Understanding social needs screening and demographic data collection in primary care practices serving Maryland Medicare patients.

BACKGROUND: Health outcomes are strongly impacted by social determinants of health, including social risk factors and patient demographics, due to structural inequities and discrimination. Primary care is viewed as a potential medical setting to assess and address individual health-related social needs and to collect detailed patient demographics to assess and advance health equity, but limited literature evaluates such processes. METHODS: We conducted an analysis of cross-sectional survey data collected from n = 507 Maryland Primary Care Program (MDPCP) practices through Care Transformation Requirements (CTR) reporting in 2022. Descriptive statistics were used to summarize practice responses on social needs screening and demographic data collection. A stepwise regression analysis was conducted to determine factors predicting screening of all vs. a targeted subset of beneficiaries for unmet social needs. RESULTS: Almost all practices (99%) reported conducting some form of social needs screening and demographic data collection. Practices reported variation in what screening tools or demographic questions were employed, frequency of screening, and how information was used. More than 75% of practices reported prioritizing transportation, food insecurity, housing instability, financial resource strain, and social isolation. CONCLUSIONS: Within the MDPCP program there was widespread implementation of social needs screenings and demographic data collection. However, there was room for additional supports in addressing some challenging social needs and increasing detailed demographics. Further research is needed to understand any adjustments to clinical care in response to identified social needs or application of data for uses such as assessing progress towards health equity and the subsequent impact on clinical care and health outcomes.

Protocol on establishing a national disease registry-Swiss Pediatric Inflammatory Brain Disease Registry.

BACKGROUND: Pediatric-onset inflammatory brain diseases are a group of potentially life-threatening central nervous system disorders. Overall, pediatric-onset inflammatory brain diseases are rare and therefore difficult to study. Patient registries are well suited to study the natural history of (rare) diseases and have markedly advanced the knowledge on pediatric-onset inflammatory brain diseases in other countries. Following their example, we established a national pediatric-onset inflammatory brain disease registry in Switzerland (Swiss-Ped-IBrainD). AIMS: The Registry aims to describe epidemiology, demographics, diagnostics, management, and treatment, since these areas remain understudied in Switzerland. Additionally, we want to promote research by fostering the knowledge exchange between study centers and setting up studies such as national quality of life surveys. We further aim to facilitate the access to national and international studies for patients with a pediatric-onset inflammatory brain disease living and/or treated in Switzerland. METHODS: The Swiss-Ped-IBrainD is a multicentric, population-based, observational cohort study (IRB number: 2019-00377) collaborating with 11 neuropediatric centers in Switzerland. Patient screening, information and recruitment is mainly conducted by the local principal investigators. The data collection is organized centrally by the Executive Office of the registry. The collected data is purely observational. Medical records are the primary data source. All patients who have been diagnosed with a pediatric-onset inflammatory brain disease since 2005 are eligible. We aim to include all pediatric-onset inflammatory brain disease patients living and/or treated in Switzerland who meet the inclusion criteria. Considering existing literature and our single-center experience we anticipate 300-400 eligible patients. STATUS: Currently, all 11 neuropediatric centers have been initiated and are recruiting. As of the first of May 2023, we have identified 275 eligible participants and obtained informed consent from 101 patients and/or families. None of the informed patients and/or families have refused participation.

Developing Sampling Weights for Statistical Analysis of Parent-Child Pair Data From the National Health Interview Survey.

The National Health Interview Survey (NHIS), conducted by the National Center for Health Statistics since 1957, is the principal source of information on the health of the U.S. civilian noninstitutionalized population. NHIS selects one adult (Sample Adult) and, when applicable, one child (Sample Child) randomly within a family (through 2018) or a household (2019 and forward). Sampling weights for the separate analysis of data from Sample Adults and Sample Children are provided annually by the National Center for Health Statistics. A growing interest in analysis of parent-child pair data using NHIS has been observed, which necessitated the development of appropriate analytic weights. Objective This report explains how dyad weights were created such that data users can analyze NHIS data from both Sample Children and their mothers or fathers, respectively. Methods Using data from the 2019 NHIS, adult-child pair-level sampling weights were developed by combining each pair's conditional selection probability with their household-level sampling weight. The calculated pair weights were then adjusted for pair-level nonresponse, and large sampling weights were trimmed at the 99th percentile of the derived sampling weights. Examples of analyzing parent-child pair data by means of domain estimation methods (that is, statistical analysis for subpopulations or subgroups) are included in this report. Conclusions The National Center for Health Statistics has created dyad or pair weights that can be used for studies using parent-child pairs in NHIS. This method could potentially be adapted to other surveys with similar sampling design and statistical needs.

