Smartphone as a Sensor in mHealth: Narrative Overview, SWOT Analysis, and Proposal of Mobile Biomarkers.

Alessio Antonini, Serhan Coşar, Iman Naja, Muhammad Salman Haleem, Jamie Hugo Macdonald, Paquale Innominato, Giacinto Barresi

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In one paragraph

Review in Sensors (Basel, Switzerland), 2025. 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

7 authors.

Alessio AntoniniKnowledge Media Institute, The Open University, Milton Keynes MK7 6AA, UK.ORCID 0000-0002-3639-3622

Serhan CoşarSchool of Engineering & Innovation, The Open University, Milton Keynes MK7 6AA, UK.

Iman NajaKnowledge Media Institute, The Open University, Milton Keynes MK7 6AA, UK.ORCID 0000-0001-6634-3266

Muhammad Salman HaleemSchool of Electronic Engineering and Computer Science, Queen Mary University, London E1 4NS, UK.

Jamie Hugo MacdonaldSchool of Psychology and Sport Science, Bangor University, Bangor LL57 2DG, UK.ORCID 0000-0002-2375-146X

Paquale InnominatoOncology Department, Betsi Cadwaladr University Health Board, Bangor LL57 2PW, UK.

Giacinto BarresiKnowledge Media Institute, The Open University, Milton Keynes MK7 6AA, UK.ORCID 0000-0001-7116-4967

Funding

Horizon Europe 857223

6 · The paper itself

Abstract

Digital applications for supporting health management often fail to achieve large-scale adoption. Costs related to purchasing, maintaining, and using medical or sensor devices, such as smartwatches, currently hinder uptake and sustained engagement, particularly in the prevention and monitoring of lifelong conditions. As an alternative, smartphone-based passive monitoring could provide a viable strategy for lifelong use, removing hardware-related costs and exploiting the synergies between mobile health (mHealth) and ambient assisted living (AAL). However, smartphone sensor toolkits are not designed for diagnostic purposes, and their quality varies depending on the model, maker, and generation. This narrative overview of recent reviews (narrative meta-review) on the current state of smartphone-based passive monitoring highlights the strengths, weaknesses, opportunities, and threats (SWOT analysis) of this approach, which pervasively encompasses digital health, mHealth, and AAL. The results are then consolidated into a newly defined concept of a

Indexed as

BiomarkersBiosensing TechniquesSmartphoneTelemedicineHumansMobile ApplicationsMonitoring, PhysiologicBiomarkersdigital healthmHealthpassive monitoringsmartphone

Identifiers

PMID40573543

DOI10.3390/s25123655

PMCPMC12196979

What OpenQuestion holds

Textmetadata

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.