The impact of utilizing mobile phones to promote physical activity among post-secondary students: a scoping review
Bibliographic record
Abstract
A commitment to regular physical activity may reduce the risks of chronic diseases for young adults. Internationally, the majority of post-secondary students are insufficiently active for health benefits. Novel health strategies and interventions utilizing mobiles phones could increase post-secondary students' physical activity levels. However, there is contradictory evidence to support the use of mobile phones to promote physical activity, and a scoping review could provide further insights into this topic. The purpose of this study was to conduct a scoping review to explore the existing literature and investigate what is currently known about the use of mobile phones to enhance physical activity levels among post-secondary students. A total of 84 articles were identified from the literature search, and six studies were selected for data analysis. Two major themes were supported by the evidence, which included: (I) the relationship between mobile phones and physical activity levels; and (II) students' perceptions of mobile phones. Goal setting principles combined with text message interventions were reported to have significant influences on students' physical activity levels. Students expressed mixed feelings about coaching elements of apps and posting personal results on social networking websites. No studies reported the use of objective physical activity measurements. In conclusion, mobile phone technologies such as text message reminders could be included in health interventions to enhance post-secondary students' physical activity levels. There is limited evidence available on this topic and additional research is warranted to establish a clearer understanding of the relationship between mobile phones and post-secondary students' physical activity.
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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".