Improving Fitness and Quality of Life of Lymphoma Survivors Using Fitbit^TM Monitors
Bibliographic record
Abstract
Lymphomas are among the most common cancers in Canada, with a relatively highsurvival rate due to treatment, specifically chemotherapy. However, chemotherapy can causevarious side effects that can affect a patient’s quality of life. These side effects, which includechanges in body composition, reduced physical functioning, cancer-related fatigue, depression,anxiety, and insomnia, may be improved through physical activity. To date, no interventionaiming to increase physical activity using objective monitoring among individuals withlymphoma during the COVID-19 pandemic have been conducted, neglecting the potentiallydeleterious effects of quarantine and sedentary behavior in lymphoma patients. This is clinicallymeaningful, as improving adherence to physical activity is crucial in mitigating the severity ofmany chemotherapy-induced side effects.The purpose of this study was to determine the implementation feasibility of our proof of-concept study for future application in a randomized controlled trial. This included addressingretention rate, technical and safety issues that occurred throughout the intervention. Furthermore,this trial was designed to explore the preliminary effects of the Lymfit exercise intervention onparticipant adherence to physical activity, as well as on improvements in their overall health andwell-being. We hypothesized that a FitbitTM monitor would improve exercise adherence, as wellas fitness and quality of life domains. We also examined improvements in side effects, barriersand facilitators to exercise and the sustainability of the program for the promotion of a healthy,active lifestyle.This proof-of-concept study was designed as a single-armed trial with a pre- and post-testdesign in which 20 participants were prescribed a 12-week, personalized, remotely delivered,home-based exercise program. FitbitTM monitors were given to participants to track their dailyactivity pre-, during and post-exercise prescription. The FitbitTM monitors also served thepurpose of motivating participants to increase their physical activity levels by quantifying theirefforts and motivating them to improve upon their FitbitTM outcomes (i.e., activity levels).FitbitTM data was collected via our Lymfit database and analyzed each week to assess participantchanges in activity levels and exercise adherence. Participants were contacted bi-weekly toaddress the progress made and to adjust the program, where needed. Questionnaires were filledout at baseline and week 12 to explore changes in both health and well-being.We found that activity levels and exercise adherence remained relatively stable and didnot increase over 12 weeks. However, significant improvements were seen in several quality oflife domains, including social participation, physical functioning, and sleep disturbance. Themost frequently reported barriers were fatigue, lack of time, and lack of motivation. In contrast,the most frequently reported facilitators included wearing the FitbitTM, improved well-being andimproved physical capacity.The results indicate that the exercise program did not seemingly improve adherence andfitness outcomes, though it did improve health and well-being. Limiting factors, includinglimitations pertaining to the FitbitTM monitor, participants baseline fitness characteristics, andCOVID-19 may explain the lack of fitness improvements. More importantly, feasibility testingof the Lymfit intervention proved successful, with only a few minor technical issues reported.These technical issues included the inability of the FitbitsTM to track resistance training and aserver issue that prevented a participant from receiving the quality of life questionnaire. Bothissues were quickly resolved, and large-scale testing in a randomized controlled trial can now beconducted
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".