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Abstract Over the past decade, phase-targeted auditory stimulation (PTAS), a neuromodulation approach which presents auditory stimuli locked to the ongoing phase of slow waves during sleep, has shown potential to enhance specific aspects of sleep functions. However, the complexity of PTAS responses complicates the establishment of causality between specific electroencephalographic events and observed benefits. Here, we used down-PTAS during sleep to specifically evoke the early, K-complex (KC)-like response following PTAS without leading to a sustained increase in slow-wave activity throughout the stimulation window. Over the course of two nights, one with down-PTAS, the other without, high-density electroencephalography (hd-EEG) was recorded from 14 young healthy adults. The early response exhibited striking similarities to evoked KCs and was associated with improved verbal memory consolidation via stimulus-evoked spindle events nested into the up-phase of ongoing 1 Hz waves in a central region. These findings suggest that the early, KC-like response is sufficient to boost memory, potentially by orchestrating aspects of the hippocampal-neocortical dialogue.

Abstract Background Supplementary treatment options after pediatric severe traumatic brain injury (TBI) are needed to improve neurodevelopmental outcome. Evidence suggests enhancement of brain delta waves via auditory phase-targeted stimulation might support neuronal reorganization, however, this method has never been applied in analgosedated patients on the pediatric intensive care unit (PICU). Therefore, we conducted a feasibility study to investigate this approach: In a first recording phase, we examined feasibility of recording over time and in a second stimulation phase, we applied stimulation to address tolerability and efficacy. Methods Pediatric patients (> 12 months of age) with severe TBI were included between May 2019 and August 2021. An electroencephalography (EEG) device capable of automatic delta wave detection and sound delivery through headphones was used to record brain activity and for stimulation (MHSL-SleepBand version 2). Stimulation tolerability was evaluated based on report of nurses, visual inspection of EEG data and clinical signals (heart rate, intracranial pressure), and whether escalation of therapy to reduce intracranial pressure was needed. Stimulation efficacy was investigated by comparing EEG power spectra of active stimulation versus muted stimulation (unpaired t-tests). Results In total, 4 out of 32 TBI patients admitted to the PICU (12.5%) between 4 and 15 years of age were enrolled in the study. All patients were enrolled in the recording phase and the last one also to the stimulation phase. Recordings started within 5 days after insult and lasted for 1–4 days. Overall, 23–88 h of EEG data per patient were collected. In patient 4, stimulation was enabled for 50 min: No signs of patient stress reactions were observed. Power spectrums between active and muted stimulation were not statistically different (all P > .05). Conclusion Results suggests good feasibility of continuously applying devices needed for auditory stimulation over multiple days in pediatric patients with TBI on PICU. Very preliminary evidence suggests good tolerability of auditory stimuli, but efficacy of auditory stimuli to enhance delta waves remains unclear and requires further investigation. However, only low numbers of severe TBI patients could be enrolled in the study and, thus, future studies should consider an international multicentre approach.

In epidemiological studies related to winter sports, especially alpine skiing, an unresolved methodological challenge is the quantification of actual on-snow activity exposure. Such information would be relevant for reporting meaningful measures of injury incidence, which refers to the number of new injuries that occur in a given population and time period. Accordingly, accurate determination of the denominator, i.e., actual “activity exposure time”, is critical for injury surveillance and reporting. In this perspective article, we explore the question of whether wearable sensors in combination with mHealth applications are suitable tools to accurately quantify the periods in a ski day when the skier is physically skiing and not resting or using a mechanical means of transport. As a first proof of concept, we present exemplary data from a youth competitive alpine skier who wore his smartphone with embedded sensors on his body on several ski days during one winter season. We compared these data to self-reported estimates of ski exposure, as used in athletes' training diaries. In summary, quantifying on-snow activity exposure in alpine skiing using sensor data from smartphones is technically feasible. For example, the sensors could be used to track ski training sessions, estimate the actual time spent skiing, and even quantify the number of runs and turns made as long as the smartphone is worn. Such data could be very useful in determining actual exposure time in the context of injury surveillance and could prove valuable for effective stress management and injury prevention in athletes.

Lustenberger et al. perform a randomized crossover trial of auditory deep sleep stimulation in older adults, conducted over multiple weeks within the participants’ homes. The authors report substantial inter- and intra-individual differences in the slow wave response to auditory stimulation and identify predictors of response.