The National Healthcare Safety Network's digital quality measures: CDC's automated measures for surveillance of patient safety.

OBJECTIVE: This article presents the National Healthcare Safety Network (NHSN)'s approach to automation for public health surveillance using digital quality measures (dQMs) via an open-source tool (NHSNLink) and piloting of this approach using real-world data in a newly established collaborative program (NHSNCoLab). The approach leverages Health Level Seven Fast Healthcare Interoperability Resources (FHIR) application programming interfaces to improve data collection and reporting for public health and patient safety beginning with common, clinically significant, and preventable patient harms, such as medication-related hypoglycemia, healthcare facility-onset Clostridioides difficile infection, and healthcare-associated venous thromboembolism. CONCLUSIONS: The NHSN's FHIR dQMs hold the promise of minimizing the burden of reporting, improving accuracy, quality, and validity of data collected by NHSN, and increasing speed and efficiency of public health surveillance.

Use of Ecological Momentary Assessment to Measure Dyspnea in COPD.

Dyspnea is an unpredictable and distressing symptom of chronic obstructive pulmonary disease (COPD). Dyspnea is challenging to measure due to the heterogeneity of COPD and recall bias associated with retrospective reports. Ecological Momentary Assessment (EMA) is a technique used to collect symptoms in real-time within a natural environment, useful for monitoring symptom trends and risks of exacerbation in COPD. EMA can be integrated into mobile health (mHealth) platforms for repeated data collection and used alongside physiological measures and behavioral activity monitors. The purpose of this paper is to discuss the use of mHealth and EMA for dyspnea measurement, consider clinical implications of EMA in COPD management, and identify needs for future research in this area.

Designing user-centered decision support systems for climate disasters: What information do communities and rescue responders need during floods?

Flooding events are the most common natural hazard globally, resulting in vast destruction and loss of life. An effective flood emergency response is necessary to lessen the negative impacts of flood disasters. However, disaster management and response efforts face a complex scenario. Simultaneously, regular citizens attempt to navigate the various sources of information being distributed and determine their best course of action. One thing is evident across all disaster scenarios: having accurate information and clear communication between citizens and rescue personnel is critical. This research aims to identify the diverse needs of two groups, rescue operators and citizens, during flood disaster events by investigating the sources and types of information they rely on and information that would improve their responses in the future. This information can improve the design and implementation of existing and future spatial decision support systems (SDSSs) during flooding events. This research identifies information characteristics crucial for rescue operators and everyday citizens' response and possible evacuation to flooding events by qualitatively coding survey responses from rescue responders and the public. The results show that including local input in SDSS development is crucial for improving higher-resolution flood risk quantification models. Doing so democratizes data collection and analysis, creates transparency and trust between people and governments, and leads to transformative solutions for the broader scientific community.

Insect detect: An open-source DIY camera trap for automated insect monitoring.

Insect monitoring is essential to design effective conservation strategies, which are indispensable to mitigate worldwide declines and biodiversity loss. For this purpose, traditional monitoring methods are widely established and can provide data with a high taxonomic resolution. However, processing of captured insect samples is often time-consuming and expensive, which limits the number of potential replicates. Automated monitoring methods can facilitate data collection at a higher spatiotemporal resolution with a comparatively lower effort and cost. Here, we present the Insect Detect DIY (do-it-yourself) camera trap for non-invasive automated monitoring of flower-visiting insects, which is based on low-cost off-the-shelf hardware components combined with open-source software. Custom trained deep learning models detect and track insects landing on an artificial flower platform in real time on-device and subsequently classify the cropped detections on a local computer. Field deployment of the solar-powered camera trap confirmed its resistance to high temperatures and humidity, which enables autonomous deployment during a whole season. On-device detection and tracking can estimate insect activity/abundance after metadata post-processing. Our insect classification model achieved a high top-1 accuracy on the test dataset and generalized well on a real-world dataset with captured insect images. The camera trap design and open-source software are highly customizable and can be adapted to different use cases. With custom trained detection and classification models, as well as accessible software programming, many possible applications surpassing our proposed deployment method can be realized.

EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition.

