Brief Report: Quantifying Speech Production Coordination from Non- and Minimally-Speaking Individuals

PURPOSE

Non-verbal utterances are an important tool of communication for individuals who are non- or minimally-speaking. While these utterances are typically understood by caregivers, they can be challenging to interpret by their larger community. To date, there has been little work done to detect and characterize the vocalizations produced by non- or minimally-speaking individuals. This paper aims to characterize five categories of utterances across a set of 7 non- or minimally-speaking individuals.

METHODS

The characterization is accomplished using a correlation structure methodology, acting as a proxy measurement for motor coordination, to localize similarities and differences to specific speech production systems.

RESULTS

We specifically find that frustrated and dysregulated utterances show similar correlation structure outputs, especially when compared to self-talk, request, and delighted utterances. We additionally witness higher complexity of coordination between articulatory and respiratory subsystems and lower complexity of coordination between laryngeal and respiratory subsystems in frustration and dysregulation as compared to self-talk, request, and delight. Finally, we observe lower complexity of coordination across all three speech subsystems in the request utterances as compared to self-talk and delight.

CONCLUSION

The insights from this work aid in understanding of the modifications made by non- or minimally-speaking individuals to accomplish specific goals in non-verbal communication.

Deep learning-aided decision support for diagnosis of skin disease across skin tones

Although advances in deep learning systems for image-based medical diagnosis demonstrate their potential to augment clinical decision-making, the effectiveness of physician-machine partnerships remains an open question, in part because physicians and algorithms are both susceptible to systematic errors, especially for diagnosis of underrepresented populations. Here we present results from a large-scale digital experiment involving board-certified dermatologists (n = 389) and primary-care physicians (n = 459) from 39 countries to evaluate the accuracy of diagnoses submitted by physicians in a store-and-forward teledermatology simulation. In this experiment, physicians were presented with 364 images spanning 46 skin diseases and asked to submit up to four differential diagnoses. Specialists and generalists achieved diagnostic accuracies of 38% and 19%, respectively, but both specialists and generalists were four percentage points less accurate for the diagnosis of images of dark skin as compared to light skin. Fair deep learning system decision support improved the diagnostic accuracy of both specialists and generalists by more than 33%, but exacerbated the gap in the diagnostic accuracy of generalists across skin tones. These results demonstrate that well-designed physician-machine partnerships can enhance the diagnostic accuracy of physicians, illustrating that success in improving overall diagnostic accuracy does not necessarily address bias.

Testing a Behavioral Activation Gaming App for Depression During Pregnancy: Multimethod Pilot Study

BACKGROUND

Depression during pregnancy is increasingly recognized as a worldwide public health problem. If untreated, there can be detrimental outcomes for the mother and child. Anxiety is also often comorbid with depression. Although effective treatments exist, most women do not receive treatment. Technology is a mechanism to increase access to and engagement in mental health services.

OBJECTIVE

The Guardians is a mobile app, grounded in behavioral activation principles, which seeks to leverage mobile game mechanics and in-game rewards to encourage user engagement. This study seeks to assess app satisfaction and engagement and to explore changes in clinical symptoms of depression and anxiety among a sample of pregnant women with elevated depressive symptoms.

METHODS

This multimethod pilot test consisted of a single-arm, proof-of-concept trial to examine the feasibility and acceptability of The Guardians among a pregnant sample with depression (N=18). Participation included two web-based study visits: (1) a baseline assessment to collect demographic and obstetric information and to assess clinical symptoms and (2) an exit interview to administer follow-up measures and explore user experience. Participants completed biweekly questionnaires (ie, Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7) during the trial to assess depression and anxiety symptom severity. App satisfaction was measured using 2 self-report scales (ie, Mobile Application Rating Scale and Player Experience of Needs Satisfaction scale). Engagement with The Guardians was captured using game interaction metric data. We used backward-eliminated mixed effects longitudinal models to examine the effects of app engagement and satisfaction and length of time in the study on symptoms of depression and anxiety. Content analysis was conducted on qualitative data from exit interviews.

RESULTS

The 15-day and 30-day overall app retention rates were 26.6% and 15.1%, respectively. Mixed effects models found significant negative main effects of week in study (β=-.35; t61=-3.05; P=.003), number of activities completed (β=-.12; t61=-2.05; P=.04), days played (β=-.12; t58=-2.9; P=.005), and satisfaction, according to the Mobile Application Rating Scale (β=-3.05; t45=-2.19; P=.03) on depressive symptoms. We have reported about similar analyses for anxiety. There is preliminary evidence suggesting harder activities are associated with greater mood improvement than easier activities. Qualitative content analysis resulted in feedback falling under the following themes: activities, app design, engagement, fit of the app with lifestyle, perceived impact of the app on mood, and suggestions for app modifications.

CONCLUSIONS

Preliminary results from this multimethod study of The Guardians indicate feasibility and acceptability among pregnant women with depression. Retention and engagement levels were more than double those of previous public mental health apps, and use of the app was associated with significant decrease in depressive symptom scores over the 10-week trial. The Guardians shows promise as an effective and scalable digital intervention to support women experiencing depression.

Prospective clinical validation of the Empatica EmbracePlus wristband as a reflective pulse oximeter

Introduction

Respiratory diseases such as chronic obstructive pulmonary disease, obstructive sleep apnea syndrome, and COVID-19 may cause a decrease in arterial oxygen saturation (SaO2). The continuous monitoring of oxygen levels may be beneficial for the early detection of hypoxemia and timely intervention. Wearable non-invasive pulse oximetry devices measuring peripheral oxygen saturation (SpO2) have been garnering increasing popularity. However, there is still a strong need for extended and robust clinical validation of such devices, especially to address topical concerns about disparities in performances across racial groups. This prospective clinical validation aimed to assess the accuracy of the reflective pulse oximeter function of the EmbracePlus wristband during a controlled hypoxia study in accordance with the ISO 80601-2-61:2017 standard and the Food & Drug Administration (FDA) guidance.

Methods

Healthy adult participants were recruited in a controlled desaturation protocol to reproduce mild, moderate, and severe hypoxic conditions with SaO2 ranging from 100% to 70% (ClinicalTrials.gov registration #NCT04964609). The SpO2 level was estimated with an EmbracePlus device placed on the participant's wrist and the reference SaO2 was obtained from blood samples analyzed with a multiwavelength co-oximeter.

