Expressive Interviewing Agents to Support Health-Related Behavior Change: Randomized Controlled Study of COVID-19 Behaviors
Notice bibliographique
Résumé
BACKGROUND: Expressive writing and motivational interviewing are well-known approaches to help patients cope with stressful life events. Although these methods are often applied by human counselors, it is less well understood if an automated artificial intelligence approach can benefit patients. Providing an automated method would help expose a wider range of people to the possible benefits of motivational interviewing, with lower cost and more adaptability to sudden events like the COVID-19 pandemic. OBJECTIVE: This study presents an automated writing system and evaluates possible outcomes among participants with respect to behavior related to the COVID-19 pandemic. METHODS: We developed a rule-based dialogue system for "Expressive Interviewing" to elicit writing from participants on the subject of how COVID-19 has impacted their lives. The system prompts participants to describe their life experiences and emotions and provides topic-specific prompts in response to participants' use of topical keywords. In May 2021 and June 2021, we recruited participants (N=151) via Prolific to complete either the Expressive Interviewing task or a control task. We surveyed participants immediately before the intervention, immediately after the intervention, and again 2 weeks after the intervention. We measured participants' self-reported stress, general mental health, COVID-19-related health behavior, and social behavior. RESULTS: Participants generally wrote long responses during the task (53.3 words per response). In aggregate, task participants experienced a significant decrease in stress in the short term (~23% decrease, P<.001) and a slight difference in social activity compared with the control group (P=.03). No significant differences in short-term or long-term outcomes were detected between participant subgroups (eg, male versus female participants) except for some within-condition differences by ethnicity (eg, higher social activity among African American people participating in Expressive Interviewing vs participants of other ethnicities). For short-term effects, participants showed different outcomes based on their writing. Using more anxiety-related words was correlated with a greater short-term decrease in stress (r=-0.264, P<.001), and using more positive emotion words was correlated with a more meaningful experience (r=0.243, P=.001). As for long-term effects, writing with more lexical diversity was correlated with an increase in social activity (r=0.266, P<.001). CONCLUSIONS: Expressive Interviewing participants exhibited short-term, but not long-term, positive changes in mental health, and some linguistic metrics of writing style were correlated with positive change in behavior. Although there were no significant long-term effects observed, the positive short-term effects suggest that the Expressive Interviewing intervention could be used in cases in which a patient lacks access to traditional therapy and needs a short-term solution. TRIAL REGISTRATION: Clincaltrials.gov NCT05949840; https://www.clinicaltrials.gov/study/NCT05949840.
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 | 0,007 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».