The widespread adoption of cryoEM technologies for structural biology has pushed the discipline to new frontiers. A significant worldwide effort has refined the single-particle analysis (SPA) workflow into a reasonably standardized procedure. Significant investments of development time have been made, particularly in sample preparation, microscope data-collection efficiency, pipeline analyses and data archiving. The widespread adoption of specific commercial microscopes, software for controlling them and best practices developed at facilities worldwide has also begun to establish a degree of standardization to data structures coming from the SPA workflow. There is opportunity to capitalize on this moment in the maturation of the field, to capture metadata from SPA experiments and correlate the metadata with experimental outcomes, which is presented here in a set of programs called EMinsight. This tool aims to prototype the framework and types of analyses that could lead to new insights into optimal microscope configurations as well as to define methods for metadata capture to assist with the archiving of cryoEM SPA data. It is also envisaged that this tool will be useful to microscope operators and facilities looking to rapidly generate reports on SPA data-collection and screening sessions.

FoodEx2 Harmonization of the Food Consumption Database from the Italian IV SCAI Children's Survey.

Estimating the habitual food and nutrient intakes of a population is based on dietary assessment methods that collect detailed information on food consumption. Establishing the list of foods to be used for collecting data in dietary surveys is central to standardizing data collection. Comparing foods across different data sources is always challenging. Nomenclatures, detail, and classification into broad food groups and sub-groups can vary considerably. The use of a common system for classifying and describing foods is an important prerequisite for analyzing data from different sources. At the European level, EFSA has addressed this need through the development and maintenance of the FoodEx2 classification system. The aim of this work is to present the FoodEx2 harmonization of foods, beverages, and food supplements consumed in the IV SCAI children's survey carried out in Italy. Classifying foods into representative food categories predefined at European level for intake and exposure assessment may lead to a loss of information. On the other hand, a major advantage is the comparability of data from different national databases. The FoodEx2 classification of the national food consumption database represented a step forward in the standardization of the data collection and registration. The large use of FoodEx2 categories at a high level of detail (core and extended terms) combined with the use of descriptors (facets) has minimized information loss and made the reference food categories at country level comparable with different food databases at national and international level.

Understanding the implementation and sustainability needs of evidence-based programs for racial and ethnic minoritized older adults in under-resourced communities with limited aging services.

BACKGROUND: Evidence-based programs (EBPs) for older adults effectively improve health outcomes. However, there is a limited understanding of the unique needs of service providers as they consider adopting, implementing, and maintaining programs for older minority adults in low-income communities with limited aging services. METHODS: We conducted semi-structured interviews with key informants of community-based organizations (CBOs) to understand implementation and sustainability needs of CBOs within four racial and ethnically diverse Los Angeles County geographic areas. We performed thematic analysis of interview transcripts. RESULTS: Interviews were conducted with representatives from 25 senior-serving agencies providing aging-related EBPs. CBO representatives reported implementing EBPs in 8 domains: Falls Prevention (68%), Mental Health (64%), Caregiver Health (48%), Chronic Disease Management (48%), Diabetes Management (36%), Arthritis Management (28%), Physical Activity (24%), and Multiple Conditions Management (8%). Themes are presented using the six domains of the Bass and Judge framework for factors impacting successful and sustained EBP implementation. CBOs in low-income and diverse communities described unique challenges with tailoring interventions based on local community context (literacy, language), cultural context, and locally available resources (technology, safe community spaces, transportation) and faced resource-intensive administrative burdens through staff turnover, data collection, sustainable funding, and networking. CONCLUSIONS: Serving racial and ethnic communities has unique challenges that require tailored approaches and additional resources to ensure equitable access to EBPs for all communities. We describe suggestions for enhancing the effective adoption of EBPs among service agencies in under-resourced and diverse aging communities serving populations with aging-related health disparities.

'Joining the Dots: Linking Prenatal Drug Exposure to Childhood and Adolescence' - research protocol of a population cohort study.

