User-adaptive Reminders for Home-based Medical Tasks

Summary

Objectives: We present a prototype adaptive reminder system for home-based medical tasks. The system consists of a mobile device for reminder presentation and ambient sensors to determine opportune moments for reminder delivery. Our objective was to study interaction with the prototype under naturalistic living conditions and gain insight into factors affecting the longterm acceptability of context-sensitive reminder systems for the home setting.

Methods: A volunteer participant used the prototype in a residential research facility while adhering to a regimen of simulated medical tasks for ten days. Some reminders were scheduled at fixed times during the day and some were automatically time-shifted based on sensor data. We made a complete video and sensor record of the stay. Finally, the participant commented about his experiences with the system in a debriefing interview.

Results: Based on this case study, including direct observation of individual alert-action sequences, we make four recommendations for designers of context-sensitive adaptive reminder systems. Captured metrics suggest that adaptive reminders led to faster reaction times and were perceived by the participant as being more useful.

Conclusions: The evaluation of context-sensitive systems that overlap into domestic lives is challenging. We believe that the ideal experiment is to deploy such systems in real homes and assess performance longitudinally. This case study in an instrumented live-in facility is a step toward that long-term goal.

Keywords

Adaptive assistance - smart homes - mobile computing - medication adherence - in-home acceptability evaluation

• References

• 1 National Council on Patient Information and Education.. Enhancing Prescription Medicine Ad herence: A National Action Plan. NCPIE; 2007
• 2 McDonald HP, Garg AX, Haynes RB. Interventions to enhance patient adherence to medication prescriptions: Scientific review. JAMA 2002; 288: 2868-2879.
• 3 Leirer VO, Morrow DG, Tanke ED, Pariante GM. Elders’ nonadherence: Its assessment and medication reminding by voice mail. Gerontologist 1991; 313: 514-520.
• 4 Cramer JA, Mattson RH, Prevey ML, Scheyer RD, Ouellette VL. How often is medication taken as prescribed? A novel assessment technique. JAMA 1989; 261: 3273-3277.
• 5 Ho J, Intille SS. Using context-aware computing to reduce the perceived burden of interruptions from mobile devices. Proc CHI 2005, ACM Press; pp 909-918.
• 6 Fook V, Tee J, Yap K, Wai A, Maniyeri J, Jit B, Lee P. Smart mote-based medical system for monitoring and handling medication among persons with Dementia. Proc ICOST 2007; LNCS 4541: 54-62.
• 7 Haigh K, Kiff L, Ho G. The independent lifestyle assistant: Lessons learned. Assistive Tech 2006; 18: 87-106.
• 8 Vurgun S, Philipose M, Pavel M. A statistical reasoning system for medication prompting. Proc UbiComp 2007; LNCS 4717: 1-18.
• 9 Intille SS, Larson K, Tapia EM, Beaudin J, Kaushik P, Nawyn J, Rockinson R. Using a live-in laboratory for ubiquitous computing research. Proc Pervasive 2006; LNCS 3968: 349-365.
• 10 Fleming B, Pulliam C, Perfetto E, Hanlon J. Medication use by home health patients. Jour Geriatric Drug Therapy 1993; 7: 33-45.
• 11 Pearce M, Narasimhan N, Janssen C, Song Y. A lightweight remote display management protocol for mobile devices. Proc Consumer Communications Networking Conf 2007 pp 711-715.
• 12 Bickmore T, Mauer D, Crespo F, Brown T. Persuasion, task interruption and health regimen adherence. Proc Conf Persuasive Tech 2007; LNCS 4744: 1-11.
• 13 Tapia EM, Intille SS, Larson K. Portable wireless sensors for object usage sensing in the home: Challenges and practicalities. Proc European Ambient Intelligence Conf 2007; LNCS 4794: 19-37.