Objective: To investigate how participants self-schedule their engagement with domestic rehabilitation gaming platform, and how their scheduling behavior in turn influence overall compliance.

Design: Cohort of individuals randomized to receive in-home rehabilitation gaming during a multi-site randomized controlled trial.

Setting: In-home self-managed rehabilitation.

Participants: Eighty community-dwelling participants who were >6 months post-stroke and had mild to moderate upper extremity impairment (N=80).

Interventions: Participants were prescribed 15 hours of independent in-home self-scheduled game play for upper extremity mobility over 3 weeks.

Main outcome measures: Total number of hours of active game play was objectively measured by the rehabilitation gaming system. Cluster analysis identified scheduling patterns from the following scheduling characteristics: total number of sessions, average session length, and consistency of play schedule.

Results: Four distinct scheduling profiles were revealed, 3 of which were associated with complete or near-complete compliance, while a fourth (inconsistent schedule of short, infrequent sessions) was associated with very poor compliance. Poor compliance could be predicted within the first 7 days of the program with 78% accuracy based on the same play pattern metrics used to identify player profiles.

Conclusions: Our findings support client autonomy in selecting the home practice schedule that works best for them, as compliance can successfully be achieved through a variety of different scheduling patterns. The objective measurements of compliance provided through rehabilitation gaming can assist therapists to identify individuals early on who exhibit scheduling behavior that is predictive of poor compliance.

Materials:

• Paper: pdf

See also:

• Wouter Durnez, Aleksandra Zheleva, Mark Claypool, Klaas Bombeke, Mathias Maes, Jan Van Looy and Lieven De Marez. Spaz! The Effects of Local Latency on Player Actions in an Desktop-based Exergame, IEEE Transactions on Games, Volume 14, Issue 4, DOI: 10.1109/TG.2021.3128714, 2022. Online at: http://www.cs.wpi.edu/~claypool/papers/spaz-22/

• Alexander Fitzgerald, Sam Huang, Kyle Sposato, Dongjie Wang, Mark Claypool, and Emmanuel Agu. The Exergame Enjoyment Questionnaire (EEQ): An Instrument for Measuring Exergame Enjoyment, In Proceedings of the Hawaii International Conference on System Sciences (HICSS), Maui, Hawaii, January 7-10, 2020. Online at: http://www.cs.wpi.edu/~claypool/papers/eeq/

• Anthony Gallo, Philipp Baumann, Emmanuel Agu and Mark Claypool. Exerwalls - an Exercise Alternative to Paywalls in Mobile Games, In Proceedings of the International Academic Conference on Meaningful Play, East Lansing, Michigan, USA, October 20-22, 2016. Online at: http://www.cs.wpi.edu/~claypool/papers/exerwall/

• Emmanuel Agu and Mark Claypool. Cypress: A Cyber-Physical Recommender System to Discover SmartPhone Exergame Enjoyment, In Proceedings of the ACM Workshop on Engendering Health with Recommender Systems, Boston, Massachusetts, USA, September 15, 2016. Online at: http://www.cs.wpi.edu/~claypool/papers/cypress-recsys/