User-Friendly Chatbot to Mitigate the Psychological Stress of Older Adults During the COVID-19 Pandemic: Development and Usability Study
Notice bibliographique
Résumé
BACKGROUND: To safeguard the most vulnerable individuals during the COVID-19 pandemic, numerous governments enforced measures such as stay-at-home orders, social distancing, and self-isolation. These social restrictions had a particularly negative effect on older adults, as they are more vulnerable and experience increased loneliness, which has various adverse effects, including increasing the risk of mental health problems and mortality. Chatbots can potentially reduce loneliness and provide companionship during a pandemic. However, existing chatbots do not cater to the specific needs of older adult populations. OBJECTIVE: We aimed to develop a user-friendly chatbot tailored to the specific needs of older adults with anxiety or depressive disorders during the COVID-19 pandemic and to examine their perspectives on mental health chatbot use. The primary research objective was to investigate whether chatbots can mitigate the psychological stress of older adults during COVID-19. METHODS: Participants were older adults belonging to two age groups (≥65 years and <65 years) from a psychiatric outpatient department who had been diagnosed with depressive or anxiety disorders by certified psychiatrists according to the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) criteria. The participants were required to use mobile phones, have internet access, and possess literacy skills. The chatbot's content includes monitoring and tracking health data and providing health information. Participants had access to the chatbot for at least 4 weeks. Self-report questionnaires for loneliness, depression, and anxiety were administered before and after chatbot use. The participants also rated their attitudes toward the chatbot. RESULTS: A total of 35 participants (mean age 65.21, SD 7.51 years) were enrolled in the trial, comprising 74% (n=26) female and 26% (n=9) male participants. The participants demonstrated a high utilization rate during the intervention, with over 82% engaging with the chatbot daily. Loneliness significantly improved in the older group ≥65 years. This group also responded positively to the chatbot, as evidenced by changes in University of California Los Angeles Loneliness Scale scores, suggesting that this demographic can derive benefits from chatbot interaction. Conversely, the younger group, <65 years, exhibited no significant changes in loneliness after the intervention. Both the older and younger age groups provided good scores in relation to chatbot design with respect to usability (mean scores of 6.33 and 6.05, respectively) and satisfaction (mean scores of 5.33 and 5.15, respectively), rated on a 7-point Likert scale. CONCLUSIONS: The chatbot interface was found to be user-friendly and demonstrated promising results among participants 65 years and older who were receiving care at psychiatric outpatient clinics and experiencing relatively stable symptoms of depression and anxiety. The chatbot not only provided caring companionship but also showed the potential to alleviate loneliness during the challenging circumstances of a pandemic.
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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 | 0,005 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».