Evidence map›Paper›PMID 41472724›Full record

ReviewFrontiers in immunology2025

Remote patient monitoring in autoimmune related interstitial lung diseases: a narrative review.

Malik A Althobiani, Maryam Almoagal

Abstract readReview
In one paragraph

Review in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

2 authors.

Malik A AlthobianiDepartment of Respiratory Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.
Maryam AlmoagalRespiratory Therapy Department, College of Applied Medical Sciences, King Saud Bin Abdulaziz University for Health Sciences, Al Ahsa, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autoimmune related interstitial lung disease can worsen between clinic visits, and episodic assessment may miss clinically important change. Digital health extends observation into daily life through home spirometry, wearable sensors, application based patient reported outcomes, and therapist supported telerehabilitation. This Review synthesizes recent evidence on feasibility and adherence, data quality and agreement with clinic assessments, patient experience and safety, and service integration for remote monitoring in autoimmune related interstitial lung disease. Device derived signals and patient generated health data show useful agreement with clinic measures when interpreted across repeated time points, and remote monitoring data can reveal actionable trends and support rehabilitation and self-management. Important limitations remain, including variability and artifacts, missing data, uneven interoperability, workload implications for services, and inequities in digital access. We outline a practical workflow for adoption that includes enrolment, training, quality checks, alert thresholds, and escalation to the multidisciplinary team, with attention to privacy, cost, and record integration. Remote monitoring can complement standard care by increasing observation frequency and patient support. Priorities for the field are to define clinically meaningful digital endpoints, evaluate effects on outcomes and use of resources, and develop strategies that sustain long term engagement.

Indexed as

Autoimmune DiseasesLung Diseases, InterstitialHumansMonitoring, PhysiologicRemote Patient MonitoringTelemedicineartificial intelligence - AIautoimmune rheumatic diseasesinterstitial lung disease (ILD)machine learningremote monitorwearables

Identifiers

PMID41472724
PMCPMC12745459

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.