Evidence map›Paper›PMID 42239502›Full record

ArticleProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies2025

From Verbal Reports to Personalized Activity Trackers: Understanding the Challenges of Ground Truth Data Collection with Older Adults in the Wild.

Hossein Khayami, Lining Wang, Young-Ho Kim, Bongshin Lee, David E Conroy, Amanda Lazar, Eun Kyoung Choe, Hernisa Kacorri

Abstract read
In one paragraph

Article in Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 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. Enabling Older Adults to Provide High-quality Activity Labels: Unpacking Accuracy, Precision, and Granularity in Activity Labeling.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025
    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

8 authors.

Hossein KhayamiUniversity of Maryland, College Park, USA.ORCID 0000-0002-4485-9322
Lining WangUniversity of Maryland, College Park, USA.ORCID 0000-0002-7191-8762
Young-Ho KimNAVER AI Lab, Republic of Korea.ORCID 0000-0002-2681-2774
Bongshin LeeYonsei University, Republic of Korea.ORCID 0000-0002-4217-627X
David E ConroyUniversity of Michigan, USA.ORCID 0000-0003-0204-4093
Amanda LazarUniversity of Maryland, College Park, USA.ORCID 0000-0001-8110-4239
Eun Kyoung ChoeUniversity of Maryland, College Park, USA.ORCID 0000-0001-5038-8320
Hernisa KacorriUniversity of Maryland, College Park, USA.ORCID 0000-0002-7798-308X

Funding

ACL HHS 90REGE0024
6 · The paper itself

Abstract

Tracking activities holds great potential to improve the well-being of older adults, yet the accuracy of activity trackers for this demographic remains in question. Evaluating this accuracy requires ground-truth data directly from older adults, which has largely been gathered in controlled laboratory settings or labeled by researchers. Moreover, considering the diversity in older adults' activity engagement and tracking preferences, personalized activity tracking appears necessary. We demonstrate that older adults can benefit from personalized activity trackers by showing that cadence thresholds for stepping intensities vary within this group. However, collecting ground-truth data from older adults in real-world settings poses unique challenges. This paper examines two sources of ground-truth labels for the smartwatch Inertial Measurement Unit (IMU) data collected with older adults. Using verbal self-reports and a thigh-worn activity tracker, we assess their viability as ground-truth sources in natural settings. Additionally, we evaluate the costs and benefits of triangulating these sources as a ground-truth proxy. Our findings reveal two main costs: data shrinkage and notable effort from both contributors and data stewards. Simultaneously, we observe improved data quality and a greater ability to identify error sources when evaluating a trained model.

Indexed as

Human Activity RecognitionMachine LearningOlder AdultsSelf TrackingSeniorsSmartwatches

Identifiers

PMID42239502
PMCPMC13227613

What OpenQuestion holds

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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 OpenQuestion graph.