INTRODUCTION: Prenatal drug exposure (PDE) is one of the most important causes of child harm, but comprehensive information about the long-term outcomes of the families is difficult to ascertain. The Joining the Dots cohort study uses linked population data to understand the relationship between services, therapeutic interventions and outcomes of children with PDE. METHODS AND ANALYSIS: Information from routinely collected administrative databases was linked for all births registered in New South Wales (NSW), Australia between 1 July 2001 and 31 December 2020 (n=1 834 550). Outcomes for seven mutually exclusive groups of children with varying prenatal exposure to maternal substances of addiction, including smoking, alcohol, prescription/illicit drugs and neonatal abstinence syndrome will be assessed. Key exposure measures include maternal drug use type, maternal social demographics or social determinants of health, and maternal physical and mental health comorbidities. Key outcome measures will include child mortality, academic standardised testing results, rehospitalisation and maternal survival. Data analysis will be conducted using Stata V.18.0. ETHICS AND DISSEMINATION: Approvals were obtained from the NSW Population and Health Services Research Ethics Committee (29 June 2020; 2019/ETH12716) and the Australian Capital Territory Health Human Research Ethics Committee (11 October 2021; 2021-1231, 2021-1232, 2021-1233); and the Aboriginal Health and Medical Research Council (5 July 2022; 1824/21), and all Australian educational sectors: Board of Studies (government schools), Australian Independent Schools and Catholic Education Commission (D2014/120797). Data were released to researchers in September 2022. Results will be presented in peer-reviewed academic journals and at international conferences. Collaborative efforts from similar datasets in other countries are welcome.

Birthweight trends and their explanatory factors in Hungary between 1999 and 2018: an analysis of the Hungarian Tauffer registry.

BACKGROUND: The increasing birthweight trend stopped and even reversed in several high income countries in the last 20 years, however the reason for these changes is not well characterized. We aimed to describe birthweight trends of term deliveries in Hungary between 1999 and 2018 and to investigate potential maternal and foetal variables that could drive these changes. METHODS: We analysed data from the Hungarian Tauffer registry, a compulsory anonymized data collection of each delivery. We included all singleton term deliveries in 1999-2018 (n = 1,591,932). We modelled birthweight trends separately in 1999-2008 and 2008-2018 in hierarchical multiple linear regression models adjusted for calendar year, newborn sex, maternal age, gestational age at delivery, and other important determinants. RESULTS: Median birthweights increased from 3250/3400 g (girl/boy) to 3300/3440 g from 1999 to 2008 and decreased to 3260/3400 g in 2018. When we adjusted for gestational age at delivery the increase in the first period became more pronounced (5.4 g/year). During the second period, similar adjustment substantially decreased the rate of decline from 2.5 to 1.4 g/year. Further adjustment for maternal age halved the rate of increase to 2.4 g/year in the first period. During the second period, adjustment for maternal age had little effect on the estimate. CONCLUSIONS: Our findings of an increasing birthweight trend (mostly related to the aging of the mothers) in 1999-2008 may forecast an increased risk of cardiometabolic diseases in offsprings born in this period. In contrast, the decreasing birthweight trends after 2008 may reflect some beneficial effects on perinatal morbidity. However, the long-term effect cannot be predicted, as the trend is mostly explained by the shorter pregnancies.

Birthweights showed an increase followed by a decrease in several high income countries in the last 20 years, however the reasons for these changes is not well described. Thus, we aimed to investigate birthweight trends and their potential explanatory factors in Hungary between 1999 and 2018. We used registry data of all deliveries from Hungary in 1999­2018 (n = 1 591 932). Birthweights increased from 3250/3400 g (girl/boy) to 3300/3440 g from 1999 to 2008 and decreased to 3260/3400 g until 2018. Maternal age explained approximately half of increase in the first period, while a substantial part of the decrease in the second period was explained by the presence of shorter pregnancies. The increasing birthweights in 1999­2008 may forecast an increased risk of cardiometabolic diseases in offsprings born in this period. In contrast, the decreasing birthweight trends after 2008 may reflect some beneficial effects on perinatal morbidity. However, its long-term consequences cannot be predicted, as the trend is mostly explained by the shorter pregnancies.

Introducing the Pi-CON Methodology to Overcome Usability Deficits during Remote Patient Monitoring.