Results

The controlled hypoxia study yielded 373 conclusive measurements on 15 subjects, including 30% of participants with dark skin pigmentation (V-VI on the Fitzpatrick scale). The accuracy root mean square (Arms) error was found to be 2.4%, within the 3.5% limit recommended by the FDA. A strong positive correlation between the wristband SpO2 and the reference SaO2 was observed (r = 0.96, P < 0.001), and a good concordance was found with Bland-Altman analysis (bias, 0.05%; standard deviation, 1.66; lower limit, -4.7%; and upper limit, 4.8%). Moreover, acceptable accuracy was observed when stratifying data points by skin pigmentation (Arms 2.2% in Fitzpatrick V-VI, 2.5% in Fitzpatrick I-IV), and sex (Arms 1.9% in females, and 2.9% in males).

Discussion

This study demonstrates that the EmbracePlus wristband could be used to assess SpO2 with clinically acceptable accuracy under no-motion and high perfusion conditions for individuals of different ethnicities across the claimed range. This study paves the way for further accuracy evaluations on unhealthy subjects and during prolonged use in ambulatory settings.

Combining Electrodermal Activity With the Peak-Pain Time to Quantify Three Temporal Regions of Pain Experience

Background

Self-reported pain levels, while easily measured, are often not reliable for quantifying pain. More objective methods are needed that supplement self-report without adding undue burden or cost to a study. Methods that integrate multiple measures, such as combining self-report with physiology in a structured and specific-to-pain protocol may improve measures.

Method

We propose and study a novel measure that combines the timing of the peak pain measured by an electronic visual-analog-scale (eVAS) with continuously-measured changes in electrodermal activity (EDA), a physiological measure quantifying sympathetic nervous system activity that is easily recorded with a skin-surface sensor. The new pain measure isolates and specifically quantifies three temporal regions of dynamic pain experience: I. Anticipation preceding the onset of a pain stimulus, II. Response rising to the level of peak pain, and III. Recovery from the peak pain level. We evaluate the measure across two pain models (cold pressor, capsaicin), and four types of treatments (none, A=pregabalin, B=oxycodone, C=placebo). Each of 24 patients made four visits within 8 weeks, for 96 visits total: A training visit (TV), followed by three visits double-blind presenting A, B, or C (randomized order). Within each visit, a participant experienced the cold pressor, followed by an hour of rest during which one of the four treatments was provided, followed by a repeat of the cold pressor, followed by capsaicin.

Results

The novel method successfully discriminates the pain reduction effects of the four treatments across both pain models, confirming maximal pain for no-treatment, mild pain reduction for placebo, and the most pain reduction with analgesics. The new measure maintains significant discrimination across the test conditions both within a single-day's visit (for relative pain relief within a visit) and across repeated visits spanning weeks, reducing different-day-physiology affects, and providing better discriminability than using self-reported eVAS.

Conclusion

The new method combines the subjectively-identified time of peak pain with capturing continuous physiological data to quantify the sympathetic nervous system response during a dynamic pain experience. The method accurately discriminates, for both pain models, the reduction of pain with clinically effective analgesics.

Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit

Background: Using machine learning to combine wrist accelerometer (ACM) and electrodermal activity (EDA) has been shown effective to detect primarily and secondarily generalized tonic-clonic seizures, here termed as convulsive seizures (CS). A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm. Here we present its performance on pediatric and adult patients in epilepsy monitoring units (EMUs).

Methods: Patients diagnosed with epilepsy participated in a prospective multi-center clinical study. Three board-certified neurologists independently labeled CS from video-EEG. The Detection Algorithm was evaluated in terms of Sensitivity and false alarm rate per 24 h-worn (FAR) on all the data and on only periods of rest. Performance were analyzed also applying the Detection Algorithm offline, with a less sensitive but more specific parameters configuration (“Active mode”).

Results: Data from 152 patients (429 days) were used for performance evaluation (85 pediatric aged 6–20 years, and 67 adult aged 21–63 years). Thirty-six patients (18 pediatric) experienced a total of 66 CS (35 pediatric). The Sensitivity (corrected for clustered data) was 0.92, with a 95% confidence interval (CI) of [0.85-1.00] for the pediatric population, not significantly different (p > 0.05) from the adult population's Sensitivity (0.94, CI: [0.89–1.00]). The FAR on the pediatric population was 1.26 (CI: [0.87–1.73]), higher (p < 0.001) than in the adult population (0.57, CI: [0.36–0.81]). Using the Active mode, the FAR decreased by 68% while reducing Sensitivity to 0.95 across the population. During rest periods, the FAR's were 0 for all patients, lower than during activity periods (p < 0.001).

Conclusions: Performance complies with FDA's requirements of a lower bound of CI for Sensitivity higher than 0.7 and of a FAR lower than 2, for both age groups. The pediatric FAR was higher than the adult FAR, likely due to higher pediatric activity. The high Sensitivity and precision (having no false alarms) during sleep might help mitigate SUDEP risk by summoning caregiver intervention. The Active mode may be advantageous for some patients, reducing the impact of the FAR on daily life. Future work will examine the performance and usability outside of EMUs.

Robust stability of melatonin circadian phase, sleep metrics, and chronotype across months in young adults living in real-world settings

Appropriate synchronization of the timing of behaviors with the circadian clock and adequate sleep are both important for almost every physiological process. The timing of the circadian clock relative to social (ie, local) clock time and the timing of sleep can vary greatly among individuals. Whether the timing of these processes is stable within an individual is not well-understood. We examined the stability of circadian-controlled melatonin timing, sleep timing, and their interaction across ~ 100 days in 15 students at a single university. At three time points ~ 35-days apart, circadian timing was determined from the dim-light melatonin onset (DLMO). Sleep behaviors (timing and duration) and chronotype (ie, mid-sleep time on free days corrected for sleep loss on school/work days) were determined via actigraphy and analyzed in ~ 1-month bins. Melatonin timing was stable, with an almost perfect relationship strength as determined via intraclass correlation coefficients ([ICC]=0.85); average DLMO timing across all participants only changed from the first month by 21 minutes in month 2 and 5 minutes in month 3. Sleep behaviors also demonstrated high stability, with ICC relationship strengths ranging from substantial to almost perfect (ICCs = 0.65-0.85). Average DLMO was significantly associated with average chronotype (r²  = 0.53, P <.01), with chronotype displaying substantial stability across months (ICC = 0.61). These findings of a robust stability in melatonin timing and sleep behaviors in young adults living in real-world settings holds promise for a better understanding of the reliability of previous cross-sectional reports and for the future individualized strategies to combat circadian-associated disease and impaired safety (ie, "chronomedicine").

