Thinking about the environment and theorising change: how could Life History Strategy Theory inform mHealth interventions in low- and middle-income countries?

Morgan, Barak; Hunt, Xanthe; Tomlinson, Mark

doi:10.1080/16549716.2017.1320118

Cited by 6 publications

(9 citation statements)

References 45 publications

(64 reference statements)

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“…Another review found that IMB was the most common theory employed in eHealth interventions targeting medication adherence [46]. The use of theory was less common among studies in low- and middle-income countries (LMIC;19%) relative to high-income countries (HIC; 48%) [3,23]. The lack of theory-informed eHealth interventions is not unique to the field of HIV prevention and treatment.…”

Section: How Well Does Ehealth Intervention Research Incorporate Healmentioning

confidence: 99%

Health Behavior Theory to Enhance eHealth Intervention Research in HIV: Rationale and Review

2018

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Purpose of Review Optimal design and evaluation of eHealth interventions requires the specification of behavioral targets and hypothesized mechanisms of action – both of which can be enhanced with the use of established health behavior theories (HBTs). In this paper, we describe the major HBTs and examine their use in studies of eHealth interventions for HIV prevention and treatment, and assess the contribution of HBT in developing and evaluating eHealth interventions. Recent Finding Based on our review of the literature, we argue the field can benefit from more systematic selection, application and reporting of HBT. We highlight theories specifically designed for eHealth and describe ways that HBT can be used by researchers and practitioners to improve the rigor and impact of eHealth interventions for individuals living with or at risk for HIV. Summary This brief overview of HBTs and their application to eHealth intervention in HIV research has underscored the importance of a theoretically intentional approach. The theory should be used to inform the design of the eHealth intervention; the intervention should not determine the theory. A theory-driven iterative model of eHealth intervention development may not only improve our repertoire of effective strategies but also has the potential to expand our theoretical and empirical knowledge of health behavior change.

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Section: How Well Does Ehealth Intervention Research Incorporate Healmentioning

confidence: 99%

Health Behavior Theory to Enhance eHealth Intervention Research in HIV: Rationale and Review

2018

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“…Although users may briefly accommodate the innovation, they will be less invested in making long-term commitments requiring individual and organizational realignment, for short-term gains. In addition to being user-centered and accounting for the context, it is imperative that multiple stakeholder perspectives are leveraged during innovation design [6,7,35]. High-ranking supervisors might seem distant from the usage process but could compel or foster accountability and usage.…”

Section: Discussionmentioning

confidence: 99%

Unveiling the Black Box of Diagnostic and Clinical Decision Support Systems for Antenatal Care: Realist Evaluation

Abejirinde¹,

Zweekhorst²,

Bardají³

et al. 2018

JMIR Mhealth Uhealth

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BackgroundDigital innovations have shown promise for improving maternal health service delivery. However, low- and middle-income countries are still at the adoption-utilization stage. Evidence on mobile health has been described as a black box, with gaps in theoretical explanations that account for the ecosystem of health care and their effect on adoption mechanisms. Bliss4Midwives, a modular integrated diagnostic kit to support antenatal care service delivery, was piloted for 1 year in Northern Ghana. Although both users and beneficiaries valued Bliss4Midwives, results from the pilot showed wide variations in usage behavior and duration of use across project sites.ObjectiveTo strengthen the design and implementation of an improved prototype, the study objectives were two-fold: to identify causal factors underlying the variation in Bliss4Midwives usage behavior and understand how to overcome or leverage these in subsequent implementation cycles.MethodsUsing a multiple case study design, a realist evaluation of Bliss4Midwives was conducted. A total of 3 candidate program theories were developed and empirically tested in 6 health facilities grouped into low and moderate usage clusters. Quantitative and qualitative data were collected and analyzed using realist thinking to build configurations that link intervention, context, actors, and mechanisms to program outcomes, by employing inductive and deductive reasoning. Nonparametric t test was used to compare the perceived usefulness and perceived ease of use of Bliss4Midwives between usage clusters.ResultsWe found no statistically significant differences between the 2 usage clusters. Low to moderate adoption of Bliss4Midwives was better explained by fear, enthusiasm, and high expectations for service delivery, especially in the absence of alternatives. Recognition from pregnant women, peers, supervisors, and the program itself was a crucial mechanism for device utilization. Other supportive mechanisms included ownership, empowerment, motivation, and adaptive responses to the device, such as realignment and negotiation. Champion users displayed high adoption-utilization behavior in contexts of participative or authoritative supervision, yet used the device inconsistently. Intervention-related (technical challenges, device rotation, lack of performance feedback, and refresher training), context-related (staff turnover, competing priorities, and workload), and individual factors (low technological self-efficacy, baseline knowledge, and internal motivation) suppressed utilization mechanisms.ConclusionsThis study shed light on optimal conditions necessary for Bliss4Midwives to thrive in a complex social and organizational setting. Beyond usability and viability studies, advocates of innovative technologies for maternal care need to consider how implementation strategies and contextual factors, such as existing collaborations and supervision styles, trigger mechanisms that influence program outcomes. In addition to informing scale-up of the Bliss4Midwives prototype, our resu...

