The Patient Role in a Federal National-Scale Health Information Exchange.

The federal Trusted Exchange Framework and Common Agreement (TEFCA) aims to reduce fragmentation of patient records by expanding query-based health information exchange with nationwide connectivity for diverse purposes. TEFCA provides a common agreement and security framework allowing clinicians, and possibly insurance company staff, public health officials, and other authorized users, to query for health information about hundreds of millions of patients. TEFCA presents an opportunity to weave information exchange into the fabric of our national health information economy. We define 3 principles to promote patient autonomy and control within TEFCA: (1) patients can query for data about themselves, (2) patients can know when their data are queried and shared, and (3) patients can configure what is shared about them. We believe TEFCA should address these principles by the time it launches. While health information exchange already occurs on a large scale today, the launch of TEFCA introduces a major, new, and cohesive component of 21st-century US health care information infrastructure. We strongly advocate for a substantive role for the patient in TEFCA, one that will be a model for other systems and policies.

Development and evaluation of the HRSD-D, an image-based digital measure of the Hamilton rating scale for depression.

The Hamilton rating scale for depression (HRSD) is considered the gold standard for the assessment of major depressive disorder. Nevertheless, it has drawbacks such as reliance on retrospective reports and a relatively long administration time. Using a combination of an experience sampling method with mobile health technology, the present study aimed at developing and conducting initial validation of HRSD-D, the first digital image-based assessment of the HRSD. Fifty-three well-trained HRSD interviewers selected the most representative image for each item from an initial sample of images. Based on their responses, we developed the prototype of HRSD-D in two versions: trait-like (HRSD-DT) and state-like (HRSD-DS). HRSD-DT collects one-time reports on general tendencies to experience depressive symptoms; HRSD-DS collects daily reports on the experience of symptoms. Using a total of 1933 responses collected in a preclinical sample (N = 86), we evaluated the validity and feasibility of HRSD-D, based on participant reports of HRSD-DT at baseline, and 28 consecutive daily reports of HRSD-DS, using smartphone devices. HRSD-D showed good convergent validity with respect to the original HRSD, as evident in high correlations between HRSD-DS and HRSD (up to Bstd = 0.80). Our combined qualitative and quantitative analyses indicate that HRSD-D captured both dynamic and stable features of symptomatology, in a user-friendly monitoring process. HRSD-D is a promising tool for the assessment of trait and state depression and contributes to the use of mobile technologies in mental health research and practice.

Standardized Integration of Person-Generated Data Into Routine Clinical Care.

Person-generated data (PGD) are a valuable source of information on a person's health state in daily life and in between clinic visits. To fully extract value from PGD, health care organizations must be able to smoothly integrate data from PGD devices into routine clinical workflows. Ideally, to enhance efficiency and flexibility, such integrations should follow reusable processes that can easily be replicated for multiple devices and data types. Instead, current PGD integrations tend to be one-off efforts entailing high costs to build and maintain custom connections with each device and their proprietary data formats. This viewpoint paper formulates the integration of PGD into clinical systems and workflow as a PGD integration pipeline and reviews the functional components of such a pipeline. A PGD integration pipeline includes PGD acquisition, aggregation, and consumption. Acquisition is the person-facing component that includes both technical (eg, sensors, smartphone apps) and policy components (eg, informed consent). Aggregation pools, standardizes, and structures data into formats that can be used in health care settings such as within electronic health record-based workflows. PGD consumption is wide-ranging, by different solutions in different care settings (inpatient, outpatient, consumer health) for different types of users (clinicians, patients). The adoption of data and metadata standards, such as those from IEEE and Open mHealth, would facilitate aggregation and enable broader consumption. We illustrate the benefits of a standards-based integration pipeline for the illustrative use case of home blood pressure monitoring. A standards-based PGD integration pipeline can flexibly streamline the clinical use of PGD while accommodating the complexity, scale, and rapid evolution of today's health care systems.

HabitWorks: Development of a CBM-I Smartphone App to Augment and Extend Acute Treatment.

