Evidence map›Paper›PMID 35389351›Full record

ArticleJMIR mental health2022

Characterizing Use of a Multicomponent Digital Intervention to Predict Treatment Outcomes in First-Episode Psychosis: Cluster Analysis.

Shaunagh O'Sullivan, Lianne Schmaal, Simon D'Alfonso, Yara Jo Toenders, Lee Valentine, Carla McEnery, Sarah Bendall, Barnaby Nelson, John F Gleeson, Mario Alvarez-Jimenez

Abstract read
In one paragraph

Article in JMIR mental health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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

10 authors.

Shaunagh O'SullivanOrygen, Parkville, Australia.ORCID https://orcid.org/0000-0002-5234-5727
Lianne SchmaalOrygen, Parkville, Australia.ORCID https://orcid.org/0000-0001-9822-048X
Simon D'AlfonsoOrygen, Parkville, Australia.ORCID https://orcid.org/0000-0001-7407-8730
Yara Jo ToendersOrygen, Parkville, Australia.ORCID https://orcid.org/0000-0002-4117-1143
Lee ValentineOrygen, Parkville, Australia.ORCID https://orcid.org/0000-0001-7481-6537
Carla McEneryOrygen, Parkville, Australia.ORCID https://orcid.org/0000-0001-9145-9068
Sarah BendallOrygen, Parkville, Australia.ORCID https://orcid.org/0000-0003-1486-6190
Barnaby NelsonOrygen, Parkville, Australia.ORCID https://orcid.org/0000-0002-6263-2332
John F GleesonHealth Brain and Mind Research Centre, Australian Catholic University, Melbourne, Australia.ORCID https://orcid.org/0000-0001-7969-492X
Mario Alvarez-JimenezOrygen, Parkville, Australia.ORCID https://orcid.org/0000-0002-3535-9086

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMulticomponent digital interventions offer the potential for tailored and flexible interventions that aim to address high attrition rates and increase engagement, an area of concern in digital mental health. However, increased flexibility in use makes it difficult to determine which components lead to improved treatment outcomes.

objectiveThis study aims to identify user profiles on Horyzons, an 18-month digital relapse prevention intervention for first-episode psychosis that incorporates therapeutic content and social networking, along with clinical, vocational, and peer support, and to examine the predictive value of these user profiles for treatment outcomes. A secondary objective is to compare each user profile with young people receiving treatment as usual (TAU).

methodsParticipants comprised 82 young people (aged 16-27 years) with access to Horyzons and 84 receiving TAU, recovering from first-episode psychosis. In addition, 6-month use data from the therapy and social networking components of Horyzons were used as features for K-means clustering for joint trajectories to identify user profiles. Social functioning, psychotic symptoms, depression, and anxiety were assessed at baseline and 6-month follow-up. General linear mixed models were used to examine the predictive value of user profiles for treatment outcomes and between each user profile with TAU.

resultsA total of 3 user profiles were identified based on the following system use metrics: low use, maintained use of social components, and maintained use of both therapy and social components. The maintained therapy and social group showed improvements in social functioning (F

conclusionsContinued engagement with both therapy and social components might be key in achieving long-term recovery. Maintained social use and low use outcomes were broadly comparable with TAU, emphasizing the importance of maintaining engagement for improved treatment outcomes. Although the social network may be a key ingredient to increase sustained engagement, as users engaged with this more consistently, it should be leveraged as a tool to engage young people with therapeutic content to bring about social and clinical benefits.

Indexed as

clusteringdigital healthdigital interventionlog datapsychotic disorderssocial networkingusage metricsyouth mental health

Identifiers

PMID35389351
PMCPMC9030973

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.