Using Crowdsourcing Internet of Things Technology to Reduce Caregiver Worry in Dementia-Related Lost Episodes: Longitudinal Observational Study
Notice bibliographique
Résumé
Background: Dementia increases the risk of individuals getting lost due to cognitive decline, impacting daily functioning and heightening caregiver worry. Traditional search methods are often time-consuming and stressful, whereas GPS-based technologies face limitations such as battery dependency. A crowdsourcing Internet of Things (IoT) technology using energy-efficient Bluetooth Low Energy (BLE) offers a potential solution to locate missing individuals with dementia more effectively by harnessing the power of the crowd and fostering a caring and inclusive community. Objective: This study aimed to evaluate the effectiveness of a BLE-based privacy-preserving crowdsourcing IoT system consisting of a BLE tag and an Android and iOS app in improving lost-related behavior and psychological well-being by facilitating searches, after-care arrangements, and reducing caregiver worry, as well as to assess its usability among caregivers of individuals with dementia in Hong Kong. Methods: A single-arm, prospective observational study was conducted from November 2020 to October 2023. Caregivers (N=1034) of individuals with dementia used a staff-assisted crowdsourcing IoT technology comprising a BLE tag, mobile app sensor, and location cloud server. Outcomes included search strategies, post-getting lost care arrangements, caregiver worry and distress (10-point scale), and usability (modified Quebec User Evaluation of Satisfaction with Assistive Technology 2.0 survey). Data were collected at 6- and 12-month follow-ups and analyzed using generalized estimating equations and linear mixed models. Results: Of the 1034 participants, 143 (13.82%) reported lost episodes, with 51 (35.7%) using BLE tags for searches. Worry about future lost episodes decreased significantly over time (P=.008), especially among BLE tag users (P=.04). There was an association between BLE tag use and adoption of proactive search strategies (eg, going out to search: adjusted odds ratio 2.78, 95% CI 1.33-5.82; P=.007) and preventative measures (eg, IoT devices or CCTV: adjusted odds ratio 2.92, 95% CI 1.61-5.29; P<.001). Usability satisfaction was high for design and data security, whereas approximately half of the participants (309/707, 43.7%) were satisfied with accuracy. Conclusions: The BLE crowdsourcing system may reduce caregiver worry and encourage proactive search behaviors, although accuracy depends on broader community adoption. Integration into dementia care plans could enhance safety and autonomy. Further research with a randomized controlled trial design is needed to confirm these findings.
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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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 ».