The Prevalence and Predictors of Digital Proxy Behavior in the United States: Cross-Sectional Survey Study
Bibliographic record
Abstract
Background: As dependent adults increasingly require help with managing web-based financial and medical tasks, caregivers often step in to assist-sometimes informally, by sharing login credentials. Despite growing reliance on such digital proxies, limited data exist on the scale of this behavior, who performs it, and how they access accounts. Informal access practices may pose privacy and security risks and increase the potential for elder abuse. Objective: The aim of this study is to quantify the prevalence of digital proxy behavior in the United States, identify demographic and caregiving predictors of proxy roles, and examine the extent of formal versus informal access methods in medical and financial domains. Methods: A nationally representative survey (n=657) was conducted among US adults from December 2022 to February 2023. The survey assessed the participants' life experience of digital proxy behavior, physical caregiving, demographics, and methods and motivations for accessing others' digital accounts. Logistic regression models were used to analyze predictors of financial and medical digital proxy behavior separately. Results: In the United States, digital proxy behavior is prevalent and often driven by usability challenges rather than the care recipient's physical or cognitive limitations. Roughly 49% of respondents reported having had digital proxy duties, with 59% assisting in both medical and financial domains. Predictors of being a digital proxy included being younger, male, more educated, and providing physical care. Additionally, higher income predicted financial proxy behavior, while having more siblings and being from a majority ethnicity predicted medical proxy behavior. Approximately one-third of digital proxies used informal access methods-translating to about 18 million adults nationally. About 10% of the sample logged on by knowing the account owner's credentials, and about 7% of the sample used the accounts without the owner's presence. Older female proxies were more likely to use formal access methods. Conclusions: Informal access practices are widespread and pose potential risks to vulnerable adults. Although most proxies used formal access when available, a significant portion have used insecure, informal methods. Policymakers and system designers should aim to promote secure, user-friendly proxy features and balance access with monitoring mechanisms that balance usability, privacy, and abuse prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".