Mobile Data Collection Platform Research Articles

Continued adherence to community-based health insurance scheme in two districts of northeast Ethiopia: application of accelerated failure time shared frailty models

BackgroundThe sustainability of a voluntary community-based health insurance scheme depends to a greater extent on its ability to retain members. In low- and middle-income countries, high rate of member dropout has been a great concern for such schemes. Although several studies have investigated the factors influencing dropout decisions, none of these looked into how long and why members adhere to the scheme. The purpose of this study was to determine the factors affecting time to drop out while accounting for the influence of cluster-level variables.MethodsA community-based cross-sectional study was conducted among 1232 rural households who have ever been enrolled in two community-based health insurance schemes. Data were collected using an interviewer-administered questionnaire via a mobile data collection platform. The Kaplan–Meier estimates were used to compare the time to drop out among subgroups. To identify predictors of time to drop out, a multivariable analysis was done using the accelerated failure time shared frailty models. The degree of association was assessed using the acceleration factor (δ) and statistical significance was determined at 95% confidence interval.ResultsResults of the multivariable analysis revealed that marital status of the respondents (δ = 1.610; 95% CI: 1.216, 2.130), household size (δ = 1.168; 95% CI: 1.013, 1.346), presence of chronic illness (δ = 1.424; 95% CI: 1.165, 1.740), hospitalization history (δ = 1.306; 95% CI: 1.118, 1.527), higher perceived quality of care (δ = 1.322; 95% CI: 1.100, 1.587), perceived risk protection (δ = 1.218; 95% CI: 1.027, 1.444), and higher trust in the scheme (δ = 1.731; 95% CI: 1.428, 2.098) were significant predictors of time to drop out. Contrary to the literature, wealth status did not show a significant correlation with the time to drop out.ConclusionsThe fact that larger households and those with chronic illness remained longer in the scheme is suggestive of adverse selection. It is needed to reconsider the premium level in line with household size to attract small size households. Resolving problems related to the quality of health care can be a cross-cutting area of ​​intervention to retain members by building trust in the scheme and enhancing the risk protection ability of the schemes.

Концепция применения и моделирование морского автономного smart профилографа

The article focuses on the development of technical support in terms of expanding the measuring base and improving marine profilers for operational observation systems as part of a new and developing scientific and applied field – operational oceanography. The concept of using a marine autonomous intelligent profiler for operational measurements of the thermohaline profile parameters of a stratified ocean environment with a significant reduction in the time to conduct an experiment using smart profiling is presented. At the same time, time savings are achieved due to the flexible control of the high-speed modes of vertical movement of the marine autonomous profiler with adjustable buoyancy. Low profiling speeds avoid significant dynamic distortions in the measurements obtained from inertial sensors. However, in a homogeneous environment, after taking measurements, the speed of the profiler can be significantly increased. The purpose of the smart profiler as a mobile data collection platform is to analyze its own motion and the properties of the surrounding ocean environment and choose, on this basis, a high-speed profiling mode that provides an acceptable level of dynamic distortion in the sensor data installed on board the measuring equipment. The results of computer simulation of the proposed smart structure in the MATLAB & Simulink environment based on the original mathematical models that make up its subsystems are presented. We studied the process of “smart” profiling during the transition of the profiler from a cruising speed mode (fast) to a working speed mode (slow), as well as its return to cruising speed in a stratified ocean environment. In this case, the behavior strategy of the smart profiler (ensuring the specified accuracy of thermohaline measurements) was implemented by choosing a speed mode based on the analysis of dynamic measurements of its motion parameters and stratification of the profile by density.

Development and application of an electronic treatment register: a system for enumerating populations and monitoring treatment during mass drug administration

ABSTRACT We developed an electronic treatment register for the DeWorm3 Project, a cluster-randomised, controlled trial in Benin, India, and Malawi testing the feasibility of interrupting transmission of soil-transmitted helminths through community-wide mass drug administration. The electronic treatment register was designed in xlsform, deployed via the SurveyCTO mobile data collection platform, and implemented on smartphones running the Android operating system. The versatile system enables collection of census and treatment status information, facilitates data aggregation and visualisation, and permits real-time feedback loops during implementation of mass drug administration. Here we describe the system’s design and use within the DeWorm3 Project and key features, and by sharing the register here, we hope our readers will further explore its use within their research and disease-control activities.

