Evidence map›Paper›PMID 26084626›Full record

ArticleBMC medical informatics and decision making2015

Using a mobile health application to support self-management in chronic obstructive pulmonary disease: a six-month cohort study.

Maxine Hardinge, Heather Rutter, Carmelo Velardo, Syed Ahmar Shah, Veronika Williams, Lionel Tarassenko, Andrew Farmer

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 3 pooled it
–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

38 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Differentiating the start of an exacerbation from day-to-day variation in people with COPD: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026
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  9. Digital health system for personalised COPD long-term management.BMC medical informatics and decision making · 2017
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  15. Fractional Dynamics Foster Deep Learning of COPD Stage Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
    Article
  16. Mobile heath applications for self-management in chronic lung disease: a systematic review.Network modeling and analysis in health informatics and bioinformatics · 2023
    Review
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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.

Maxine HardingeOxford University Hospitals NHS Trust, Oxford, UK.
Heather RutterOxford Health Foundation Trust, Oxford, UK.
Carmelo VelardoDepartment of Engineering Science, University of Oxford, Oxford, UK. carmelo.velardo@eng.ox.ac.uk.
Syed Ahmar ShahDepartment of Engineering Science, University of Oxford, Oxford, UK.
Veronika WilliamsNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Lionel TarassenkoDepartment of Engineering Science, University of Oxford, Oxford, UK.
Andrew FarmerNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.

Funding

Department of Health HICF-1010-032Wellcome Trust
6 · The paper itself

Abstract

backgroundSelf-management strategies have the potential to support patients with chronic obstructive pulmonary disease (COPD). Telehealth interventions may have a role in delivering this support along with the opportunity to monitor symptoms and physiological variables. This paper reports findings from a six-month, clinical, cohort study of COPD patients' use of a mobile telehealth based (mHealth) application and how individually determined alerts in oxygen saturation levels, pulse rate and symptoms scores related to patient self-initiated treatment for exacerbations.

methodsThe development of the mHealth intervention involved a patient focus group and multidisciplinary team of researchers, engineers and clinicians. Individual data thresholds to set alerts were determined, and the relationship to exacerbations, defined by the initiation of stand-by medications, was measured. The sample comprised 18 patients (age range of 50-85 years) with varied levels of computer skills.

resultsPatients identified no difficulties in using the mHealth application and used all functions available. 40% of exacerbations had an alert signal during the three days prior to a patient starting medication. Patients were able to use the mHealth application to support self- management, including monitoring of clinical data. Within three months, 95% of symptom reporting sessions were completed in less than 100 s.

conclusionsHome based, unassisted, daily use of the mHealth platform is feasible and acceptable to people with COPD for reporting daily symptoms and medicine use, and to measure physiological variables such as pulse rate and oxygen saturation. These findings provide evidence for integrating telehealth interventions with clinical care pathways to support self-management in COPD.

Indexed as

AgedAged, 80 and overCohort StudiesHumansMiddle AgedMobile ApplicationsPulmonary Disease, Chronic ObstructiveSelf CareTelemedicine

Identifiers

PMID26084626
PMCPMC4472616

What OpenQuestion 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 OpenQuestion graph.