Digital health games offer one innovative approach to engage older adults to support healthy aging. Multiple reviews have described the positive impact of health games. Limited research has examined multi-focus health games implemented in senior centers. Informed by healthy aging theory and community-engaged methods, our multi-disciplinary team developed/refined an educational exergame with a combined focus on educating about healthy lifestyle behaviors (i.e., physical activity, healthy eating), stimulating cognitive functioning, and engaging movement to support healthy aging. A pilot study (Nf13; mean age = 78, 100% female) examined team gameplay (4 sessions in two weeks) in a senior center. Teams (2–3 members) worked together to answer knowledge, trivia, and cognitive challenge questions and competed for the highest score. A post-gameplay survey asked about acceptability, usability (i.e., adapted System Usability Scale), and perceived game impact. Preliminary results suggest team gameplay was engaging and nearly all (>90%) agreed/strongly agreed that they enjoyed playing with others (i.e., on teams); were comfortable doing the physical movements during gameplay; were satisfied with game educational, trivia, and cognitive questions; enjoyed the social part of team gameplay; would recommend the game to others; and the game increased their knowledge and motivation regarding physical activity and healthy eating. The System Usability Scale was above 70, on average, suggesting above average usability for the game. Findings support use of this educational exergame as an innovative way to engage older adults in health promotion. Presentation will describe game development/refinement, senior center pilot, and implications for future research and senior center translation.
This work presents advances in predictive modeling of weed growth, as well as an improved planning index to be used in conjunction with these techniques, for the purpose of improving the performance of coordinated weeding algorithms being developed for industrial agriculture. We demonstrate that the evolving Gaussian process (E-GP) method applied to measurements from the agents can predict the evolution of the field within the realistic simulation environment, Weed World. This method also provides physical insight into the seed bank distribution of the field. In this work, we extend the E-GP model in two important ways. First, we have developed a model that has a bias term, and we show how it is connected to the seed bank distribution. Second, we show that one may decouple the component of the model representing weed growth from the component, which varies with the seed bank distribution, and adapt the latter online. We compare this predictive approach with one that relies on known properties of the weed growth model and show that the E-GP method can drive down the total weed biomass for fields with high seed bank densities using less agents, without assuming this model information. We use an improved planning index, the Whittle index, which allows a balanced tradeoff between exploiting a row or allowing it to accrue reward and conforms to what we show is the theoretical limit for the fewest number of agents, which can be used in this domain.
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