The month following discharge from acute psychiatric care is associated with increased risk of relapse, rehospitalization, and suicide. Effective and accessible interventions tailored to this critical transition are urgently needed. Cognitive bias modification for interpretation (CBM-I) is a low-intensity intervention that targets interpretation bias, a transdiagnostic process implicated in the development and maintenance of emotional disorders. We describe the development of a CBM-I smartphone app called HabitWorks as an augmentation to acute care that extends through the high-risk month postdischarge. We first obtained input from various stakeholders, including adults who had completed partial hospital treatment (patient advisory board), providers, CBM experts, and clinic program directors. We then iteratively tested versions of the app, incorporating feedback over three waves of users. Participants were recruited from a partial hospital program and completed CBM-I sessions via the HabitWorks app while attending the hospital program and during the month postdischarge. In this Stage 1A treatment development work, we obtained preliminary data regarding feasibility and acceptability, adherence during acute care, and target engagement. Pilot data met our a priori benchmarks. While adherence during acute treatment was good, it decreased during the postacute period. Qualitative feedback was generally positive and revealed themes of usability and helpfulness of app features. Participants varied in their perception of skill generalization to real-life situations. The feasibility and acceptability data suggest that a controlled trial of HabitWorks is warranted.

Correction: The Digital Marshmallow Test (DMT) Diagnostic and Monitoring Mobile Health App for Impulsive Behavior: Development and Validation Study.

[This corrects the article DOI: 10.2196/25018.].

The Digital Marshmallow Test (DMT) Diagnostic and Monitoring Mobile Health App for Impulsive Behavior: Development and Validation Study.

BACKGROUND: The classic Marshmallow Test, where children were offered a choice between one small but immediate reward (eg, one marshmallow) or a larger reward (eg, two marshmallows) if they waited for a period of time, instigated a wealth of research on the relationships among impulsive responding, self-regulation, and clinical and life outcomes. Impulsivity is a hallmark feature of self-regulation failures that lead to poor health decisions and outcomes, making understanding and treating impulsivity one of the most important constructs to tackle in building a culture of health. Despite a large literature base, impulsivity measurement remains difficult due to the multidimensional nature of the construct and limited methods of assessment in daily life. Mobile devices and the rise of mobile health (mHealth) have changed our ability to assess and intervene with individuals remotely, providing an avenue for ambulatory diagnostic testing and interventions. Longitudinal studies with mobile devices can further help to understand impulsive behaviors and variation in state impulsivity in daily life. OBJECTIVE: The aim of this study was to develop and validate an impulsivity mHealth diagnostics and monitoring app called Digital Marshmallow Test (DMT) using both the Apple and Android platforms for widespread dissemination to researchers, clinicians, and the general public. METHODS: The DMT app was developed using Apple's ResearchKit (iOS) and Android's ResearchStack open source frameworks for developing health research study apps. The DMT app consists of three main modules: self-report, ecological momentary assessment, and active behavioral and cognitive tasks. We conducted a study with a 21-day assessment period (N=116 participants) to validate the novel measures of the DMT app. RESULTS: We used a semantic differential scale to develop self-report trait and momentary state measures of impulsivity as part of the DMT app. We identified three state factors (inefficient, thrill seeking, and intentional) that correlated highly with established measures of impulsivity. We further leveraged momentary semantic differential questions to examine intraindividual variability, the effect of daily life, and the contextual effect of mood on state impulsivity and daily impulsive behaviors. Our results indicated validation of the self-report sematic differential and related results, and of the mobile behavioral tasks, including the Balloon Analogue Risk Task and Go-No-Go task, with relatively low validity of the mobile Delay Discounting task. We discuss the design implications of these results to mHealth research. CONCLUSIONS: This study demonstrates the potential for assessing different facets of trait and state impulsivity during everyday life and in clinical settings using the DMT mobile app. The DMT app can be further used to enhance our understanding of the individual facets that underlie impulsive behaviors, as well as providing a promising avenue for digital interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT03006653; https://www.clinicaltrials.gov/ct2/show/NCT03006653.

mPulse Mobile Sensing Model for Passive Detection of Impulsive Behavior: Exploratory Prediction Study.

BACKGROUND: Mobile health technology has demonstrated the ability of smartphone apps and sensors to collect data pertaining to patient activity, behavior, and cognition. It also offers the opportunity to understand how everyday passive mobile metrics such as battery life and screen time relate to mental health outcomes through continuous sensing. Impulsivity is an underlying factor in numerous physical and mental health problems. However, few studies have been designed to help us understand how mobile sensors and self-report data can improve our understanding of impulsive behavior. OBJECTIVE: The objective of this study was to explore the feasibility of using mobile sensor data to detect and monitor self-reported state impulsivity and impulsive behavior passively via a cross-platform mobile sensing application. METHODS: We enrolled 26 participants who were part of a larger study of impulsivity to take part in a real-world, continuous mobile sensing study over 21 days on both Apple operating system (iOS) and Android platforms. The mobile sensing system (mPulse) collected data from call logs, battery charging, and screen checking. To validate the model, we used mobile sensing features to predict common self-reported impulsivity traits, objective mobile behavioral and cognitive measures, and ecological momentary assessment (EMA) of state impulsivity and constructs related to impulsive behavior (ie, risk-taking, attention, and affect). RESULTS: Overall, the findings suggested that passive measures of mobile phone use such as call logs, battery charging, and screen checking can predict different facets of trait and state impulsivity and impulsive behavior. For impulsivity traits, the models significantly explained variance in sensation seeking, planning, and lack of perseverance traits but failed to explain motor, urgency, lack of premeditation, and attention traits. Passive sensing features from call logs, battery charging, and screen checking were particularly useful in explaining and predicting trait-based sensation seeking. On a daily level, the model successfully predicted objective behavioral measures such as present bias in delay discounting tasks, commission and omission errors in a cognitive attention task, and total gains in a risk-taking task. Our models also predicted daily EMA questions on positivity, stress, productivity, healthiness, and emotion and affect. Perhaps most intriguingly, the model failed to predict daily EMA designed to measure previous-day impulsivity using face-valid questions. CONCLUSIONS: The study demonstrated the potential for developing trait and state impulsivity phenotypes and detecting impulsive behavior from everyday mobile phone sensors. Limitations of the current research and suggestions for building more precise passive sensing models are discussed. TRIAL REGISTRATION: ClinicalTrials.gov NCT03006653; https://clinicaltrials.gov/ct2/show/NCT03006653.

An mHealth App for Self-Management of Chronic Lower Back Pain (Limbr): Pilot Study.

BACKGROUND: Although mobile health (mHealth) interventions can help improve outcomes among patients with chronic lower back pain (CLBP), many available mHealth apps offer content that is not evidence based. Limbr was designed to enhance self-management of CLBP by packaging self-directed rehabilitation tutorial videos, visual self-report tools, remote health coach support, and activity tracking into a suite of mobile phone apps, including Your Activities of Daily Living, an image-based tool for quantifying pain-related disability. OBJECTIVE: The aim is to (1) describe patient engagement with the Limbr program, (2) describe patient-perceived utility of the Limbr program, and (3) assess the validity of the Your Activities of Daily Living module for quantifying functional status among patients with CLBP. METHODS: This was a single-arm trial utilizing a convenience sample of 93 adult patients with discogenic back pain who visited a single physiatrist from January 2016 to February 2017. Eligible patients were enrolled in 3-month physical therapy program and received the Limbr mobile phone app suite for iOS or Android. The program included three daily visual self-reports to assess pain, activity level, and medication/coping mechanisms; rehabilitation video tutorials; passive activity-level measurement; and chat-based health coaching. Patient characteristics, patient engagement, and perceived utility were analyzed descriptively. Associations between participant characteristics and program interaction were analyzed using multiple linear regression. Associations between Your Activities of Daily Living and Oswestry Disability Index (ODI) assessments were examined using Pearson correlation and hierarchical linear modeling. RESULTS: A total of 93 participants were enrolled; of these, 35 (38%) completed the program (age: mean 46, SD 16 years; female: 22/35, 63%). More than half of completers finished assessments at least every 3 days and 70% (19/27) used the rehabilitation component at least once a week. Among respondents to a Web-based feedback survey, 76% (16/21) found the daily notifications helped them remember to complete their exercises, 81% (17/21) found the system easy to use, and 62% (13/21) rated their overall experience good or excellent. Baseline Your Activities of Daily Living score was a significant predictor of baseline ODI score, with ODI increasing by 0.30 units for every 1-unit increase in Your Activities of Daily Living (P<.001). Similarly, hierarchical linear modeling analysis indicated that Your Activities of Daily Living daily assessment scores were significant predictors of ODI scores over the course of the study (P=.01). CONCLUSIONS: Engagement among participants who completed the Limbr program was high, and program utility was rated positively by most respondents. Your Activities of Daily Living was significantly associated with ODI scores, supporting the validity of this novel tool. Future studies should assess the effect of Limbr on clinical outcomes, evaluate its use among a wider patient sample, and explore strategies for reducing attrition. TRIAL REGISTRATION: ClinicalTrials.gov NCT03040310; https://clinicaltrials.gov/ct2/show/NCT03040310 (Archived by WebCite at http://www.webcitation.org/722mEvAiv).