The adoption of telehealth has soared, and with that the acceptance of Remote Patient Monitoring (RPM) and virtual care. A review of the literature illustrates, however, that poor device usability can impact the generated data when using Patient-Generated Health Data (PGHD) devices, such as wearables or home use medical devices, when used outside a health facility. The Pi-CON methodology is introduced to overcome these challenges and guide the definition of user-friendly and intuitive devices in the future. Pi-CON stands for passive, continuous, and non-contact, and describes the ability to acquire health data, such as vital signs, continuously and passively with limited user interaction and without attaching any sensors to the patient. The paper highlights the advantages of Pi-CON by leveraging various sensors and techniques, such as radar, remote photoplethysmography, and infrared. It illustrates potential concerns and discusses future applications Pi-CON could be used for, including gait and fall monitoring by installing an omnipresent sensor based on the Pi-CON methodology. This would allow automatic data collection once a person is recognized, and could be extended with an integrated gateway so multiple cameras could be installed to enable data feeds to a cloud-based interface, allowing clinicians and family members to monitor patient health status remotely at any time.

Effectiveness of an online recovery training for employees exposed to blurred boundaries between work and non-work: Bayesian analysis of a randomised controlled trial.

BACKGROUND: Blurred work-non-work boundaries can have negative effects on mental health, including sleep. OBJECTIVES: In a randomised control trial, we aimed to assess the effectiveness of an online recovery training programme designed to improve symptoms of insomnia in a working population exposed to blurred boundaries. METHODS: 128 participants with severe insomnia symptoms (Insomnia Severity Index ≥15) and working under blurred work and non-work conditions (segmentation supplies <2.25) were randomly assigned to either the recovery intervention or a waitlist control group (WLC). The primary outcome was insomnia severity, assessed at baseline, after 2 months (T2) and 6 months (T3). FINDINGS: A greater reduction in insomnia was observed in the intervention compared with the WLC group at both T2 (d=1.51; 95% CI=1.12 o 1.91) and T3 (d=1.63; 95% CI=1.23 to 2.03]. This was shown by Bayesian analysis of covariance (ANCOVA), whereby the ANCOVA model yielded the highest Bayes factor (BF 10=3.23×e60] and a 99.99% probability. Likewise, frequentist analysis revealed significantly reduced insomnia at both T2 and T3. Beneficial effects were found for secondary outcomes including depression, work-related rumination, and mental detachment from work. Study attrition was 16% at T2 and 44% at T3. CONCLUSIONS: The recovery training was effective in reducing insomnia symptoms, work related and general indicators of mental health in employees exposed to blurred boundaries, both at T2 and T3. CLINICAL IMPLICATIONS: In addition to demonstrating the intervention's effectiveness, this study exemplifies the utilisation of the Bayesian approach in a clinical context and shows its potential to empower recipients of interventional research by offering insights into result probabilities, enabling them to draw informed conclusions. TRIAL REGISTRATION NUMBER: German Clinical Trial Registration (DRKS): DRKS00006223, https://drks.de/search/de/trial/DRKS00006223.

A practical guide to cross-cultural and multi-sited data collection in the biological and behavioural sciences.

Researchers in the biological and behavioural sciences are increasingly conducting collaborative, multi-sited projects to address how phenomena vary across ecologies. These types of projects, however, pose additional workflow challenges beyond those typically encountered in single-sited projects. Through specific attention to cross-cultural research projects, we highlight four key aspects of multi-sited projects that must be considered during the design phase to ensure success: (1) project and team management; (2) protocol and instrument development; (3) data management and documentation; and (4) equitable and collaborative practices. Our recommendations are supported by examples from our experiences collaborating on the Evolutionary Demography of Religion project, a mixed-methods project collecting data across five countries in collaboration with research partners in each host country. To existing discourse, we contribute new recommendations around team and project management, introduce practical recommendations for exploring the validity of instruments through qualitative techniques during piloting, highlight the importance of good documentation at all steps of the project, and demonstrate how data management workflows can be strengthened through open science practices. While this project was rooted in cross-cultural human behavioural ecology and evolutionary anthropology, lessons learned from this project are applicable to multi-sited research across the biological and behavioural sciences.

Emergency Nurses' Reasons for Not Recommending Their Hospital to Clinicians as a Good Place to Work.

