Promoting Physical Activity With Self-Tracking and Mobile-Based Coaching for Cardiac Surgery Patients During the Discharge–Rehabilitation Gap: Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Home-based cardiac rehabilitations (CRs) with digital technologies have been researched and implemented to replace, augment, and complement traditional center-based CR in recent years with considerable success. One problem that technology-enhanced home-based CR can potentially address is the gap between cardiac interventions and formal CR programs. In the Netherlands and some other countries (eg, Australia), patients after cardiac interventions stay at home for 3-4 weeks without much support from their physicians, and often engage in very little physical activity (PA). A home-based exercise program enabled by digital technologies may help patients to better prepare for the later center-based CR programs, potentially increasing the uptake rate of those programs. OBJECTIVE: In a randomized controlled trial (RCT), we will evaluate the effectiveness of a home-based walking exercise program enhanced by self-tracking and mobile-based coaching (treatment condition), comparing it with a version of the same program without these technologies (control condition). The added value of the digital technologies is justified if patients in the treatment group walk more steps on average (primary outcome) and show better physical fitness in a bicycle ergometer test and higher self-efficacy toward PA (secondary outcomes). METHODS: Based on a power analysis, we will recruit 100 cardiac patients and assign them evenly to the 2 parallel groups. Eligible patients are those who are scheduled in the postanesthesia care unit, know the Dutch language, have basic literacy of using smartphones, and are without medical conditions that may increase risks associated with PA. In a face-to-face meeting with a nurse practitioner, all patients are prescribed a 3-week exercise program at home (2 walking exercises per day with increasing duration), based on national and international guidelines and tailored to their physical conditions after cardiac intervention. Their physical activities (daily steps) will be measured by the Axivity AX3 accelerometer worn at hip position. Patients in the treatment group will also be supported by a Neo Health One self-tracking device and a mobile platform called Heart Angel, through which they are monitored and coached by their nurses. After the study, all patients will perform a bicycle ergometer test and return the devices within 1 week. In addition, 5 questionnaires will be sent to the patients by emails to assess their self-efficacy toward PA and other psychological states for exploratory analyses (at discharge, at the end of each monitoring week, and 1 week after the study). To minimize bias, the randomization procedure will be performed after introducing the exercise program, so the nurse practitioners are blind to the experimental conditions until that point. RESULTS: The study protocol has been approved by the Medical Research Ethics Committees United on February 26, 2018 (NL 62142.100.17/R17.51). By the end of 2018, we completed a small pilot study with 8 patients and the results based on interviews and app usage data suggest that a larger clinical trial with the targeted population is feasible. We expect to complete the RCT by the end of 2021, and statistical analyses will follow. CONCLUSIONS: Results of the RCT will help us to test the hypothesized benefits of self-tracking and mobile-based coaching for cardiac patients in home-based exercise programs during the discharge-rehabilitation gap. If the results are positive, cost-effectiveness analysis will be performed based on the insights of the study to inform the translation of the technology-enhanced program to clinical practice. We also note limitations of the trial in the discussion. TRIAL REGISTRATION: Registered at Netherlands Trial Register NL8040; https://www.trialregister.nl/trial/8040. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/16737.
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 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 | 0,043 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,009 | 0,004 |
| Méta-épidémiologie (sens large) | 0,019 | 0,009 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,010 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,080 | 0,015 |
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 ».