Early-life antibiotic use and subsequent diagnosis of food allergy and allergic diseases.

BACKGROUND: Antibiotic use in early life has been linked to disruptions in the microbiome. Such changes can disturb immune system development. Differences have been observed in the microbiota of children with and without allergies, but there have been few studies on antibiotic use and allergic disease. OBJECTIVE: We evaluated associations of early-life antibiotic use with subsequent occurrence of food allergy and other allergies in childhood using electronic health record data. METHODS: We used longitudinal data on 30 060 children up to age 7 years from Geisinger Clinic's electronic health record to conduct a sex- and age-matched case-control study to evaluate the association between antibiotic use and milk allergy, non-milk food allergies, and other allergies. For each outcome, we estimated conditional logistic regression models adjusting for race/ethnicity, history of Medical Assistance, and mode of birth delivery. Models were repeated separately for penicillins, cephalosporins and macrolides. RESULTS: There were 484 milk allergy cases, 598 non-milk food allergy cases and 3652 other allergy cases. Children with three or more antibiotic orders had a greater odds of milk allergy (Odds Ratio; 95% Confidence interval) (1.78; 1.28-2.48), non-milk food allergy (1.65; 1.27-2.14), and other allergies (3.07; 2.72-3.46) compared with children with no antibiotic orders. Associations were strongest at younger ages and differed by antibiotic class. CONCLUSIONS AND CLINICAL RELEVANCE: We observed associations between antibiotic orders and allergic diseases, providing evidence of a potentially modifiable clinical practice associated with paediatric allergic disease. Differences by antibiotic class should be further explored, as this knowledge could inform paediatric treatment decisions.

Antibiotic use and childhood body mass index trajectory.

BACKGROUND/OBJECTIVES: Antibiotics are commonly prescribed for children. Use of antibiotics early in life has been linked to weight gain but there are no large-scale, population-based, longitudinal studies of the full age range among mainly healthy children. SUBJECTS/METHODS: We used electronic health record data on 163 820 children aged 3-18 years and mixed effects linear regression to model associations of antibiotic orders with growth curve trajectories of annual body mass index (BMI) controlling for confounders. Models evaluated three kinds of antibiotic associations-reversible (time-varying indicator for an order in year before each BMI), persistent (time-varying cumulative orders up to BMIj) and progressive (cumulative orders up to prior BMI (BMIj-1))-and whether these varied by age. RESULTS: Among 142 824 children under care in the prior year, a reversible association was observed and this short-term BMI gain was modified by age (P<0.001); effect size peaked in mid-teen years. A persistent association was observed and this association was stronger with increasing age (P<0.001). The addition of the progressive association among children with at least three BMIs (n=79 752) revealed that higher cumulative orders were associated with progressive weight gain; this did not vary by age. Among children with an antibiotic order in the prior year and at least seven lifetime orders, antibiotics (all classes combined) were associated with an average weight gain of approximately 1.4 kg at age 15 years. When antibiotic classes were evaluated separately, the largest weight gain at 15 years was associated with macrolide use. CONCLUSIONS: We found evidence of reversible, persistent and progressive effects of antibiotic use on BMI trajectories, with different effects by age, among mainly healthy children. The results suggest that antibiotic use may influence weight gain throughout childhood and not just during the earliest years as has been the primary focus of most prior studies.

Leveraging Multi-Modal Sensing for Mobile Health: A Case Review in Chronic Pain.

Active and passive mobile sensing has garnered much attention in recent years. In this paper, we focus on chronic pain measurement and management as a case application to exemplify the state of the art. We present a consolidated discussion on the leveraging of various sensing modalities along with modular server-side and on-device architectures required for this task. Modalities included are: activity monitoring from accelerometry and location sensing, audio analysis of speech, image processing for facial expressions as well as modern methods for effective patient self-reporting. We review examples that deliver actionable information to clinicians and patients while addressing privacy, usability, and computational constraints. We also discuss open challenges in the higher level inferencing of patient state and effective feedback with potential directions to address them. The methods and challenges presented here are also generalizable and relevant to a broad range of other applications in mobile sensing.

