Missing Data Statistics Provide Causal Insights into Data Loss in Diabetes Health Monitoring by Wearable Sensors.

BACKGROUND: Data loss in wearable sensors is an inevitable problem that leads to misrepresentation during diabetes health monitoring. We systematically investigated missing wearable sensors data to get causal insight into the mechanisms leading to missing data. METHODS: Two-week-long data from a continuous glucose monitor and a Fitbit activity tracker recording heart rate (HR) and step count in free-living patients with type 2 diabetes mellitus were used. The gap size distribution was fitted with a Planck distribution to test for missing not at random (MNAR) and a difference between distributions was tested with a Chi-squared test. Significant missing data dispersion over time was tested with the Kruskal-Wallis test and Dunn post hoc analysis. RESULTS: Data from 77 subjects resulted in 73 cleaned glucose, 70 HR and 68 step count recordings. The glucose gap sizes followed a Planck distribution. HR and step count gap frequency differed significantly (p < 0.001), and the missing data were therefore MNAR. In glucose, more missing data were found in the night (23:00-01:00), and in step count, more at measurement days 6 and 7 (p < 0.001). In both cases, missing data were caused by insufficient frequency of data synchronization. CONCLUSIONS: Our novel approach of investigating missing data statistics revealed the mechanisms for missing data in Fitbit and CGM data.

Early Warning Scores to Support Continuous Wireless Vital Sign Monitoring for Complication Prediction in Patients on Surgical Wards: Retrospective Observational Study.

BACKGROUND: Wireless vital sign sensors are increasingly being used to monitor patients on surgical wards. Although early warning scores (EWSs) are the current standard for the identification of patient deterioration in a ward setting, their usefulness for continuous monitoring is unknown. OBJECTIVE: This study aimed to explore the usability and predictive value of high-rate EWSs obtained from continuous vital sign recordings for early identification of postoperative complications and compares the performance of a sensor-based EWS alarm system with manual intermittent EWS measurements and threshold alarms applied to individual vital sign recordings (single-parameter alarms). METHODS: Continuous vital sign measurements (heart rate, respiratory rate, blood oxygen saturation, and axillary temperature) collected with wireless sensors in patients on surgical wards were used for retrospective simulation of EWSs (sensor EWSs) for different time windows (1-240 min), adopting criteria similar to EWSs based on manual vital signs measurements (nurse EWSs). Hourly sensor EWS measurements were compared between patients with (event group: 14/46, 30%) and without (control group: 32/46, 70%) postoperative complications. In addition, alarms were simulated for the sensor EWSs using a range of alarm thresholds (1-9) and compared with alarms based on nurse EWSs and single-parameter alarms. Alarm performance was evaluated using the sensitivity to predict complications within 24 hours, daily alarm rate, and false discovery rate (FDR). RESULTS: The hourly sensor EWSs of the event group (median 3.4, IQR 3.1-4.1) was significantly higher (P<.004) compared with the control group (median 2.8, IQR 2.4-3.2). The alarm sensitivity of the hourly sensor EWSs was the highest (80%-67%) for thresholds of 3 to 5, which was associated with alarm rates of 2 (FDR=85%) to 1.2 (FDR=83%) alarms per patient per day respectively. The sensitivity of sensor EWS-based alarms was higher than that of nurse EWS-based alarms (maximum=40%) but lower than that of single-parameter alarms (87%) for all thresholds. In contrast, the (false) alarm rates of sensor EWS-based alarms were higher than that of nurse EWS-based alarms (maximum=0.6 alarm/patient/d; FDR=80%) but lower than that of single-parameter alarms (2 alarms/patient/d; FDR=84%) for most thresholds. Alarm rates for sensor EWSs increased for shorter time windows, reaching 70 alarms per patient per day when calculated every minute. CONCLUSIONS: EWSs obtained using wireless vital sign sensors may contribute to the early recognition of postoperative complications in a ward setting, with higher alarm sensitivity compared with manual EWS measurements. Although hourly sensor EWSs provide fewer alarms compared with single-parameter alarms, high false alarm rates can be expected when calculated over shorter time spans. Further studies are recommended to optimize care escalation criteria for continuous monitoring of vital signs in a ward setting and to evaluate the effects on patient outcomes.

