Evidence map›Paper›PMID 42446006›Full record

ArticleSociology of health & illness2026

Algorithmic Choreography: Redefining Human and Non-Human Relations in Digital Health.

Francesco Miele, Ludovica Rubini, Veronica Moretti, Martina Consoloni

Abstract read
In one paragraph

Article in Sociology of health & illness, 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

4 authors.

Francesco MieleDepartment of Political and Social Sciences, University of Trieste, Trieste, Italy.ORCID 0000-0003-1337-9112
Ludovica RubiniDepartment of Political and Social Sciences, University of Trieste, Trieste, Italy.ORCID 0000-0002-6343-1858
Veronica MorettiDepartment of Sociology and Business Law, University of Bologna, Bologna, Italy.ORCID 0000-0002-6506-0501
Martina ConsoloniDepartment of Sociology and Business Law, University of Bologna, Bologna, Italy.ORCID 0000-0001-6600-2824

Funding

Ministero dell'Università e della Ricerca P202254N7P
6 · The paper itself

Abstract

This article contributes to the growing debate on algorithms in digital health, which has expanded alongside the integration of algorithmic technologies into diverse healthcare practices and contexts. Although much of the existing literature adopts a deterministic perspective, focusing on the impact of algorithms on society, we propose a different approach. Drawing on the concept of algorithmic choreography, we show how humans and algorithmic technologies "gear together" in shared performances. Based on an ethnographic study in a nursing home for people with dementia, we examine a telemonitoring algorithmic system designed to prevent adverse events and how it enters into relations with the humans inhabiting this healthcare organization. The system gives rise to two distinct choreographies: night monitoring, in which staff use it for real-time observation of residents' movements and quantified decision-making, where algorithmically generated indexes support longitudinal assessments of residents' health status. We argue that in both choreographies, humans remain central rather than passive recipients of innovation. Care professionals give or withhold agency from algorithms, sustain their functioning and engage in collective improvisations through which human-nonhuman interactions are continually reshaped.

Indexed as

AlgorithmsDigital HealthAnthropology, CulturalDecision MakingDementiaHumansNursing Home ResidentsNursing HomesTelemedicine

Identifiers

PMID42446006
PMCPMC13366560

What OpenQuestion holds

Textmetadata
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.