BackgroundFear of movement is thought to interfere with the recovery from low back pain (LBP). To date, the relationship between fear of movement and postural balance has not been adequately elucidated. Recent findings suggest that more specific fears need to be assessed and put in relation to a specific movement task. We propose that the fear to bend the trunk in a certain direction is distinctly related to the amount of postural sway in different directions. Therefore, our aim was to investigate whether fear of movement in general and fear of bending the trunk in a certain plane is related to postural sway.MethodsData was collected from participants with LBP during two assessments ~3 weeks apart. Postural sway was measured with a force-platform during quiet standing with the eyes closed. Fear of movement was assessed with an abbreviated version of the Tampa Scale of Kinesiophobia (TSK-11) and custom items referring to fear of bending the trunk in the sagittal and the frontal plane.ResultsBased on data from 25 participants, fear of bending the trunk in the frontal plane was positively related to displacement in the sagittal and frontal plane and to velocity in the frontal plane (χ2 = 4.35, p = 0.04; χ2 = 8.15, p = 0.004; χ2 = 9.79, p = 0.002). Fear of bending the trunk in the sagittal plane was not associated with any direction specific measure of sway. A positive relation of the TSK-11 with velocity of the frontal plane (χ2 = 7.14, p = 0.008) was found, but no association with undirected measures of sway.DiscussionFear of bending the trunk in the frontal plane may be especially relevant to postural sway under the investigated stance conditions. It is possible that fear of bending the trunk in the frontal plane could interfere with balance control at the hip, shifting the weight from side to side to control balance.ConclusionFor the first time the directional relationship of fear of movement and postural sway was studied. Fear of bending the trunk in the frontal plane was positively associated with several measures of postural sway.

BackgroundPostural balance is compromised in people with low back pain, possibly by changes in motor control of the trunk. Augmenting exercising interventions with sensor-based feedback on trunk posture and movements might improve postural balance in people with low back pain. ObjectiveWe hypothesized that exercising with feedback on trunk movements reduces sway in anterior-posterior direction during quiet standing in people with low back pain. Secondary outcomes were lumbar spine and hip movement assessed during box lift and waiter bow tasks, as well as participant-reported outcomes. Adherence to the exercising intervention was also examined. MethodsA randomized controlled trial was conducted with the intervention group receiving unsupervised home exercises with visual feedback using the Valedo Home, an exergame based on 2 inertial measurement units. The control group received no intervention. Outcomes were recorded by blinded staff during 4 visits (T1-T4) at University Hospital Zurich. The intervention group performed 9 sessions of 20 minutes in the 3 weeks between T2 and T3 and were instructed to exercise at their own convenience between T3 and T4. Postural balance was assessed on a force platform. Lumbar spine and hip angles were obtained from 3 inertial measurement units. The assessments included pain intensity, disability, quality of life, and fear of movement questionnaires. ResultsA total of 32 participants with nonspecific low back pain completed the first assessment T1, and 27 (84%) participants were randomized at T2 (n=14, 52% control and n=13, 48% intervention). Intention-to-treat analysis revealed no significant difference in change in anterior-posterior sway direction during the intervention period with a specified schedule (T2-T3) between the groups (W=99; P=.36; r=0.07). None of the outcomes showed significant change in accordance with our hypotheses. The intervention group completed a median of 61% (55/90; range 2%-99%) of the exercises in the predefined training program. Adherence was higher in the first intervention period with a specified schedule. ConclusionsThe intervention had no significant effect on postural balance or other outcomes, but the wide range of adherence and a limited sample size challenged the robustness of these conclusions. Future work should increase focus on improving adherence to digital interventions. Trial RegistrationClinicalTrials.gov NCT04364243; https://clinicaltrials.gov/ct2/show/NCT04364243 International Registered Report Identifier (IRRID)RR2-10.2196/26982

Abstract Background Ethics review is the process of assessing the ethics of research involving humans. The Ethics Review Committee (ERC) is the key oversight mechanism designated to ensure ethics review. Whether or not this governance mechanism is still fit for purpose in the data-driven research context remains a debated issue among research ethics experts. Main text In this article, we seek to address this issue in a twofold manner. First, we review the strengths and weaknesses of ERCs in ensuring ethical oversight. Second, we map these strengths and weaknesses onto specific challenges raised by big data research. We distinguish two categories of potential weakness. The first category concerns persistent weaknesses, i.e., those which are not specific to big data research, but may be exacerbated by it. The second category concerns novel weaknesses, i.e., those which are created by and inherent to big data projects. Within this second category, we further distinguish between purview weaknesses related to the ERC’s scope (e.g., how big data projects may evade ERC review) and functional weaknesses, related to the ERC’s way of operating. Based on this analysis, we propose reforms aimed at improving the oversight capacity of ERCs in the era of big data science. Conclusions We believe the oversight mechanism could benefit from these reforms because they will help to overcome data-intensive research challenges and consequently benefit research at large.