Missing data imputation techniques for wireless continuous vital signs monitoring.

Wireless vital signs sensors are increasingly used for remote patient monitoring, but data analysis is often challenged by missing data periods. This study explored the performance of various imputation techniques for continuous vital signs measurements. Wireless vital signs measurements (heart rate, respiratory rate, blood oxygen saturation, axillary temperature) from surgical ward patients were used for repeated random simulation of missing data periods (gaps) of 5-60 min in two-hour windows. Gaps were imputed using linear interpolation, spline interpolation, last observation- and mean carried forwards technique, and cluster-based prognosis. Imputation performance was evaluated using the mean absolute error (MAE) between original and imputed gap samples. Besides, effects on signal features (window's slope, mean) and early warning scores (EWS) were explored. Gaps were simulated in 1743 data windows, obtained from 52 patients. Although MAE ranges overlapped, median MAE was structurally lowest for linear interpolation (heart rate: 0.9-2.6 beats/min, respiratory rate: 0.8-1.8 breaths/min, temperature: 0.04-0.17 °C, oxygen saturation: 0.3-0.7% for 5-60 min gaps) but up to twice as high for other techniques. Three techniques resulted in larger ranges of signal feature bias compared to no imputation. Imputation led to EWS misclassification in 1-8% of all simulations. Imputation error ranges vary between imputation techniques and increase with gap length. Imputation may result in larger signal feature bias compared to performing no imputation, and can affect patient risk assessment as illustrated by the EWS. Accordingly, careful implementation and selection of imputation techniques is warranted.

COVID-BEHAVE dataset: measuring human behaviour during the COVID-19 pandemic.

Aiming to illuminate the effects of enforced confinements on people's lives, this paper presents a novel dataset that measures human behaviour holistically and longitudinally during the COVID-19 outbreak. In particular, we conducted a study during the first wave of the lockdown, where 21 healthy subjects from the Netherlands and Greece participated, collecting multimodal raw and processed data from smartphone sensors, activity trackers, and users' responses to digital questionnaires. The study lasted more than two months, although the duration of the data collection varies per participant. The data are publicly available and can be used to model human behaviour in a broad sense as the dataset explores physical, social, emotional, and cognitive domains. The dataset offers an exemplary perspective on a given group of people that could be considered to build new models for investigating behaviour changes as a consequence of the lockdown. Importantly, to our knowledge, this is the first dataset combining passive sensing, experience sampling, and virtual assistants to study human behaviour dynamics in a prolonged lockdown situation.

Determining the Reliable Measurement Period for Preoperative Baseline Values With Telemonitoring Before Major Abdominal Surgery: Pilot Cohort Study.

BACKGROUND: Preoperative telemonitoring of vital signs, physical activity, and well-being might be able to optimize prehabilitation of the patient's physical and mental condition prior to surgery, support setting alarms during in-hospital monitoring, and allow personalization of the postoperative recovery process. OBJECTIVE: The primary aim of this study was to evaluate when and how long patients awaiting major abdominal surgery should be monitored to get reliable preoperative individual baseline values of heart rate (HR), daily step count, and patient-reported outcome measures (PROMs). The secondary aim was to describe the perioperative course of these measurements at home. METHODS: In this observational single-center cohort study, patients used a wearable sensor during waking hours and reported PROMs (pain, anxiety, fatigue, nausea) on a tablet twice a day. Intraclass correlation coefficients (ICCs) were used to evaluate the reliability of mean values on 2 specific preoperative days (the first day of telemonitoring and the day before hospital admission) and randomly selected preoperative periods compared to individual reference values. Mean values of HR, step count, and PROMs per day were visualized in a boxplot from 14 days before hospital admission until 30 days after surgery. RESULTS: A total of 16 patients were included in the data analyses. The ICCs of mean values on the first day of telemonitoring were 0.91 for HR, 0.71 for steps, and at least 0.86 for PROMs. The day before hospital admission showed reliability coefficients of 0.76 for HR, 0.71 for steps, and 0.92-0.99 for PROMs. ICC values of randomly selected measurement periods increased over the continuous period of time from 0.68 to 0.99 for HR and daily step counts. A lower bound of the 95% CI of at least 0.75 was determined after 3 days of measurements. The ICCs of randomly selected PROM measurements were 0.89-0.94. Visualization of mean values per day mainly showed variable preoperative daily step counts (median 2409, IQR 1735-4661 steps/day) and lower postoperative daily step counts (median 884, IQR 474-1605 steps/day). In addition, pain was visually reduced until 30 days after surgery at home. CONCLUSIONS: In this prospective pilot study, for patients awaiting major abdominal surgery, baseline values for HR and daily step count could be measured reliably by a wearable sensor worn for at least 3 consecutive days and PROMs during any preoperative day. No clear conclusions were drawn from the description of the perioperative course by showing mean values of HR, daily step count, and PROM values over time in the home situation.

