Evidence map›Paper›PMID 41993037›Full record

ReviewFrontiers in digital health2026

Single-item measures in digital mental healthcare: a mini narrative review of challenges and opportunities.

Lauren Staples, Alana Fisher, Blake Dear, Olav Nielssen, Nickolai Titov

Abstract readReview
In one paragraph

Review in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

5 authors.

Lauren StaplesSchool of Psychological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.
Alana FisherSchool of Psychological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.
Blake DearSchool of Psychological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.
Olav NielssenSchool of Psychological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.
Nickolai TitovSchool of Psychological Sciences, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-item measures (SIMs) are increasingly used by digital mental health services for assessment, outcome monitoring, and population-level surveillance. Their simplicity offers clear advantages, including good face validity, practical efficiency, and the potential to integrate across digital platforms. However, concerns persist regarding their reliability and suitability for complex psychological constructs. This mini narrative review synthesises recent literature examining the use of SIMs in mental healthcare over the past decade. A total of 31 articles underwent full-text review, and key themes were identified. Findings suggest that SIMs demonstrate acceptable validity for narrowly defined constructs and are valuable for large-scale screening and digital integration, though systematic validation across diverse populations is essential. These results suggest that SIMs should not replace comprehensive assessments, but they can complement integrated, person-centred models of measurement in digital mental healthcare.

Indexed as

cultural differencesimplementationinternet delivered therapymeasurementmental healthoutcome monitoringperson-centred carepsychometric validation

Identifiers

PMID41993037
PMCPMC13079382

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.