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Use of mobile phones as research instrument for data collection

Authors:
  • La Trobe Regional Hospital

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.
... Electronic tools and solutions offer many advantages over traditional paper-based data collection, including more rapid data collection and transfer, use of checks/validation to improve data accuracy, and collection of more diverse data types including images, audio and barcodes. They can provide cost savings and are more environmentally friendly [8][9][10][11]. However, electronic tools also have limitations in some contexts, for example if there are requirements for stable electricity, internet or phone connectivity. ...
... Survey participants were asked an open question on what influenced their choice of tool, and 10 factors emerged as key influencers: [1] cost [2]; user friendliness [3]; speed to configure data collection forms/deploy the system [4]; availability of the tool [5]; previous experience with a tool either by individuals, within specific organisations or by Ministry of Health personnel [6]; open source tool [7]; offline use [8]; availability of specific functionalities e.g. audit log [9]; if the tool was viewed as the "sector standard" and [10] presence of a strong userbase/community of support. ...
... Survey participants were also asked another open question to comment on how their currently used electronic tools could be improved. Ten improvements were recommended: [1] reduce the cost [2]; improve the interoperability [3]; improve user-friendliness [4]; increase the flexibility and customisability of the tool, which would reduce the reliance of local staff on centralised teams/deployment of specialists [5]; provide more training resources and in more languages, with a suggestion to build training modules within the tools [6]; simplify the local hosting procedures [7]; improve data visualisations [8]; allow more advanced analyses to be performed [9]; improve multiple user management and [10] improve support for longitudinal data. ...
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Background Use of electronic data collection, management and analysis tools to support outbreak response is limited, especially in low income countries. This can hamper timely decision-making during outbreak response. Identifying available tools and assessing their functions in the context of outbreak response would support appropriate selection and use, and likely more timely data-driven decision-making during outbreaks. Methods We conducted a systematic review and a stakeholder survey of the Global Outbreak Alert and Response Network and other partners to identify and describe the use of, and technical characteristics of, electronic data tools used for outbreak response in low- and middle-income countries. Databases included were MEDLINE, EMBASE, Global Health, Web of Science and CINAHL with publications related to tools for outbreak response included from January 2010–May 2020. Software tool websites of identified tools were also reviewed. Inclusion and exclusion criteria were applied and counts, and proportions of data obtained from the review or stakeholder survey were calculated. Results We identified 75 electronic tools including for data collection (33/75), management (13/75) and analysis (49/75) based on data from the review and survey. Twenty-eight tools integrated all three functionalities upon collection of additional information from the tool developer websites. The majority were open source, capable of offline data collection and data visualisation. EpiInfo, KoBoCollect and Open Data Kit had the broadest use, including for health promotion, infection prevention and control, and surveillance data capture. Survey participants highlighted harmonisation of data tools as a key challenge in outbreaks and the need for preparedness through training front-line responders on data tools. In partnership with the Global Health Network, we created an online interactive decision-making tool using data derived from the survey and review. Conclusions Many electronic tools are available for data -collection, −management and -analysis in outbreak response, but appropriate tool selection depends on knowledge of tools’ functionalities and capabilities. The online decision-making tool created to assist selection of the most appropriate tool(s) for outbreak response helps by matching requirements with functionality. Applying the tool together with harmonisation of data formats, and training of front-line responders outside of epidemic periods can support more timely data-driven decision making in outbreaks.
... 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.
... 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.
... A cellphone was used to record each conversation with the authorisation of the participants and the researcher transcribed the interviews in English to Microsoft Word™ for analysis. Pakhare, Bali and Kalra (2013) believe that the use of a mobile phone speeds capturing of reporting with accuracy and has the potential for enriched data collection. This study adopted content analysis as the technique for 723 interpreting the findings obtained from empirical material collected. ...
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ABSTRACT The government is committed to improving the health system by providing universal coverage to all South Africans as articulated in national health policies. The biggest threat facing the health sector today is the shortage of well-trained healthcare workers and the increasing demand for healthcare services. A quantitative study was used to examine the role of task-shifting as response to human resource crisis facing the Ngwelezana Tertiary Hospital in KwaZulu-Natal. Data was analysed using descriptive statistics, chi-square tests of association and the Cramer’s V test. The results show that task-shifting was adopted to address staff shortages, delays in serving patients, long waiting periods for patients, increased risks of error and patient mortality. However, task-shifting presented its own challenges such as legal and professional risks and staff morale issues. The paper concludes that task shifting should be used as a relief measure for reducing the impact of staff shortages in hospitals.
... A cellphone was used to record each conversation with the authorisation of the participants and the researcher transcribed the interviews in English to Microsoft Word™ for analysis. Pakhare, Bali and Kalra (2013) believe that the use of a mobile phone speeds capturing of reporting with accuracy and has the potential for enriched data collection. This study adopted content analysis as the technique for 723 interpreting the findings obtained from empirical material collected. ...
