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Abstract

Data collection is a crucial step in any research design or program. In order to be analysed, this collected data needs to be entered into aspreadsheet or statistical software. Transcribing paper based data is time consuming and often associated with errors. Such errors may be due toan inability to read the data-collector’s handwriting,human mistakes during data entry etc. A system wherein data automatically gets transcribed and uploaded in a database during data collection would be of immense use in this situation. A possible solution for this is mobile phone based data collection, a type of electronic data capture method wherein the processes of data collection and data entry are merged1. Initially electronic data collection was done by hand-helddevices such as Personal Digital Assistants (PDAs). However with the entry of the newer and more sophisticated smartphones in the market,there is a growing possibility of extendingthe success achieved on PDAs to a phone-based platform2. Withthe advent of newer technology software solutions this process can even be done on a standard entry level mobile phone. This paper discusses the use and advantages of using mobile phones for data collection and also provides information about resources for mobile based data collection.
... Although this practice is well established, it is time and resource consuming, as well as error prone [6]. Common errors may be due to an inability to read the data collectors' handwriting [7]. The financial and opportunity costs associated with data entry, double entry and storage are generally high. ...
... The major advantages of EDC is the ability to enter and review data, implement online data validation checks for more effective data quality assurance, monitor data collection progress, and analyse data, all in real time [6]. Additionally, EDC systems provide opportunity for daily, remote quality control and supervision of field-based data collectors which makes it an attractive management tool and preferable to a pen and paper-based approach [7]. The access to real time data enables better decision making for researchers, yielding faster results that will influence program action and/or policy making decisions and improved allocation of limited financial resources [7,8]. ...
... Additionally, EDC systems provide opportunity for daily, remote quality control and supervision of field-based data collectors which makes it an attractive management tool and preferable to a pen and paper-based approach [7]. The access to real time data enables better decision making for researchers, yielding faster results that will influence program action and/or policy making decisions and improved allocation of limited financial resources [7,8]. ...
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Background: Although the use of technology viz. mobile phones, personalised digital assistants, smartphones, notebook and tablets to monitor health and health care (mHealth) is mushrooming, only small, localised studies have described their use as a data collection tool. This paper describes the complexity, functionality and feasibility of mHealth for large scale surveillance at national and sub-national levels in South Africa, a high HIV-prevalence setting. Methods: In 2010, 2011-12 and 2012-13 three nationally representative surveys were conducted amongst infants attending 580 facilities across all 51 districts, within all nine provinces of South Africa, to monitor the effectiveness of the programme to prevent mother-to-child transmission of HIV (PMTCT). In all three surveys a technical protocol and iterative system for mobile data collection was developed. In 2012-13 the system included automated folders to store information about upcoming interviews. Paper questionnaires were used as a back-up, in case of mHealth failure. These included written instructions per question on limits, skips and compulsory questions. Data collectors were trained on both systems. Results: In the 2010, 2011-12 and 2012-2013 surveys respectively, data from 10,554, 10,071, and 10,536 interviews, and approximately 186 variables per survey were successfully uploaded to 151 mobile phones collecting data from 580 health facilities in 51 districts, across all nine provinces of South Africa. A technician, costing approximately U$D20 000 p.a. was appointed to support field-based staff. Two percent of data were gathered using paper- questionnaires. The time needed for mHealth interviews was approximately 1,5 times less than the time needed for paper questionnaires 30-45 min versus approximately 120 min (including 60-70 min for the interview with an additional 45 min for data capture). In 2012-13, 1172 data errors were identified via the web-based console. There was a four-week delay in resolving data errors from paper-based surveys compared with a 3-day turnaround time following direct capture on mobile phones. Conclusion: Our experiences demonstrate the feasibility of using mHealth during large-scale national surveys, in the presence of a supportive data management team. mHealth systems reduced data collection time by almost 1.5 times, thus reduced data collector costs and time needed for data management.
... Advancement in mobile computing has led to increased reliance on mobile devices for Electronic Data Capture (EDC) in place of paper-based data collection [1]. Mobile Electronic Data Collection Tools (MEDCTs) consist of mobile devices like phones and tablets (hardware) together with a number of different possible programs (software), also known as form creation software [2,3]. To collect data, form developers have to create a data collection form by using form creation software like Open Data Kit (ODK), OpenMRS, etc, which may be open source or proprietary. ...
... To collect data, form developers have to create a data collection form by using form creation software like Open Data Kit (ODK), OpenMRS, etc, which may be open source or proprietary. The form developers do not need to have any prior software programming training in order to create a mobile form [3]. ...
