Investigating the motivational interviewing techniques and behaviour change techniques in physical activity counselling sessions: Preliminary results
Bibliographic record
Abstract
Over the past 20 years, there has been a wealth of research on the implementation and evaluation of physical activity promoting interventions (Kahn et al., 2002). Among these is Physical Activity Counselling (PAC), an individual, face-to-face intervention that uses a Motivational Interviewing (MI) counselling style and other behaviour change techniques for eliciting physical activity behaviour change (Fortier et al., 2007; Fortier, William, et al., 2011). Recently, the Behaviour Change Technique Taxonomy version 1 (BCTTv1) was developed to provide an agreed and standard method of describing intervention content (Michie et al., 2013). Given the poor representation of MI in the BCTTv1 and its proven effectiveness for changing health behaviours (Lundahl et al., 2013), a recent conceptual review was conducted to identify relational and content techniques specific to MI (Hardcastle, Fortier, Blake, & Hagger, 2016). Therefore, the purpose of this study is to identify and quantify the specific BCTs and MI techniques applied during PAC sessions. Videotaped recordings of six PAC sessions were analyzed. Results indicated that the most utilized BCTs include 3.1 Social support (unspecified) (k=6), 1.1 Goal setting (behaviour) (k=4) and 1.2 Problem Solving (k=3). The most utilized relational techniques of MI include Open-ended questions (k=6), Affirmation (k=6), Reflective statements (k=6), Summary statements (k=6) and Permission to provide information and advice (k=5), whereas Consider change options (k=4) and Values exploration (k=3) were the content techniques of MI most frequently used. These preliminary findings shed light on the techniques applied and the relational-content interplay during PAC.
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 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 | 0.029 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".