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Updated: Sep 10, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
Staged, life-cycle approach to evidence generation for digital health interventions in low- and middle-income
Gareth Obery1, Eva Weicken2, Shubhanan Upadhyay3
1Prova Health, London, UK.
Abstract:
Digital health interventions (DHIs) are rapidly expanding across low- and middle-income countries (LMICs), driven by their potential to address persistent health system constraints, workforce shortages and health inequities. However, the evaluation of these interventions has not kept pace with their adoption. Traditional evidence-generation approaches, particularly those centred on rigid, resource-intensive study designs, are often poorly aligned with the iterative nature of digital technologies and the realities of resource-constrained settings. This misalignment can exacerbate existing system pressures, slow validation and contribute to an enduring evidence-implementation gap that limits sustainable adoption and scale. This narrative review synthesises peer-reviewed biomedical literature, grey literature, institutional reports and applied case studies published between 2015 and 2025 to examine how evidence for DHIs is generated in LMICs. The analysis is organised across three interrelated domains: contextual and stakeholder dynamics shaping evaluations, methodological innovations suited to LMIC realities and top-down policy and regulatory mechanisms influencing adoption and scale. Drawing on this synthesis, the review argues for a staged, life-cycle-based approach to evidence generation, in which evaluation methods and evidence strategies evolve as the intervention matures and the implementation context evolves. This approach emphasises pragmatic, context-sensitive strategies that balance scientific rigour with feasibility. This review aims to provide decision-oriented guidance for innovators, implementers, policy-makers and regulators seeking to generate appropriate, actionable and policy-relevant evidence to support the adoption, integration and scale of DHIs in LMIC health systems.
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