Abstract Background Exposure to unhealthy environments and inadequate child stimulation are main risk factors that affect children’s health and wellbeing in low- and middle-income countries. Interventions that simultaneously address several risk factors at the household level have great potential to reduce these negative effects. We present the design and baseline findings of a cluster-randomised controlled trial to evaluate the impact of an integrated home-environmental intervention package and an early child development programme to improve diarrhoea, acute respiratory infections and childhood developmental outcomes in children under 36 months of age living in resource-limited rural Andean Peru. Methods We collected baseline data on children’s developmental performance, health status and demography as well as microbial contamination in drinking water. In a sub-sample of households, we measured indoor kitchen 24-h air concentration levels of carbon monoxide (CO) and fine particulate matter (PM2.5) and CO for personal exposure. Results We recruited and randomised 317 children from 40 community-clusters to four study arms. At baseline, all arms had similar health and demographic characteristics, and the developmental status of children was comparable between arms. The analysis revealed that more than 25% of mothers completed primary education, a large proportion of children were stunted and diarrhoea prevalence was above 18%. Fifty-two percent of drinking water samples tested positive for thermo-tolerant coliforms and the occurrence of E.coli was evenly distributed between arms. The mean levels of kitchen PM2.5 and CO concentrations were 213 μg/m3 and 4.8 ppm, respectively. Conclusions The trial arms are balanced with respect to most baseline characteristics, such as household air and water pollution, and child development. These results ensure the possible estimation of the trial effectiveness. This trial will yield valuable information for assessing synergic, rational and cost-effective benefits of the combination of home-based interventions. Trial Registry ISRCTN-26548981 .

Sufficient recovery during sleep is the basis of physical and psychological well-being. Understanding the physiological mechanisms underlying this restorative function is essential for developing novel approaches to promote recovery during sleep. Phase-targeted auditory stimulation (PTAS) is an increasingly popular technique for boosting the key electrophysiological marker of recovery during sleep, slow-wave activity (SWA, 1–4 Hz EEG power). However, it is unknown whether PTAS induces physiological sleep. In this study, we demonstrate that, when applied during deep sleep, PTAS accelerates SWA decline across the night which is associated with an overnight improvement in attentional performance. Thus, we provide evidence that PTAS enhances physiological sleep and demonstrate under which conditions this occurs most efficiently. These findings will be important for future translation into clinical populations suffering from insufficient recovery during sleep.

Chronic wounds significantly impact quality of life. They can rapidly deteriorate and require close monitoring of healing progress. Image-based wound analysis is a way of objectively assessing the wound status by quantifying important features that are related to healing. However, high heterogeneity of the wound types and imaging conditions challenge the robust segmentation of wound images. We present Detect-and-Segment (DS), a deep learning approach to produce wound segmentation maps with high generalization capabilities. In our approach, dedicated deep neural networks detected the wound position, isolated the wound from the perturbing background, and computed a wound segmentation map. We tested this approach on a diabetic foot ulcers data set and compared it to a segmentation method based on the full image. To evaluate its generalizability on out-of-distribution data, we measured the performance of the DS approach on 4 additional independent data sets, with larger variety of wound types from different body locations. The Matthews’ correlation coefficient (MCC) improved from 0.29 (full image) to 0.85 (DS) on the diabetic foot ulcer data set. When the DS was tested on the independent data sets, the mean MCC increased from 0.17 to 0.85 . Furthermore, the DS enabled the training of segmentation models with up to 90% less training data without impacting the segmentation performance. The proposed DS approach is a step towards automating wound analysis and reducing efforts to manage chronic wounds.