... Depending on the type of mobile device, other types of data can be collected for example location coordinates using GPS, pictures and video recordings. In addition, to improve the data accuracy and completeness of the tool, certain validation constraints and skip logics are also implemented during form creation [3]. MEDCTs provide an array of tools for designing electronic forms, however, the usability of these forms relies on the capabilities provided by the software developers, and this should be of great concern [4]. ...
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Mobile Electronic Data Collection Tools (MEDCTs) are created by form developers to collect data. Usability being one of the top quality attributes is of great concern to developers of any interactive applications. However, little is known about the form developers' understanding of usability, how they measure usability and their limitations in designing for usability. We conducted an empirical study where we aimed at getting the developers' views on usability by interviewing 8 form developers. These are creators of forms used for data collection. We found that developers knew about usability, but it was not their main focus during form development. Challenges included constraining deadlines, software limitations and the insufficient communication with the field users to establish the usability needs. Furthermore, the methods used to evaluate the usability of created forms varied amongst developers and these included in-house evaluations and feedback from piloting sessions with end users.
... Over the years, electronic data collection systems are increasingly being used in health care particularly for data collection and management in health surveys, surveillance and patient monitoring [1]. Electronic data collection tools consist of mobile devices like phones, computers and tablets (hardware) together with a number of different possible programs (software), also known as form creation software [2] which maybe open-source or proprietary. For mobile electronic data collection systems, data collection is done using mobile forms, known as Mobile Electronic Data Collection Forms (MEDCFs), which are developed and designed by software developers and form developers respectively. ...
... For mobile electronic data collection systems, data collection is done using mobile forms, known as Mobile Electronic Data Collection Forms (MEDCFs), which are developed and designed by software developers and form developers respectively. The form developers do not need to have any prior software programming training, but rely on the array of tools provided by the software [2] to create the forms. These electronic forms usually consist of numeric fields and multiple choice menus, among others [3] and their main role is to collect data through direct data capture. ...
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Background New Specific Application Domain (SAD) heuristics or design principles are being developed to guide the design and evaluation of mobile applications in a bid to improve on the usability of these applications. This is because the existing heuristics are rather generic and are often unable to reveal a large number of mobile usability issues related to mobile specific interfaces and characteristics. Mobile Electronic Data Capturing Forms (MEDCFs) are one of such applications that are being used to collect health data particularly in hard to reach areas, but with a number of usability challenges especially when used in rural areas by semi literate users. Existing SAD design principles are often not used to evaluate mobile forms because their focus on features specific to data capture is minimal. In addition, some of these lists are extremely long rendering them difficult to use during the design and development of the mobile forms. The main aim of this study therefore was to generate a usability evaluation checklist that can be used to design and evaluate Mobile Electronic Data Capturing Forms in a bid to improve their usability. We also sought to compare the novice and expert developers’ views regarding usability criteria. Methods We conducted a literature review in August 2016 using key words on articles and gray literature, and those with a focus on heuristics for mobile applications, user interface designs of mobile devices and web forms were eligible for review. The data bases included the ACM digital library, IEEE-Xplore and Google scholar. We had a total of 242 papers after removing duplicates and a total of 10 articles which met the criteria were finally reviewed. This review resulted in an initial usability evaluation checklist consisting of 125 questions that could be adopted for designing MEDCFs. The questions that handled the five main categories in data capture namely; form content, form layout, input type, error handling and form submission were considered. A validation study was conducted with both novice and expert developers using a validation tool in a bid to refine the checklist which was based on 5 criteria. The criteria for the validation included utility, clarity, question naming, categorization and measurability, with utility and measurability having a higher weight respectively. We then determined the proportion of participants who agreed (scored 4 or 5), disagreed (scored 1 or 2) and were neutral (scored 3) to a given criteria regarding a particular question for each of the experts and novice developers. Finally, we selected questions that had an average of 85% agreement (scored 4 or 5) across all the 5 criteria by both novice and expert developers. ‘Agreement’ stands for capturing the same views or sentiments about the perceived likeness of an evaluation question. Results The validation study reduced the initial 125 usability evaluation questions to 30 evaluation questions with the form layout category having the majority questions. Results from the validation showed higher levels of affirmativeness from the expert developers compared to those of the novice developers across the different criteria; however the general trend of agreement on relevance of usability questions was similar across all the criteria for the developers. The evaluation questions that were being validated were found to be useful, clear, properly named and categorized, however the measurability of the questions was found not to be satisfactory by both sets of developers. The developers attached great importance to the use of appropriate language and to the visibility of the help function, but in addition expert developers felt that indication of mandatory and optional fields coupled with the use of device information like the Global Positioning System (GPS) was equally important. And for both sets of developers, utility had the highest scores while measurability scored least. Conclusion The generated checklist indicated the design features the software developers found necessary to improve the usability of mobile electronic data collection tools. In the future, we thus propose to test the effectiveness of the measure for suitability and performance based on this generated checklist, and test it on the end users (data collectors) with a purpose of picking their design requirements. Continuous testing with the end users will help refine the checklist to include only that which is most important in improving the data collectors’ experience.
