Becoming Your Quantified Self: A Study of the Effects of Personal Avatars in Self-Tracking Sports Apps
Bibliographic record
Abstract
Following the Quantified Self (QS) movement, sports apps increasingly adopt self-tracking technologies that offer data-driven insights into personal competencies to enhance self-efficacy. However, sustained engagement with QS technology remains challenging, as interpreting data is complex. One potential solution is to make QS data more meaningful to users. To address this, we present a player card feature within a QS sports app, incorporating a personalized avatar to enhance users’ enjoyment, data meaningfulness, and self-efficacy to promote continued use. We evaluate the app through a field experiment in a soccer context to examine the impact of the avatar feature. Results indicate that avatar identification positively affects self-efficacy, data meaningfulness, continued use intention, and enjoyment, though enjoyment did not impact continued use. Our findings suggest that (1) avatar identification can be enhanced through personalization, (2) personalized avatars effectively boost self-efficacy, and (3) sustained engagement may rely more on meaning than on enjoyment alone.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".