Evidence map›Paper›PMID 36355633›Full record

Trial reportJournal of medical Internet research2022

Mental Health Chatbot for Young Adults With Depressive Symptoms During the COVID-19 Pandemic: Single-Blind, Three-Arm Randomized Controlled Trial.

Yuhao He, Li Yang, Xiaokun Zhu, Bin Wu, Shuo Zhang, Chunlian Qian, Tian Tian

Open access · goldAbstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 17 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
77citing papers in PubMed, 17 pooled it
19.6field-weighted citation impact, top 1% 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

77 citing papers in PubMed, 17 syntheses or guidelines pooled it, 143 citations in OpenAlex.

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17 more citing papers are in PubMed but not listed here.

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

7 authors at 2 institutions in 1 country.

Yuhao HeInstitute of Applied Psychology, College of Education, Tianjin University, Tianjin, China.ORCID 0000-0003-3969-7530
Li YangInstitute of Applied Psychology, College of Education, Tianjin University, Tianjin, China.ORCID 0000-0002-7947-0392
Xiaokun ZhuTianjin Vocational Institute, Tianjin, China.ORCID 0000-0002-4072-3778
Bin WuTianjin Quesoar Intelligent Technology Co, Ltd, Tianjin, China.ORCID 0000-0001-8349-0188
Shuo ZhangTianjin Quesoar Intelligent Technology Co, Ltd, Tianjin, China.ORCID 0000-0001-5621-6300
Chunlian QianInstitute of Applied Psychology, College of Education, Tianjin University, Tianjin, China.ORCID 0000-0001-7389-092X
Tian TianCollege of Intelligence and Computing, Tianjin University, Tianjin, China.ORCID 0000-0003-4197-3186
Tianjin University · CNTianjin Vocational Institute · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDepression has a high prevalence among young adults, especially during the COVID-19 pandemic. However, mental health services remain scarce and underutilized worldwide. Mental health chatbots are a novel digital technology to provide fully automated interventions for depressive symptoms.

objectiveThe purpose of this study was to test the clinical effectiveness and nonclinical performance of a cognitive behavioral therapy (CBT)-based mental health chatbot (XiaoE) for young adults with depressive symptoms during the COVID-19 pandemic.

methodsIn a single-blind, 3-arm randomized controlled trial, participants manifesting depressive symptoms recruited from a Chinese university were randomly assigned to a mental health chatbot (XiaoE; n=49), an e-book (n=49), or a general chatbot (Xiaoai; n=50) group in a ratio of 1:1:1. Participants received a 1-week intervention. The primary outcome was the reduction of depressive symptoms according to the 9-item Patient Health Questionnaire (PHQ-9) at 1 week later (T1) and 1 month later (T2). Both intention-to-treat and per-protocol analyses were conducted under analysis of covariance models adjusting for baseline data. Controlled multiple imputation and δ-based sensitivity analysis were performed for missing data. The secondary outcomes were the level of working alliance measured using the Working Alliance Questionnaire (WAQ), usability measured using the Usability Metric for User Experience-LITE (UMUX-LITE), and acceptability measured using the Acceptability Scale (AS).

resultsParticipants were on average 18.78 years old, and 37.2% (55/148) were female. The mean baseline PHQ-9 score was 10.02 (SD 3.18; range 2-19). Intention-to-treat analysis revealed lower PHQ-9 scores among participants in the XiaoE group compared with participants in the e-book group and Xiaoai group at both T1 (F

conclusionsA CBT-based chatbot is a feasible and engaging digital therapeutic approach that allows easy accessibility and self-guided mental health assistance for young adults with depressive symptoms. A systematic evaluation of nonclinical metrics for a mental health chatbot has been established in this study. In the future, focus on both clinical outcomes and nonclinical metrics is necessary to explore the mechanism by which mental health chatbots work on patients. Further evidence is required to confirm the long-term effectiveness of the mental health chatbot via trails replicated with a longer dose, as well as exploration of its stronger efficacy in comparison with other active controls.

trial registrationChinese Clinical Trial Registry ChiCTR2100052532; http://www.chictr.org.cn/showproj.aspx?proj=135744.

Indexed as

COVID-19Mental HealthAdolescentDepressionFemaleHumansMalePandemicsSingle-Blind MethodYoung Adultchatbotcognitive behavioral therapyconversational agentCOVID-19depressiondigital medicineevaluationhealth servicemental healthmHealthmobile healthrandomized controlled trialyoung adultyouth

Identifiers

PMID36355633
PMCPMC9680932
OpenAlexW4308687068

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.