Evaluating physical activity behaviour change techniques delivered online for people with multiple sclerosis
Bibliographic record
Abstract
Physical activity can aid in the management of symptoms for people with multiple sclerosis (MS; Motl et al., 2010). Thus, promoting physical activity among people with MS is critical. One method to achieve this aim is the use of behaviour change techniques targeting determinants of physical activity (Ellis & Motl, 2013). These may be delivered online, as the Internet is a preferred source of physical activity information for people with MS (Sweet et al., 2013). However, there are concerns regarding the quality and comprehensiveness of techniques delivered online, in addition to the overall quality (e.g., accountability and transparency) of MS websites (Marrie et al., 2013). The purpose of this study was to (a) examine the coverage and quality of behaviour change techniques delivered on websites for adults with MS; and (b) evaluate overall website quality. Twenty websites were coded for quality and coverage of behaviour change techniques using an adapted version of the Taxonomy of Behaviour Change Techniques (Abraham & Michie, 2008). Overall website quality was determined using the European Commission Quality Criteria for Health Related Websites (CEC, 2002). Results demonstrated low coverage (M=12.75, SD=7.49) and quality (M=12.55, SD=7.57) for behaviour change techniques (maximum possible score=40.00), with only one of the twenty techniques, provide information on behaviour-health link and consequences, being delivered on all websites. None of the websites met the Quality Criteria for Health-Related Websites. This study illustrates that behaviour change techniques are not consistently used when delivering online physical activity information to people with MS.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".