... Using smartphone technology-based tools for data collection has many advantages and can provide a broader range of options. It is economically and environmentally friendly, and it can provide faster reporting with more accuracy [11]. It is also more efficient. ...
... In contrast, data security and connectivity can be a concern, and data collectors need to be familiar and comfortable with using an automated tool [4]. Accidental loss of data, battery life, loss or theft of the device, security of the device, and network connectivity in rural areas are also major concerns [5,11,13]. ...
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Introduction Data collection using paper-based questionnaires can be time consuming and return errors affect data accuracy, completeness, and information quality in health surveys. We compared smartphone and paper-based data collection systems in the Burden of Obstructive Lung Disease (BOLD) study in rural Sudan. Methods This exploratory pilot study was designed to run in parallel with the cross-sectional household survey. The Open Data Kit was used to programme questionnaires in Arabic into smartphones. We included 100 study participants (83% women; median age = 41.5 ± 16.4 years) from the BOLD study from 3 rural villages in East-Gezira and Kamleen localities of Gezira state, Sudan. Questionnaire data were collected using smartphone and paper-based technologies simultaneously. We used Kappa statistics and inter-rater class coefficient to test agreement between the two methods. Results Symptoms reported included cough (24%), phlegm (15%), wheezing (17%), and shortness of breath (18%). One in five were or had been cigarette smokers. The two data collection methods varied between perfect to slight agreement across the 204 variables evaluated (Kappa varied between 1.00 and 0.02 and inter-rater coefficient between 1.00 and -0.12). Errors were most commonly seen with paper questionnaires (83% of errors seen) vs smartphones (17% of errors seen) administered questionnaires with questions with complex skip-patterns being a major source of errors in paper questionnaires. Automated checks and validations in smartphone-administered questionnaires avoided skip-pattern related errors. Incomplete and inconsistent records were more likely seen on paper questionnaires. Conclusion Compared to paper-based data collection, smartphone technology worked well for data collection in the study, which was conducted in a challenging rural environment in Sudan. This approach provided timely, quality data with fewer errors and inconsistencies compared to paper-based data collection. We recommend this method for future BOLD studies and other population-based studies in similar settings.
... Data collection and processing Annum (2015) refers to research instruments as tools used in collection of data, such as interviews, questionnaires, observations and document readings. Semi-structured interviews were conducted in order to gain knowledge of LULC issues in the study area and to collect primary data from interaction with planners. ...
Conference Paper
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The Western Cape Province is currently faced with population growth, declining household sizes, increasing household numbers, high levels of migration, urbanization and escalating development pressures. These factors have consequently triggered changes in land use and land cover (LULC) and incited issues such as urban sprawl, marginalization of the poor, limited public access to resources, land degradation and climate change. This paper seeks to understand the most significant drivers of LULC change in the Western Cape Province. Focus is given to the major LULC changes which have occurred in the Province in past 24 years by integrating a desktop study of LULC changes using the 1990 and 2013-2014 South African National LULC datasets; document analysis; and expert opinion in the form of semi-structured interviews with municipal town planners. An adapted Driver-Pressure-State-Impact-Response (DPSIR) Framework is used to analyse and understand LULC changes in the study area. LULC changes are driven by political, economic, technological, demographic, biophysical and cultural factors that must be considered in strategies and policies in future planning to avoid detrimental impacts on the environment whilst maintaining socioeconomic benefits.
... Do mesmo modo, os erros comumente cometidos durante as transcrições tornam-se reduzidos, contribuindo para um resultado da pesquisa mais confiável e que ajuda na disseminação das informações geradas de maneira mais rápida. Além disso, o uso de mídias para o registro de dados, como fotografias e áudios, e da aquisição de coordenadas geográficas e altitude, no caso de análises espaciais, são outras vantagens que o uso de dispositivos móveis exercem sobre as metodologias baseadas em material analógico (Pakhare et al., 2013). ...