Smartphone Data in Rheumatoid Arthritis - What Do Rheumatologists Want?

OBJECTIVE: To create a relevant and clinically informative visualization of passively collected patient mobility data from smartphones of rheumatoid arthritis (RA) patients for rheumatologists. METHODS: (1) Pilot analysis of smartphone mobility data in RA; (2) Assessment of rheumatologists' needs for patient data through semi-structured interviews; and (3) Evaluation of the visual format of the RA data using scenario-based usability methods. RESULTS: We created a color-scale mobility index superimposed on a calendar to summarize the passive mobility measures from the smartphone that the rheumatologists confirmed would be clinically relevant. CONCLUSION: This assessment of clinician data needs and preferences demonstrates the potential value of passively collected smartphone data to resolve an important data question in RA. Efforts such as these are necessary to ensure that any smartphone data that patients share with their doctors will not exacerbate clinician information overload, but actually facilitate clinical decisions.

Does social support predict pregnant mothers' information seeking behaviors on an educational website?

We examine how social support (perceived support and support from a spouse, or committed partner) may influence pregnant women's information seeking behaviors on a pregnancy website. We assess information seeking behavior among participants in a trial testing the effectiveness of a web-based intervention for appropriate gestational weight gain. Participants were pregnant women (N = 1,329) recruited from clinics and private practices in one county in the Northeast United States. We used logistic regression models to estimate the likelihood of viewing articles, blogs, frequently asked questions (FAQs), and resources on the website as a function of perceived social support, and support from a spouse or relationship partner. All models included socio-demographic controls (income, education, number of adults and children living at home, home Internet use, and race/ethnicity). Compared to single women, women who were married or in a committed relationship were more likely to information seek online by viewing articles (OR 1.95, 95 % CI [1.26-3.03]), FAQs (OR 1.64 [1.00-2.67]), and blogs (OR 1.88 [1.24-2.85]). Women who felt loved and valued (affective support) were more likely to seek information by viewing articles on the website (OR 1.19 [1.00-1.42]). While the Internet provides a space for people who have less social support to access health information, findings from this study suggest that for pregnant women, women who already had social support were most likely to seek information online. This finding has important implications for designing online systems and content to encourage pregnant women with fewer support resources to engage with content.

Metamodels for transdisciplinary analysis of wildlife population dynamics.

Wildlife population models have been criticized for their narrow disciplinary perspective when analyzing complexity in coupled biological - physical - human systems. We describe a "metamodel" approach to species risk assessment when diverse threats act at different spatiotemporal scales, interact in non-linear ways, and are addressed by distinct disciplines. A metamodel links discrete, individual models that depict components of a complex system, governing the flow of information among models and the sequence of simulated events. Each model simulates processes specific to its disciplinary realm while being informed of changes in other metamodel components by accessing common descriptors of the system, populations, and individuals. Interactions among models are revealed as emergent properties of the system. We introduce a new metamodel platform, both to further explain key elements of the metamodel approach and as an example that we hope will facilitate the development of other platforms for implementing metamodels in population biology, species risk assessments, and conservation planning. We present two examples - one exploring the interactions of dispersal in metapopulations and the spread of infectious disease, the other examining predator-prey dynamics - to illustrate how metamodels can reveal complex processes and unexpected patterns when population dynamics are linked to additional extrinsic factors. Metamodels provide a flexible, extensible method for expanding population viability analyses beyond models of isolated population demographics into more complete representations of the external and intrinsic threats that must be understood and managed for species conservation.

A population-based study of the epidemiology and clinical features of methicillin-resistant Staphylococcus aureus infection in Pennsylvania, 2001-2010.