Conference Paper
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Procuring and Disposing Entities (PDEs) in state departments are challenged with missing records, inadequate training, poor enforcement mechanisms, limited funding and poor reports. Taking into account the huge government investment into public contracts, research to understand factors enabling contract compliance is imperative. This study explored the enabling factors for contract compliance in PDEs in Uganda. A qualitative study of twenty-nine telephone interviews and focus group discussions via Zoom were conducted. Thematic and content analysis were used to identify the fourteen major themes. Findings suggest capacity building, funding, reporting, evaluation, contract management, records management, institutional structure, enforcement, legal and regulatory framework, communication, openness, compliance checks, ethical orientation and stakeholder involvement as enablers for a suitable contract compliance environment in state departments in Uganda. The proposed conceptual framework serves as a valuable resource for researchers and practitioners especially those involved in public procurement.
... Pengumpulan data seluler menggunakan telepon pintar semakin populer karena banyak keuntungannya, lebih efisien dan efektif. peneliti dapat memperoleh laporan secara realtime, mempersingkat proses inputasi dan cleaning data (Pakhare et al., 2013;Seifert et al., 2018). Dengan Open Data Kit (ODK) memungkinkan proses pengumpulan data secara luar jaringan (luring) dan melakukan submit data setelah smartphone terkoneksi ke jaringan internet (Wahana Visi Indonesia, 2019) Jumlah pengguna internet dan kepemilikan smartphone yang makin meningkat di Indonesia menjadi peluang untuk menggunakan ODK dalam pengumpulan data (Haryanto, 2019;Nafi, 2019;Rahmayani, 2015). ...
... Pengumpulan data seluler menggunakan telepon pintar semakin populer karena banyak keuntungannya, lebih efisien dan efektif. peneliti dapat memperoleh laporan secara realtime, mempersingkat proses inputasi dan cleaning data (Pakhare et al., 2013;Seifert et al., 2018). Dengan Open Data Kit (ODK) memungkinkan proses pengumpulan data secara luar jaringan (luring) dan melakukan submit data setelah smartphone terkoneksi ke jaringan internet (Wahana Visi Indonesia, 2019) Jumlah pengguna internet dan kepemilikan smartphone yang makin meningkat di Indonesia menjadi peluang untuk menggunakan ODK dalam pengumpulan data (Haryanto, 2019;Nafi, 2019;Rahmayani, 2015). ...
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Increasing number of smartphone users, especially among students, allows in maximizing the implementation of learning, especially research. The COVID-19 pandemic has caused a shift in the method of collecting research data from paper-based to digital surveys. The most frequently used application is Google Form (GF). The use of GF has several weaknesses, such as the errors in data cleaning process, and it does not accommodate logical questions. The Open Data Kit (ODK) application can cover all the shortcomings of GF. This activity aims to train lecturers to make ODK-based questionnaires. The method of implementing this activity is in the form of training through the Zoom application. The stages of this activity are starting from delivering theoretical material, practicum, and submitting XLS forms to the server. The participants of this activity are lecturers from various higher education institutions in Indonesia, totaling 30 people. The activity was divided into 5 batches, from 18 to 30 July 2021. At the end of the activity, 100% of participants succeeded in uploading the XLS Form questionnaire to the kobotoolbox server. Keywords: Open Data; Kobotoolbox; Smartphone; Questionnaires; ODK
... Pengumpulan data merupakan langkah penting dalam setiap desain atau program penelitian [1]. Praktek pengumpulan data selama ini masih metode manual yaitu menggunakan media kertas yang menyebabkan rendahnya kualitas bila volume datanya besar. ...
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Until the end of 2019, there were 60 RPTRA's located in several sites in South Jakarta. However, there is none of Official RPTRA's Web that contains such profiles, in order to obtain the fast, accurate and complete information about it.. This research aims to facilitate RPTRA's South Jakarta a fast and high-quality data collection tools in a centralized server using ODK System and Android Smartphone. Furthermore, it also aims to generate an android application that can be used to read RPTRA's South Jakarta data survey. This research uses quantitative approach with survey type of research. Using Open Data Kit as a data collection tool for RPTRA's South Jakarta. The result is the use of ODK system makes the process of data collection faster and easier to obtain. The RPTRA's South Jakarta data collection can be accessed online or offline on mobile phone, so it can be used in any situations. Another result of this research is that the RPTRA's result survey can be easily accessed by the public through android application.
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In any organization, human resources stand as vital assets for success; these are to be highly valued in order to achieve the competitive edge. Therefore, human capital policies and practices should be tied to organizational goals. The impact of practices of HRM can be the channel that fuels sustained competitive advantage; therefore, enhancement of organizational performance should be the focus for an organization. Performance management comprises setting expectations, assessing behaviors and results of the employee, giving coaching and feedback, and performance evaluation over time to use in decision making. The aim is to line up individual efforts to achieve organizational goals. To this end, this study attempts to examine result-based management which is synonymous with performance management system, and how it drives employees’ productivity. Utilising the purposive sampling technique, staff within an educational institution were selected in Lagos. It was revealed that setting targets and performance standards would engender the desired productivity in employees. The study therefore recommends a comprehensive performance management system which should also be automated in order to capture key performance indicators of employees.
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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.