The exact text we saved

This is the version exactly as it was saved, character for character. The SHA-256 hash below is a 64-character code worked out from that text. Run the same text through SHA-256 and you get the same code; change a single letter and the code comes out completely different. Each version also stores the previous version’s hash, which chains them together so no version can be swapped out quietly.

TRUVACE RECORD VERSION
record: TRV-2026-2531
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:49:09.999898Z
status: published
lens: p_space
sector: lifestyle
headline: Problematic smartphone use and AI chatbot dependence: A cross-sectional study using latent profile and dimensional analyses
dek: ABSTRACT As generative artificial intelligence becomes increasingly embedded in everyday, educational, and work-related contexts, increasing attention has been directed to dependency-related forms of AI chatbot use. Problematic smartphone use (PSU) may reflect broader dysregulated digital engagement and may therefore be relevant to understanding dependency-related AI chatbot use. However, little is known about whether PSU is associated with AI chatbot dependency or whether AI-related evaluative factors show diff…
gain_title: (none)
problem_title: In a cross-sectional study of 538 AI chatbot users, higher problematic smartphone use was positively associated with AI chatbot dependency, and 38% of users fell in the high-symptom group.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: In a cross-sectional study of 538 AI chatbot users, higher problematic smartphone use was positively associated with AI chatbot dependency, and 38% of users fell in the high-symptom group.
problem_evidence: The supplementary continuous-PSU model supported the positive association between PSU and AI chatbot dependency | high-symptom PSU (38%)
quick_read: A cross-sectional study of 538 AI chatbot users found that people with more severe problematic smartphone use also showed more dependency on AI chatbots. Latent profile analysis split users into low-symptom (16%), moderate-symptom (46%) and high-symptom (38%) smartphone-use groups.

The groups formed an ordered severity gradient rather than distinct symptom types, and chatbot dependency differed across them. A second model that treated smartphone use as a continuous measure supported the same positive link.

The authors say the findings suggest chatbot dependency may sit within broader patterns of dysregulated digital engagement. Because the data are self-reported and collected at one point in time, the work cannot show that one behavior causes the other.
limitation: The study used a cross-sectional, self-report design, and the profile-specific performance expectancy finding was not reproduced in the continuous model, so it is exploratory.
tag: Evidence-backed problem
key_points: The study analyzed data from 538 AI chatbot users and was guided by the I-PACE model. | Latent profile analysis found three smartphone-use groups: low-symptom (16%), moderate-symptom (46%) and high-symptom (38%). | The three groups formed an ordered severity gradient, not distinct symptom configurations. | AI chatbot dependency differed across the smartphone-use groups. | Emotional experience was involved in several statistical indirect associations between AI-use evaluations and chatbot dependency. | Formal between-group differences were limited to the link between performance expectancy and chatbot dependency, and the continuous model did not reproduce it.
rundown: Researchers recruited 538 people who use AI chatbots and applied latent profile analysis to their problematic smartphone use scores, guided by the I-PACE model of addictive behavior. The analysis produced three severity-based groups, with 16% in the low-symptom group, 46% in the moderate group and 38% in the high-symptom group.

The groups differed mainly in severity rather than in kind. A supplementary model using continuous smartphone-use scores supported a positive association between smartphone-use problems and chatbot dependency.

Emotional experience appeared in several significant indirect associations between how users evaluated AI and their chatbot dependency. The only formal difference between groups involved performance expectancy and dependency, and the authors call that finding exploratory because the continuous model did not reproduce it.
sources:
- peer_reviewed | Computers in Human Behavior Reports | https://doi.org/10.1016/j.chbr.2026.101337 | 2026-09-24
prev: 0000000000000000000000000000000000000000000000000000000000000000

SHA-256 hash

b92e4f027cd8704dfc83a5d8ec6adcd8e1262c9a6132b3ff3902d65b1570083e

Previous version’s hash

0000000000000000000000000000000000000000000000000000000000000000