Providing facility-based care to the billions of people living in Low- and Middle-Income Countries (LMICs) is a challenge due to the multitude of barriers people face in accessing these sites. Unaffordable transportation costs, limited child care options, poor health and inconsistent staffing and services of facilities are just some of the many reasons facility-based primary health care interventions struggle to recruit and retain patients in efficacious programs (1). Home-based interventions have been shown to be a viable alternative across a broad range of health initiatives including infectious disease (e.g., HIV screening), mental health (e.g., postpartum depression) and non-communicable disease risk reduction and education (e.g., obesity and nutrition) interventions (2-4). One of their key weaknesses is lack of cost-effectiveness. Multiple follow-up visits to the home are required to deliver the intervention and to ensure that uptake and behavioural change are taking place. A second limitation of home-based interventions is that they are typically one-size-fits-all. Lay health workers are trained in the intervention and the sessions and manuals are routinized to simplify deliver and to increase the likelihood of intervention fidelity. We believe a solution to these challenges is available in the use of passive sensor data to provide robust, evidence-based feedback on what happens in the home after the health worker walks out the door.
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