Importance: Half of emergency nurses report high burnout and intend to leave their job in the next year. Whether emergency nurses would recommend their workplace to other clinicians may be an important indicator of a hospital's ability to recruit clinicians. Objective: To examine why emergency nurses do not recommend their hospital to other clinicians as a good place to work. Design, Setting, and Participants: This qualitative study used directed content analysis of open-text responses (n = 142) from the RN4CAST-NY/IL survey of registered nurses licensed in New York and Illinois between April 13 and June 22, 2021. Inductive and deductive analytic approaches guided study theme development informed by the Social Ecological Model. The collected data were analyzed from April to June 2023. Main Outcomes and Measures: Nurses who answered "probably not" or "definitely not" to the survey question, "Would you recommend your place of employment as a good place to work?" were prompted to provide a rationale in an open-text response. Results: In this qualitative study of 142 emergency nurses (mean [SD] age, 43.5 [12.5] years; 113 [79.6%] female; mean [SD] experience, 14.0 [12.2] years), 94 (66.2%) were licensed to work in New York and the other 48 (33.8%) in Illinois. Five themes and associated subthemes emerged from the data. Themes conveyed understaffing of nurses and ancillary support (theme 1: unlimited patients with limited support); inadequate responsiveness from unit management to work environment safety concerns (theme 2: unanswered calls for help); perceptions that nurses' licenses were in jeopardy given unsafe working conditions and compromised care quality (theme 3: license always on the line); workplace violence on a patient-to-nurse, clinician-to-nurse, and systems level (theme 4: multidimensional workplace violence); and nurse reports of being undervalued by hospital management and unfulfilled at work in delivering suboptimal care to patients in unsafe working conditions (theme 5: undervalued and unfulfilled). Conclusions and Relevance: This study found that emergency department nurses did not recommend their workplace to other clinicians as a good place to work because of poor nurse and ancillary staffing, nonresponsive hospital leadership, unsafe working conditions, workplace violence, and a lack of feeling valued. These findings inform aspects of the work environment that employers can address to improve nurse recruitment and retention.

On investigating drivers' attention allocation during partially-automated driving.

The use of partially-automated systems require drivers to supervise the system functioning and resume manual control whenever necessary. Yet literature on vehicle automation show that drivers may spend more time looking away from the road when the partially-automated system is operational. In this study we answer the question of whether this pattern is a manifestation of inattentional blindness or, more dangerously, it is also accompanied by a greater attentional processing of the driving scene. Participants drove a simulated vehicle in manual or partially-automated mode. Fixations were recorded by means of a head-mounted eye-tracker. A surprise two-alternative forced-choice recognition task was administered at the end of the data collection whereby participants were quizzed on the presence of roadside billboards that they encountered during the two drives. Data showed that participants were more likely to fixate and recognize billboards when the automated system was operational. Furthermore, whereas fixations toward billboards decreased toward the end of the automated drive, the performance in the recognition task did not suffer. Based on these findings, we hypothesize that the use of the partially-automated driving system may result in an increase in attention allocation toward peripheral objects in the road scene which is detrimental to the drivers' ability to supervise the automated system and resume manual control of the vehicle.

[Registration of national health registry-based research]. / Registrering av forskning baserad på nationella hälsoregister.

In medical research as a whole, frequent inaccurate or biased findings are of international concern. One measure against reporting biases is study registration before the start of data collection (preregistration), preferably together with the statistical analysis plan. This meta-research study systematically evaluated registration of Swedish observational research based on national health registries. In a random sample of registry-based observational studies published 2010-2022, very few were preregistered with a publicly available analysis plan (<1 procent). Ideas from the meta-research literature can be leveraged to strengthen the brand of Swedish registry-based observational studies and counteract reporting bias.

From physiological complexity to data interactions-A case study of recordings from exercise monitoring.

The popularity of nonlinear analysis has been growing simultaneously with the technology of effort monitoring. Therefore, considering the simple methods of physiological data collection and the approaches from the information domain, we proposed integrating univariate and bivariate analysis for the rest and effort comparison. Two sessions separated by an intensive training program were studied. Nine subjects participated in the first session (S1) and seven in the second session (S2). The protocol included baseline (BAS), exercise, and recovery phase. During all phases, electrocardiogram (ECG) was recorded. For the analysis, we selected corresponding data lengths of BAS and exercise usually lasting less than 5 min. We found the utility of the differences between original data and their surrogates for sample entropy Sdiff and Kullback-Leibler divergence KLDdiff. Sdiff of heart rate variability was negative in BAS and exercise but its sensitivity for phases discrimination was not satisfactory. We studied the bivariate analysis of RR intervals and corresponding QT peaks by Interlayer Mutual Information (IMI) and average edge overlap (AVO) markers. While the IMI parameter decreases in exercise conditions, AVO increased in effort compared to BAS. These findings conclude that researchers should consider a bivariate analysis of extracted RR intervals and corresponding QT datasets, when only ECG is recorded during tests.