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The popularity of mobile devices has been encouraged a lot of geoprocessing applications development. This scenery is inciting a collaborative way around free softwares. This study shows a methodology to collect field data using free geotechnologies like QGIS for Android and EpiCollect. The use of these computational tools was efficient to data collection and real time navigation in field. This tools combined use shown the versatility to apply to both academic and professional activities with no extra costs. Keywords: Free geoprocessing, QGIS, EpiCollect, methodology, data collection, geosciences.
... Epicollect5 is available for both Android (4.4+) and iOS (8+)-based mobile phones in play store and app store, respectively. There are various resources for mobile phone-based data collection solutions (6). ...
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Dear Editor, Re-Orientation of Medical Education (ROME), a one month posting of undergraduate students in department of Community Medicine, is used not only to build up the knowledge, attitude, communication and clinical skills, but also to make the physician in contact with the community efficiently. In 1977, the ROME scheme aimed at developing medical doctors for the rural community in the vision of medical education, which later envisaged training a basic doctor to serve better at the first contact with rural and urban community (1, 2). During the posting, the students conducted house to house survey in rural and urban communities and prepared a detailed report of the research activities and planned intervention based on the needs of the community. World consumption of paper has grown 400% in the last four decades. About 35% of the total trees cut around the world are used in paper industries (3). To reduce the paper consumption, The Ugandan Ministry of Health advocated the use of smart phones in integrated community case management approach for health care providers for child health (4). Every year in ROME more than thousands of pages of papers are used for data collection. As a small initiative to save paper in this year (2018) ROME training, completely paper-less questionnaires were made and used. Smart phones were used as a tool for data collection and data entry. Even in the large scale surveys, data capture can be easily done (5). Epicollect5 software (developed by Imperial College London funded by the Wellcome trust) was used to collect data through a mobile app. The mobile based data collection has benefits over paper-based approaches. The Epicollect5 software enables the creators or managers to identify the errors and allows mid-course correction in minimal time. The instances of data entry error/missing data can be avoided by applying checks at the data entry point. The data are transferred to a central server near – instantaneously; therefore, the data are stored and backed up securely, and the risk of data loss is minimal. Epicollect5 is available for both Android (4.4+) and iOS (8+)-based mobile phones in play store and app store, respectively. There are various resources for mobile phone-based data collection solutions (6). As the students are familiar with the smart phones, training requirements were almost minimal. The students grasped it in almost realtime. It would be right to say the technology has imbibed the younger generation more, than being learnt by them. Use of mobile phones was so convenient, feasible and user-friendly to capture data (data collection as well as data entry). By the end of day 5 of data collection by a group of 40 undergraduate students, the total numbers of households covered were 963 and 3527 individuals. During the survey period, a batch of 150 undergraduate students collected the data of around 15575 individuals by using this mobile app. This actually reduced the time and costs involved in acquiring or maintaining dedicated tools for data capture. The app also gives an opportunity to collect audios, videos and GPS co-ordinates. As compared to previous years' ROME posting which were done in a similar setting with paper-based questionnaire; the total number of participants interviewed by a group of 30-35 students were around 2200 to 2600 over a period of 10 days of data collection which was followed by another 5 days of data entry in MS Excel/ EpiData software. Appropriate use of technology in research helped the students to minimize the hours spent in data collection and entry and provoked their interest in research since the app-based data collection was more interesting as compared to paper-based approach for them.
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Mobile Electronic Data Capturing Forms (MEDCFs) are electronic form applications that are primarily used for data capture using mobile devices in the place of paper-based routines. Translating paper-based forms to MEDCFs presents several usability challenges due to the design limitations of using mobile devices. The main objective of this study therefore was to define the most important design features that need to be considered when developing MEDCFs. Fifteen mobile form developers each received a semi-structured questionnaire via Email. The questions were derived from sub heuristics for mobile applications and were based on features that are common to forms such as form content, form layout, input type, error handling and form submission. The study identified the eighteen most important design features that all MEDCFs should have in order to provide a usable tool. These include feedback, logic implementation, form navigation, data input format requirements, unique identification, language translation and error handling among others. With a shorter design feature checklist specific to MEDCFs, and collaboration efforts amongst the various stakeholders, it will be possible to develop usable mobile electronic data collection forms.
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This quasi-comparative-experimental study explained student achievements scores based on the level of disparity in the means within the control and experimental groups. Four secondary schools (two-government and two-private owned) were studied in Enugu State. The study was comprised of 182 Agricultural Science students in 4 intact classes drawn from four secondary schools in Enugu state. A Student Achievement Test (SAT) was used for data collection. The instrument was faced and content validated. Kuder-Richardson formula (K-R21) was used to determine the internal consistency of the instrument after trial testing, which yielded a reliability index of 0.79. Mean and standard deviation were used for data analysis. Findings of the study strongly supported the use of instructional materials in content delivery but cautions on the diverging effects that could arise among student as revealed in the widening gap between the highest and lowest mean achievement scores.
