Evidence map›Paper›PMID 38875600›Full record

ArticleJMIR AI2022

Adolescents' Well-being While Using a Mobile Artificial Intelligence-Powered Acceptance Commitment Therapy Tool: Evidence From a Longitudinal Study.

Dana Vertsberger, Navot Naor, Mirène Winsberg

Abstract read
In one paragraph

Article in JMIR AI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 pooled it
–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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

3 authors.

Dana VertsbergerStanford University, Stanford, CA, United States.ORCID https://orcid.org/0000-0002-0971-9236
Navot NaorKai.ai, Haifa, Israel.ORCID https://orcid.org/0000-0002-6488-7662
Mirène WinsbergStanford University, Stanford, CA, United States.ORCID https://orcid.org/0000-0002-6767-5403

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdolescence is a critical developmental period to prevent and treat the emergence of mental health problems. Smartphone-based conversational agents can deliver psychologically driven intervention and support, thus increasing psychological well-being over time.

objectiveThe objective of the study was to test the potential of an automated conversational agent named Kai.ai to deliver a self-help program based on Acceptance Commitment Therapy tools for adolescents, aimed to increase their well-being.

methodsParticipants were 10,387 adolescents, aged 14-18 years, who used Kai.ai on one of the top messaging apps (eg, iMessage and WhatsApp). Users' well-being levels were assessed between 2 and 5 times using the 5-item World Health Organization Well-being Index questionnaire over their engagement with the service.

resultsUsers engaged with the conversational agent an average of 45.39 (SD 46.77) days. The average well-being score at time point 1 was 39.28 (SD 18.17), indicating that, on average, users experienced reduced well-being. Latent growth curve modeling indicated that participants' well-being significantly increased over time (β=2.49; P<.001) and reached a clinically acceptable well-being average score (above 50).

conclusionsMobile-based conversational agents have the potential to deliver engaging and effective Acceptance Commitment Therapy interventions.

Indexed as

acceptanceadolescentsautomatedchatbotscommitmentconversational agentsengagementinterventionmental healthmobile mental healthpsychologicalpsychologyself-helpself-managementsmartphonesupporttherapywell-being

Identifiers

PMID38875600
PMCPMC11041447

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.