BackgroundMultisensor fitness trackers offer the ability to longitudinally estimate sleep quality in a home environment with the potential to outperform traditional actigraphy. To benefit from these new tools for objectively assessing sleep for clinical and research purposes, multisensor wearable devices require careful validation against the gold standard of sleep polysomnography (PSG). Naturalistic studies favor validation. ObjectiveThis study aims to validate the Fitbit Charge 2 against portable home PSG in a shift-work population composed of 59 first responder police officers and paramedics undergoing shift work. MethodsA reliable comparison between the two measurements was ensured through the data-driven alignment of a PSG and Fitbit time series that was recorded at night. Epoch-by-epoch analyses and Bland-Altman plots were used to assess sensitivity, specificity, accuracy, the Matthews correlation coefficient, bias, and limits of agreement. ResultsSleep onset and offset, total sleep time, and the durations of rapid eye movement (REM) sleep and non–rapid-eye movement sleep stages N1+N2 and N3 displayed unbiased estimates with nonnegligible limits of agreement. In contrast, the proprietary Fitbit algorithm overestimated REM sleep latency by 29.4 minutes and wakefulness after sleep onset (WASO) by 37.1 minutes. Epoch-by-epoch analyses indicated better specificity than sensitivity, with higher accuracies for WASO (0.82) and REM sleep (0.86) than those for N1+N2 (0.55) and N3 (0.78) sleep. Fitbit heart rate (HR) displayed a small underestimation of 0.9 beats per minute (bpm) and a limited capability to capture sudden HR changes because of the lower time resolution compared to that of PSG. The underestimation was smaller in N2, N3, and REM sleep (0.6-0.7 bpm) than in N1 sleep (1.2 bpm) and wakefulness (1.9 bpm), indicating a state-specific bias. Finally, Fitbit suggested a distribution of all sleep episode durations that was different from that derived from PSG and showed nonbiological discontinuities, indicating the potential limitations of the staging algorithm. ConclusionsWe conclude that by following careful data processing processes, the Fitbit Charge 2 can provide reasonably accurate mean values of sleep and HR estimates in shift workers under naturalistic conditions. Nevertheless, the generally wide limits of agreement hamper the precision of quantifying individual sleep episodes. The value of this consumer-grade multisensor wearable in terms of tackling clinical and research questions could be enhanced with open-source algorithms, raw data access, and the ability to blind participants to their own sleep data.

Background: Heart rate (HR) and respiratory rate (RR) can be challenging to measure accurately and reliably in neonates. The introduction of innovative, non-invasive measurement technologies suitable for resource-constrained settings is limited by the lack of appropriate clinical thresholds for accuracy comparison studies. Methods: We collected measurements of photoplethysmography-recorded HR and capnography-recorded exhaled carbon dioxide across multiple 60-second epochs (observations) in enrolled neonates admitted to the neonatal care unit at Aga Khan University Hospital in Nairobi, Kenya. Trained study nurses manually recorded HR, and the study team manually counted individual breaths from capnograms. For comparison, HR and RR also were measured using an automated signal detection algorithm. Clinical measurements were analyzed for repeatability. Results: A total of 297 epochs across 35 neonates were recorded. Manual HR showed a bias of -2.4 (-1.8%) and a spread between the 95% limits of agreement (LOA) of 40.3 (29.6%) compared to the algorithm-derived median HR. Manual RR showed a bias of -3.2 (-6.6%) and a spread between the 95% LOA of 17.9 (37.3%) compared to the algorithm-derived median RR, and a bias of -0.5 (1.1%) and a spread between the 95% LOA of 4.4 (9.1%) compared to the algorithm-derived RR count. Manual HR and RR showed repeatability of 0.6 (interquartile range (IQR) 0.5-0.7), and 0.7 (IQR 0.5-0.8), respectively. Conclusions: Appropriate clinical thresholds should be selected a priori when performing accuracy comparisons for HR and RR. Automated measurement technologies typically use a smoothing or averaging filter, which significantly impacts accuracy. A wider spread between the LOA, as much as 30%, should be considered to account for the observed physiological nuances and within- and between-neonate variability and different averaging methods. Wider adoption of thresholds by data standards organizations and technology developers and manufacturers will increase the robustness of clinical comparison studies.

