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
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».