Evidence map›Paper›PMID 41714862›Full record

ArticleDiabetic medicine : a journal of the British Diabetic Association2026

The impact of sleep and movement behaviour on daily mood in people with type 2 diabetes: A smartphone-based digital phenotyping study.

Amy M McInerney, Norbert Schmitz, Mark Matthews, Sonya S Deschênes

Abstract read
In one paragraph

Article in Diabetic medicine : a journal of the British Diabetic Association, 2026. 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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Amy M McInerneyDepartment of Population-Based Medicine, University of Tübingen, Tübingen, Germany.ORCID https://orcid.org/0000-0002-1576-1801
Norbert SchmitzDepartment of Population-Based Medicine, University of Tübingen, Tübingen, Germany.
Mark MatthewsSchool of Computer Science, University College Dublin, Dublin, Ireland.
Sonya S DeschênesSchool of Psychology, University College Dublin, Dublin, Ireland.ORCID https://orcid.org/0000-0002-9258-0895

Funding

Ad Astra Fellows PhD Studentship
6 · The paper itself

Abstract

objectiveTo examine how sleep and movement behaviours, measured on smartphones via ecological momentary assessment (EMA), GPS and accelerometer, impact subsequent daily mood in people with type 2 diabetes (T2D) compared to those without.

methodsSixty-one participants with (n = 32) and without (n = 29) T2D underwent 2 months of smartphone-based data collection through phone sensors (GPS, accelerometer) and EMAs. Daily sleep, movement and mood (happiness, sadness, stress, anger) were assessed. Dynamic structural equation modelling examined the impact of sleep and movement on subsequent mood, adjusted for age, gender and employment status.

resultsWe found 18 significant within-person effects between smartphone-derived behaviour and subsequent mood, with 17 within-person effects indicating behaviour had a positive effect on mood. For people with and without T2D, higher physical activity, better sleep quality and visiting more locations predicted increased happiness, and higher physical activity predicted lower sadness. However, unique behaviour-mood effects were also found for each group, such as greater actigraphy-derived step count predicting greater anger in people with T2D (0.13 [0.05, 0.2]) but having no effect for those without.

conclusionsThough effects were small, results indicate smartphone-derived behaviour influences daily mood for both people with and without T2D, but that the nuances of these relationships may differ. If daily mood correlates differ between people with and without T2D, digital phenotyping for early detection and intervention may need to be tailored to those with T2D.

Indexed as

AffectDiabetes Mellitus, Type 2ExerciseSleepSmartphoneAccelerometryAgedAngerDigital HealthEcological Momentary AssessmentFemaleHumansMaleMiddle AgedMovementPhenotypedaily mooddaily movementdigital phenotypingecological momentary assessmentsleeptype 2 diabetes

Identifiers

PMID41714862
PMCPMC13074140

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LicenceCC BY-NC-ND
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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.