BackgroundPhysical exercise is a common treatment for people with low back pain (LBP). Wearable sensors that provide feedback on body movements and posture during exercise may enhance postural balance and motor control in people with LBP. ObjectiveThis study aims to investigate whether physical exercising with postural feedback (EPF) improves postural balance, motor control, and patient-reported outcomes in people with LBP. MethodsThe study was an assessor-blinded 2×2 factorial trial. We planned to recruit 80 participants with nonspecific LBP who did not receive treatment for LBP. In addition, we aimed to recruit 40 patients with chronic, nonspecific LBP who were receiving exercise therapy (ET) at the University Hospital Zurich. Both ET patients and participants without treatment were randomized to receive either an additional EPF intervention or no additional intervention. This resulted in four different combinations of interventions: ET+EPF, ET, EPF, and no intervention. The participants underwent outcome assessments at inclusion (T1); 3 weeks later, at randomization (T2); after an intervention period of 3 weeks with a predefined exercise schedule for participants receiving EPF (T3); and after an additional 6 weeks, during which participants assigned to the EPF groups could exercise as much as they wished (T4). Patients receiving ET completed their regularly prescribed therapies during the study period. Balance was assessed during quiet standing on a force platform, and motor control was assessed during a lifting task and a waiter’s bow task. Physical activity was recorded using an activity tracker and the participants’ mobile phones during the study. The predefined EPF schedule consisted of nine sessions of 20 minutes of exercise with a tablet and inertial measurement unit sensors at home. Participants performed a series of trunk and hip movements and received feedback on their movements in a gamified environment displayed on the tablet. ResultsThe first participant was recruited in May 2019. Data collection was completed in October 2020, with 3 patients and 32 eligible people without therapy who passed the eligibility check. ConclusionsAlthough it will not be possible to investigate differences in patients and people without other therapies, we expect this pilot study to provide insights into the potential of EPF to improve balance in people with LBP and adherence to such interventions. International Registered Report Identifier (IRRID)DERR1-10.2196/26982

BackgroundTravel 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. ObjectiveOur 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. MethodsWe 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. ResultsA 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

BackgroundSleep 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.MethodsOvernight 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).ResultsOur 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.ConclusionWith a reliable testing protocol, the performance of dry electrodes can be compared to reference technologies and objectively assessed also in free-living conditions.

Abstract Objective Normobaric (NH) and hypobaric hypoxia (HH) are associated with acute mountain sickness (AMS) and cognitive dysfunction. Only few variables, like heart-rate-variability, are correlated with AMS. However, prediction of AMS remains difficult. We therefore designed an expedition-study with healthy volunteers in NH/HH to investigate additional non-invasive hemodynamic variables associated with AMS. Methods Eleven healthy subjects were examined in NH (FiO2 13.1%; equivalent of 3.883 m a.s.l; duration 4 h) and HH (3.883 m a.s.l.; duration 24 h) before and after an exercise of 120 min. Changes in parameters of electrical cardiometry (cardiac index (CI), left-ventricular ejection time (LVET), stroke volume (SV), index of contractility (ICON)), near-infrared spectroscopy (cerebral oxygenation, rScO2), Lake-Louise-Score (LLS) and cognitive function tests were assessed. One-Way-ANOVA, Wilcoxon matched-pairs test, Spearman’s-correlation-analysis and Student’s t-test were performed. Results HH increased heart rate (HR), mean arterial pressure (MAP) and CI and decreased LVET, SV and ICON, whereas NH increased HR and decreased LVET. In both NH and HH cerebral oxygenation decreased and LLS increased significantly. After 24 h in HH, 6 of 11 subjects (54.6%) developed AMS. LLS remained increased until 24 h in HH, whereas cognitive function remained unaltered. In HH, HR and LLS were inversely correlated (r = − 0.692; p

Abstract Malaria, together with HIV/AIDS, tuberculosis and hepatitis are the four most deadly infectious diseases globally. Progress in eliminating malaria has saved millions of lives, but also creates new challenges in detecting the ‘last parasite’. Effective and accurate detection of malaria infections, both in symptomatic and asymptomatic individuals are needed. In this review, the current progress in developing new diagnostic tools to fight malaria is presented. An ideal rapid test for malaria elimination is envisioned with examples to demonstrate how innovative technologies can assist the global defeat against this disease. Diagnostic gaps where technology can bring an impact to the elimination campaign for malaria are identified. Finally, how a combination of microfluidic-based technologies and smartphone-based read-outs could potentially represent the next generation of rapid diagnostic tests is discussed.