Feasibility and patient's experiences of perioperative telemonitoring in major abdominal surgery: an observational pilot study.

BACKGROUND: Telemonitoring during the perioperative trajectory may improve patient outcomes and self-management. The aim of this study is to assess the feasibility of and patient's experiences with telemonitoring before and after major abdominal surgery to inform future study design. METHODS: Patients planned for elective major abdominal surgery wore a sensor and answered well-being questions on a tablet daily for at least 2 weeks preoperatively up to 30-days postoperatively. Feasibility was assessed by participation and completion rate, compliance per day, weekly satisfaction scores, and reasons for nonscheduled contact. RESULTS: Twenty-three patients were included (participation rate of 54.5%) with a completion rate of 69.6%. Median compliance with the wearable sensor and well-being questions was respectively: 94.7% and 83.3% preoperatively at home; 100% and 66.7% postoperatively in-hospital; and 95.4% and 85.8% postoperatively at home. Median weekly satisfaction scores for both wearing the sensor and well-being questions were 5 (IQR, 4-5). Contact moments were related to absence of sensor data and technological issues (76.0%) or patient discomfort and insecurity (24.0%). CONCLUSIONS: In this study, telemonitoring showed high satisfaction and compliance during the perioperative trajectory. Future trial design regarding the effectiveness of telemonitoring requires embedding in clinical practice and support for patients, relatives, and healthcare personnel.

A guide for standardized interpretation of lumbar multifidus ultrasonography; an observational study.

BACKGROUND: Inconsistent descriptions of Lumbar multifidus (LM) morphology were previously identified, especially in research applying ultrasonography (US), hampering its clinical applicability with regard to diagnosis and therapy. The aim of this study is to determine the LM-sonoanatomy by comparing high-resolution reconstructions from a 3-D digital spine compared to standard LM-ultrasonography. METHODS: An observational study was carried out. From three deeply frozen human tissue blocks of the lumbosacral spine, a large series of consecutive photographs at 78 µm interval were acquired and reformatted into 3-D blocks. This enabled the reconstruction of (semi-)oblique cross-sections that could match US-images obtained from a healthy volunteer. Transverse and oblique short-axis views were compared from the most caudal insertion of LM to L1. RESULTS: Based on the anatomical reconstructions, we could distinguish the LM from the adjacent erector spinae (ES) in the standard US imaging of the lower spine. At the lumbosacral junction, LM is the only dorsal muscle facing the surface. From L5 upwards, the ES progresses from lateral to medial. A clear distinction between deep and superficial LM could not be discerned. We were only able to identify five separate bands between every lumbar spinous processes and the dorsal part of the sacrum in the caudal anatomical cross-sections, but not in the standard US images. CONCLUSION: The detailed cross-sectional LM-sonoanatomy and reconstructions facilitate the interpretations of standard LM US-imaging, the position of the separate LM-bands, the details of deep interspinal muscles, and demarcation of the LM versus the ES. Guidelines for electrode positioning in EMG studies should be refined to establish reliable and verifiable findings. For clinical practice, this study can serve as a guide for a better characterisation of LM compared to ES and for a more reliable placement of US-probe in biofeedback.

Game not over: Explaining older adults' use and intention to continue using a gamified eHealth service.

