Evidence map›Paper›PMID 42197949›Full record

ArticleSensors (Basel, Switzerland)2026

"Are You Okay, Honey?": Recognizing Emotions Among Couples Managing Diabetes in Daily Life Using Multimodal Real-World Smartwatch Data.

George Boateng, Xiangyu Zhao, Malgorzata Speichert, Elgar Fleisch, Janina Lüscher, Theresa Pauly, Urte Scholz, Guy Bodenmann, Tobias Kowatsch

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

George BoatengDepartment of Management, Technology, and Economics, ETH Zurich, 8092 Zurich, Switzerland.ORCID 0000-0003-4540-9582
Xiangyu ZhaoChair of Data Science in Earth Observation, Technical University of Munich (TUM), 80333 Munich, Germany.ORCID 0000-0003-4887-1924
Malgorzata SpeichertDepartment of Computer Science, ETH Zurich, 8092 Zurich, Switzerland.ORCID 0009-0008-0968-6957
Elgar FleischDepartment of Management, Technology, and Economics, ETH Zurich, 8092 Zurich, Switzerland.ORCID 0000-0002-4842-1117
Janina LüscherSwiss Paraplegic Research, 6207 Nottwil, Switzerland.ORCID 0000-0003-2453-1925
Theresa PaulyDepartment of Gerontology, Simon Fraser University, Vancouver, BC V6B 5K3, Canada.ORCID 0000-0002-6855-1787
Urte ScholzDepartment of Psychology, University of Zurich, 8050 Zurich, Switzerland.ORCID 0000-0003-0184-5921
Guy BodenmannDepartment of Psychology, University of Zurich, 8050 Zurich, Switzerland.ORCID 0000-0003-0964-6409
Tobias KowatschDepartment of Management, Technology, and Economics, ETH Zurich, 8092 Zurich, Switzerland.ORCID 0000-0001-5939-4145

Funding

Swiss National Science Foundation CR12I1_166348/1
6 · The paper itself

Abstract

Couples generally manage chronic diseases together and the management takes an emotional toll on both patients and their romantic partners. Consequently, recognizing the emotions of each partner in daily life could provide insight into their emotional well-being in chronic disease management. Currently, the process of assessing each partner's emotions is manual, time-intensive, and costly. Despite the existence of works on emotion recognition among couples, none of these works have used data collected from couples' interactions in daily life. In this work, we collected 85 h (1021 5-min samples) of real-world multimodal smartwatch sensor data (speech, heart rate, accelerometer, and gyroscope) and self-reported emotion data (

Indexed as

Diabetes Mellitus, Type 2EmotionsAccelerometryFemaleHeart RateHumansMachine LearningMaleSupport Vector MachineWearable Electronic Devicesaffective computingBERTchronic disease managementcouplesdeep learningemotion recognitionmachine learningmultimodal sensor datanatural language processingsmartwatchesspeech processingtransfer learningwearable computing

Identifiers

PMID42197949
PMCPMC13210981

What OpenQuestion holds

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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.