Multi-Sensor-Fusion Approach for a Data-Science-Oriented Preventive Health Management System: Concept and Development of a Decentralized Data Collection Approach for Heterogeneous Data Sources.

Investigations in preventive and occupational medicine are often based on the acquisition of data in the customer's daily routine. This requires convenient measurement solutions including physiological, psychological, physical, and sometimes emotional parameters. In this paper, the introduction of a decentralized multi-sensor-fusion approach for a preventive health-management system is described. The aim is the provision of a flexible mobile data-collection platform, which can be used in many different health-care related applications. Different heterogeneous data sources can be integrated and measured data are prepared and transferred to a superordinated data-science-oriented cloud-solution. The presented novel approach focuses on the integration and fusion of different mobile data sources on a mobile data collection system (mDCS). This includes directly coupled wireless sensor devices, indirectly coupled devices offering the datasets via vendor-specific cloud solutions (as e.g., Fitbit, San Francisco, USA and Nokia, Espoo, Finland) and questionnaires to acquire subjective and objective parameters. The mDCS functions as a user-specific interface adapter and data concentrator decentralized from a data-science-oriented processing cloud. A low-level data fusion in the mDCS includes the synchronization of the data sources, the individual selection of required data sets and the execution of pre-processing procedures. Thus, the mDCS increases the availability of the processing cloud and in consequence also of the higher level data-fusion procedures. The developed system can be easily adapted to changing health-care applications by using different sensor combinations. The complex processing for data analysis can be supported and intervention measures can be provided.

Mental health informed physical activity for first responders and their support partner: a protocol for a stepped-wedge evaluation of an online, codesigned intervention

BackgroundFirst responders (police, fire and ambulance officers) are at a significantly increased risk of experiencing poor mental health, including depression and post-traumatic stress disorder. These conditions are associated with high...

Fistula care in the digital era: A discussion on the digitization of fistula care in five UNFPA countries

Aims: The objective of this project was to partner with five different UNFPA countries in order to collect archived data on the occurrence of obstetric fistula, and to visualize our initial findings on the various contributors to the condition, using data visualization dashboards and mapping tools. One year into its partnership with UNFPA, Operation Fistula takes this opportunity to share the initial findings from the digitization of obstetric fistula data from its five partner countries. Operation Fistula focuses on using technology to facilitate coordination between key stakeholders in the obstetric fistula sector.
 Methods: The first phase of the project involved data collection via the digitizing of nine unique patient record sheets onto a mobile data collection platform (CommCare) from our five partner countries: Bangladesh, Cameroon, Madagascar, Malawi and Nepal. We have visualized the data we have received thus far in an effort to gain clarity on sociodemographic data, location, surgical and patient outcomes in these five countries. Once their data has been visualized, our partners are able to access and analyze their data with the provision of a Tableau license.
 Results: We have collected 1,046 patient records across our five partner countries. An initial analysis of these countries has begun to show key trends within the fistula sector, in sociodemographic background, geographic location, and obstetric history.
 Conclusions: While there have been limitations to our data collection, the preliminary outcomes of the data visualizations, produced have been insightful, and have provided us with a strong basis for further study.

Data collection and upload under dynamicity in smart community Internet-of-Things deployments

The Internet of Things has enabled new services to communities in many domains, e.g. smart healthcare, environmental awareness, and public safety. These services require timely and accurate event delivery, but such in-situ deployments are often limited by the coverage of sensing/communication infrastructures. In this paper we develop effective, scalable, and realistic data collection and upload solutions using mobile data collectors in community IoT systems. Specifically, we address the optimized upload planning problem, i.e. determine the optimal schedule for communication to enable timely data delivery under dynamicity in network connectivity, data characteristics/heterogeneity, and mobility. We develop a two-phase approach and associated policies, where an initial upload plan is generated offline with prior knowledge of networks and data, and a subsequent runtime adaptation alters the plan under multiple dynamics. To validate our approach, we designed and built SCALECycle, our mobile data collection platform, and deployed it in real communities in Rockville, MD and Irvine, CA. Measurements from these testbeds are used to drive extensive simulations. Experimental results indicate that compared with opportunistic operation, our two-phase approach using a judicious combination of policies can result in 30%–60% improvement in overall data utility, 30% reduction in collection delays, along with greater resilience to dynamicity and improved scalability.

