A scoping review of interventions to improve strength training participation
Bibliographic record
Abstract
BACKGROUND: Low participation rates (1-31%) and unique barriers to strength training (e.g., specialized knowledge, equipment, perceived complexity) suggest effective strength training interventions may differ from effective aerobic or general physical activity interventions. The purpose of this scoping review was to examine interventions used to improve strength training participation through mapping theory, intervention characteristics, prescription parameters, and behaviour change techniques. METHODS: Recommendations by Levac et al. (2010) and PRISMA-ScR were followed in the conduct and reporting of this review, respectively. Patients and exercise professionals participated in developing the research question and data extraction form, interpreting the findings, and drafting the manuscript. Medline, Embase, PsycINFO, CINAHL, SPORTDiscus, and PubMed databases (inception-December 2020) were searched. The inclusion criteria were (a) original peer-reviewed articles and grey literature, (b) intervention study design, and (c) behavioural interventions targeted towards improving strength training participation. Two reviewers performed data screening, extraction, and coding. The interventions were coded using the Behaviour Change Technique Taxonomy version 1. Data were synthesized using descriptive and frequency reporting. RESULTS: Twenty-seven unique interventions met the inclusion criteria. Social cognitive theory (n = 9), the transtheoretical model (n = 4), and self-determination theory (n = 2) were the only behaviour change theories used. Almost all the interventions were delivered face-to-face (n = 25), with the majority delivered by an exercise specialist (n = 23) in community or home settings (n = 24), with high variability in exercise prescription parameters. Instructions on how to perform the behaviour, behavioural practice, graded tasks, goal setting, adding objects to the environment (e.g., providing equipment), and using a credible source (e.g., exercise specialist delivery) comprised the most common behaviour change techniques. CONCLUSIONS: Our results highlight gaps in theory, intervention delivery, exercise prescription parameters, and behaviour change techniques for future interventions to examine and improve our understanding of how to most effectively influence strength training participation.
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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 | 0.029 | 0.109 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".