Social marketing interventions to promote physical activity among 60 years and older: a systematic review of the literature
Bibliographic record
Abstract
BACKGROUND: Falls are a significant source of morbidity in people aged 65 and over, affecting one in three people in this age group. The scientific evidence indicates that physical activity is the most effective method for preventing falls among seniors. Although public health professionals often use social marketing to design and plan successful interventions, its use to promote physical activity and prevent falls among older people remains low. This article aims to provide a new systematic literature review of social marketing interventions promoting physical activity and targeting people aged 60 and over. METHODS: Following CRD's guidance and PRISMA guidelines, we searched between January 2008 and July 2019 for relevant articles in five primary databases using predefined search and inclusion criteria. Two independent reviewers analysed the selected articles to identify evidence of the seven social marketing benchmark criteria, defined by experts in the field as the common elements that contribute to social marketing success. RESULTS: The final review included nine studies. Of the studies selected, three specifically targeted over 60-year-olds, whereas the others segmented the population into several age-based subcategories, including over 60-year-olds. Eight studies highlighted positive results for the participants with an increase in participation or an increase in physical activity level. None of the nine studies selected for this systematic review implemented the entire social marketing approach. CONCLUSION: Few published interventions use the seven social marketing criteria. Further research is required to encourage uptake and inclusion in successful social marketing interventions to increase program effectiveness in this target population.
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".