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Enregistrement W4281871093 · doi:10.1136/bmjebm-2022-podabstracts.61

133 Mobile phone mental health applications: a novel pathway for overdiagnosis of depression

2022· article· en· W4281871093 sur OpenAlex

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Notice bibliographique

RevueAbstracts · 2022
Typearticle
Langueen
DomainePsychology
ThématiqueDigital Mental Health Interventions
Établissements canadiensUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésOverdiagnosisMental healthMobile phoneDepression (economics)PsychologyPsychiatryMedicineInternet privacyComputer science

Résumé

récupéré en direct d'OpenAlex

<h3></h3> Mobile phone mental health applications (apps) are widely used, and usage has only been accelerated by the global COVID-19 pandemic. These apps often offer a suggested diagnosis as well as treatment guidance. Despite the extensive use of these apps, there is no evidence on the effect they may have on overdiagnosis of mental health conditions, specifically depression. Given the nature of mental health, overdiagnosis can be particularly difficult to quantify and attempts to study overdiagnosis in mental health have often focused on misdiagnosis. Overdiagnosis is separate from misdiagnosis and occurs when overdetection or overdefinition occur. Overdefinition is of relevance in mental health as the DSM-V criteria have been critiqued as being overinclusive such that normal aspects of life are medicalized. Overdiagnosis of depression can have significant effects on the user in the forms of treatment side effects, stigma and labelling harms as well as direct and indirect costs. From a systems level, overdiagnosis results in inefficient use of resources and potential diversion of resources away from those most in need. <h3>Objectives</h3> To understand if mobile phone mental health apps have the potential to contribute to the overdiagnosis of depression in users with milder and self-limited depressive symptoms or grief reactions. <h3>Methods</h3> A review of the relevant literature was conducted using PubMed. The top 25 apps using the search term ‘depression’ on the Apple App Store and Google Play App Store were reviewed. <h3>Results</h3> Numerous apps (8/25 on each app store) inappropriately used a general screening and treatment response tracking tool, the PHQ-9 questionnaire, as a diagnostic tool. These apps and others provided users with a suggested diagnosis of depression in the context of short term mild depressive symptoms that do not meet DSM-V criteria for Major Depressive Disorder (MDD). This may reflect misdiagnosis, however beyond this phenomenon, these apps appear to be susceptible to overdiagnosis as well. Users may meet diagnostic criteria for MDD however symptoms may be sufficiently transient or mild that clinicians using their clinical judgement would not make a diagnosis of depression. Mental health apps lack this fine-tuned clinical judgment and have the potential to indiscriminately make the diagnosis in those experiencing non-pathologic aspects of everyday experiences. The top 12 apps in each store represent greater than 90% of the monthly active users of all depression apps. Among the top 12 apps, 4/12 after making a suggested diagnosis of depression then offered links to for profit web-based therapy services that in some cases have funded the app itself or reimbursed the app for successful referrals. <h3>Conclusion</h3> Physicians need to be aware of this potential for overdiagnosis of depression and should clarify how a patient’s previous history of depression was diagnosed. Additionally, patients presenting with depressive symptoms may have already used these apps to reach a premature diagnostic conclusion and expect a certain level of treatment based on the recommendations of these apps. This can strain the therapeutic relationship and care must be taken to explain the nuances of diagnosis and treatment that these apps often overlook.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,808
Score d'incertitude au seuil0,677

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,036
Tête enseignante GPT0,385
Écart entre enseignants0,349 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle