Using disparate data to personalize digital health behavior change interventions

Amos Adler
Bill Simpson


Despite advances in the use of mobile and digital health technology, there is still an enormous gap between delivery, adoption and long-term use of digital health interventions. Much of the work of the last few years has focused on short-term outcomes (<= 3-6 months ) and the initial uptake of digital health technology. 

MEMOTEXT has shown that by collecting disparate sources of data from patient self-report, claims-data, wearables and EMRs, our personalized and evidence-based digital health interventions can achieve long term adoption and significant changes in patient behaviour.

Validated in a number of clinical and commercial settings MEMOTEXT has spent the last few years creating a development methodology, building personalization algorithms and improving adherence to medications, blood-glucose testing, and overall treatment adherence. 

With a case study approach, we can demonstrate that the use of multi-dimensional data from patient self-report (e.g. mood, quality of life, perceptual barriers to adherence) combined with objective data sources (e.g prescription claims  data, real-time blood glucose levels) can extend digital engagement over the long-term. MEMOTEXT has live pilot implementations in place with stakeholders including Pharmacy Benefit Management (PerformRx), Health plans (GreenShield Canada), pharmaceutical manufacturers (Genentech) and providers (Johns Hopkins University) to demonstrate the efficacy of employing a two-tiered data driven digital health intervention. 

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