A systematic review of the dose-response relationship between usage and outcomes of online physical activity weight-loss interventions
Bibliographic record
Abstract
BACKGROUND: Online physical activity interventions can be an effective strategy for weight loss. However, there is a lack of systematic reviews examining the relationship between intervention usage (dose) and participants' response to online physical activity interventions for weight loss. It remains unclear whether certain usage metrics (e.g. login frequency, percent of content accessed) would be associated with improvements in behavioral outcomes. Understanding the dose-response relationship for online physical activity interventions for weight loss would be important for designing and evaluating future interventions. OBJECTIVE: 1) Review the methods used to assess intervention usage and 2) to explore the association between intervention usage metrics and outcomes for online physical activity interventions for weight-loss. METHODS: We conducted a systematic review following the PRISMA guidelines to examine the dose-response relationship of online-based interventions targeting physical activity. We used the following keywords: web OR internet OR online OR eHealth AND physical activity OR exercise, AND engagement OR dose OR dose-response OR usage AND obesity OR weight*. Peer-reviewed articles published between 2006 and 2019 were included. RESULTS: A total of five articles met the inclusion criteria. The mean intervention length was 10 ± 6 months (range 2-30 months). The usage metrics were total number of logins, login frequency, and usage of online tools. All usage metrics reported were found to be related to outcomes in physical activity interventions for weight-loss. CONCLUSION: Our findings suggest that usage metrics for online physical activity interventions for weight-loss included login frequency, login duration, and use of online tools. Increased intervention usage appeared to be associated with an improvement in participant's weight, physical activity behaviors, and intervention retention. Future research should examine innovative ways to maintain intervention usage throughout the intervention.
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 | 0.063 | 0.276 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".