Research Article
Addressing Medication Adherence Technology Needs in an Aging Population
@INPROCEEDINGS{10.1145/3154862.3154872, author={Jessica Pater and Shane Owens and Sarah Farmer and Elizabeth Mynatt and Brad Fain}, title={Addressing Medication Adherence Technology Needs in an Aging Population}, proceedings={11th EAI International Conference on Pervasive Computing Technologies for Healthcare}, publisher={ACM}, proceedings_a={PERVASIVEHEALTH}, year={2018}, month={1}, keywords={medication adherence wearable technology mobile application behavior change}, doi={10.1145/3154862.3154872} }
- Jessica Pater
Shane Owens
Sarah Farmer
Elizabeth Mynatt
Brad Fain
Year: 2018
Addressing Medication Adherence Technology Needs in an Aging Population
PERVASIVEHEALTH
ACM
DOI: 10.1145/3154862.3154872
Abstract
Using technology to inspire behavior change motivated by a health goal is a challenge. Technologies, often rooted in sound scientific principles, sometimes do not perform as expected in real world scenarios. Quite often the barriers to use are not inherent in the behavior change model of the product or service, but are issues associated with the failure to appropriately consider the needs of the end users when designing an intervention. We deployed a large, multi-stage research study with aging adults to assess the facilitators and barriers of technologies aimed to create or support behavior changes related to medication adherence. Using the Fogg Behavior model, we analyzed the triggers made on behavior change through data from surveys, in-home interviews, participatory design workshops and the deployed technologies. Our results indicate that the user experience associated with delivery of the content is at least as important as the content. Additionally, experienced users are far better prepared to help researchers identify potential design issues than novice users. Because our participants were knowledgeable about the technologies and the features that worked and did not work, the concluding participatory design sessions were highly efficient in providing feedback on the type of mechanisms that resonate with this population and could lead to higher levels of behavior change in future technologies.