Evidence map›Paper›PMID 34855607›Full record

ArticleJMIR formative research2021

A Conversational Artificial Intelligence Agent for a Mental Health Care App: Evaluation Study of Its Participatory Design.

Morena Danieli, Tommaso Ciulli, Seyed Mahed Mousavi, Giuseppe Riccardi

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 pooled it
4.1field-weighted citation impact, top 6% of its field
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

22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 58 citations in OpenAlex.

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  12. Loneliness by Design: The Structural Logic of Isolation in Engagement-Driven Systems.International journal of environmental research and public health · 2025
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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

4 authors at 1 institution in 1 country.

Morena Danieli *Speech and Interactive Signal Lab, Department of Engineering and Computer Science, Università degli Studi di Trento, Trento, Italy.ORCID https://orcid.org/0000-0001-7888-5935
Tommaso CiulliIDEGO srl, Rome, Italy.ORCID https://orcid.org/0000-0002-0828-164X
Seyed Mahed Mousavi *Speech and Interactive Signal Lab, Department of Engineering and Computer Science, Università degli Studi di Trento, Trento, Italy.ORCID https://orcid.org/0000-0002-7790-7730
Giuseppe Riccardi *Speech and Interactive Signal Lab, Department of Engineering and Computer Science, Università degli Studi di Trento, Trento, Italy.ORCID https://orcid.org/0000-0002-0739-8184
University of Trento · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMobile apps for mental health are available on the market. Although they seem to be promising for improving the accessibility of mental health care, little is known about their acceptance, design methodology, evaluation, and integration into psychotherapy protocols. This makes it difficult for health care professionals to judge whether these apps may help them and their patients.

objectiveOur aim is to describe and evaluate a protocol for the participatory design of mobile apps for mental health. In this study, participants and psychotherapists are engaged in the early phases of the design and development of the app empowered by conversational artificial intelligence (AI). The app supports interventions for stress management training based on cognitive behavioral theory.

methodsA total of 21 participants aged 33-61 years with mild to moderate levels of stress, anxiety, and depression (assessed by administering the Italian versions of the Symptom Checklist-90-Revised, Occupational Stress Indicator, and Perceived Stress Scale) were assigned randomly to 2 groups, A and B. Both groups received stress management training sessions along with cognitive behavioral treatment, but only participants assigned to group A received support through a mobile personal health care agent, designed for mental care and empowered by AI techniques. Psychopathological outcomes were assessed at baseline (T1), after 8 weeks of treatment (T2), and 3 months after treatment (T3). Focus groups with psychotherapists who administered the therapy were held after treatment to collect their impressions and suggestions.

resultsAlthough the intergroup statistical analysis showed that group B participants could rely on better coping strategies, group A participants reported significant improvements in obsessivity and compulsivity and positive distress symptom assessment. The psychotherapists' acceptance of the protocol was good. In particular, they were in favor of integrating an AI-based mental health app into their practice because they could appreciate the increased engagement of patients in pursuing their therapy goals.

conclusionsThe integration into practice of an AI-based mobile app for mental health was shown to be acceptable to both mental health professionals and users. Although it was not possible in this experiment to show that the integration of AI-based conversational technologies into traditional remote psychotherapy significantly decreased the participants' levels of stress and anxiety, the experimental results showed significant trends of reduction of symptoms in group A and their persistence over time. The mental health professionals involved in the experiment reported interest in, and acceptance of, the proposed technology as a promising tool to be included in a blended model of psychotherapy.

Indexed as

conversational AImental health caremHealthparticipatory designpersonal health care agentspsychotherapy

Identifiers

PMID34855607
PMCPMC8686486
OpenAlexW3200694407

What OpenQuestion holds

Textmetadata
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.