Evaluating the use of mobile phone technology to enhance cardiovascular disease screening by community health workers

https://doi.org/10.1016/j.ijmedinf.2014.06.008Get rights and content

Highlights

  • Enhanced screening with the mHealth tool compared to the paper-based chart tool.

  • Reduction in screening times by 40% (21 min vs 35 min).

  • Reduction in community health worker training times by 76% (3 h vs 12.3 h).

  • Elimination in the margin of error in calculating a CVD Risk score.

Abstract

Background

Primary prevention of cardiovascular disease (CVD),by identifying individuals at risk is a well-established, but costly strategy when based on measurements that depend on laboratory analyses. A non-laboratory, paper-based CVD risk assessment chart tool has previously been developed to make screening more affordable in developing countries. Task shifting to community health workers (CHWs) is being investigated to further scale CVD risk screening. This study aimed to develop a mobile phone CVD risk assessment application and to evaluate its impact on CHW training and the duration of screening for CVD in the community by CHWs.

Methods

A feature phone application was developed using the open source online platform, CommCare©. CHWs (n = 24) were trained to use both paper-based and mobile phone CVD risk assessment tools. They were randomly allocated to using one of the risk tools to screen 10–20 community members and then crossed over to screen the same number, using the alternate risk tool. The impact on CHW training time, screening time and margin of error in calculating risk scores was recorded. A focus group discussion evaluated experiences of CHWs using the two tools.

Results

The training time was 12.3 h for the paper-based chart tool and 3 h for the mobile phone application. 537 people were screened. The mean screening time was 36 min (SD = 12.6) using the paper-base chart tool and 21 min (SD = 8.71) using the mobile phone application, p = <0.0001. Incorrect calculations (4.3% of average systolic BP measurements, 10.4% of BMI and 3.8% of CVD risk score) were found when using the paper-based chart tool while all the mobile phone calculations were correct. Qualitative findings from the focus group discussion corresponded with the findings of the pilot study.

Conclusion

The reduction in CHW training time, CVD risk screening time, lack of errors in calculation of a CVD risk score and end user satisfaction when using a mobile phone application, has implications in terms of adoption and sustainability of this primary prevention strategy to identify people with high CVD risk who can be referred for appropriate diagnoses and treatment.

Introduction

Cardiovascular disease (CVD) is the leading cause of death worldwide; with developing countries affected the worst [1], [2], [3]. Screening for the risk of developing CVD is a well-recognised primary prevention strategy. This is usually done by calculating a risk score based on assessing a combination of risk factors, including, age, gender, tobacco use, blood pressure levels, blood cholesterol levels, diabetes or family history of CVD [4], [5], [6]. The human resource requirements, laboratory costs as well as inconvenience to the individual of risk scores that depend on biochemical tests has led to the development of a non-laboratory based CVD risk assessment model. This simplified model substitutes the body-mass index for blood lipid level to calculate the absolute CVD risk score thus making CVD risk screening far more feasible and potentially cost effective in both high and low resource settings [7]. The model uses data from a clinical history and physical examination, making a number of basic arithmetic calculations and decision support charts to calculate the CVD risk score (Fig. 1). This method has been found to perform as well as the common laboratory-based risk score in identifying people at high CVD risk in a South African setting [8].

Given the limited work force of nurses and doctors across all resource settings, the concept of task shifting is gaining increasing traction. Community health workers (CHWs) defined by the World Health Organization (WHO) as community members that have shorter training than professional workers, have been identified as potential candidates for task shifting in the health sector in general. CHWs have been used to provide a wide range of basic health services and it is well established that they play a crucial role in improving access to health services in under resourced settings [9]. There are, however, a number of challenges in using CHWs as they tend to have a limited amount of formal education and training. Mobile health tools are increasingly being used to assist and enable lay health workers in performing basic tasks. These interventions are thought to strengthen health systems by enabling a wide range of activities including data collection, disease surveillance, monitoring and evaluation and supporting clinic based health workers [10], [11], [12], [13].

Development of a mobile phone application that automatically calculates a CVD risk score further simplifies the task of risk assessment in the community because it allows for the risk assessment tool to be carried into the community and because it can potentially limit errors due to manual calculations. Finally, it can be used by health workers with limited formal education who may be less skilled and numerate.

The aim of this study was to develop a mobile phone CVD risk assessment application, based on a non-laboratory CVD risk assessment model and to evaluate its impacts on the training of CHWs and the screening for CVD in the community by CHWs compared to them using the paper-based chart tool.

Section snippets

Methods

This pilot study used quantitative and qualitative research methods. A mobile phone CVD risk assessment application was developed based on the non-laboratory paper-based CVD risk assessment model developed and validated by Gaziano et al. [7].The online CommCareHQ platform was used to develop the mobile phone version of this tool. CommCareHQ is an open-source software application with mobile phone and cloud infrastructure designed to enable creation of mobile phone job aids for CHWs. Relevant

Results

The mean age of the CHWs was 33 years (range 21–52 years) with 21 being female and 3 male. The level of basic education was Grade 12 (twelve years of schooling; completion of high school), n = 8; Grade 11, n = 14 and Grade 10, n = 2. In addition, most CHWs also had some basic healthcare training in the form of Home-Based Community Care skills (n = 17) or Chronic Diseases of lifestyle skills (n = 4). Every participating CHW owned and was familiar with using a mobile phone with 71% (n = 17) owning feature

Qualitative results

A number of themes were identified during the focus group discussion and are summarized in Table 2. CHWs felt the mobile phone application was easier, faster and more accurate to use, but noted that it was inferior to the chart as a visual aid when explaining risk.

Discussion

The major findings of this study were that the mobile phone application of a non-blood based CVD risk tool used by CHWs was associated with a major reduction in the time taken for training, reaching adequate proficiency, screening for CVD risk and an elimination of errors in calculating a CVD risk score. Further that a quarter (25.4%) of screened participants had moderate to high risk of having a CVD event in the next five years. These are individuals that were previously unaware of having any

Conclusion

This study has found that when CHWs were trained to use a non-blood based CVD risk tool, compared to the paper-based chart tool, the mobile phone application was associated with a major reduction in the time taken for training and reaching adequate proficiency, in the time taken to screen individuals and there was elimination of errors in calculating a CVD risk score. The increased efficiency with reduced screening times and faster and easier training could have cost saving implications and the

Author contributions

Dr Sam Surka is involved in the conception and design of the study, (2) acquisition of data, analysis and interpretation of data, and (3) drafting the article, revising it critically for important intellectual content. Dr Sisira Edirippulige is involved in (1) interpretation of data, (2) revising it critically for important intellectual content, and (3) final approval of the version to be submitted. Prof Krisela Steyn is involved in (1) the conception and design of the study, (2) interpretation

Competing interests

None declared by any of the authors.

Sources of support

This Seed Grant has been funded in whole with Federal funds by the United States National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Purchase Order No. UCT HHSN268200900030C. Dr Surka was also supported by the Discovery Foundation Academic Fellowship Award and the South African Medical Research Council. Funding for this research project is gratefully acknowledged.

Summary points

What was already known about the topic?

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Acknowledgements

We are grateful to the following institutions for their support: CDIA at the University of Cape Town, School of Public Health at the University of the Western Cape and the Division of Telemedicine and mHealth at the South African Medical Research Council, Ms Jabu Zulu's contribution in the training and field work is also gratefully acknowledged. Dimagi provided technical support and use of their Commcare platform.

References (16)

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