Investigating avatar customization as a motivational design strategy for improving engagement with technology-enabled services for health
Bibliographic record
Abstract
Technology-enabled services for physical and mental health are a promising approach to improve healthcare globally. Unfortunately, the largest barrier for effective technology-based treatment is participants' gradually fading engagement with effective novel training applications, such as exercise apps or online mental health training programs. Engaging users through design presents an elegant solution to the problem; however, research on technology-enabled services is primarily focused on the efficacy of novel interventions and not on improving adherence through engaging interaction design. As a result, motivational design strategies to improve engagement---both in the moment of use and over time---are underutilized. Drawing from game-design, I investigate avatar customization as a game-based motivational design strategy in four studies. In Study 1, I examine the effect of avatar customization on experience and behaviour in an infinite runner game. In Study 2, I induce different levels of motivation to research the effects of financial rewards on self-reported motivation and performance in a gamified training task over 11 days. In Study 3, I apply avatar customization to investigate the effects of attrition in an intervention context using a breathing exercise over three weeks. In Study 4, I investigate the immediate effects of avatar customization on the efficacy of an anxiety reducing attentional retraining task. My results show that avatar customization increases motivation over time and in the moment of use, suggesting that avatar customization is a viable strategy to address the engagement barrier that thwarts the efficacy of technology-enabled services for health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".