Digital Health Interventions to Reduce Cancer-Related Fatigue Among Adolescents and Young Adults: Scoping Review
Bibliographic record
Abstract
Background: Cancer-related fatigue is a common and significant symptom experienced by patients with cancer and survivors across all age groups, profoundly impacting their quality of life. Adolescents and young adults often encounter substantial academic, career, and personal demands, which pose unique challenges in managing this symptom and may have a more profound overall impact on their lives. While digital health interventions show considerable promise in managing cancer-related fatigue, few reviews have specifically addressed their use among adolescents and young adults. Objective: This scoping review aimed to identify and assess the types and effectiveness of digital health interventions in managing cancer-related fatigue among adolescents and young adults. Methods: A comprehensive literature search was conducted using the keywords "digital health," "adolescent," "young adult," "fatigue," and "neoplasms" across 6 databases: PubMed, CINAHL, PsycINFO, Embase, Cochrane Library, and Web of Science. The search included English-language publications from the inception of each database to August 2024. Two researchers independently screened the studies based on predetermined inclusion and exclusion criteria. Results: A total of 2965 articles were retrieved during the initial search, of which 10 (0.34%) satisfied the inclusion criteria of this review. The 10 included studies comprised 5 (50%) randomized controlled trials, 2 (20%) quasi-experimental studies, 2 (20%) mixed methods studies, and 1 (10%) cohort study. On the basis of the functions and forms of digital health interventions, the interventions included in this review were categorized into the following 5 types: dynamic health monitoring and feedback, automated online guidance and feedback, live remote coaching and instruction, gamified interventions, and robot-assisted interventions. Multiple studies (7/10, 70%) demonstrated that digital health interventions are effective in reducing cancer-related fatigue in adolescents and young adults and show potential in improving physical function and emotional well-being in this population. Conclusions: Digital health interventions overcome the time and spatial limitations of traditional treatments and provide holistic support across physical, psychological, and social domains. They hold significant potential to alleviate cancer-related fatigue in adolescents and young adults. Future research should integrate various fatigue measurement scales and conduct large-scale studies and long-term follow-ups to capture a more comprehensive range of fatigue experiences, validate these findings, and enhance the effectiveness of digital health interventions.
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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".