Objective Assessment of Financial Decision-Making With a Simulated Online Money Management Task in Older Adults: Protocol for a Prospective Observational Study
Notice bibliographique
Résumé
Background: Technology-enabled tasks to conduct financial transactions are ubiquitous around the world. In a recent survey, about 75% of the respondents endorsed the use of technology to perform financial activities such as reviewing bank statements and keeping track of money spent. However, assessment of financial decision-making (FDM) is limited by tasks that use traditional paper-and-pencil methods or by relying on self or informant reports. Furthermore, such tools have weak psychometric properties, are prone to biases, and are at times limited in scope. Thus, there is an urgent need to develop modern, technology-based tools that have strong psychometric properties and that can assess FDM comprehensively and accurately. Objective: This study aimed to develop and establish the psychometric properties of a novel, simulated Online Money Management (OMM) credit card task. Based on existing gaps identified in the literature, this task relied on objective measurement, assessed multiple dimensions within a single task, and mimicked a real-world task to bridge the gap between a controlled, clinical setting and real-world functioning. Methods: This was a prospective cohort study that enrolled cognitively healthy older adults. This study was funded by the National Institutes of Health. Various recruitment sites were involved, which allowed for the recruitment of older adults across the United States. The OMM task was developed in collaboration with an interdisciplinary team of computer scientists, economists, psychologists, and geriatricians. The tasks consist of both online and offline components, with subcomponents examining the ability to navigate, basic and complex credit card literacy, and statement monitoring. Data about participants' perception of their financial abilities and a self-report survey on financial exploitation were collected. The test battery consisted of an array of cognitive, financial, and psychosocial tasks. Participants provided written informed consent, and all procedures received institutional review board approval. Results: Data collection began in September 2019, and enrollment stopped in July 2025. A total of 272 participants completed the baseline visit, while 147 completed the longitudinal follow-up visit. Data analysis is underway as of August 2025, and results are expected to be published in 2026. Conclusions: Rigorous standards have been deployed for developing this novel OMM credit card task. If the measurement properties of the task are found adequate, the OMM task can be used to assess FDM in clinical evaluations for early detection and prevention or mitigation of financial mismanagement.
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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 | 0,021 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,005 |
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 ».