133 Mobile phone mental health applications: a novel pathway for overdiagnosis of depression
Notice bibliographique
Résumé
<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.
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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 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,006 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,002 |
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 ».