Coherent Dynamics in Quantum Emitters under Dichromatic Excitation

We characterize the coherent dynamics of a two-level quantum emitter driven by a pair of symmetrically detuned phase-locked pulses. The promise of dichromatic excitation is to spectrally isolate the excitation laser from the quantum emission, enabling background-free photon extraction from the emitter. While excitation is not possible without spectral overlap between the exciting pulse and the quantum emitter transition for ideal two-level systems due to cancellation of the accumulated pulse area, we find that any additional interactions that interfere with cancellation of the accumulated pulse area may lead to a finite stationary population inversion. Our spectroscopic results of a solid-state two-level system show that, while coupling to lattice vibrations helps to improve the inversion efficiency up to 50% under symmetric driving, coherent population control and a larger amount of inversion are possible using asymmetric dichromatic excitation, which we achieve by adjusting the ratio of the intensities between the red- and blue-detuned pulses. Our measured results, supported by simulations using a real-time path-integral method, offer a new perspective toward realizing efficient, background-free photon generation and extraction.

Wireless Measurement of Sympathetic Arousal During in vivo Occupational Therapy Sessions

Purpose

One goal of occupational therapists working with children who have sensory processing challenges is the regulation of arousal. Regulation strategies have not been evaluated using an empirical measure of physiological arousal.

Objective

To establish the feasibility of using an objective physiologic measure of sympathetic arousal in therapeutic settings and explore the relation between therapeutic activities and sympathetic arousal. To evaluate changes in electrodermal activity (EDA) during occupational therapy sessions.

Methods

Twenty-two children identified with sensory modulation dysfunction (SMD) wore a wireless EDA sensor during 50 min occupational therapy sessions (n = 77 sessions).

Results

All children were able to wear the sensor on the lower calf without being distracted by the device. The five insights below are based on a comparison of EDA recordings in relation to therapists' reflections describing how sympathetic arousal might correspond to therapeutic activities.

Conclusion

Objective physiological assessment of a child's sympathetic arousal during therapy is possible using a wireless EDA measurement system. Changes in EDA may correspond directly with therapeutic activities. The article provides a foundation for designing future therapeutic studies that include continuous measures of EDA.

Correlation between work impairment, scores of rhinitis severity and asthma using the MASK-air® App

BACKGROUND

In allergic rhinitis, a relevant outcome providing information on the effectiveness of interventions is needed. In MASK-air (Mobile Airways Sentinel Network), a visual analogue scale (VAS) for work is used as a relevant outcome. This study aimed to assess the performance of the work VAS work by comparing VAS work with other VAS measurements and symptom-medication scores obtained concurrently.

METHODS

All consecutive MASK-air users in 23 countries from 1 June 2016 to 31 October 2018 were included (14 189 users; 205 904 days). Geolocalized users self-assessed daily symptom control using the touchscreen functionality on their smart phone to click on VAS scores (ranging from 0 to 100) for overall symptoms (global), nose, eyes, asthma and work. Two symptom-medication scores were used: the modified EAACI CSMS score and the MASK control score for rhinitis. To assess data quality, the intra-individual response variability (IRV) index was calculated.

RESULTS

A strong correlation was observed between VAS work and other VAS. The highest levels for correlation with VAS work and variance explained in VAS work were found with VAS global, followed by VAS nose, eye and asthma. In comparison with VAS global, the mCSMS and MASK control score showed a lower correlation with VAS work. Results are unlikely to be explained by a low quality of data arising from repeated VAS measures.

CONCLUSIONS

VAS work correlates with other outcomes (VAS global, nose, eye and asthma) but less well with a symptom-medication score. VAS work should be considered as a potentially useful AR outcome in intervention studies.

Deep Reinforcement Learning for Optimal Critical Care Pain Management with Morphine using Dueling Double-Deep Q Networks

Opioids are the preferred medications for the treatment of pain in the intensive care unit. While under-treatment leads to unrelieved pain and poor clinical outcomes, excessive use of opioids puts patients at risk of experiencing multiple adverse effects. In this work, we present a sequential decision making framework for opioid dosing based on deep reinforcement learning. It provides real-time clinically interpretable dosing recommendations, personalized according to each patient's evolving pain and physiological condition. We focus on morphine, one of the most commonly prescribed opioids. To train and evaluate the model, we used retrospective data from the publicly available MIMIC-3 database. Our results demonstrate that reinforcement learning may be used to aid decision making in the intensive care setting by providing personalized pain management interventions.

Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health

While accurately predicting mood and wellbeing could have a number of important clinical benefits, traditional machine learning (ML) methods frequently yield low performance in this domain. We posit that this is because a one-size-fits-all machine learning model is inherently ill-suited to predicting outcomes like mood and stress, which vary greatly due to individual differences. Therefore, we employ Multitask Learning (MTL) techniques to train personalized ML models which are customized to the needs of each individual, but still leverage data from across the population. Three formulations of MTL are compared: i) MTL deep neural networks, which share several hidden layers but have final layers unique to each task; ii) Multi-task Multi-Kernel learning, which feeds information across tasks through kernel weights on feature types; and iii) a Hierarchical Bayesian model in which tasks share a common Dirichlet Process prior. We offer the code for this work in open source. These techniques are investigated in the context of predicting future mood, stress, and health using data collected from surveys, wearable sensors, smartphone logs, and the weather. Empirical results demonstrate that using MTL to account for individual differences provides large performance improvements over traditional machine learning methods and provides personalized, actionable insights.

Continuous Pain Intensity Estimation from Autonomic Signals with Recurrent Neural Networks

Pain is usually measured by patient's self-report, which requires patient collaboration. Hence, the development of an objective automatic pain detection method would be useful in many clinical applications and patient populations. Previous studies have explored the feasibility of using physiological autonomic signals to detect the presence of pain. In this study, we focused on continuously estimating experimental heat pain intensity with high temporal resolution from autonomic signals. Specifically, we employed skin conductance deconvolution and point process heart rate variability analysis to continuously evaluate time-varying autonomic parameters, and presented a regression algorithm based on recurrent neural networks.

Guidance to 2018 good practice: ARIA digitally-enabled, integrated, person-centred care for rhinitis and asthma

Aims

Mobile Airways Sentinel NetworK (MASK) belongs to the Fondation Partenariale MACVIA-LR of Montpellier, France and aims to provide an active and healthy life to rhinitis sufferers and to those with asthma multimorbidity across the life cycle, whatever their gender or socio-economic status, in order to reduce health and social inequities incurred by the disease and to improve the digital transformation of health and care. The ultimate goal is to change the management strategy in chronic diseases.

Methods

MASK implements ICT technologies for individualized and predictive medicine to develop novel care pathways by a multi-disciplinary group centred around the patients.

Stakeholders

Include patients, health care professionals (pharmacists and physicians), authorities, patient’s associations, private and public sectors.

Results

MASK is deployed in 23 countries and 17 languages. 26,000 users have registered.

EU grants (2018)

MASK is participating in EU projects (POLLAR: impact of air POLLution in Asthma and Rhinitis, EIT Health, DigitalHealthEurope, Euriphi and Vigour).

Lessons learnt

(i) Adherence to treatment is the major problem of allergic disease, (ii) Self-management strategies should be considerably expanded (behavioural), (iii) Change management is essential in allergic diseases, (iv) Education strategies should be reconsidered using a patient-centred approach and (v) Lessons learnt for allergic diseases can be expanded to chronic diseases.

MASK 2017: ARIA digitally-enabled, integrated, person-centred care for rhinitis and asthma multimorbidity using real-world-evidence

mHealth, such as apps running on consumer smart devices is becoming increasingly popular and has the potential to profoundly affect healthcare and health outcomes. However, it may be disruptive and results achieved are not always reaching the goals. Allergic Rhinitis and its Impact on Asthma (ARIA) has evolved from a guideline using the best evidence-based approach to care pathways suited to real-life using mobile technology in allergic rhinitis (AR) and asthma multimorbidity. Patients largely use over-the-counter medications dispensed in pharmacies. Shared decision making centered around the patient and based on self-management should be the norm. Mobile Airways Sentinel networK (MASK), the Phase 3 ARIA initiative, is based on the freely available MASK app (the Allergy Diary, Android and iOS platforms). MASK is available in 16 languages and deployed in 23 countries. The present paper provides an overview of the methods used in MASK and the key results obtained to date. These include a novel phenotypic characterization of the patients, confirmation of the impact of allergic rhinitis on work productivity and treatment patterns in real life. Most patients appear to self-medicate, are often non-adherent and do not follow guidelines. Moreover, the Allergy Diary is able to distinguish between AR medications. The potential usefulness of MASK will be further explored by POLLAR (Impact of Air Pollution on Asthma and Rhinitis), a new Horizon 2020 project using the Allergy Diary.

Identifying Objective Physiological Markers and Modifiable Behaviors for Self-Reported Stress and Mental Health Status Using Wearable Sensors and Mobile Phones: Observational Study

Background

Wearable and mobile devices that capture multimodal data have the potential to identify risk factors for high stress and poor mental health and to provide information to improve health and well-being.

Objective

We developed new tools that provide objective physiological and behavioral measures using wearable sensors and mobile phones, together with methods that improve their data integrity. The aim of this study was to examine, using machine learning, how accurately these measures could identify conditions of self-reported high stress and poor mental health and which of the underlying modalities and measures were most accurate in identifying those conditions.

Methods

We designed and conducted the 1-month SNAPSHOT study that investigated how daily behaviors and social networks influence self-reported stress, mood, and other health or well-being-related factors. We collected over 145,000 hours of data from 201 college students (age: 18-25 years, male:female=1.8:1) at one university, all recruited within self-identified social groups. Each student filled out standardized pre- and postquestionnaires on stress and mental health; during the month, each student completed twice-daily electronic diaries (e-diaries), wore two wrist-based sensors that recorded continuous physical activity and autonomic physiology, and installed an app on their mobile phone that recorded phone usage and geolocation patterns. We developed tools to make data collection more efficient, including data-check systems for sensor and mobile phone data and an e-diary administrative module for study investigators to locate possible errors in the e-diaries and communicate with participants to correct their entries promptly, which reduced the time taken to clean e-diary data by 69%. We constructed features and applied machine learning to the multimodal data to identify factors associated with self-reported poststudy stress and mental health, including behaviors that can be possibly modified by the individual to improve these measures.

Results

We identified the physiological sensor, phone, mobility, and modifiable behavior features that were best predictors for stress and mental health classification. In general, wearable sensor features showed better classification performance than mobile phone or modifiable behavior features. Wearable sensor features, including skin conductance and temperature, reached 78.3% (148/189) accuracy for classifying students into high or low stress groups and 87% (41/47) accuracy for classifying high or low mental health groups. Modifiable behavior features, including number of naps, studying duration, calls, mobility patterns, and phone-screen-on time, reached 73.5% (139/189) accuracy for stress classification and 79% (37/47) accuracy for mental health classification.

Conclusions

New semiautomated tools improved the efficiency of long-term ambulatory data collection from wearable and mobile devices. Applying machine learning to the resulting data revealed a set of both objective features and modifiable behavioral features that could classify self-reported high or low stress and mental health groups in a college student population better than previous studies and showed new insights into digital phenotyping.

Common data elements for epilepsy mobile health systems

OBJECTIVE

Common data elements (CDEs) are currently unavailable for mobile health (mHealth) in epilepsy devices and related applications. As a result, despite expansive growth of new digital services for people with epilepsy, information collected is often not interoperable or directly comparable. We aim to correct this problem through development of industry-wide standards for mHealth epilepsy data.

METHODS

Using a group of stakeholders from industry, academia, and patient advocacy organizations, we offer a consensus statement for the elements that may facilitate communication among different systems.

RESULTS

A consensus statement is presented for epilepsy mHealth CDEs.

SIGNIFICANCE

Although it is not exclusive, we believe that the use of a minimal common information denominator, specifically these CDEs, will promote innovation, accelerate scientific discovery, and enhance clinical usage across applications and devices in the epilepsy mHealth space. As a consequence, people with epilepsy will have greater flexibility and ultimately more powerful tools to improve their lives.

Toward Impactful Collaborations on Computing and Mental Health

We describe an initiative to bring mental health researchers, computer scientists, human-computer interaction researchers, and other communities together to address the challenges of the global mental ill health epidemic. Two face-to-face events and one special issue of the Journal of Medical Internet Research were organized. The works presented in these events and publication reflect key state-of-the-art research in this interdisciplinary collaboration. We summarize the special issue articles and contextualize them to present a picture of the most recent research. In addition, we describe a series of collaborative activities held during the second symposium and where the community identified 5 challenges and their possible solutions.

ARIA 2016: Care pathways implementing emerging technologies for predictive medicine in rhinitis and asthma across the life cycle

The Allergic Rhinitis and its Impact on Asthma (ARIA) initiative commenced during a World Health Organization workshop in 1999. The initial goals were (1) to propose a new allergic rhinitis classification, (2) to promote the concept of multi-morbidity in asthma and rhinitis and (3) to develop guidelines with all stakeholders that could be used globally for all countries and populations. ARIA—disseminated and implemented in over 70 countries globally—is now focusing on the implementation of emerging technologies for individualized and predictive medicine. MASK [MACVIA (Contre les Maladies Chroniques pour un Vieillissement Actif)-ARIA Sentinel NetworK] uses mobile technology to develop care pathways for the management of rhinitis and asthma by a multi-disciplinary group and by patients themselves. An app (Android and iOS) is available in 20 countries and 15 languages. It uses a visual analogue scale to assess symptom control and work productivity as well as a clinical decision support system. It is associated with an inter-operable tablet for physicians and other health care professionals. The scaling up strategy uses the recommendations of the European Innovation Partnership on Active and Healthy Ageing. The aim of the novel ARIA approach is to provide an active and healthy life to rhinitis sufferers, whatever their age, sex or socio-economic status, in order to reduce health and social inequalities incurred by the disease.

Automatic identification of artifacts in electrodermal activity data

Recently, wearable devices have allowed for long term, ambulatory measurement of electrodermal activity (EDA). Despite the fact that ambulatory recording can be noisy, and recording artifacts can easily be mistaken for a physiological response during analysis, to date there is no automatic method for detecting artifacts. This paper describes the development of a machine learning algorithm for automatically detecting EDA artifacts, and provides an empirical evaluation of classification performance. We have encoded our results into a freely available web-based tool for artifact and peak detection.

Scaling up strategies of the chronic respiratory disease programme of the European Innovation Partnership on Active and Healthy Ageing (Action Plan B3: Area 5)

Action Plan B3 of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) focuses on the integrated care of chronic diseases. Area 5 (Care Pathways) was initiated using chronic respiratory diseases as a model. The chronic respiratory disease action plan includes (1) AIRWAYS integrated care pathways (ICPs), (2) the joint initiative between the Reference site MACVIA-LR (Contre les MAladies Chroniques pour un VIeillissement Actif) and ARIA (Allergic Rhinitis and its Impact on Asthma), (3) Commitments for Action to the European Innovation Partnership on Active and Healthy Ageing and the AIRWAYS ICPs network. It is deployed in collaboration with the World Health Organization Global Alliance against Chronic Respiratory Diseases (GARD). The European Innovation Partnership on Active and Healthy Ageing has proposed a 5-step framework for developing an individual scaling up strategy: (1) what to scale up: (1-a) databases of good practices, (1-b) assessment of viability of the scaling up of good practices, (1-c) classification of good practices for local replication and (2) how to scale up: (2-a) facilitating partnerships for scaling up, (2-b) implementation of key success factors and lessons learnt, including emerging technologies for individualised and predictive medicine. This strategy has already been applied to the chronic respiratory disease action plan of the European Innovation Partnership on Active and Healthy Ageing.

Predicting students' happiness from physiology, phone, mobility, and behavioral data

In order to model students' happiness, we apply machine learning methods to data collected from undergrad students monitored over the course of one month each. The data collected include physiological signals, location, smartphone logs, and survey responses to behavioral questions. Each day, participants reported their wellbeing on measures including stress, health, and happiness. Because of the relationship between happiness and depression, modeling happiness may help us to detect individuals who are at risk of depression and guide interventions to help them. We are also interested in how behavioral factors (such as sleep and social activity) affect happiness positively and negatively. A variety of machine learning and feature selection techniques are compared, including Gaussian Mixture Models and ensemble classification. We achieve 70% classification accuracy of self-reported happiness on held-out test data.

Remote measurement of cognitive stress via heart rate variability

Remote detection of cognitive load has many powerful applications, such as measuring stress in the workplace. Cognitive tasks have an impact on breathing and heart rate variability (HRV). We show that changes in physiological parameters during cognitive stress can be captured remotely (at a distance of 3m) using a digital camera. A study (n=10) was conducted with participants at rest and under cognitive stress. A novel five band digital camera was used to capture videos of the face of the participant. Significantly higher normalized low frequency HRV components and breathing rates were measured in the stress condition when compared to the rest condition. Heart rates were not significantly different between the two conditions. We built a person-independent classifier to predict cognitive stress based on the remotely detected physiological parameters (heart rate, breathing rate and heart rate variability). The accuracy of the model was 85% (35% greater than chance).

MACVIA-ARIA Sentinel NetworK for allergic rhinitis (MASK-rhinitis): the new generation guideline implementation

Several unmet needs have been identified in allergic rhinitis: identification of the time of onset of the pollen season, optimal control of rhinitis and comorbidities, patient stratification, multidisciplinary team for integrated care pathways, innovation in clinical trials and, above all, patient empowerment. MASK-rhinitis (MACVIA-ARIA Sentinel NetworK for allergic rhinitis) is a simple system centred around the patient which was devised to fill many of these gaps using Information and Communications Technology (ICT) tools and a clinical decision support system (CDSS) based on the most widely used guideline in allergic rhinitis and its asthma comorbidity (ARIA 2015 revision). It is one of the implementation systems of Action Plan B3 of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA). Three tools are used for the electronic monitoring of allergic diseases: a cell phone-based daily visual analogue scale (VAS) assessment of disease control, CARAT (Control of Allergic Rhinitis and Asthma Test) and e-Allergy screening (premedical system of early diagnosis of allergy and asthma based on online tools). These tools are combined with a clinical decision support system (CDSS) and are available in many languages. An e-CRF and an e-learning tool complete MASK. MASK is flexible and other tools can be added. It appears to be an advanced, global and integrated ICT answer for many unmet needs in allergic diseases which will improve policies and standards.

