BACKGROUND: Ecological momentary assessment (EMA) is a measurement methodology that involves the repeated collection of real-time data on participants\' behavior and experience in their natural environment. While EMA allows researchers to gain valuable insights into dynamic behavioral processes, the need for frequent self-reporting can be burdensome and disruptive. Compliance with EMA protocols is important for accurate, unbiased sampling; yet, there is no \"gold standard\" for EMA study design to promote compliance.
OBJECTIVE: The purpose of this study was to use a factorial design to identify optimal study design factors, or combinations of factors, for achieving the highest completion rates for smartphone-based EMAs.
METHODS: Participants recruited from across the United States were randomized to 1 of 2 levels on each of 5 design factors in a 2×2×2×2×2 design (32 conditions): factor 1-number of questions per EMA survey (15 vs 25); factor 2-number of EMAs per day (2 vs 4); factor 3-EMA prompting schedule (random vs fixed times); factor 4-payment type (US $1 paid per EMA vs payment based on the percentage of EMAs completed); and factor 5-EMA response scale type (ie, slider-type response scale vs Likert-type response scale; this is the only within-person factor; each participant was randomized to complete slider- or Likert-type questions for the first 14 days or second 14 days of the study period). All participants were asked to complete prompted EMAs for 28 days. The effect of each factor on EMA completion was examined, as well as the effects of factor interactions on EMA completion. Finally, relations between demographic and socioenvironmental factors and EMA completion were examined.
RESULTS: Participants (N=411) were aged 48.4 (SD 12.1) years; 75.7% (311/411) were female, 72.5% (298/411) were White, 18.0% (74/411) were Black or African American, 2.7% (11/411) were Asian, 1.5% (6/411) were American Indian or Alaska Native, 5.4% (22/411) belonged to more than one race, and 9.6% (38/396) were Hispanic/Latino. On average, participants completed 83.8% (28,948/34,552) of scheduled EMAs, and 96.6% (397/411) of participants completed the follow-up survey. Results indicated that there were no significant main effects of the design factors on compliance and no significant interactions. Analyses also indicated that older adults, those without a history of substance use problems, and those without current depression tended to complete more EMAs than their counterparts. No other demographic or socioenvironmental factors were related to EMA completion rates. Finally, the app was well liked (ie, system usability scale score=82.7), and there was a statistically significant positive association between liking the app and EMA compliance.
CONCLUSIONS: Study results have broad implications for developing best practices guidelines for future studies that use EMA methodologies.
BACKGROUND: ClinicalTrials.gov number NCT05194228; https://clinicaltrials.gov/study/NCT05194228.

Harsh driving events such as harsh brakings (HBs) and harsh accelerations (HAs) are promising Surrogate Safety Measures, already extensively utilised in road safety research. However, their occurrence relative to normal driving conditions has not been the explicit target of research, as they are typically used as inputs for crash prediction. The present study addresses this research gap by investigating factors influencing HB and HA occurrence using real-time naturalistic driving telematics data recorded from smartphones, traffic data and road geometry & network characteristics data. These multisource data were matched in order to capture the specific circumstances under which HBs and HAs occur. The utilized telematics dataset included trips from 314 anonymous drivers in an urban arterial of Athens, Greece. Subsequently, Synthetic Minority Oversampling TEchnique (SMOTE) was applied due to class imbalance and then binary classification was conducted to detect factors leading to HB and HA occurrence. Imbalanced Machine Learning (ML) XGBoost algorithms predicted over 75% of HBs and over 84% of HAs for the test dataset, indicating suitability for real-time monitoring. The algorithms were also augmented with SHapley Additive exPlanation (SHAP) values, aiming to increase outcome explainability. Results reveal strong nonlinear effects on harsh event occurrence, with individual speed and traffic flow parameters showing the highest influence, followed by exposure parameters such as segment length and pass count. Network characteristics such as number of lanes, and speed limit had limited influence on HA and HB occurrence, as did behaviors such as mobile phone engagement and speeding.

