Connectivity for Healthcare and Well-Being Management: Examples from Six European Projects

Technological advances and societal changes in recent years have contributed to a shift in traditional care models and in the relationship between patients and their doctors/carers, with (in general) an increase in the patient-carer physical distance and corresponding changes in the modes of access to relevant care information by all groups. The objective of this paper is to showcase the research efforts of six projects (that the authors are currently, or have recently been, involved in), CAALYX, eCAALYX, COGKNOW, EasyLine+, I2HOME, and SHARE-it, all funded by the European Commission towards a future where citizens can take an active role into managing their own healthcare. Most importantly, sensitive groups of citizens, such as the elderly, chronically ill and those suffering from various physical and cognitive disabilities, will be able to maintain vital and feature-rich connections with their families, friends and healthcare providers, who can then respond to, and prevent, the development of adverse health conditions in those they care for in a timely manner, wherever the carers and the people cared for happen to be

Validating a GPS-based approach to detect health facility visits against maternal response to prompted recall survey

INTRODUCTION: Common approaches to measure health behaviors rely on participant responses and are subject to bias. Technology-based alternatives, particularly using GPS, address these biases while opening new channels for research. This study describes the development and implementation of a GPS-based approach to detect health facility visits in rural Pune district, India. METHODS: Participants were mothers of under-five year old children within the Vadu Demographic Surveillance area. Participants received GPS-enabled smartphones pre-installed with a location-aware application to continuously record and transmit participant location data to a central server. Data were analyzed to identify health facility visits according to a parameter-based approach, optimal thresholds of which were calibrated through a simulation exercise. Lists of GPS-detected health facility visits were generated at each of six follow-up home visits and reviewed with participants through prompted recall survey, confirming visits which were correctly identified. Detected visits were analyzed using logistic regression to explore factors associated with the identification of false positive GPS-detected visits. RESULTS: We enrolled 200 participants and completed 1098 follow-up visits over the six-month study period. Prompted recall surveys were completed for 694 follow-up visits with one or more GPS-detected health facility visits. While the approach performed well during calibration (positive predictive value (PPV) 78%), performance was poor when applied to participant data. Only 440 of 22 251 detected visits were confirmed (PPV 2%). False positives increased as participants spent more time in areas of high health facility density (odds ratio (OR) = 2.29, 95% confidence interval (CI) = 1.62-3.25). Visits detected at facilities other than hospitals and clinics were also more likely to be false positives (OR = 2.78, 95% CI = 1.65-4.67) as were visits detected to facilities nearby participant homes, with the likelihood decreasing as distance increased (OR = 0.89, 95% CI = 0.82-0.97). Visit duration was not associated with confirmation status. CONCLUSIONS: The optimal parameter combination for health facility visits simulated by field workers substantially overestimated health visits from participant GPS data. This study provides useful insights into the challenges in detecting health facility visits where providers are numerous, highly clustered within urban centers and located near residential areas of the population which they serve

Assessing the Determinants of Care-seeking for Childhood Illness in Rural Pune District, Maharashtra State, India

An estimated 1.2 million children under five years of age die each year in India, with pneumonia and diarrhea among the leading causes. Interventions exist to reduce mortality and morbidity from these causes, though approaches to measure intervention coverage typically rely on respondent self-report and may be subject to bias. Technology-based approaches, such as those using Global Positioning System (GPS) data, provide an alternative while potentially reducing these biases. Recently, the Improving Coverage Measurement (ICM) study compared maternally-reported care-seeking for childhood illness with care-seeking assessed through a GPS-based approach in rural Pune district, Maharashtra, India. We analyzed data collected through the ICM study to: 1. Evaluate the association between care-seeking for childhood cough, fever, or diarrhea and child, maternal, and household factors 2. Evaluate the feasibility of a smartphone-based approach for tracking participant movement and explore factors associated with data completeness and participant compliance with phone-related protocols 3. Evaluate the positive predictive value of a GPS-based method to detect health facility visits and explore the factors associated with method’s performance Care-seeking for childhood illness was high overall and most care was provided through the private sector. Characteristics of the illness, especially perceived severity, were the primary contributors to the decision to seek care. Maternal employment was associated with decreased care-seeking for illnesses perceived as non-severe though not associated with care-seeking for more severe illness. Smartphone-based movement tracking through a location-aware application resulted in both high data completeness and participant compliance. Data completeness increased among participants complying with phone-related protocols and residing in rural villages. Compliance increased during the second half of the study and was highest among participants of higher socioeconomic status. Overall performance of the GPS-based method to detect health facility visits was low with most detected visits subsequently classified as false positives. The probability of detecting false positives increased among participants who spent more time in urban centers and at facilities located nearer to participants’ homes. Alternative approaches to monitor health behavior may be preferable while GPS-based approaches are refined. As their performance improves, smartphone-based approaches may provide a platform to integrate behavioral observations with other smartphone applications