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Enregistrement W7162032557 · doi:10.82308/9768

Improving Fitness and Quality of Life of Lymphoma Survivors Using Fitbit^TM Monitors

2023· dissertation· en· W7162032557 sur OpenAlexaboutno aff
Christopher Angelillo

Notice bibliographique

Revuenon disponible
Typedissertation
Langueen
DomaineMedicine
ThématiqueCancer survivorship and care
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésQuality of life (healthcare)Randomized controlled trialPhysical activityIntervention (counseling)Affect (linguistics)Physical exercisePhysical fitness

Résumé

récupéré en direct d'OpenAlex

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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,004
Score d'incertitude au seuil0,013

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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.

Tête enseignante Opus0,049
Tête enseignante GPT0,340
Écart entre enseignants0,291 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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