Development and Evaluation of a Digital App for Patient Self-Management of Opioid Use Disorder: Usability, Acceptability, and Utility Study
Notice bibliographique
Résumé
BACKGROUND: Self-management of opioid use disorder (OUD) is an important component of treatment. Many patients receiving opioid agonist treatment in methadone maintenance treatment settings benefit from counseling treatments to help them improve their recovery skills but have insufficient access to these treatments between clinic appointments. In addition, many addiction medicine clinicians treating patients with OUD in a general medical clinic setting do not have consistent access to counseling referrals for their patients. This can lead to decreases in both treatment retention and overall progress in the patient's recovery from substance misuse. Digital apps may help to bridge this gap by coaching, supporting, and reinforcing behavioral change that is initiated and directed by their psychosocial and medical providers. OBJECTIVE: This study aimed to conduct an acceptability, usability, and utility pilot study of the KIOS app to address these clinical needs. METHODS: We developed a unique, patient-centered computational software system (KIOS; Biomedical Development Corporation) to assist in managing OUD in an outpatient, methadone maintenance clinic setting. KIOS tracks interacting self-reported symptoms (craving, depressed mood, anxiety, irritability, pain, agitation or restlessness, difficulty sleeping, absenteeism, difficulty with usual activities, and conflicts with others) to determine changes in both the trajectory and severity of symptom patterns over time. KIOS then applies a proprietary algorithm to assess the individual's patterns of symptom interaction in accordance with models previously established by OUD experts. After this analysis, KIOS provides specific behavioral advice addressing the individual's changing trajectory of symptoms to help the person self-manage their symptoms. The KIOS software also provides analytics on the self-reported data that can be used by patients, clinicians, and researchers to track outcomes. RESULTS: In a 4-week acceptability, usability (mean System Usability Scale-Modified score 89.5, SD 9.2, maximum of 10.0), and utility (mean KIOS utility questionnaire score 6.32, SD 0.25, maximum of 7.0) pilot study of 15 methadone-maintained participants with OUD, user experience, usability, and software-generated advice received high and positive assessment scores. The KIOS clinical variables closely correlated with craving self-report measures. Therefore, managing these variables with advice generated by the KIOS software could have an impact on craving and ultimately substance use. CONCLUSIONS: KIOS tracks key clinical variables and generates advice specifically relevant to the patient's current and changing clinical state. Patients in this pilot study assigned high positive values to the KIOS user experience, ease of use, and the appropriateness, relevance, and usefulness of the specific behavioral guidance they received to match their evolving experiences. KIOS may therefore be useful to augment in-person treatment of opioid agonist patients and help fill treatment gaps that currently exist in the continuum of care. A National Institute on Drug Abuse-funded randomized controlled trial of KIOS to augment in-person treatment of patients with OUD is currently being conducted.
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 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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) | 0,002 | 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 ».