BACKGROUND:Children discharged from hospitals in developing countries are at high risk of morbidity and mortality. However, few data describe these outcomes among children seen and discharged from rural outpatient centers. OBJECTIVE:The objective of this exploratory study was to identify predictors of immediate and follow-up morbidity and mortality among children visiting a rural health center in Uganda. METHODS:Subjects 0-12 years of age seeking care with a caregiver were consecutively enrolled from a single rural health center in Southwestern Uganda. Baseline variables were collected by research nurses and outcomes of referral, admission or death were recorded (immediate events). Death, hospital admission and health seeking occurring during the 30 days following the clinic visit were also determined (follow-up events). Univariate logistic regression was performed to identify baseline variables associated with immediate outcome and follow-up outcomes. RESULTS:Over the four-month recruitment period 717 subjects were enrolled. There were 85 (11.9%) immediate events (10.1% were admitted, 2.2% were referred, none died). Forty-seven (7.8%) events occurred within 30 days after the visit (7.3% sought care from a health provider, 1.5% were admitted and 0.5% died). Variables associated with immediate events included living more than 30 minutes from the health center, age older than 5 years, having received an antimalarial prior to the visit, having seen a community health worker prior to the visit, elevated respiratory rate or temperature, and depressed weight-for-age z score or decreased oxygen saturation. These variables were not associated with follow-up events. CONCLUSIONS:Sick-child visits at a rural health center in South Western Uganda were associated with rates of mortality and subsequent admission of less than 2% in the period following the sick child visits. Other types of health seeking behavior occurred in approximately 7% of subjects during this same period. Several variables were associated with immediate events but there were no reliable predictors of follow-up events, possibly due to low statistical power.

The reduction in the deaths of millions of children who die from infectious diseases requires early initiation of treatment and improved access to care available in health facilities. A major challenge is the lack of objective evidence to guide front line health workers in the community to recognize critical illness in children earlier in their course.We undertook a prospective observational study of children less than 5 years of age presenting at the outpatient or emergency department of a rural tertiary care hospital between October 2012 and April 2013. Study physicians collected clinical signs and symptoms from the facility records, and with a mobile application performed recordings of oxygen saturation, heart rate and respiratory rate. Facility physicians decided the need for hospital admission without knowledge of the oxygen saturation. Multiple logistic predictive models were tested.Twenty-five percent of the 3374 assessed children, with a median (interquartile range) age of 1.02 (0.42-2.24), were admitted to hospital. We were unable to contact 20% of subjects after their visit. A logistic regression model using continuous oxygen saturation, respiratory rate, temperature and age combined with dichotomous signs of chest indrawing, lethargy, irritability and symptoms of cough, diarrhea and fast or difficult breathing predicted admission to hospital with an area under the receiver operating characteristic curve of 0.89 (95% confidence interval -CI: 0.87 to 0.90). At a risk threshold of 25% for admission, the sensitivity was 77% (95% CI: 74% to 80%), specificity was 87% (95% CI: 86% to 88%), positive predictive value was 70% (95% CI: 67% to 73%) and negative predictive value was 91% (95% CI: 90% to 92%).A model using oxygen saturation, respiratory rate and temperature in combination with readily obtained clinical signs and symptoms predicted the need for hospitalization of critically ill children. External validation of this model in a community setting will be required before adoption into clinical practice.

The recommended method for measuring respiratory rate (RR) is counting breaths for 60 s using a timer. This method is not efficient in a busy clinical setting. There is an urgent need for a robust, low-cost method that can help front-line health care workers to measure RR quickly and accurately. Our aim was to develop a more efficient RR assessment method. RR was estimated by measuring the median time interval between breaths obtained from tapping on the touch screen of a mobile device. The estimation was continuously validated by measuring consistency (% deviation from the median) of each interval. Data from 30 subjects estimating RR from 10 standard videos with a mobile phone application were collected. A sensitivity analysis and an optimization experiment were performed to verify that a RR could be obtained in less than 60 s; that the accuracy improves when more taps are included into the calculation; and that accuracy improves when inconsistent taps are excluded. The sensitivity analysis showed that excluding inconsistent tapping and increasing the number of tap intervals improved the RR estimation. Efficiency (time to complete measurement) was significantly improved compared to traditional methods that require counting for 60 s. There was a trade-off between accuracy and efficiency. The most balanced optimization result provided a mean efficiency of 9.9 s and a normalized root mean square error of 5.6%, corresponding to 2.2 breaths/min at a respiratory rate of 40 breaths/min. The obtained 6-fold increase in mean efficiency combined with a clinically acceptable error makes this approach a viable solution for further clinical testing. The sensitivity analysis illustrating the trade-off between accuracy and efficiency will be a useful tool to define a target product profile for any novel RR estimation device.