BACKGROUND: Gamification within eHealth services can increase eHealth adoption. However, little is known about factors affecting adoption of gamified eHealth among older adults. In this study, we sought to explain the (continued) use of a gamified eHealth service among older adults (55+). METHODS: Participants used a gamified eHealth service, focusing on falls prevention, for 4 weeks and completed a post-test questionnaire based on the Technology Acceptance Model. We used Partial Least Squares Structural Equation Modeling to analyse our data. RESULTS: Seventy-two older adults participated with a mean age of 65.1 years (SD = 7.0). Our results show that first, perceived ease of use affected use of the service (use duration: ß = 0.303, R2 = 0.130, and use frequency: ß = 0.304, R2 = 0.107). Second, perceived usefulness affected the intention to continue using the service (ß = 0.754, R2 = 0.640). Third, use of the service did not predict the intention to continue using it. Furthermore, enjoyment affected perceived usefulness (ß = 0.783, R2 = 0.563) and aesthetics affected perceived ease of use (ß = 0.634, R2 = 0.652). CONCLUSIONS: This study refutes the expected relation between use and intention to continue use a gamified eHealth service. Additionally, we learned that using theoretical approaches focusing on technology acceptance, are not suitable for explaining (continued) use of gamified eHealth services.

Evaluation of a virtual coaching system eHealth intervention: A mixed methods observational cohort study in the Netherlands.

BACKGROUND: With the rise in human life expectancy, the prevalence of chronic disease has increased significantly. Adopting a healthy lifestyle can decrease the risk of chronic disease. Virtual coaching systems can help older adults adopt a healthy lifestyle.AimThe primary objective of this study was to assess the use, user experience and potential health effects of a conversational agent-based eHealth platform (Council of Coaches) implemented in a real-world setting among older adults. METHODS: An observational cohort study was conducted with older adults aged 55 years or older in the Netherlands. Participants were enrolled for 5-9 weeks during which they had access to Council of Coaches. They completed three questionnaires: pre-test, post-test, and at follow-up. After five weeks, an interview was conducted, and participants chose whether they wanted to use the eHealth intervention for another four weeks during the facultative phase. RESULTS: The study population consisted of 51 older adults (70.6% female) with a mean age of 65.3 years (SD = 7.4). Of these, 94.1% started interacting with Council of Coaches, and most participants interacted once per week. During the facultative phase, 21 participants were still interacting with Council of Coaches. Minimal clinical important differences in quality of life were found among the study population after interacting with Council of Coaches. CONCLUSION: Our results demonstrate that eHealth interventions with virtual coaching can be used among older adults. This may increase quality of life for older adults, and decrease their healthcare needs. Future research into such eHealth interventions should take into account the inclusion of sufficient personalised content and the use of a mixed methods study for assessing the eHealth intervention.

Automatic topic selection for long-term interaction with embodied conversational agents in health coaching: A micro-randomized trial.

INTRODUCTION: Embodied Conversational Agents (ECAs) can be included in health coaching applications as virtual coaches. The engagement with these virtual coaches could be improved by presenting users with tailored coaching dialogues. In this article, we investigate if the suggestion of an automatically tailored topic by an ECA leads to higher engagement by the user and thus longer sessions of interaction. METHODS: A Micro-Randomized Trial (MRT) was conducted in which two types of interaction with an ECA were compared: (a) the coach suggests a relevant topic to discuss, and (b) the coach asks the user to select a topic from a set of options. Every time the user would interact with the ECA, one of those conditions would be randomly selected. Participants interacted in their daily life with the ECA that was part of a multi-agent health coaching application for 4-8 weeks. RESULTS: In two rounds, 82 participants interacted with the micro-randomized coach a total of 1011 times. Interactions in which the coach took the initiative were found to be of equal length as interactions in which the user was allowed to choose the topic, and the acceptance of topic suggestions was high (71.1% overall, 75.8% for coaching topics). CONCLUSION: Tailoring coaching conversations with ECAs by letting the coach automatically suggest a topic that is tailored to the user is perceived as a natural variation in the flow of interaction. Future research could focus on improving the novel coaching engine component that supports the topic selection process for these suggestions or on investigating how the amount of initiative and coaching approach by the ECA could be tailored.

Continuous Monitoring of Vital Signs With Wearable Sensors During Daily Life Activities: Validation Study.