OBJECTIVE: Real-time data (RTD) are data that are delivered immediately after creation. The key feature of RTD is low delivery latency. Information systems in health care are extremely time-sensitive and their building block is the electronic health record (EHR). Real-time data from EHRs play an important role to support decision-making, analytics and coordination of care. This is well mentioned in the literature, but the process has not yet been described, providing reference implementations and testing. Real-time data delivery can technically be achieved using several methods. The objective of this work is to evaluate the performance of different transfer methods of RTD from EHRs by measuring delivery latency.
METHODS: In our work we used four approaches to transfer RTD from EHRs: REST hooks, WebSocket notifications, reverse proxy and database triggers. We deployed a Fast Health Interoperability Resources (FHIR) server as it is one of the most widely used EHR standard. For the reference implementations we used Python and Golang. Delivery latency was selected as performance metric, derived by subtracting the timestamp of the EHR resource creation from the timestamp of the EHR resource receipt in millisecond. The data was analyzed using descriptive statistics, cumulative distribution function (CDF), Kruskal-Wallis and post-hoc tests.
RESULTS: The database trigger approach had the best mean delivery latency 13.52±5.56 ms, followed by the reverse proxy 14.43±4.58 ms, REST hooks 19.26±5.76 ms and WebSocket 27.32±9.44 ms. The reverse proxy showed a tighter range of the values and lower variability. There were significant differences in the latencies between all pairs of approaches, except for reverse proxy and database trigger.
CONCLUSIONS: Real-time data transfer is vital for the development of robust and innovative healthcare applications. Properties of current EHR systems as a data source predefine the approaches for transfer. In our work for the first time the performance of RTD transfer from the EHRs with reference implementations is measured and evaluated. We found that database triggers achieve lowest delivery latency. Reverse proxy performed slightly slower, but offered more stability, followed by REST hooks and WebSocket notifications.

In the face of rising population, erratic climate, resource depletion, and increased exposure to natural hazards, environmental monitoring is increasingly important. Satellite data form most of our observations of Earth. On-the-ground observations based on in situ sensor systems are crucial for these remote measurements to be dependable. Providing open-source options to rapidly prototype environmental datalogging systems allows quick advancement of research and monitoring programs. This paper introduces Loom, a development environment for low-power Arduino-programmable microcontrollers. Loom accommodates a range of integrated components including sensors, various datalogging formats, internet connectivity (including Wi-Fi and 4G Long Term Evolution (LTE)), radio telemetry, timing mechanisms, debugging information, and power conservation functions. Additionally, Loom includes unique applications for science, technology, engineering, and mathematics (STEM) education. By establishing modular, reconfigurable, and extensible functionality across components, Loom reduces development time for prototyping new systems. Bug fixes and optimizations achieved in one project benefit all projects that use Loom, enhancing efficiency. Although not a one-size-fits-all solution, this approach has empowered a small group of developers to support larger multidisciplinary teams designing diverse environmental sensing applications for water, soil, atmosphere, agriculture, environmental hazards, scientific monitoring, and education. This paper not only outlines the system design but also discusses alternative approaches explored and key decision points in Loom\'s development.

Icebergs and ice islands (large, tabular icebergs) present a significant hazard to marine vessels and infrastructure at a time when demand for access to Arctic waters is increasing. There is a growing demand for in situ iceberg tracking data to monitor their drift trajectories and improve models used for operational forecasting of ice hazards, yet the high cost of commercial tracking devices often prevents monitoring at optimal spatial and temporal resolutions. Here, we provide a detailed description of the Cryologger Ice Tracking Beacon (ITB), a low-cost, robust, and user-friendly data logger and telemeter for tracking icebergs and ice islands based on the Arduino open-source electronics platform. Designed for deployments of at least 2 years with an hourly sampling interval that is remotely modifiable by the end user, the Cryologger ITB provides long-term measurements of position, temperature, pressure, pitch, roll, heading, and battery voltage. Data are transmitted via the Iridium satellite network at user-specified intervals. We present the results of field campaigns in 2018 and 2019, which saw the deployment of 16 ITBs along the coasts of Greenland and Ellesmere and Baffin islands. The overall success of these ITB deployments has demonstrated that inexpensive, open-source hardware and software can provide a reliable and cost-effective method of monitoring icebergs and ice islands in the polar regions.

UNASSIGNED: in Nigeria, supportive supervision of Supplementary Immunization Activities (SIA) is a quality improvement strategy for providing support to vaccination teams administering the poliovirus vaccines to children under 5 years of age. Supervision activities were initially reported in paper forms. This had significant limitations, which led to Open Data Kit (ODK) technology being adopted in March 2017. A review was conducted to assess the impact of ODK for supervision reporting in place of paper forms.
UNASSIGNED: issues with paper-based reporting and the benefits of ODK were recounted. We determined the average utilization of ODK per polio SIA rounds and assessed the supervision coverage over time based on the proportion of local government areas with ODK geolocation data per round.
UNASSIGNED: a total of 17 problematic issues were identified with paper-based reporting, and ODK addressed all the issues. Open Data Kit-based supervision reports increased from 3,125 in March 2017 to 51,060 in February 2020. Average ODK submissions for national rounds increased from 84 in March 2017 to 459 in February 2020 and for sub-national rounds increased from 533 in July 2017 to 1,596 in October 2019. Supportive supervision coverage improved from 42.5% in March 2017 to 97% in February 2020.
UNASSIGNED: the use of digital technologies in public health has comparative advantages over paper forms, and the adoption of ODK for supervision reporting during polio SIAs in Nigeria experienced the advantages. The visibility and coverage of supportive supervision improved, consequentially contributing to the improved quality of polio SIAs.