Taking Personalized Medicine Seriously: Biomarker Approaches in Phase IIb/III Studies in Major Depression and Schizophrenia

The success rate in the development of psychopharmacological compounds is insufficient. Two main reasons for failure have been frequently identified: 1) treating the wrong patients and 2) using the wrong dose. This is potentially based on the known heterogeneity among patients, both on a syndromal and a biological level. A focus on personalized medicine through better characterization with biomarkers has been successful in other therapeutic areas. Nevertheless, obstacles toward this goal that exist are 1) the perception of a lack of validation, 2) the perception of an expensive and complicated enterprise, and 3) the perception of regulatory hurdles. The authors tackle these concerns and focus on the utilization of biomarkers as predictive markers for treatment outcome. The authors primarily cover examples from the areas of major depression and schizophrenia. Methodologies covered include salivary and plasma collection of neuroendocrine, metabolic, and inflammatory markers, which identified subgroups of patients in the Netherlands Study of Depression and Anxiety. A battery of vegetative markers, including sleep-electroencephalography parameters, heart rate variability, and bedside functional tests, can be utilized to characterize the activity of a functional system that is related to treatment refractoriness in depression (e.g., the renin-angiotensin-aldosterone system). Actigraphy and skin conductance can be utilized to classify patients with schizophrenia and provide objective readouts for vegetative activation as a functional marker of target engagement. Genetic markers, related to folate metabolism, or folate itself, has prognostic value for the treatment response in patients with schizophrenia. Already, several biomarkers are routinely collected in standard clinical trials (e.g., blood pressure and plasma electrolytes), and appear to be differentiating factors for treatment outcome. Given the availability of a wide variety of markers, the further development and integration of such markers into clinical research is both required and feasible in order to meet the benefit of personalized medicine. This article is based on proceedings from the "Taking Personalized Medicine Seriously-Biomarker Approaches in Phase IIb/III Studies in Major Depression and Schizophrenia" session, which was held during the 10th Annual Scientific Meeting of the International Society for Clinical Trials Meeting (ISCTM) in Washington, DC, February 18 to 20, 2014.

First Report of 'Candidatus Liberibacter asiaticus' Associated with Huanglongbing on Citrus latifolia in Martinique and Guadeloupe, French West Indies

Huanglongbing is an unculturable vascular citrus pathogen transmitted from infected to healthy plants through grafting or by citrus psyllids, Diaphorina citri mainly in Asia and America and Trioza erytreae in Africa. This phloem limited gram-negative bacterium causes dramatic yield losses and is classified into three species based on 16S rDNA sequence analysis (2): (i) 'Candidatus Liberibacter asiaticus' (Las), the most epidemiologically active, widespread and heat tolerant species; (ii) 'Ca. L. africanus' (Laf), only found in Africa; and (iii) the newly described 'Ca. L. americanus' (Lam), which appeared in 2005 in Brazil (5). Considered as a quarantine organism in America and Europe, Las is actively affecting North America and Asia, and research is leading toward psyllid management and resistance breeding. Despite the fact that Reunion Island has successfully controlled Las by introducing a psyllid parasitoid, Tamarixia radiata (1), this strategy was less effective or reproducible within other territories. D. citri was first detected in Guadeloupe in 1998, where the control of the the psyllid population has been effective with T. radiata (3); and was first detected in Martinique in 2012. Following the outbreak in the United States and the Caribbean, and also supported by reports of symptoms in citrus orchards, local National Plant Protection Organizations (NPPO) organized a detection survey across both islands to verify the occurrence of Huanglongbing. Since 2012, 450 sites were prospected each year in Martinique and Guadeloupe, where 20 leaves from 10 to 30 trees were analyzed. DNA extraction was performed (DNeasy Plant Mini Kit, Qiagen) on fresh or dried leaf midribs, along with negative control midribs (Citrus paradisi 'Star Rubis') and PCR amplification was done with the species-specific primers A2/J5 (4) and GB1/GB3 (5). Only Las-specific 703-bp amplicons were obtained (n = 43) and 20 were sequenced (Beckman Coulter Genomics, United Kingdom; sequences available through GenBank Accession Nos. KF699074 to KF699093) and blasted against the National Center for Biotechnology Information non-redondant database (NCBI-nr). BLAST analysis revealed 100% identity with the 50S ribosomal protein subunit L1 (rplA) and L10 (rplJ) of 'Ca. L. asiaticus' (all strains), and no significant homology to other organisms. Additionally, sequence assembly on a reference genome (NC_012985) showed 100% homology. Huanglongbing was detected in Guadeloupe on March 2012 at Le Moule (East coast) in a Tahiti lime orchard (C. latifolia) and crossed the island in 6 months. Las was detected in Martinique on May 2013 on Tahiti lime (C. latifolia) at Bellefontaine (Northwest) in a private garden and at Le Lorrain (Northeast) in an orchard. Other species from the Rutaceae family were affected by HLB (C. reticulat and C. sinensis) on both islands; however, few of the positive samples showed HLB symptoms (blotchy mottle patterns and green islands on leaves), but presented symptoms similar to nutrient deficiencies. Despite the former presence of T. radiata in Guadeloupe and its detection in Martinique a few weeks after the detection of D. citri, where it had a mean parasitism rate of 70%, an outbreak of HLB spread across both islands. These analyses confirm the presence of HLB in Martinique and Guadeloupe and to our knowledge represent the first report of Las in the French West Indies. Introduction events remain unclear, but this report raises the importance of plant certification, psyllid population control, and surveillance of territories close to the French West Indies, with regards to the risk that HLB presents to citrus production worldwide. References: (1) B. Aubert et al. Fruits. 38, 1983. (2) J. M. Bové. J. Plant Pathol. 88:1, 2006. (3) J. Etienne et al. Fruits. 56:05, 2001. (4) A. Hocquellet et al. Mol. Cell. Probes 13:5, 1999. (5) D. C. Teixeira et al. Mol. Cell. Probes 19:3, 2005.

Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying

Cyberbullying (harassment on social networks) is widely recognized as a serious social problem, especially for adolescents. It is as much a threat to the viability of online social networks for youth today as spam once was to email in the early days of the Internet. Current work to tackle this problem has involved social and psychological studies on its prevalence as well as its negative effects on adolescents. While true solutions rest on teaching youth to have healthy personal relationships, few have considered innovative design of social network software as a tool for mitigating this problem. Mitigating cyberbullying involves two key components: robust techniques for effective detection and reflective user interfaces that encourage users to reflect upon their behavior and their choices.

Spam filters have been successful by applying statistical approaches like Bayesian networks and hidden Markov models. They can, like Google’s GMail, aggregate human spam judgments because spam is sent nearly identically to many people. Bullying is more personalized, varied, and contextual. In this work, we present an approach for bullying detection based on state-of-the-art natural language processing and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying (e.g. appearance, intelligence, racial and ethnic slurs, social acceptance, and rejection), and construct BullySpace , a common sense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with common sense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace common sense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Formspring, a social networking website that is popular with teenagers.

On the intervention side, we explore a set of reflective user-interaction paradigms with the goal of promoting empathy among social network participants. We propose an “air traffic control”-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that in-context, targeted, and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies.

Affective computing and autism

This article highlights the overlapping and converging goals and challenges of autism research and affective computing. We propose that a collaboration between autism research and affective computing could lead to several mutually beneficial outcomes--from developing new tools to assist people with autism in understanding and operating in the socioemotional world around them, to developing new computational models and theories that will enable technology to be modified to provide an overall better socioemotional experience to all people who use it. This article describes work toward this convergence at the MIT Media Lab, and anticipates new research that might arise from the interaction between research into autism, technology, and human socioemotional intelligence.

Establishing the computer-patient working alliance in automated health behavior change interventions

Current user interfaces for automated patient and consumer health care systems can be improved by leveraging the results of several decades of research into effective patient-provider communication skills. A research project is presented in which several such "relational" skills - including empathy, social dialogue, nonverbal immediacy behaviors, and other behaviors to build and maintain good working relationships over multiple interactions - are explicitly designed into a computer interface within the context of a longitudinal health behavior change intervention for physical activity adoption. Results of a comparison among 33 subjects interacting near-daily with the relational system and 27 interacting near-daily with an identical system with the relational behaviors ablated, each for 30 days indicate, that the use of relational behaviors by the system significantly increases working alliance and desire to continue working with the system. Comparison of the above groups to another group of 31 subjects interacting with a control system near-daily for 30 days also indicated a significant increase in proactive viewing of health information.

Ethical evaluation of displays that adapt to affect

We are developing new kinds of sensors and algorithms that can sense and begin to interpret information related to affect. Starting from the standpoint of value-sensitive design, which considers values such as calmness, autonomy, and informed consent, displays that adapt to affect can be ethically evaluated. The question of how adaptive technologies act on these values is of critical importance to the affective computing group. We are seeking to better understand ethical implications for designers through empirical experiments.

Health0: a new health and lifestyle management paradigm

The paper describes Health0: an innovative healthcare and lifestyle management wearable system that aims to bring an individual focused approach to healthcare. A modular architecture is proposed that combines a Linux-based PDA with a distributed wireless sensor-network and innovative bandage-sized (2.5 cm2) sensor hardware. Real-world scenarios that can use the Health0 system are discussed.

Quantifying driver stress: developing a system for collecting and processing bio-metric signals in natural situations

A system for quantifying the physiological features of emotional stress is being developed for use during a driving task. Two prototypes, using sensors that measure the driver's skin conductance, respiration, muscle activity, and heart activity are presented. The first system allows sampling rates of 200 Hz on two fast channels and 20 Hz on six additional channels. It uses a wearable computer to do real-time processing on the signals and has an attached digital camera which was used to capture images of the driver's facial expression once every minute. The second system uses a car-based computer that allows a sampling rate of 1984 samples per second on eight channels. This system uses multiple video cameras to continuously capture the driver's facial expression and road conditions. The data is then synchronized with the physiological signals using a video quad-splitter. The methods for extracting physiological features in the driving environment are discussed, including measurement of the skin conductance orienting response, muscle activity, pulse, and respiration patterns. Preliminary studies show how using multiple modalities of sensors can help discriminate reactions to driving events and how individual's response to similar driving conditions can vary from day to day.

Coupling between agonist and chloride ionophore sites of the GABA(A) receptor: agonist/antagonist efficacy of 4-PIOL

Eight gamma-aminobutyric acid (GABA) mimetics were tested on their ability to differentiate native GABA(A) receptor subtypes present in various rat brain regions. In rat brain cryostat sections, little regional variations by the agonistic actions of muscimol, thiomuscimol, 4,5,6,7-tetrahydroisoazolo(5,4-c)pyridin-3-ol, piperidine-4-sulphonic acid, taurine and beta-alanine on [35S]t-butylbicyclophosphorothionate ([35S]TBPS) binding to GABA(A) receptor channels were found. They were very similar to those found for GABA itself and indicated no direct correlation with single subunit distributions for any of these compounds. Only the low-efficacy GABA mimetic 5-(4-piperidyl)isoxazol-3-ol (4-PIOL) acted like a weak partial agonist or antagonist depending on the brain area. As the cerebellar granule cell layer was relatively insensitive to both modes of action, we tested 4-PIOL in recombinant alpha1beta2gamma2 (widespread major subtype) and alpha6beta2gamma2 (cerebellar granule cell restricted) receptors where it had different effects on GABA-modulated [35S]TBPS binding and on electrophysiological responses. 4-PIOL may thus serve as a potential lead for receptor subtype selective compounds.

