Tracking feels oppressive and ‘punishy’: Exploring the costs and benefits of self-monitoring for health and wellness
Bibliographic record
Abstract
Self-monitoring is the cornerstone of many health and wellness persuasive interventions. However, applications designed to promote health and wellness that use this strategy have recorded varying degrees of success. In this study, we investigated why the self-monitoring strategy might work in some contexts and fail in others. We conducted a series of large-scale studies, with a total of 1768 participants, to explore the strengths and weaknesses of the self-monitoring strategy. Our results uncover important strengths and weaknesses that could facilitate or hinder the effectiveness of self-monitoring to promote the health and wellness of its users. The strengths include its tendency to reveal problem behaviours, provide real and concrete information, foster reflection, make people accept responsibility, create awareness and raise users’ consciousness about their health and wellness. Some of the weaknesses include its tendency to provoke health disorder, be tedious and boring. We contribute to the digital health community by offering design guidelines for operationalising self-monitoring to overcome its weaknesses and amplify its strengths.
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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 | 0.019 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".