BACKGROUND: Continuous telemonitoring of vital signs in a clinical or home setting may lead to improved knowledge of patients' baseline vital signs and earlier detection of patient deterioration, and it may also facilitate the migration of care toward home. Little is known about the performance of available wearable sensors, especially during daily life activities, although accurate technology is critical for clinical decision-making. OBJECTIVE: The aim of this study is to assess the data availability, accuracy, and concurrent validity of vital sign data measured with wearable sensors in volunteers during various daily life activities in a simulated free-living environment. METHODS: Volunteers were equipped with 4 wearable sensors (Everion placed on the left and right arms, VitalPatch, and Fitbit Charge 3) and 2 reference devices (Oxycon Mobile and iButton) to obtain continuous measurements of heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), and temperature. Participants performed standardized activities, including resting, walking, metronome breathing, chores, stationary cycling, and recovery afterward. Data availability was measured as the percentage of missing data. Accuracy was evaluated by the median absolute percentage error (MAPE) and concurrent validity using the Bland-Altman plot with mean difference and 95% limits of agreement (LoA). RESULTS: A total of 20 volunteers (median age 64 years, range 20-74 years) were included. Data availability was high for all vital signs measured by VitalPatch and for HR and temperature measured by Everion. Data availability for HR was the lowest for Fitbit (4807/13,680, 35.14% missing data points). For SpO2 measured by Everion, median percentages of missing data of up to 100% were noted. The overall accuracy of HR was high for all wearable sensors, except during walking. For RR, an overall MAPE of 8.6% was noted for VitalPatch and that of 18.9% for Everion, with a higher MAPE noted during physical activity (up to 27.1%) for both sensors. The accuracy of temperature was high for VitalPatch (MAPE up to 1.7%), and it decreased for Everion (MAPE from 6.3% to 9%). Bland-Altman analyses showed small mean differences of VitalPatch for HR (0.1 beats/min [bpm]), RR (-0.1 breaths/min), and temperature (0.5 °C). Everion and Fitbit underestimated HR up to 5.3 (LoA of -39.0 to 28.3) bpm and 11.4 (LoA of -53.8 to 30.9) bpm, respectively. Everion had a small mean difference with large LoA (-10.8 to 10.4 breaths/min) for RR, underestimated SpO2 (>1%), and overestimated temperature up to 2.9 °C. CONCLUSIONS: Data availability, accuracy, and concurrent validity of the studied wearable sensors varied and differed according to activity. In this study, the accuracy of all sensors decreased with physical activity. Of the tested sensors, VitalPatch was found to be the most accurate and valid for vital signs monitoring.

Contextual Health Information Behavior in the Daily Lives of People with Type 2 Diabetes: A Diary Study in Scotland.

Changes in lifestyle can have positive effects on treating type 2 diabetes (T2D), like sporting or healthy eating. Therefore, a person diagnosed with T2D is often advised to make healthy choices throughout the day, in addition to other interventions such as medication. To do this, he or she needs health information to support decision-making. Literature describes ample categorizations of types of (health) information behavior and theoretical models that explain the factors that drive people to search for, encounter or avoid information. However, there are few longitudinal studies about triggers and factors in daily life that affect health information behavior (HIB). This study was set up to identify triggers, actions and outcomes for active, passive and avoidant HIB situations in daily life among Scots with Type 2 diabetes (T2D) to identify points of attention for communication strategies. Twelve participants took part in a four-week diary study. Every day, participants received an online diary form to describe active, passive or avoidant HIB situations. Data collection resulted in 53 active, 120 passive and 25 avoidant diary entries. Seven active HIB contexts (e.g., experiencing symptoms, cooking dinner, sports training) and five passive HIB contexts (e.g., home, work, medical facility) were identified. Four motivations for avoidance were found (e.g., time constraints, no health trigger). These results can be used to supplement the theoretical models of health information behavior. Furthermore, health professionals can use these results to support their clients with T2D in the self-management of their health, by guiding them to trustworthy sources of health information and lowering barriers for searching health information.

Outdoor E-trike cycling: A low intensity physical activity.