Expectant management of post-term patients: observations and outcome

Post-term pregnancy is associated with increased perinatal mortality. In a retrospective study based on our post-term protocol from 1990 until May 1995 1,798 post-term pregnant women with reliable dating were evaluated for expectant management. A group of 2,633 pregnant women who delivered between 37 and 41 weeks during 1994 served as a control group. The perinatal mortality (0.56 per 1,000 vs. 0.75 per 1,000 in the control group) was similar in both groups. The incidence of induction of labor (7.45% vs. 7% in the control group), meconium of more than +1 (5.2% vs. 4% in the control group), shoulder dystocia (0.33% vs. 0.19%), high birthweight (> 4,500 g) (1% vs. 1%), and cesarean section rates (7.5% vs. 7% in the control group) were similar. However the fetal distress rates (11.6% vs 16%; P = .004), instrumental deliveries (10.1% vs. 13%, P = .002), and the rate of 5-minute Apgar score of less then 7 (1.1% vs. 5%, P = .000001) were found to be significantly lower in the post-date group than in the control group. We conclude that the expectant management and our intensive observation and follow-up in post-term is indicated for both mother and fetus.

Birth weight discordance, intrauterine growth retardation and perinatal outcomes in twins

In a population-based study, the association between twin perinatal outcome, birth weight discordance and intrauterine growth retardation (IUGR) was investigated. Mortality was examined in 1,145 twin pairs, and neonatal morbidity was studied in 315 live-born pairs. In this study, birth weight discordance was defined as > or = 25% of the weight of the heavier twin, and IUGR was defined as < 10th percentile of gender- and gestational age-specific singleton birth weight. In univariate analyses IUGR and discordance were associated with mortality but not with morbidity. When twins were categorized by discordance and IUGR simultaneously, stillbirths were associated with IUGR, while discordant twins had elevated risks of early neonatal death. In multiple logistic regression analyses, which also included gestational age, mode of delivery, birth order, birth weight discordance and IUGR, low birth weight was the single consistent factor associated with elevated risks of mortality and morbidity. For every 250-g increase in birth weight, the risks for each condition examined fell by about 40%. Discordance was an independent risk factor only for hypoglycemia, while IUGR was not associated with any neonatal outcome. Birth weight itself seems to be the most important factor associated with perinatal outcome in twin births. Therefore, the weight of the individual fetus should be the focus of concern of the medical profession in the effort to reduce adverse outcomes in twin pregnancies.

Ethnicity and sex ratio in twin births

The patterns of sex ratio in Israeli twins by maternal age and parity are described in two ethnic subpopulation. Jews and Bedouins differ one from the other in genetic, environmental, lifestyle, and reproductive factors. In the Jewish subpopulation, the proportion of males was significantly lower in twin than in singleton births; parity increased the odds of male twin births while maternal age had the inverse effect. In the Bedouin subpopulation, the sex ratio did not differ significantly from that of singletons, and no consistent patterns were found by maternal age and parity. The data suggest that several factors may influence the sex ratio in twin births. In Jewish twin births, the findings are consistent with the literature and can be largely explained in terms of high level of maternal gonadotropins. In the Bedouin subpopulation, paternal influence which could be related to lifestyle may be present, and as yet undefined genetic factors may also be involved. It would be of great interest, therefore, to continue monitoring of the effects of changes in lifestyle on the Bedouin population in order to tease out the relative importance of the varying factors on the sex ratio of twin births.

Factors associated with neonatal problems in twin gestations

We examined the neonatal outcome of 644 twins weighing 500 g or more and 656 singletons, born in the years 1984-1986 in the Soroka Medical Center, Beer-Sheva, Israel. There was nearly a four-fold risk of antepartum death in twins vs singletons, which disappeared when birth weight was controlled for. The risks for intrapartum and early neonatal mortality were not raised in this population. A statistically significant relative risk for congenital heart malformations in twins vs singletons remained (RR = 5.0, 95% CI = 1.5-16.3), after controlling for maternal age. Significantly higher rates of hyalin membrane disease, hypoglycemia, hyperbilirubinemia, anemia and septicemia were found in twins. Controlling for the confounding of the association between twinning and mortality or morbidity caused by differences in distributions of mode of delivery or gestational age between twins and singletons, was not as efficient as the controlling for birth weight. Thus, adjustment for birth weight removed all the excess risks detected except in hypoglycemia. Our findings suggest that the lower birth weight of twins, which is so intimately associated with multiple gestations, is probably the single most important factor associated with neonatal problems found in twin births.

[Congenital mitral and tricuspid double stenosis]

The authors report the case of a child followed up from the age of 4 months to the age of 15 for asymptomatic congenital heart disease. Initial clinical examination showed the presence of a systolic murmur and a diastolic rumble in the xiphoid area with signs of right atrial dilatation-hypertrophy. During follow-up, an apical diastolic rumble was detected with left atrial dilatation from the age of 3. Doppler echocardiography carried out recently showed double mitral and tricuspid valve stenosis with two-dimensional appearances of doming of both valves and moderate transvalvular pressure gradients with slight elevation of pulmonary artery pressures on the Doppler study. The very early detection of the malformation and the continuous follow-up of this patient indicate the congenital and not rheumatic origin of this double valve stenosis.

Twinning in southern Israel. Seasonal variation and effects of ethnicity, maternal age and parity

Seasonality of births in southern Israel was examined in two populations, Jews and Bedouins, with distinctly different life-styles. The study included 1,444 twin births that occurred between the years 1970 and 1986. The peak month for the birth of monozygotic twins was September in both populations, while the maximum number of deliveries occurred in January for the Bedouins and August for the Jews. Of Jewish dizygotic twin births, higher rates were found from July to December. In addition, the peak months of singleton births in women aged 35 years and older and in women of high parity did not coincide with the peak months of multiple births. Maternal age and parity have been shown to greatly influence twin birth rates. The autumnal peak we found, which was independent of ethnic origin and its associated cultural and sociologic differences, was also independent of maternal age and parity and was consistent with findings in other populations in the northern hemisphere. That finding suggests that a seasonal factor, as yet undefined, affects the rates of multiple births.

Uterine rupture complicating midtrimester abortion. A report of two cases

Uterine rupture occurring during a midtrimester abortion is rare. This complication may lead to profound shock and death as well as to interference with the patient's future fertility. Two patients sustained a uterine rupture during midtrimester abortion. This complication seems to be preventable. The risk of uterine rupture due to overstimulation is higher when amnioinfusion with prostaglandin or hypertonic saline is combined with the use of other oxytocic drugs. Grand multiparas undergoing amnioinfusion should not be given oxytocin; in the rare cases in which oxytocin is needed, it should be administered cautiously and monitored continuously. When a supplemental agent, such as an oxytocic, is needed, it should not be started until several hours after the amnioinfusion.