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Record W4415106067 · doi:10.2196/preprints.85801

Goal Setting and Anchoring Effects on Meditation Using a Digital Platform: A Large‑Scale Digital Field Study (Preprint)

2025· article· en· W4415106067 on OpenAlexaboutno aff
Michael Bowen, Michael A. Beam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsMeditationPopularitySet (abstract data type)Test (biology)Goal settingAnchoringField (mathematics)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.021
GPT teacher head0.380
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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