Evidence map›Paper›PMID 42637834›Full record

ArticleScientific reports2026

Estimating time of death from smartwatch data: initial empirical results.

Tanja Gosseling, Bartholomeus G H Latten, Jan Peter van Zandwijk

Abstract read
In one paragraph

Article in Scientific reports, 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
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0citing papers in PubMed
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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

3 authors.

Tanja GosselingSafety and Health Region Gelderland-Midden (VGGM), Eusebiusbuitensingel 43, Arnhem, 6828 HZ, The Netherlands.
Bartholomeus G H LattenDepartment of Forensic Pathology, Netherlands Forensic Institute, Laan van Ypenburg 6, The Hague, 2497 GB, The Netherlands.
Jan Peter van ZandwijkDepartment of Digital and Biometric Traces, Netherlands Forensic Institute, Laan van Ypenburg 6, The Hague, 2497 GB, The Netherlands. j.p.van.zandwijk@nfi.nl.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Determining the time of death is a critical factor in forensic death investigations. Currently, the most widely used method is rectal temperature measurement. However, there are many, often unknown, factors influencing the results of this method and furthermore it has the limitation that it can only be applied within a restricted timeframe. Therefore, there is a need for new alternative methods. The research question of this exploratory study is whether digital information from smartwatches allows for a reliable determination of the time of death. With this purpose in mind, we collected data from Garmin, Apple and Samsung smartwatches, worn by participants dying at a scheduled moment during a euthanasia procedure. Analysis of experimental data (N = 27) indicates that data registered by smartwatches showed changes around the participant's death, allowing for an estimation of time of death. Although the number of variables and time granularity of registrations varied across types of devices, in most cases the difference between the time of death determined by the physician and the estimates derived from smartwatch data, was smaller than roughly 30 min. For unexplained reasons, however, in some instances there were gaps in the data recorded by smartwatches. This highlights the need for caution when applying this data for determining the time of death. Nonetheless, current first results do indicate that data from smartwatches has potential of providing relevant information about time of death in addition to existing methods.

Indexed as

DeathWearable Electronic DevicesHumansTime FactorsForensic death investigationPost-mortem intervalSmartwatchesTime of deathWearable device forensics

Identifiers

PMID42637834
PMCPMC13503888

What Socratic holds

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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 Socratic graph.