Evidence map›Paper›PMID 38188856›Full record

ArticleDigital health

Evaluating a smartphone-based symptom self-monitoring app for psychosis in China (YouXin): A non-randomised validity and feasibility study with a mixed-methods design.

Xiaolong Zhang, Shôn Lewis, Lesley-Anne Carter, Xu Chen, Jiaojiao Zhou, Xingyu Wang, Sandra Bucci

Open access · goldAbstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
2.9field-weighted citation impact, top 10% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
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 3 institutions in 2 countries.

Xiaolong ZhangDivision of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.ORCID https://orcid.org/0000-0003-2964-0485
Shôn LewisDivision of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
Lesley-Anne CarterDivision of Population Health, Health Services Research & Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
Xu ChenThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Jiaojiao ZhouThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
Xingyu WangDivision of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
Sandra BucciDivision of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.ORCID https://orcid.org/0000-0002-6197-5333
Capital Medical University · CNGreater Manchester Mental Health NHS Foundation Trust · GBManchester Academic Health Science Centre · GB

Funding

Wellcome Trust
6 · The paper itself

Abstract

Background: Psychosis causes a significant burden globally, including in China, where limited mental health resources hinder access to care. Smartphone-based remote monitoring offers a promising solution. This study aimed to assess the validity, feasibility, acceptability, and safety of a symptom self-monitoring smartphone app, YouXin, for people with psychosis in China. Methods: A pre-registered non-randomised validity and feasibility study with a mixed-methods design. Participants with psychosis were recruited from a major tertiary psychiatric hospital in Beijing, China. Participants utilised the YouXin app to self-monitor psychosis and mood symptoms for four weeks. Feasibility outcomes were recruitment, retention and outcome measures completeness. Active symptom monitoring (ASM) validity was tested against corresponding clinical assessments (PANSS and CDS) using Spearman correlation. Ten participants completed qualitative interviews at study end to explore acceptability of the app and trial procedures. Results: Feasibility parameters were met. The target recruitment sample of 40 participants was met, with 82.5% completing outcome measures, 60% achieving acceptable ASM engagement (completing >33% of all prompts), and 33% recording sufficient passive monitoring data to extract mobility indicators. Five ASM domains (hallucinations, suspiciousness, guilt feelings, delusions, grandiosity) achieved moderate correlation with clinical assessment. Both quantitative and qualitative evaluation showed high acceptability of YouXin. Clinical measurements indicated no symptom and functional deterioration. No adverse events were reported, suggesting YouXin is safe to use in this clinical population. Conclusions: The trial feasibility, acceptability and safety parameters were met and a powered efficacy study is indicated. However, refinements are needed to improve ASM validity and increase passive monitoring data completeness.

Indexed as

Chinadigital mental healthpsychosisself-managementsmartphone app

Identifiers

PMID38188856
PMCPMC10768587
OpenAlexW4390542397

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.