For people with disabilities or chronic diseases, an electrically supported tricycle (e-trike) could facilitate independence and participation in physical activity, and improve health conditions. This study investigates the exercise intensity and perceived exertion of e-trike cycling. Twenty healthy participants cycled on an e-trike with different speeds (12 and 18 km/h) and different levels of electric pedal support at an outdoor athletics track. Exercise intensity was measured with oxygen consumption (V˙O2) using a Cosmed K4B2 analysis unit, perceived exertion was measured with the Borg Rating of Perceived Exertion scale, pedaling power, and engine power were measured with a torque sensor. The effect of speed and support was analyzed with a Linear Mixed Effects model. V˙O2 was 18.67 ± 3.13 ml/kg/min without support, with electric support the exercise intensity was significantly below moderate intensity (i.e. 10.5 ml/kg/min) at t = 11.37, p < .001, 95% CI: 1.90, 2.77. The Borg score without support was 9.79 ± 1.72 and all other conditions below this, which were significantly below moderate intensity (i.e. 11) at t = -3.07, p = .007, 95% CI: -2.04, -0.38. Speed and support significantly affected V˙O2 (F = 185.49, p < .001). E-trike cycling is a low intensity activity, but intensity can be influenced by changing speed and support level.

Adaptive threshold-based alarm strategies for continuous vital signs monitoring.

Continuous vital signs monitoring in post-surgical ward patients may support early detection of clinical deterioration, but novel alarm approaches are required to ensure timely notification of abnormalities and prevent alarm-fatigue. The current study explored the performance of classical and various adaptive threshold-based alarm strategies to warn for vital sign abnormalities observed during development of an adverse event. A classical threshold-based alarm strategy used for continuous vital signs monitoring in surgical ward patients was evaluated retrospectively. Next, (combinations of) six methods to adapt alarm thresholds to personal or situational factors were simulated in the same dataset. Alarm performance was assessed using the overall alarm rate and sensitivity to detect adverse events. Using a wireless patch-based monitoring system, 3999 h of vital signs data was obtained in 39 patients. The clinically used classical alarm system produced 0.49 alarms/patient/day, and alarms were generated for 11 out of 18 observed adverse events. Each of the tested adaptive strategies either increased sensitivity to detect adverse events or reduced overall alarm rate. Combining specific strategies improved overall performance most and resulted in earlier presentation of alarms in case of adverse events. Strategies that adapt vital sign alarm thresholds to personal or situational factors may improve early detection of adverse events or reduce alarm rates as compared to classical alarm strategies. Accordingly, further investigation of the potential of adaptive alarms for continuous vital signs monitoring in ward patients is warranted.

Continuous monitoring of vital signs with the Everion biosensor on the surgical ward: a clinical validation study.

BACKGROUND: Wearable sensors enable continuous vital sign monitoring, although information about their performance on nursing wards is scarce. Vital signs measured by telemonitoring and nurse measurements on a surgical ward were compared to assess validity and reliability. METHODS: In a prospective observational study, surgical patients wore a wearable sensor (Everion, Biovotion AG, Zürich, Switzerland) that continuously measured heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), and temperature during their admittance on the ward. Validity was evaluated using repeated-measures correlation and reliability using Bland-Altman plots, mean difference, and 95% limits of agreement (LoA). RESULTS: Validity analyses of 19 patients (median age, 68; interquartile range, 62.5-72.5 years) showed a moderate relationship between telemonitoring and nurse measurements for HR (r = 0.53; 95% confidence interval, 0.44-0.61) and a poor relationship for RR, SpO2, and temperature. Reliability analyses showed that Everion measured HR close to nurse measurements (mean difference, 1 bpm; LoA, -16.7 to 18.7 bpm). Everion overestimated RR at higher values, whereas SpO2 and temperature were underestimated. CONCLUSIONS: A moderate relationship was determined between Everion and nurse measurements at a surgical ward in this study. Validity and reliability of telemonitoring should also be assessed with gold standard devices in future clinical trials.

Effectiveness of current perioperative telemonitoring on postoperative outcome in patients undergoing major abdominal surgery: A systematic review of controlled trials.

