Implementation of Regular Lifestyle Counseling During Long-Term Follow-Up Care of Childhood Cancer Survivors: Monocentric Prospective Study
Bibliographic record
Abstract
Background: Many childhood cancer survivors (CCS) develop treatment-related late effects, including an increased risk of obesity and metabolic syndrome. A healthy lifestyle can reduce the risk of associated comorbidities. Therefore, at-risk CCS could benefit from lifestyle counseling during regular long-term follow-up (LTFU). Objective: We implemented a new form of care to decrease the long-term morbidity among CCS and to gain new insights into the lifestyle of those patients. Methods: Over a 1-year study period, lifestyle counseling was integrated into LTFU care. Metabolic disorders, including hypercholesterolemia, diabetes mellitus, overweight or underweight, and low activity levels, were assessed as screening parameters for various risk groups. The perspectives of CCS, physicians, and sports scientists were compared to identify those with the highest needs. Each lifestyle counseling included general recommendations for physical activity, as well as an assessment of individual preferences for and barriers to the implementation of a healthy lifestyle. A follow-up appointment after 1 month was performed. Results: Of the 155 CCS aged 18 to 63 years (n=100, 65% female and n=55, 35% male), 112 (72%) had an indication for lifestyle counseling, identified by physicians, sports scientists, or the CCS themselves. Metabolic disorders affected 45% (n=70) of these CCS, and 46% (n=72) did not meet recommended activity levels. A total of 120 (77%) CCS received lifestyle counseling, including 8 initially uninterested individuals who became open to recommendations. Those with intensive cancer treatment history showed the greatest need. A total of 65 (54%) CCS were advised to change their lifestyle in both areas (diet and exercise) while 51 (43%) CCS received recommendations for only exercise (n=43 CCS, 36%) or diet (n=8 CCS, 7%). A total of 4 (3%) CCS, although interested in counseling, received no advice, as they already met the recommendations. Follow-up revealed high adherence to recommendations and successful integration into daily lives. In total, 97% (n=150) of survivors indicated that the provision of lifestyle counseling during LTFU would be generally beneficial. Conclusions: Incorporating specialized health care professionals such as sports scientists into survivorship care enhances the multidisciplinary approach of LTFU care. Promoting a healthy lifestyle by offering guideline-based lifestyle counseling is broadly accepted among CCS and may reduce long-term morbidity.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".