Evidence map›Paper›PMID 42830789›Full record

ArticlePreventive medicine reports2026

Associations of artificial intelligence use at work with psychological distress, loneliness, and life satisfaction among employed US adults.

Jemar R Bather, Adolfo G Cuevas, Melody S Goodman, José A Pagán

Abstract read
In one paragraph

Article in Preventive medicine reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jemar R BatherDepartment of Biostatistics, NYU School of Global Public Health, New York, NY, USA.
Adolfo G CuevasDepartment of Social and Behavioral Sciences, NYU School of Global Public Health, New York, NY, USA.
Melody S GoodmanDepartment of Biostatistics, NYU School of Global Public Health, New York, NY, USA.
José A PagánDepartment of Public Health Policy and Management, NYU School of Global Public Health, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To quantify the associations between artificial intelligence use at work and psychosocial well-being among employed adults in the United States. Methods: Nationally representative data come from the Household Trends and Outlook Pulse Survey (United States, March 2026). Artificial intelligence use at work was measured as the number of distinct assisted work tasks. Outcomes included psychological distress, loneliness, and life satisfaction. Regression models adjusted for sociodemographic characteristics, region of residence, occupation, and telework frequency. Results: The data generalized to 152 million employed U.S. adults, with 51.7% using artificial intelligence at work. The most common artificial intelligence task was "searching for information or technical help" (35.8%), followed by "writing communications, documentation, or instructions" (31.6%). Greater artificial intelligence use at work was associated with greater life satisfaction (0.11 SD increase, 95% CI: 0.06, 0.16) and a higher prevalence of feeling lonely (aPR: 1.13, 95% CI: 1.02, 1.25). There was no significant association with moderate-to-severe psychological distress. Conclusions: The findings provide evidence that artificial intelligence use at work is associated with life satisfaction and loneliness, but not with overall psychological distress. Longitudinal studies are needed to examine the impact of increasing artificial intelligence use at work on employee psychosocial well-being.

Indexed as

American workersAnxietyChatGPTCopilotDepressionGenerative artificial intelligenceMental distressSubjective well-beingTask automationWorkplace automation

Identifiers

PMID42830789
PMCPMC13634062

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.