Participants’ Perceptions on the Use of Wearable Devices to Reduce Sitting Time: Qualitative Analysis
Notice bibliographique
Résumé
BACKGROUND: Recent epidemiological evidence indicates that, on average, people are sedentary for approximately 7.7 hours per day. There are deleterious effects of prolonged sedentary behavior that are separate from participation in physical activity and include increased risk of weight gain, cancer, metabolic syndrome, diabetes, and heart disease. Previous trials have used wearable devices to increase physical activity in studies; however, additional research is needed to fully understand how this technology can be used to reduce sitting time. OBJECTIVE: The purpose of this study was to explore the potential of wearable devices as an intervention tool in a larger sedentary behavior study through a general inductive and deductive analysis of focus group discussions. METHODS: We conducted four focus groups with 15 participants to discuss 7 different wearable devices with sedentary behavior capabilities. Participants recruited for the focus groups had previously participated in a pilot intervention targeting sedentary behavior over a 3-week period and were knowledgeable about the challenges of reducing sitting time. During the focus groups, participants commented on the wearability, functionality, and feedback mechanism of each device and then identified their two favorite and two least favorite devices. Finally, participants designed and described their ideal or dream wearable device. Two researchers, who have expertise analyzing qualitative data, coded and analyzed the data from the focus groups. A thematic analysis approach using Dedoose software (SocioCultural Research Consultants, LLC version 7.5.9) guided the organization of themes that reflected participants' perspectives. RESULTS: Analysis resulted in 14 codes that we grouped into themes. Three themes emerged from our data: (1) features of the device, (2) data the device collected, and (3) how data are displayed. CONCLUSIONS: Current wearable devices for increasing physical activity are insufficient to intervene on sitting time. This was especially evident when participants voted, as several participants reported using a "process of elimination" as opposed to choosing favorites because none of the devices were ideal for reducing sitting time. To overcome the limitations in current devices, future wearable devices designed to reduce sitting time should include the following features: waterproof, long battery life, accuracy in measuring sitting time, real time feedback on progress toward sitting reduction goals, and flexible options for prompts to take breaks from sitting.
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 | 0,022 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,006 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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.
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 ».