BACKGROUND: Perioperative telemonitoring of patients undergoing major surgery might lead to improved postoperative outcomes. The aim of this systematic review is to evaluate the effectiveness of current perioperative telemonitoring interventions on postoperative clinical, patient-reported, and financial outcome measures in patients undergoing major surgery. METHODS: For this systematic review, PubMed, CINAHL, and Embase databases were searched for eligible articles published between January 1, 2009 and March 15, 2021. Studies were eligible as they described: (P) patients aged 18 years or older who underwent major abdominal surgery, (I) perioperative telemonitoring as intervention, (C) a control group receiving usual care, (O) any type of postoperative clinical, patient-reported, or financial outcome measures, and (S) an interventional study design. RESULTS: The search identified 2958 articles of which 10 were eligible for analysis, describing nine controlled trials of 2438 patients. Perioperative telemonitoring comprised wearable biosensors (n = 3), websites (n = 3), e-mail (n = 1), and mobile applications (n = 2). Outcome measures were clinical (n = 8), patient-reported (n = 5), and financial (n = 2). Results show significant improvement of recovery time, stoma self-efficacy and pain in the early postoperative phase in patients receiving telemonitoring. Other outcome measures were not significantly different between the groups. CONCLUSION: Evidence for the effectiveness of perioperative telemonitoring in major surgery is scarce. There is a need for good quality studies with sufficient patients while ensuring that the quality and usability of the technology and the adoption in care processes are optimal.

Genetic Algorithm for Feature Selection in Lower Limb Pattern Recognition.

Choosing the right features is important to optimize lower limb pattern recognition, such as in prosthetic control. EMG signals are noisy in nature, which makes it more challenging to extract useful information. Many features are used in the literature, which raises the question which features are most suited for use in lower limb myoelectric control. Therefore, it is important to find combinations of best performing features. One way to achieve this is by using a genetic algorithm, a meta-heuristic capable of searching vast feature spaces. The goal of this research is to demonstrate the capabilities of a genetic algorithm and come up with a feature set that has a better performance than the state-of-the-art feature set. In this study, we collected a dataset containing ten able-bodied subjects who performed various gait-related activities while measuring EMG and kinematics. The genetic algorithm selected features based on the performance on the training partition of this dataset. The selected feature sets were evaluated on the remaining test set and on the online benchmark dataset ENABL3S, against a state-of-the-art feature set. The results show that a feature set based on the selected features of a genetic algorithm outperforms the state-of-the-art set. The overall error decreased up to 0.54% and the transitional error by 2.44%, which represent a relative decrease in overall errors up to 11.6% and transitional errors up to 14.1%, although these results were not significant. This study showed that a genetic algorithm is capable of searching a large feature space and that systematic feature selection shows promising results for lower limb myoelectric control.

Can the Childhood Physical Activity Questionnaire Be Used to Identify Physical Activity Levels in Children With Asthma?

Objective: Children with asthma who are physically active have a better quality of life, emphasizing the importance of activity monitoring and promotion in daily life. The validity of self-reported activity measurements has been questioned in pediatric populations. In this study, we aim to compare the Physical Activity Questionnaire for Children (PAQ-C) with objectively measured PA using accelerometry. Design: In this comparison study, the pooled dataset of two cross-sectional studies was used, which prospectively home-monitored PA using the alternative self-report PAQ-C questionnaire as well as with the criterion standard accelerometry (Actigraph wGT3X-BT and GT1M). Participants:Ninety children with pediatrician-diagnosed asthma participated in the study. Main Outcome Measures:Correlation coefficients were calculated to determine the relation between the PAQ-C and accelerometer data. The predictive value of the PAQ-C in differentiating between achieving and failing the recommended daily level of moderate-to-vigorous activity (MVPA) was evaluated with receiver operator characteristic (ROC) analysis. Results: The results showed weak to moderate correlations of the PAQ-C with the accelerometer data (r = 0.29-0.47). A PAQ-C cutoff of 3.09 showed the best performance on predicting whether the recommended level of MVPA was achieved. With this cutoff, 21 of the 39 children that did achieve their daily MVPA level (53.8% sensitivity) and 33 of the 46 children that did fail their daily MVPA level (71.7% specificity) were correctly classified. A PAQ-C score of 3.5 revealed a negative predictive value of 100% for assessing physical inactivity. Conclusion: This study revealed a weak relation between the PAQ-C and PA assessed with accelerometry. However, a PAQ-C score of 3.5 or higher might be used as a low-cost and easy-to-use PA screening tool for ruling out physical inactivity in a portion of the pediatric asthma population. Clinical Trial Registration: Netherlands Trial Register: Trial NL6087.

