The dark side of streaking: Examining the backfire potential of run streaking in recreational runners who broke a long-term streak
Bibliographic record
Abstract
BACKGROUND: Run streaking is running on consecutive days for a minimum of one mile per day. Despite its benefits for supporting habit formation and long-term behaviour change, some streak runners report potential unintended negative consequences of run streaking. The aim of the study is to examine the backfire potential of run streaking in recreational runners who ended a long-term streak. METHODS: Qualitative semi-structured interviews with 17 recreational adult runners (10 male, 6 female, 1 other gender). All runners ended a run streak of ³100 consecutive days. Transcripts were analyzed using a hybrid deductive-inductive thematic analysis. RESULTS: Prior to streak cessation, some runners felt streak-related inconveniences and ran with injury to prolong the streak. Immediate consequences following the end of a streak included feelings of sadness, anger, disappointment and relief. Several run streakers described a 'grieving process' in the weeks and months following streak cessation. Unintended negative consequences were amplified in runners with higher levels of streak attachment. All physically capable runners continued to run regularly with most starting a new streak and all voiced positive views towards run streaking despite their streak ending. CONCLUSION: Run streaking as a behaviour change technique has small backfire potential in some runners. Ending a long-term run streak can lead to short-term negative affect which can develop into experiences of grief, particularly in those with high levels of streak attachment. No long-term negative consequences were reported. All participants perceived run streaking as positive overall and remained physically active following the end of their long-term streak.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".