Charting Health Challenges for Digital Preventive Interventions Among Adult Survivors of Childhood Acute Lymphoblastic Leukemia: National Long-Term Follow-Up Survey of Self-Rated Health Outcomes
Bibliographic record
Abstract
BACKGROUND: Acute lymphoblastic leukemia (ALL) is the most common malignancy in childhood, but the prognosis has remarkably improved over the last 50 years in high-income countries, and thus, there is a focus on long-term health outcomes following survival and how to best provide health care support to adult long-term survivors of childhood ALL to prevent and handle potential health problems. Digital health interventions are promising to deliver feasible health promotion and prevention programs. This is particularly relevant for ensuring long-term follow-up in cases where continuous contact with oncology care may be disrupted. Moreover, these interventions are beneficial in reaching geographically dispersed target groups and overcoming the time constraints of everyday life that often hinder participation in such programs. OBJECTIVE: This study aimed to fill the gaps in existing research on adult long-term survivors of childhood ALL and provide formative data that can inform the development of formalized follow-up services designed to meet the needs of these survivors in ways that align with their preferences for digital health interventions. METHODS: In this cross-sectional national study, adult survivors (aged ≥18 years) of childhood ALL for over 10 years after diagnosis were compared to their siblings in terms of mental and physical health-related factors, including sleep, stress, anxiety, and depression (Depression Anxiety and Stress Scale 21 [DASS-21]); several dimensions of fatigue (Multidimensional Fatigue Inventory 20 [MFI-20]); work ability (Work Ability Index); chronic pain; and prevalences of diabetes, cardiovascular disease, headache or migraine, and rheumatic disease. RESULTS: Overall, 426 of 855 eligible ALL survivors responded (mean age 30.9, SD 7.7 years), and they participated at an average of 24 (SD 6.9) years after ALL diagnosis. Siblings (n=135; mean age 31.5, SD 7.7 years) acted as controls. Sleep quality, sleep quantity, and mean work ability scores were significantly lower, and physical fatigue, reduced motivation, and reduced activity scores were higher in ALL survivors than in siblings. There were no significant differences between the groups in terms of BMI and prevalence of chronic pain, depression, anxiety, or stress. Physical and psychological complications were more frequent among adult ALL survivors who had received hematopoietic stem cell transplantation (HSCT) than among those who had not received HSCT. CONCLUSIONS: Our nationwide cross-sectional study addressed the scarcity of knowledge regarding the self-reported health outcomes of adult long-term survivors of childhood ALL. We highlighted significant disparities within this population and emphasized the potential of comprehensive digital interventions that target vitality, sleep quality, fatigue, and psychosocial well-being to enhance well-being and bolster the capacity for managing chronic health conditions in this target group. Such an intervention would align with the needs of this target group, which is a prerequisite for successfully incorporating technology into the daily lives of survivors of childhood ALL.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".