What encourages community health workers to use mobile technologies for health interventions? Emerging lessons from rural Rwanda

Mwendwa, Purity

doi:10.1111/dpr.12275

Cited by 14 publications

(10 citation statements)

References 56 publications

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“…In this study we report significant correlation between the number of deliveries and use of the USSD, however, this does not preclude inappropriate use of the intervention protocols. Although unintended use of mHealth interventions is not uncommon [50], [51], and strategies to improve appropriate use of mHealth interventions (such as reward schemes and reminders) are well documented in literature [52], [53], [54], overall, our findings suggest to carefully consider whether this kind of mHealth intervention is the most appropriate in the study context.…”

Section: Discussionmentioning

confidence: 78%

The effect of an mHealth clinical decision-making support system on neonatal mortality in a low resource setting: A cluster-randomized controlled trial

et al. 2019

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Background MHealth interventions promise to bridge gaps in clinical care but documentation of their effectiveness is limited. We evaluated the utilization and effect of an mhealth clinical decision-making support intervention that aimed to improve neonatal mortality in Ghana by providing access to emergency neonatal protocols for frontline health workers. Methods In the Eastern Region of Ghana, sixteen districts were randomized into two study arms (8 intervention and 8 control clusters) in a cluster-randomized controlled trial. Institutional neonatal mortality data were extracted from the District Health Information System-2 during an 18-month intervention period. We performed an intention-to-treat analysis and estimated the effect of the intervention on institutional neonatal mortality (primary outcome measure) using grouped binomial logistic regression with a random intercept per cluster. This trial is registered at ClinicalTrials.gov ( NCT02468310 ) and Pan African Clinical Trials Registry ( PACTR20151200109073). Findings There were 65,831 institutional deliveries and 348 institutional neonatal deaths during the study period. Overall, 47 ∙ 3% of deliveries and 56 ∙ 9% of neonatal deaths occurred in the intervention arm. During the intervention period, neonatal deaths increased from 4 ∙ 5 to 6 ∙ 4 deaths and, from 3 ∙ 9 to 4 ∙ 3 deaths per 1000 deliveries in the intervention arm and control arm respectively. The odds of neonatal death was 2⋅09 (95% CI (1 ∙ 00;4 ∙ 38); p = 0 ∙ 051) times higher in the intervention arm compared to the control arm (adjusted odds ratio). The correlation between the number of protocol requests and the number of deliveries per intervention cluster was 0 ∙ 71 (p = 0 ∙ 05). Interpretation The higher risk of institutional neonatal death observed in intervention clusters may be due to problems with birth and death registration, unmeasured and unadjusted confounding, and unintended use of the intervention. The findings underpin the need for careful and rigorous evaluation of mHealth intervention implementation and effects. Funding Netherlands Foundation for Scientific Research - WOTRO, Science for Global Development; Utrecht University.

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Section: Discussionmentioning

confidence: 78%

The effect of an mHealth clinical decision-making support system on neonatal mortality in a low resource setting: A cluster-randomized controlled trial

et al. 2019

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“…Low awareness of mHealth interventions and their functions has been documented as a barrier to mHealth intervention use 44. Reminders and reward schemes (tangible or intangible) can motivate users to use mHealth applications 42 44 45. The reasons for using the voice and SMS intervention components could be situated in the mapping of the CUG communication.…”

Section: Discussionmentioning

confidence: 99%

How and why front-line health workers (did not) use a multifaceted mHealth intervention to support maternal and neonatal healthcare decision-making in Ghana

et al. 2019

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IntroductionDespite increasing use of mHealth interventions, there remains limited documentation of ‘how and why’ they are used and therefore the explanatory mechanisms behind observed effects on beneficiary health outcomes. We explored ‘how and why’ an mHealth intervention to support clinical decision-making by front-line providers of maternal and neonatal healthcare services in a low-resource setting was used. The intervention consisted of phone calls (voice calls), text messaging (short messaging service (SMS)), internet access (data) and access to emergency obstetric and neonatal protocols via an Unstructured Supplementary Service Data (USSD). It was delivered through individual-use and shared facility mobile phones with unique Subscriber Identification Module (SIM) cards networked in a Closed User Group.MethodsA single case study with multiple embedded subunits of analysis within the context of a cluster randomised controlled trial of the impact of the intervention on neonatal health outcomes in the Eastern Region of Ghana was performed. We quantitatively analysed SIM card activity data for patterns of voice calls, SMS, data and USSD. We conducted key informant interviews and focus group discussions with intervention users and manually analysed the data for themes.ResultsOverall, the phones were predominantly used for voice calls (64%), followed by data (28%), SMS (5%) and USSD (2%), respectively. Over time, use of all intervention components declined. Qualitative analysis showed that individual health worker factors (demographics, personal and work-related needs, perceived timeliness of intervention, tacit knowledge), organisational factors (resource availability, information flow, availability, phone ownership), technological factors (attrition of phones, network quality) and client perception of health worker intervention usage explain the pattern of intervention use observed.ConclusionHow and why the mHealth intervention was used (or not) went beyond the technology itself and was influenced by individual and context-specific factors. These must be taken into account in designing similar interventions to optimise effectiveness.