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“…However, such work should be approached with caution. Indeed, the potential of mHealth, as has been widely proposed (Chang et al, 2011;Chib et al, 2015;Morgan et al, 2017), could lie in its capacity to reach underserved populations (although current analyses have cast some doubt on this, see Jennings and Gagliardi, 2013;Morgan et al, 2017). Resource constraints are endemic in many low-and middle-income countries and hinder public health efforts.…”

Section: Ai Big Data and Mhealth: Closing Or Widening Inequalitiesmentioning

confidence: 99%

“…Resource constraints are endemic in many low- and middle-income countries and hinder public health efforts. A substantial number of mHealth interventions have thus been implemented in LMIC public health in the past decade, premised on the assumption that such technologies can bridge the resources divide (Chang et al, 2011 ; Chib et al, 2015 ; Morgan et al, 2017 ). However, as commentators have noted, mHealth interventions alone are unlikely enough to achieve real behavior change, in contexts where economic, social, political, and infrastructural barriers to service access and behavior change are unlikely to be addressed by messaging alone (Morgan et al, 2017 ).…”

Section: Directions For Policy and Programming For Ai Big Data And mentioning

confidence: 99%

“…A substantial number of mHealth interventions have thus been implemented in LMIC public health in the past decade, premised on the assumption that such technologies can bridge the resources divide (Chang et al, 2011 ; Chib et al, 2015 ; Morgan et al, 2017 ). However, as commentators have noted, mHealth interventions alone are unlikely enough to achieve real behavior change, in contexts where economic, social, political, and infrastructural barriers to service access and behavior change are unlikely to be addressed by messaging alone (Morgan et al, 2017 ). Where mHealth could provide real solutions, we would offer, would be in cases where the mHealth product itself increases resource access (either by being a resource or intervention itself, such as the WoeBot) or connecting individuals to services which they would not otherwise be able to reach.…”

Section: Directions For Policy and Programming For Ai Big Data And mentioning

confidence: 99%

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Artificial Intelligence, Big Data, and mHealth: The Frontiers of the Prevention of Violence Against Children

Hunt

Tomlinson

Sikander

et al. 2020

Front. Artif. Intell.

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Violence against children is a global public health threat of considerable concern. At least half of all children worldwide experience violence every year; globally, the total number of children between the ages of 2 and 17 years who have experienced violence in any given year is one billion. Based on a review of the literature, we argue that there is substantial potential for AI (and associated machine learning and big data), and mHealth approaches to be utilized to prevent and address violence at a large scale. This potential is particularly marked in low-and middle-income countries (LMIC), although whether it could translate into effective solutions at scale remains unclear. We discuss possible entry points for Artificial Intelligence (AI), big data, and mHealth approaches to violence prevention, linking these to the World Health Organization's seven INSPIRE strategies. However, such work should be approached with caution. We highlight clear directions for future work in technology-based and technology-enabled violence prevention. We argue that there is a need for good agent-based models at the level of entire cities where and when violence can occur, where local response systems are. Yet, there is a need to develop common, reliable, and valid population-and individual/family-level data on predictors of violence. These indicators could be integrated into routine health or other information systems and become the basis of Al algorithms for violence prevention and response systems. Further, data on individual help-seeking behavior, risk factors for child maltreatment, and other information which could help us to identify the parameters required to understand what happens to cause, and in response to violence, are needed. To respond to ethical issues engendered by these kinds of interventions, there must be concerted, meaningful efforts to develop participatory and user-led work in the AI space, to ensure that the privacy and profiling concerns outlined above are addressed explicitly going forward. Finally, we make the case that developing AI and other technological infrastructure will require substantial investment, particularly in LMIC.

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Thinking about the environment and theorising change: how could Life History Strategy Theory inform mHealth interventions in low- and middle-income countries?

Cited by 6 publications

References 45 publications

Health Behavior Theory to Enhance eHealth Intervention Research in HIV: Rationale and Review

Health Behavior Theory to Enhance eHealth Intervention Research in HIV: Rationale and Review

Unveiling the Black Box of Diagnostic and Clinical Decision Support Systems for Antenatal Care: Realist Evaluation

Artificial Intelligence, Big Data, and mHealth: The Frontiers of the Prevention of Violence Against Children

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