A single-armed proof-of-concept study of Lymfit: A personalized, virtual exercise intervention to improve health outcomes in lymphoma survivors in the pandemic
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Treatments of lymphoma can lead to reduced physical functioning, cancer-related fatigue, depression, anxiety, and insomnia. These side effects can negatively impact the cancer survivor's quality of life. Mounting evidence indicates that physical activities are highly therapeutic in mitigating the short- and long-term side effects of cancer treatments. Yet, lymphoma survivors' participation in physical activities remains suboptimal, which has been further exacerbated by the deleterious effects of isolation during the COVID-19 pandemic. The Lymfit intervention aims to offer motivational support, expert guidance, and a personalized exercise prescription to optimize physical activities among lymphoma survivors. This proof-of-concept study explores implementation feasibility (retention, technical and safety), and the preliminary effects of Lymfit on various health outcomes. METHOD: This was a single-armed trial with a pre-and post-test design. Twenty lymphoma survivors were recruited to participate in the 12-week Lymfit intervention. Wearable activity trackers (Fitbit) were given to participants as a motivational tool and for data collection purposes. Participants received a personalized exercise prescription designed by a kinesiologist. Physiologic metrics were collected by the Fitbit monitors and were stored in the Lymfit database. Self-reported questionnaires measuring health outcomes were collected at baseline and post-intervention. RESULTS: The retention rate of this trial was 70%. Minimal technical issues and no adverse effects were reported. Lymfit led to significant improvements in sleep disturbances and the ability to participate in social activities and decreased fear of cancer recurrence. It also increased daily steps and decreased sedentary time in participants who did not meet the recommended physical activity guidelines. SIGNIFICANCE: With access to resources and fitness centers being limited during the pandemic, the Lymfit intervention filled an immediate need to provide physical activity guidance to lymphoma survivors. Findings provide preliminary support that implementing the Lymfit intervention is feasible and demonstrated promising results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".