Translational Journal of the American College of Sports Medicine: 2022 Paper of the Year
Bibliographic record
Abstract
The following article was chosen as the 2022 Translational Journal of the American College of Sports Medicine Paper of the Year: Collins KA, Huffman KM, Wolever RQ, Smith PJ, Siegler IC, Ross LM, Hauser ER, Jiang R, Jakicic JM, Costa PT, Kraus WE. Determinants of dropout from and variation in adherence to an exercise intervention: the STRRIDE Randomized Trials. Transl J Am Coll Sports Med. 7(1):e000190. doi: 10.1249/TJX.0000000000000190. Understanding how to support people to change their exercise behavior is an important and pressing public health issue. To extend what is known about the reasons people give for not regularly participating in exercise, Collins et al. looked at when and why people typically stopped structured exercise interventions. The goal of this work was to inform the development of future intervention studies to better support those at risk for dropout. The work will also be of interest to exercise professionals working on exercise behavior change with clients. The analysis combined data from three Studies of a Targeted Risk Reduction Intervention through Defined Exercise (STRRIDE) randomized trials that examined the differential effects of exercise amount, mode, and intensity on cardiometabolic health. All STRRIDE studies enrolled individuals who were previously sedentary with overweight or obesity, in addition to either mild-to-moderate dyslipidemia or prediabetes (1–3). The general design of the trials was a control run-in period (3–4 months), followed by an 8- to 12-wk ramp period, during which participants were asked to slowly increase exercise volume to reach the intended exercise prescription target for the 6- to 8-month exercise intervention. Participants who dropped out were on average non-White, had higher body mass index, and were less fit at baseline. In terms of timing, 66% of those who dropped out did so early, either during baseline visits, the run-in period, or the exercise ramp period. Of note, exercise adherence remained consistently high (i.e., >80% of the intended prescription) after the ramp period, suggesting if participants completed the ramp period, they were more likely to complete the 6- to 8-month intervention and meet the weekly exercise targets. The most common reason people reported for dropping out of the intervention was lack of time or a combination of lack of time due to family and/or work responsibility and lack of motivation. The article by Collins et al. provides new and novel insights that can help researchers and exercise professionals to rethink how to engage people early in the behavior change process. Additional support and tailoring of the intervention for people with demographic factors associated with higher risk of dropout were advised. For individuals who changed their minds about participating during the run-in period, health coaching or motivational interviewing at the time of enrollment and early in the intervention, including how to manage perceived barriers such as lack of time, could help to keep people engaged and motivated to participate. Collins et al. also questioned if the current approaches commonly used to ramp up exercise volume may be too ambitious for individuals who are previously sedentary or have overweight or obesity. Adjusting the ramp period, especially for those who find it a challenge to add exercise into their daily routine, was suggested as another key way to reduce dropouts. If we can overcome these early challenges to engagement in the behavior-change process, the findings of Collins et al. suggest that successful exercise adoption and adherence are possible. Editor’s note: The 2022 Paper of the Year for Translational Journal of the American College of Sports Medicine was selected based on the significance and impact of the article. To read more about the award and the articles selected for the other four American College of Sports Medicine journals, see https://www.acsm.org/education-resources/journals/paper-of-the-year-awards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".