Goal Setting and Anchoring Effects on Meditation Using a Digital Platform: A Large‑Scale Digital Field Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Meditation has grown in popularity in recent years, but many people who try meditation often fail to establish a habit. Goal setting has been demonstrated to be an effective technique in behavior change in other health related contexts, but is understudied in the meditation context. OBJECTIVE This study had two objectives: (1) to assess the effect of goal setting on the number of days people meditated, and (2) to evaluate whether anchoring bias in the goal-setting question (via response-option order) influences goal selection and subsequent meditation behavior. METHODS This large-scale quasi-experimental field study included 18,559 Spotify mobile users aged ≥18 residing in Australia, Canada, New Zealand, the United Kingdom, or the United States who had listened to ≥5 minutes of meditation content from a specified teacher. The in-app experiment consisted of two goal-setting test conditions and an active control. In the test conditions, participants selected the number of days they intended to listen to content from the meditation teacher in the next 7 days. The conditions differed only in the order of goal response options (higher goals listed first vs last). The active control rated how much they liked the teacher, but did not set a goal. Because responding was optional, selection bias is possible and the design is quasi-experimental. RESULTS The act of setting any goal had a modest positive effect on the number of days people meditated in both Treatment Condition 1 (β = 0.08, 95% CI [0.01, 0.16]) and Treatment Condition 2 (β = 0.08, 95% CI [0.002, 0.15]). People who committed to higher goals were also more likely to meditate more than people who committed to lower goals. Additionally, the distribution of goals between the treatment conditions varied (????22=84.24; P<.001) and the differences in these distributions subsequently yielded differences in the number of days each group meditated, on average (t(2,744.1) = -2.34; P = 0.02; Cohen d =-0.09). Ultimately, placing the highest goal as the first answer choice yielded higher average active days amongst those who chose a goal, but many more people opted out of answering the question itself. CONCLUSIONS Goal setting appears to be an effective tool to encourage people to engage with meditation more frequently on digital platforms and consequently may encourage meditation habit formation. However, anchoring effects play a significant role in people’s willingness to set meditation goals, the goals they set for themselves, and even incremental meditation engagement. The insights from this study are valuable for both theorists who study habit formation, goal setting, and anchoring, as well as meditation app designers and those who are seeking ways to increase engagement with their offerings.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".