th percentile: 92 % - 100 %) and very good spatial accuracy with an average root mean square error of 2 pixels in the detected ROI center when compared to manual labeling. Therefore, the implementation of a multispectral camera fusion algorithm is a valid strategy to improve the reliability of non-contact RR estimation with nearable devices featuring thermal cameras

A Protocol for Comparing Dry and Wet EEG Electrodes During Sleep

Background Sleep is commonly assessed by recording the electroencephalogram (EEG) of the sleeping brain. As sleep assessments in a lab environment are cumbersome for both the participant and researcher, it would be highly desirable to record sleep EEG with a user-friendly and mobile device. Dry electrodes that are reusable, low-cost, and easy to apply would be an essential component of such a device. In this study, we developed a testing protocol to investigate the performance of novel flat-type dry electrodes for sleep EEG recordings in free-living conditions. Methods Overnight sleep EEG, electrooculogram and electromyogram of four young and healthy participants were recorded at home. Two identical ambulatory recording devices, one using novel flat-type dry electrodes, the other using self-adhesive pre-gelled electrodes, simultaneously recorded sleep EEG. Between both electrode types, we then compared the signal quality, the incidence of artifacts, the sensitivity, specificity and inter-scoring reliability (Cohen's kappa) of sleep staging, as well as the agreement of important characteristics of sleep-specific EEG microstructure features, such as slow waves (0.5-4 Hz) and sleep spindles (10-16 Hz). Results Our testing protocol comprehensively compared the two electrode types on a macro- and microstructure level of sleep. The dry and pre-gelled electrodes both had comparable signal quality and sleep staging was feasible with both electrodes. Also, slow-wave and spindle characteristics were similar. However, sweat artifacts were more prevalent in the flat-type dry electrodes. Conclusion With a reliable testing protocol, the performance of dry electrodes can be compared to reference technologies and objectively assessed also in free-living conditions

The multidimensional aspects of sleep spindles and their relationship to word-pair memory consolidation

STUDY OBJECTIVES Several studies proposed a link between sleep spindles and sleep dependent memory consolidation in declarative learning tasks. In addition to these state-like aspects of sleep spindles, they have also trait-like characteristics, i.e., were related to general cognitive performance, an important distinction that has often been neglected in correlative studies. Furthermore, from the multitude of different sleep spindle measures, often just one specific aspect was analyzed. Thus, we aimed at taking multidimensional aspects of sleep spindles into account when exploring their relationship to word-pair memory consolidation. DESIGN Each subject underwent 2 study nights with all-night high-density electroencephalographic (EEG) recordings. Sleep spindles were automatically detected in all EEG channels. Subjects were trained and tested on a word-pair learning task in the evening, and retested in the morning to assess sleep related memory consolidation (overnight retention). Trait-like aspects refer to the mean of both nights and state-like aspects were calculated as the difference between night 1 and night 2. SETTING Sleep laboratory. PARTICIPANTS Twenty healthy male subjects (age: 23.3 ± 2.1 y). MEASUREMENTS AND RESULTS Overnight retention was negatively correlated with trait-like aspects of fast sleep spindle density and positively with slow spindle density on a global level. In contrast, state-like aspects were observed for integrated slow spindle activity, which was positively related to the differences in overnight retention in specific regions. CONCLUSION Our results demonstrate the importance of a multidimensional approach when investigating the relationship between sleep spindles and memory consolidation and thereby provide a more complete picture explaining divergent findings in the literature