Objective Assessment of Financial Decision-Making With a Simulated Online Money Management Task in Older Adults: Protocol for a Prospective Observational Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".