The photoplethysmogram (PPG) obtained from pulse oximetry measures local variations of blood volume in tissues, reflecting the peripheral pulse modulated by heart activity, respiration and other physiological effects. We propose an algorithm based on the correntropy spectral density (CSD) as a novel way to estimate respiratory rate (RR) and heart rate (HR) from the PPG. Time-varying CSD, a technique particularly well-suited for modulated signal patterns, is applied to the PPG. The respiratory and cardiac frequency peaks detected at extended respiratory (8 to 60 breaths/min) and cardiac (30 to 180 beats/min) frequency bands provide RR and HR estimations. The CSD-based algorithm was tested against the Capnobase benchmark dataset, a dataset from 42 subjects containing PPG and capnometric signals and expert labeled reference RR and HR. The RR and HR estimation accuracy was assessed using the unnormalized root mean square (RMS) error. We investigated two window sizes (60 and 120 s) on the Capnobase calibration dataset to explore the time resolution of the CSD-based algorithm. A longer window decreases the RR error, for 120-s windows, the median RMS error (quartiles) obtained for RR was 0.95 (0.27, 6.20) breaths/min and for HR was 0.76 (0.34, 1.45) beats/min. Our experiments show that in addition to a high degree of accuracy and robustness, the CSD facilitates simultaneous and efficient estimation of RR and HR. Providing RR every minute, expands the functionality of pulse oximeters and provides additional diagnostic power to this non-invasive monitoring tool.

BACKGROUND: Sleep disordered breathing (SDB) can lead to daytime sleepiness, growth failure and developmental delay in children. Polysomnography (PSG), the gold standard to diagnose SDB, is a highly resource-intensive test, confined to the sleep laboratory. AIM: To combine the blood oxygen saturation (SpO2) characterization and cardiac modulation, quantified by pulse rate variability (PRV), to identify children with SDB using the Phone Oximeter, a device integrating a pulse oximeter with a smartphone. METHODS: Following ethics approval and informed consent, 160 children referred to British Columbia Children's Hospital for overnight PSG were recruited. A second pulse oximeter sensor applied to the finger adjacent to the one used for standard PSG was attached to the Phone Oximeter to record overnight pulse oximetry (SpO2 and photoplethysmogram (PPG)) alongside the PSG. RESULTS: We studied 146 children through the analysis of the SpO2 pattern, and PRV as an estimate of heart rate variability calculated from the PPG. SpO2 variability and SpO2 spectral power at low frequency, was significantly higher in children with SDB due to the modulation provoked by airway obstruction during sleep (p-value

This paper describes hardware and behavior implementation of a miniature robot in size of a match box that simulates the behavior of cockroaches in order to establish a social interaction with them. The robot is equipped with two micro-processors dedicated to hardware processing and behavior generation. The robot can discriminate cockroaches, other robots, environment boundaries and shelters. It has also three means of communication to monitor, log, supervise the biological experiment, and detect the other robots in short range. The behavioral model of the robot is a mixture of fusion in low-level and arbitration in high-level. In arbitration level a stochastic state machine selects the proper subtask. Then in fusion level, that subtask is decomposed to a hierarchy of sub-tasks. Each sub-task generates a potential field. The resultant force is then mapped to an action.

This paper describes hardware and behavior implementation of a miniature robot in size of a match box that simulates the behavior of cockroaches in order to establish a social interaction with them. The robot is equipped with two micro-processors dedicated to hardware processing and behavior generation. The robot can discriminate cockroaches, other robots, environment boundaries and shelters. It has also three means of communication to monitor, log, supervise the biological experiment, and detect the other robots in short range. The behavioral model of the robot is a mixture of fusion in low-level and arbitration in high-level. In arbitration level a stochastic state machine selects the proper subtask. Then in fusion level, that subtask is decomposed to a hierarchy of sub-tasks. Each sub-task generates a potential field. The resultant force is then mapped to an action.