Evidence map›Paper›PMID 40966676›Full record

ArticleJMIR mental health2025

Toward Digital Self-Monitoring of Mental Health in the General Population: Scoping Review of Existing Approaches to Self-Report Measurement.

Zhao Hui Koh, Duygu Serbetci, Jason Skues, Greg Murray

Abstract readScoping Review
In one paragraph

Article in JMIR mental health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Zhao Hui KohCentre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawthorn, Australia.ORCID https://orcid.org/0000-0001-9533-6495
Duygu SerbetciCentre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawthorn, Australia.ORCID https://orcid.org/0009-0003-0117-8185
Jason SkuesDepartment of Psychological Sciences, Swinburne University of Technology, Hawthorn, Australia.ORCID https://orcid.org/0000-0001-5091-8360
Greg MurrayCentre for Mental Health and Brain Sciences, Swinburne University of Technology, Hawthorn, Australia.ORCID https://orcid.org/0000-0001-7208-5603

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the ubiquity of smartphones, digital self-report instruments have enormous potential to support the general population in monitoring their mental health. A primary challenge for researchers committed to advancing this work is simply to scope the plethora of widely used candidate instruments. The overarching aim of this study was to address this challenge to support and guide future research in this burgeoning area.

objectiveThis study aimed to conduct a literature review of self-report instruments used in empirical studies to measure mental health (1) in the general population, (2) delivered in a digital format, and (3) in longitudinal designs. Given the wide range of recognized "mental health" constructs, the review's search strategies were guided by Keyes' dual continua model of mental health, recognizing both deficits- and strengths-based constructs. This study's primary objective was to develop a first-of-its-kind ranking and synthesis of the most frequently used instruments that are potentially suitable for mental health self-monitoring. It was not an objective of this study to evaluate psychometric properties of the identified instruments-we hope the present ranking and synthesis will provide the foundation for future research into optimal digital, prospective self-report of mental health.

methodsFive major electronic databases were searched. Studies that administered digital mental health instruments (in English) repeatedly to community dwellers in the general adult population were eligible. The included studies were grouped by instruments for synthesis using a narrative approach.

resultsPreliminary screening of 95,849 records identified 8460 eligible records, among which 1000 records were randomly selected over 4 iterations for full-text screening. A total of 223 records were included. We found that the top 30 most commonly used instruments accounted for 78.4% (308/393) of the total usage across studies. These instruments predominantly measure deficits-based mental health constructs. The Patient Health Questionnaire 9 Items and Generalized Anxiety Disorder 7 Items were by far the most used instruments. The most commonly measured strengths-based constructs were life satisfaction and mental well-being.

conclusionsThe findings of this review strongly suggest that scientific investigation of mental health constructs across time on digital platforms still prioritizes deficits-focused instruments originally developed for pen-and-paper administration using classical test theory. These findings are discussed in light of evidence in the literature that deficits-focused instruments demonstrate inferior distributional properties (floor effects) in the general population and theory suggesting that both deficits- and strengths-focused measurements are required to holistically assess mental health. Limitations of the review include the restricted focus on English language instruments and the pragmatic approach to selecting records for full-text screening. It is concluded that, in the smartphone age, it would be timely to develop new digital instruments framed by holistic models of mental health and using contemporary test construction approaches.

trial registrationPROSPERO CRD42022306547; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022306547. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2022-065162.

Indexed as

Mental DisordersMental HealthSelf ReportHumansPsychometricsSmartphonedigital healthgeneral adult populationmental healthmobile phonemonitoringrepeated measurementscoping reviewself-report instrument

Identifiers

PMID40966676
PMCPMC12491901

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.