Feasibility, Efficacy, and Efficiency of eHealth-Supported Pediatric Asthma Care: Six-Month Quasi-Experimental Single-Arm Pretest-Posttest Study.

BACKGROUND: Early detection of loss of asthma control can effectively reduce the burden of the disease. However, broad implementation in clinical practice has not been accomplished so far. We are in need of research investigating the operationalization of eHealth pediatric asthma care in practice, which can provide the most potential benefits in terms of adoption, efficiency, and effectiveness. OBJECTIVE: The aim of this study was to investigate the technical and clinical feasibility, including an exploration of the efficacy and cost-efficiency, of an eHealth program implemented in daily clinical pediatric asthma practice. METHODS: We designed an eHealth-supported pediatric asthma program facilitating early detection of loss of asthma control while increasing symptom awareness and self-management. In the 6-month program, asthma control was monitored by 4 health care professionals (HCPs) by using objective home measurements and the web-based Puffer app to allow timely medical anticipation and prevent treatment delay. Technical feasibility was assessed by technology use, system usability, and technology acceptance. Clinical feasibility was assessed by participation and patient-reported health and care outcomes and via a focus group with HCPs regarding their experiences of implementing eHealth in daily practice. The efficacy and cost-efficiency were explored by comparing pretest-posttest program differences in asthma outcomes (asthma control, lung function, and therapy adherence) and medical consumption. RESULTS: Of 41 children, 35 children with moderate-to-severe asthma volunteered for participation. With regard to technical feasibility, the Puffer app scored a good usability score of 78 on the System Usability Scale and a score of 70 for technology acceptance on a scale of 1 to 100. Approximately 75% (18/24) of the children indicated that eHealth helped them to control their asthma during the program. HCPs indicated that home measurements and real time communication enabled them to make safe and substantiated medical decisions during symptom manifestations. With an average time commitment of 15 minutes by patients, eHealth care led to a 80% gross reduction (from €71,784 to €14,018, US $1=€0.85) in health care utilization, 8.6% increase (from 18.6 to 20.2, P=.40) in asthma control, 25.0% increase (from 2.8 to 3.5, P=.04) in the self-management level, and 20.4% improved (from 71.2 to 76.8, P=.02) therapy adherence. CONCLUSIONS: eHealth asthma care seems to be technically and clinically feasible, enables safe remote care, and seems to be beneficial for pediatric asthma care in terms of health outcomes and health care utilization. Follow-up research should focus on targeted effectiveness studies with the lessons learned, while also enabling individualization of eHealth for personalized health care.

Functional and morphological lumbar multifidus characteristics in subgroups with low back pain in primary care.

BACKGROUND: Since the contribution of the lumbar multifidus(LM) is not well understood in relation to non-specific low back pain(LBP), this may limit physiotherapists in choosing the most appropriate treatment strategy. OBJECTIVES: This study aims to compare clinical characteristics, in terms of LM function and morphology, between subacute and chronic LBP patients from a large clinical practice cohort compared to healthy controls. DESIGN: Multicenter case control study. METHOD: Subacute and chronic LBP patients and healthy controls between 18 and 65 years of age were included. Several clinical tests were performed: primary outcomes were the LM thickness from ultrasound measurements, trunk range of motion(ROM) from 3D kinematic tests, and median frequency and root mean square values of LM by electromyography measurements. The secondary outcomes Numeric Rating Scale for Pain(NRS) and the Oswestry Disability Index(ODI) were administered. Comparisons between groups were made with ANOVA, p-values<0.05, with Tukey's HSD post-hoc test were considered significant. RESULTS: A total of 161 participants were included, 50 healthy controls, 59 chronic LBP patients, and 52 subacute LBP patients. Trunk ROM and LM thickness were significantly larger in healthy controls compared to all LBP patients(p < 0.01). A lower LM thickness was found between subacute and chronic LBP patients although not significant(p = 0.11-0.97). All between-group comparisons showed no statistically significant differences in electromyography outcomes (p = 0.10-0.32). NRS showed no significant differences between LBP subgroups(p = 0.21). Chronic LBP patients showed a significant higher ODI score compared to subacute LBP patients(p = 0.03). CONCLUSIONS: Trunk ROM and LM thickness show differences between LBP patients and healthy controls.