The use of the Experience Sampling Method (ESM), which involves repeated assessments in people\'s daily lives, has increased in popularity in psychology and associated disciplines in recent years. A rather challenging aspect of ESM is its technical implementation. In this paper, after briefly introducing the history of ESM and the main reasons for its current popularity, we outline the ESM-Quest experience sampling app which is currently being developed at the University of Vienna. ESM-Quest runs on different operating systems, specifically on mobile devices such as smartphones and tablets running either iOS or Android. An internet connection is not necessary during the assessment. Compared to most other ESM apps, ESM-Quest allows event-based random sampling, which is very helpful when assessments need to be collected within specific situations. Currently, ESM-Quest is being utilized at the University of Vienna and will be made available for research groups worldwide upon request. We introduce the technical aspects of ESM-Quest and provide examples of analyses on ESM data collected through this app, such as examining fluctuations in constructs within individuals. Finally, we outline potential next steps in ESM research.

BACKGROUND: This study assesses the accuracy of a Bluetooth-enabled prototype activity tracker called the Sedentary behaviOR Detector (SORD) device in identifying sedentary, standing, and walking behaviors in a group of adult participants.
OBJECTIVE: The primary objective of this study was to determine the criterion and convergent validity of SORD against direct observation and activPAL.
METHODS: A total of 15 healthy adults wore SORD and activPAL devices on their thighs while engaging in activities (lying, reclining, sitting, standing, and walking). Direct observation was facilitated with cameras. Algorithms were developed using the Python programming language. The Bland-Altman method was used to assess the level of agreement.
RESULTS: Overall, 1 model generated a low level of bias and high precision for SORD. In this model, accuracy, sensitivity, and specificity were all above 0.95 for detecting sitting, reclining, standing, and walking. Bland-Altman results showed that mean biases between SORD and direct observation were 0.3% for sitting and reclining (limits of agreement [LoA]=-0.3% to 0.9%), 1.19% for standing (LoA=-1.5% to 3.42%), and -4.71% for walking (LoA=-9.26% to -0.16%). The mean biases between SORD and activPAL were -3.45% for sitting and reclining (LoA=-11.59% to 4.68%), 7.45% for standing (LoA=-5.04% to 19.95%), and -5.40% for walking (LoA=-11.44% to 0.64%).
CONCLUSIONS: Results suggest that SORD is a valid device for detecting sitting, standing, and walking, which was demonstrated by excellent accuracy compared to direct observation. SORD offers promise for future inclusion in theory-based, real-time, and adaptive interventions to encourage physical activity and reduce sedentary behavior.

With more than 1700 data rows, this data article presents data on the environmental and economic efficiency of educational buildings in 14 provinces of Iran. The presented dataset represents monthly data on climate change impact indicators such as Global Warming Potential, Acidification Potential, Human Health Particulate, Eutrophication Potential, Ozone Depletion Potential and Smog Potential. It also covers the history of monthly gas consumption and energy cost data for each school in each period read by the gas agent; In addition to the history of monthly weather information for each city/province. Information provided in this data article can be useful for research on energy prediction studies and also energy management strategies, as well as policymakers to achieve sustainability factors, and to choose the most suitable measures for educational buildings. Considering that few data sets have been published on this topic and in this type of climate, this dataset can be useful for researchers in sustainability and resiliency.

In tomato, downward leaf bending is a morphological adaptation towards waterlogging, which has been shown to induce a range of metabolic and hormonal changes. This kind of functional trait is often the result of a complex interplay of regulatory processes starting at the gene level, gated through a plethora of signaling cascades and modulated by environmental cues. Through phenotypical screening of a population of 54 tomato accessions in a Genome Wide Association Study (GWAS), we have identified target genes potentially involved in plant growth and survival during waterlogging and subsequent recovery. Changes in both plant growth rate and epinastic descriptors revealed several associations to genes possibly supporting metabolic activity in low oxygen conditions in the root zone. In addition to this general reprogramming, some of the targets were specifically associated to leaf angle dynamics, indicating these genes might play a role in the induction, maintenance or recovery of differential petiole elongation in tomato during waterlogging.