Playful Antisedentary Interactions for Online Meeting Scenarios: A Research Through Design Approach
Bibliographic record
Abstract
BACKGROUND: Online meetings have become an integral part of daily life for many people. However, prolonged periods of sitting still in front of screens can lead to significant, long-term health risks. While previous studies have explored various interventions to address sedentary lifestyles, few have specifically focused on mitigating sedentary behavior during online meetings. Furthermore, design opportunities to address this issue in the context of online meetings remain underexplored. OBJECTIVE: This study aims to investigate the design of effective antisedentary interactions for online meeting scenarios and understand user experiences with gamified bodily interactions as an antisedentary measure during online meetings. METHODS: This study adopts a "research through design" approach to develop and explore user experiences of gamified bodily interactions as interventions to mitigate sedentary behavior during online meetings. In collaboration with 11 users, we co-designed and iterated 3 prototypes, which led to the development of the Bodily Interaction Gamification towards Anti-sedentary Online Meeting Environments (BIG-AOME) framework. Using these prototypes, we conducted user studies with 3 groups totaling 15 participants. During co-design and evaluation, all group semistructured interviews were transcribed into written format and analyzed using a conventional qualitative content analysis method. RESULTS: The findings demonstrate that gamified bodily interactions encourage users to engage in physical movement while reducing the awkwardness of doing so during online meetings. Seamless integration with meeting software and the inclusion of long-term reward mechanisms can further contribute to sustained use. In addition, such games can serve as online icebreakers or playful tools for decision-making. Drawing from 3 design prototypes, this study offers a comprehensive analysis of each design dimension within the BIG-AOME framework: bodily engagement, attention, bodily interplay, timeliness, and virtual and physical environments. CONCLUSIONS: Our research findings indicate that antisedentary bodily interactions designed for online meetings have the potential to mitigate sedentary behaviors while enhancing social connections. Furthermore, the BIG-AOME framework that we propose explores the design space for antisedentary physical interactions in the context of online meetings, detailing pertinent design choices and considerations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".