Evidence map›Paper›PMID 40966661›Full record

ArticleJMIR human factors2025

Understanding the Thoughts and Preferences for Technologies Designed to Detect Feelings of Loneliness: Interview Study Among Older Adults.

Jessica Rees, John Ratcliffe, Wei Liu, Yi Zhou, Sebastien Ourselin, Michela Antonelli, Ashay Patel, Yu Shi, Jingqi Liu, Anthea Tinker and 1 more

Abstract read
In one paragraph

Article in JMIR human factors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
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

11 authors.

Jessica ReesInstitute of Gerontology, King's College London, London, United Kingdom.ORCID 0000-0002-9471-2134
John RatcliffeSchool of Healthcare, University of Leeds, Leeds, United Kingdom.ORCID 0000-0002-6835-6199
Wei LiuDepartment of Engineering, King's College London, Strand Building, Strand Campus, London, WC2R 2LS, United Kingdom, +442078482747.ORCID 0000-0002-8014-7218
Yi ZhouDepartment of Engineering, King's College London, Strand Building, Strand Campus, London, WC2R 2LS, United Kingdom, +442078482747.ORCID 0000-0002-4176-5793
Sebastien OurselinSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.ORCID 0000-0002-5694-5340
Michela AntonelliSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.ORCID 0000-0002-3005-4523
Ashay PatelSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.ORCID 0000-0003-4212-2578
Yu ShiSchool of Design, University of Leeds, Leeds, United Kingdom.ORCID 0000-0003-3891-7175
Jingqi LiuSchool of Design, University of Leeds, Leeds, United Kingdom.ORCID 0009-0002-7405-8590
Anthea TinkerInstitute of Gerontology, King's College London, London, United Kingdom.ORCID 0000-0002-0305-7198
Faith MatchamSchool of Psychology, University of Sussex, Falmer, United Kingdom.ORCID 0000-0002-4055-904X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Loneliness is a negative emotional state that is common in later life. The accumulative effects of loneliness have a significant impact on the physical and mental health of older adults. Automatic methods for detection and prediction are an emerging field to support early identification of loneliness. Objective: This study aimed to qualitatively explore the thoughts and preferences of people aged 65 years and older regarding technologies to detect feelings of loneliness in later life. Methods: We conducted 60 semistructured interviews with people aged 65 years and older between September 2022 and August 2023. Data were analyzed using a reflective thematic approach on NVivo software (Lumivero). Results: In total, three themes were identified representing what older adults considered important in a system able to detect loneliness: (1) interest and control of data, which was a priority for older adults; (2) perceived usefulness to address loneliness, which related to the importance of providing recommendations to reduce feelings of loneliness after detection; and (3) personalization as a priority, which included the level of loneliness for which an alert was sent and selection of relevant individuals who would be sent a loneliness alert. Conclusions: Findings from this in-depth qualitative study provide important perspectives from people with lived experience of loneliness on the context in which a sensor-based loneliness detection system would be most useful and acceptable to older adults. Future research will include such perspectives in the design of innovative technologies enabling the early detection of loneliness and access to timely interventions to tackle loneliness in later life.

Indexed as

LonelinessAgedAged, 80 and overFemaleHumansInterviews as TopicMaleQualitative Researchlonelinessmobile phoneolder adultspreferencesqualitativetechnology

Identifiers

PMID40966661
PMCPMC12445782

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Registered trials

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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.