No U.S. general population-based study has characterized the epidemiology and risk factors, including skin and soft tissue infection (SSTI), for healthcare-associated (HA) and community-associated (CA) methicillin-resistant Staphylococcus aureus (MRSA). We estimated the incidence of HA- and CA-MRSA and SSTI over a 9-year period using electronic health record data from the Geisinger Clinic in Pennsylvania. MRSA cases were frequency-matched to SSTI cases and controls in a nested case-control analysis. Logistic regression was used to assess risk factors, while accounting for antibiotic administration. We identified 1713 incident CA- and 1506 HA-MRSA cases and 78 216 SSTI cases. On average, from 2005 to 2009, the annual incidence of CA-MRSA increased by 34%, HA-MRSA by 7%, and SSTI by 4%. Age, season, community socioeconomic deprivation, obesity, smoking, previous SSTI, and antibiotic administration were identified as independent risk factors for CA-MRSA.

Complications of three sacrospinous ligament fixation techniques.

OBJECTIVES: To compare the complications of three techniques used to pass the suture through the sacrospinous ligament when performing sacrospinous ligament fixation (SSLF): (1) the standard needle driver with direct visualization; (2) the Deschamps ligature carrier by palpation; and (3) the Miya hook ligature carrier by palpation. MATERIALS AND METHODS: A retrospective chart review of 240 patients undergoing SSLF was performed. A standard needle driver with direct visualization was used for 46 patients; the Deschamps ligature carrier was used for 173, and the Miya hook for 21 patients. Intraoperative and postoperative complications were compared between the groups. RESULTS: Twelve women (5%) had intraoperative and forty women (17%) had postoperative complications suspected directly from the suture placement. There was no statistically significant difference in the rate of total intraoperative complications among the three groups. The proportion of patients with postoperative complications that were felt to be directly related to the suture passage technique was significantly higher in the Deschamps group compared to the direct visualization group (18% vs 2%, P=0.002). CONCLUSION: Passing the suture through the sacrospinous ligament under direct visualization may result in less intra and postoperative complications.

Effects of a desealant formulation, SR-51(®) and its individual components on the oxidative functions of mitochondria.

The Royal Australian Air Force has reported that personnel involved in F-111 fuel tank maintenance were concerned that exposure to a range of chemicals during the period 1977-mid-1990s was the cause of health problems. Particular concern was directed at a desealant chemical mixture known as SR-51(®). The current study, using in vitro submitochondrial assays, was designed to investigate the relative toxicities of the four components of SR-51(®) (Aromatic 150 solvent (Aro150), dimethylacetamide (DMA), thiophenol (TP) and triethylphosphate (TEP)). Based on the EC(50) values, TP and Aro150 were the most toxic components and were markedly more toxic than TEP and DMA.

The use of cyanoacrylate adhesives in peripheral embolization.

Although liquid adhesives or glue have been used as embolic agents for nearly three decades, experience with them outside of neurointerventional indications is generally limited. Cyanoacrylates are the main liquid adhesives used in the vascular system and have an important role in managing vascular abnormalities, especially arteriovenous malformations. Vascular occlusion results as these agents polymerize on exposure to the ions in blood. A description of the properties, biologic interactions, techniques of use, and indications for acrylic embolization in the peripheral circulation is especially pertinent at this time because of the recent approval of n-butyl cyanoacrylate by the United States Food and Drug Administration.

Evaluation of various maneuvers for prevention of air embolism during central venous catheter placement.

This study is designed to evaluate the various physiologic maneuvers (Valsalva, humming, breath-hold) for the potential prevention of air embolism during central venous catheter placement. Central venous pressure measurements were prospectively obtained in 40 patients undergoing central venous catheter placement. The average central venous pressure at baseline was 3.275 mm Hg (range = -4 to 16, SD = 5.99). The average central venous pressure during breath hold was 6.1 mm Hg (range = -6 to 24, SD = 7.99). The average central venous pressure during humming was 5.1 mm Hg (range = -4 to 20, SD = 6.4) The average central venous pressure during the Valsalva maneuver was 18.43 (range = -3 to 48, SD = 14.73). Forty percent of patients (16 of 40) had negative central venous pressures at rest, 25% (10 of 40) had negative pressures during breath hold, 20% (8 of 40) had negative pressures during humming, and 2.5% (1 of 40) had negative pressures during Valsalva maneuver. The average increases in central venous pressure during breath hold, humming, and Valsalva were 2.85, 1.82, and 15.2 mm Hg, respectively. The difference between pressures during Valsalva and other maneuvers was statistically significant (P <.05). The conclusion is that the Valsalva maneuver is superior to breath-hold and humming for increasing central venous pressure during central venous catheter placement and, therefore, it is more likely to prevent air embolism in cooperative patients.