An open source mobile platform for psychophysiological self tracking.

Self tracking is a recent trend in e-health that refers to the collection, elaboration and visualization of personal health data through ubiquitous computing tools such as mobile devices and wearable sensors. Here, we describe the design of a mobile self-tracking platform that has been specifically designed for clinical and research applications in the field of mental health. The smartphone-based application allows collecting a) self-reported feelings and activities from pre-programmed questionnaires; b) electrocardiographic (ECG) data from a wireless sensor platform worn by the user; c) movement activity information obtained from a tri-axis accelerometer embedded in the wearable platform. Physiological signals are further processed by the application and stored on the smartphone's memory. The mobile data collection platform is free and released under an open source licence to allow wider adoption by the research community (download at: http://sourceforge.net/projects/psychlog/).

A qualitative inquiry of Malawian radio listeners' attitudes and perceived outcomes of alcohol use using a mobile data collection platform.

Alcohol use has been associated with an increased risk of human immunodeficiency virus (HIV) transmission in sub-Saharan Africa. Despite this association, there are little data on drinking behaviours in Malawi, a country located in southern Africa. Utilising alternative techniques of data collection, such as mobile technology, may be necessary to eliminate this dearth of information. We use listeners' text messages (SMS) in response to a Malawian radio show to assess listeners' attitudes and perceived outcomes of drinking. Mobile phones provide a novel, low-cost platform from which to gather qualitative data directly from target audiences. To demonstrate the utility of a mobile data collection approach, we analysed listener feedback in the form of SMS in response to a Malawian radio show. As emergent themes indicate that the perceived outcomes of consumption affect different levels within a social system, results are stratified within a socioecological framework to identify perceived individual, household and community level outcomes. As the perceived effects of consumption are numerous and impact a wide swath of communities, alcohol consumption must be addressed through multiple levels. The utilisation of anonymous text responses as a data collection methodology may provide an innovative way to understand contributing factors, such as alcohol consumption, to HIV risk exposure. [Rooney M, Limaye RJ. A qualitative inquiry of Malawian radio listeners' attitudes and perceived outcomes of alcohol use using a mobile data collection platform. Drug Alcohol Rev 2015;34:477-82].

A mobile data collection platform for mental health research

Ubiquitous computing technologies offer exciting new possibilities for monitoring and analyzing user's experience in real time. In this paper, we describe the design and development of Psychlog, a ...

A Portable, PC-Based System for Making Optical and Electromagnetic Measurements of Lightning

A digital video camcorder, a four-channel analog-to-digital (A/D) converter, and a GPS time/position receiver have been interfaced to a portable personal computer (PC) to provide a mobile data-collection platform for making precise time-synchronized optical and electromagnetic measurements of lightning with correlated video imagery. Optical waveforms and the associated electric field signatures are digitized with 14-bit resolution at a sampling frequency of up to 2.5 MHz, and entire flashes can be captured and stored with less than 280 ms of dead time between events. Digital video (DV) data can be recorded either on Mini-DV tape within the camera or directly in the PC using an IEEE 1394 “Firewire” serial data link. The video format has a standard 30 interlaced frames per second (33.3 ms per frame) and 720 × 480 pixels per frame, and in postprocessing the frames are deinterlaced to double the time resolution (to 16.7 ms). The system was tested on natural lightning during the summer of 2001 near Tucson, Arizona. Video images are shown of cloud-to-ground (CG) flashes, attempted leaders, and vertical and horizontal air discharges, and several examples of correlated optical and electric field waveforms are given. An analysis of 249 CG flashes shows an average of 1.46 strike points per CG flash, and shows that flashes with a visible attempted leader are more likely (by a factor of 2) to produce multiple strike points and less likely (by 1/3) to have a long continuing luminosity, but the statistics are limited. A subset of these data shows that the flash and stroke detection efficiencies (DE) of the U.S. National Lightning Detection Network (NLDN) near Tucson are about 61% and 36%, respectively, with a 24-ms coincidence criterion, and 71% and 41%, respectively, with a 33-ms criterion. The latter DE values are similar to DEs measured near Albany, New York, and agree with model predictions.