Is sedentary behaviour a health risk? Laypersons perceive both costs and benefits in their sedentary activity
Bibliographic record
Abstract
There is now strong evidence of a relationship between sedentary behaviour (SB) and deleterious health outcomes, including mortality (Katzmarzyk & Lee, 2012) and cancer (Lynch, 2010). Such findings have been cause for concern, and intervention is an increasingly popular topic. However, social-cognitive theory suggests that behaviour change is not as simple as saying “stop!” – it requires both self-regulatory abilities (the “how”) and motivation (the “why”). Past work (Gierc & Brawley, 2014) indicates that laypersons define SB differently than researchers, and that perceptions of SB vary by context/activity (e.g., socialising over video games versus coffee). If health interventionists make SB risk statements, (1) will these reflect individuals’ own view of risks, and (2) will these risks motivate behaviour change? The current study examined individuals’ perceived risks and benefits of SB engagement. 152 participants completed an online mixed-methods survey on SB activities and risk/benefit perceptions. Qualitative data was inductively analysed, and response frequencies calculated. Participants identified many characteristically-sedentary activities, with more respondents focussing on leisure (e.g., watching television, 80.9%) than work (office jobs, 34.2%) and transportation (25.7%). Risk/benefit analysis revealed nine major benefits of SB, including rest/relaxation (51.3%) and positive moods (13.8%). Eleven major risks were identified, including reduced fitness (40.1%) and negative emotions (15.1%). Results carry three implications. First, while individuals can identify many different types of SB, responses were primarily directed toward leisure-time pursuits versus other life domains (e.g., work). Second, the perceived risks of SB in this layperson sample (e.g., fitness, stress) differ from conditions commonly identified by health research investigators (e.g., cardiometabolic disorders). Third, individuals also associate SB with positive mental and physical outcomes. Findings highlight the need for sedentary psychology research, such as determination of whether the pursuit of favourable proximal SB outcomes will interfere with intervention efforts that target distal physical health risks.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".