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Background: Traditionally, clinical research studies rely on collecting data with case report forms, which are subsequently entered into a database to create electronic records. Although well established, this method is time-consuming and error-prone. This study compares four electronic data capture (EDC) methods with the conventional approach with respect to duration of data capture and accuracy. It was performed in a West African setting, where clinical trials involve data collection from urban, rural and often remote locations. Methodology/principal findings: Three types of commonly available EDC tools were assessed in face-to-face interviews; netbook, PDA, and tablet PC. EDC performance during telephone interviews via mobile phone was evaluated as a fourth method. The Graeco Latin square study design allowed comparison of all four methods to standard paper-based recording followed by data double entry while controlling simultaneously for possible confounding factors such as interview order, interviewer and interviewee. Over a study period of three weeks the error rates decreased considerably for all EDC methods. In the last week of the study the data accuracy for the netbook (5.1%, CI95%: 3.5-7.2%) and the tablet PC (5.2%, CI95%: 3.7-7.4%) was not significantly different from the accuracy of the conventional paper-based method (3.6%, CI95%: 2.2-5.5%), but error rates for the PDA (7.9%, CI95%: 6.0-10.5%) and telephone (6.3%, CI95% 4.6-8.6%) remained significantly higher. While EDC-interviews take slightly longer, data become readily available after download, making EDC more time effective. Free text and date fields were associated with higher error rates than numerical, single select and skip fields. Conclusions: EDC solutions have the potential to produce similar data accuracy compared to paper-based methods. Given the considerable reduction in the time from data collection to database lock, EDC holds the promise to reduce research-associated costs. However, the successful implementation of EDC requires adjustment of work processes and reallocation of resources.
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With more clinical trials involving evaluations of new drugs or vaccines, monitoring for early detection of adverse events is essential. The overall goal of this study was to develop an interactive-computer system using cell phones for real-time collection and transmission of adverse events related to metronidazole administration among female sex workers (FSW) in Peru. We developed an application for cell phones in Spanish, called Cell-PREVEN, based on a system from Voxiva Inc. We used cell phones to enter data collected by interviewers from FSW in three communities. Information was stored in an online database, where it could be immediately accessed worldwide and exported over a secure Internet connection. E-mail and text messages sent to mobile devices alerted key personnel to selected symptoms. This pilot project has demonstrated that it is feasible to develop a public-health-surveillance system based on cell phones to collect data in real-time in Peru (http://www.prevenperu.org).
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Survey data are traditionally collected using pen-and-paper, with double data entry, comparison of entries and reconciliation of discrepancies before data cleaning can commence. We used Personal Digital Assistants (PDAs) for data entry at the point of collection, to save time and enhance the quality of data in a survey of over 21,000 scattered rural households in southern Tanzania. Pendragon Forms 4.0 software was used to develop a modular questionnaire designed to record information on household residents, birth histories, child health and health-seeking behaviour. The questionnaire was loaded onto Palm m130 PDAs with 8 Mb RAM. One hundred and twenty interviewers, the vast majority with no more than four years of secondary education and very few with any prior computer experience, were trained to interview using the PDAs. The 13 survey teams, each with a supervisor, laptop and a four-wheel drive vehicle, were supported by two back-up vehicles during the two months of field activities. PDAs and laptop computers were charged using solar and in-car chargers. Logical checks were performed and skip patterns taken care of at the time of data entry. Data records could not be edited after leaving each household, to ensure the integrity of the data from each interview. Data were downloaded to the laptop computers and daily summary reports produced to evaluate the completeness of data collection. Data were backed up at three levels: (i) at the end of every module, data were backed up onto storage cards in the PDA; (ii) at the end of every day, data were downloaded to laptop computers; and (iii) a compact disc (CD) was made of each team's data each day.A small group of interviewees from the community, as well as supervisors and interviewers, were asked about their attitudes to the use of PDAs. Following two weeks of training and piloting, data were collected from 21,600 households (83,346 individuals) over a seven-week period in July-August 2004. No PDA-related problems or data loss were encountered. Fieldwork ended on 26 August 2004, the full dataset was available on a CD within 24 hours and the results of initial analyses were presented to district authorities on 28 August. Data completeness was over 99%. The PDAs were well accepted by both interviewees and interviewers. The use of PDAs eliminated the usual time-consuming and error-prone process of data entry and validation. PDAs are a promising tool for field research in Africa.