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“…On the other hand, strong motivation also increases lay workers’ performance. Mwendwa et al suggest that poor performance of the community health care workers cannot be solely solved by training skills but also by highlighting the consequences of the measurements and explaining the process of data collection [38]. A properly supervised training and explanations of the benefits of the mHealth tool have the potential to increase user motivation.…”

Section: Discussionmentioning

confidence: 99%

Data Integrity–Based Methodology and Checklist for Identifying Implementation Risks of Physiological Sensing in Mobile Health Projects: Quantitative and Qualitative Analysis

Zhang¹,

Tüshaus²,

Martínez³

et al. 2018

JMIR Mhealth Uhealth

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BackgroundMobile health (mHealth) technologies have the potential to bring health care closer to people with otherwise limited access to adequate health care. However, physiological monitoring using mobile medical sensors is not yet widely used as adding biomedical sensors to mHealth projects inherently introduces new challenges. Thus far, no methodology exists to systematically evaluate these implementation challenges and identify the related risks.ObjectiveThis study aimed to facilitate the implementation of mHealth initiatives with mobile physiological sensing in constrained health systems by developing a methodology to systematically evaluate potential challenges and implementation risks.MethodsWe performed a quantitative analysis of physiological data obtained from a randomized household intervention trial that implemented sensor-based mHealth tools (pulse oximetry combined with a respiratory rate assessment app) to monitor health outcomes of 317 children (aged 6-36 months) that were visited weekly by 1 of 9 field workers in a rural Peruvian setting. The analysis focused on data integrity such as data completeness and signal quality. In addition, we performed a qualitative analysis of pretrial usability and semistructured posttrial interviews with a subset of app users (7 field workers and 7 health care center staff members) focusing on data integrity and reasons for loss thereof. Common themes were identified using a content analysis approach. Risk factors of each theme were detailed and then generalized and expanded into a checklist by reviewing 8 mHealth projects from the literature. An expert panel evaluated the checklist during 2 iterations until agreement between the 5 experts was achieved.ResultsPulse oximetry signals were recorded in 78.36% (12,098/15,439) of subject visits where tablets were used. Signal quality decreased for 1 and increased for 7 field workers over time (1 excluded). Usability issues were addressed and the workflow was improved. Users considered the app easy and logical to use. In the qualitative analysis, we constructed a thematic map with the causes of low data integrity. We sorted them into 5 main challenge categories: environment, technology, user skills, user motivation, and subject engagement. The obtained categories were translated into detailed risk factors and presented in the form of an actionable checklist to evaluate possible implementation risks. By visually inspecting the checklist, open issues and sources for potential risks can be easily identified.ConclusionsWe developed a data integrity–based methodology to assess the potential challenges and risks of sensor-based mHealth projects. Aiming at improving data integrity, implementers can focus on the evaluation of environment, technology, user skills, user motivation, and subject engagement challenges. We provide a checklist to assist mHealth implementers with a structured evaluation protocol when planning and preparing projects.

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What encourages community health workers to use mobile technologies for health interventions? Emerging lessons from rural Rwanda

Abstract: This article explores the determinants of using mobile phone technologies for public health (mHealth) through a quantitative study with community health workers

Cited by 14 publications

References 56 publications

The effect of an mHealth clinical decision-making support system on neonatal mortality in a low resource setting: A cluster-randomized controlled trial

The effect of an mHealth clinical decision-making support system on neonatal mortality in a low resource setting: A cluster-randomized controlled trial

How and why front-line health workers (did not) use a multifaceted mHealth intervention to support maternal and neonatal healthcare decision-making in Ghana

Data Integrity–Based Methodology and Checklist for Identifying Implementation Risks of Physiological Sensing in Mobile Health Projects: Quantitative and Qualitative Analysis

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