Evidence map›Paper›PMID 36171962›Full record

ArticleDigital health

Home-based digital health technologies for older adults to self-manage multiple chronic conditions: A data-informed analysis of user engagement from a longitudinal trial.

Yiyang Sheng, Julie Doyle, Raymond Bond, Rajesh Jaiswal, Shane Gavin, John Dinsmore

Open access · goldAbstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
4.2field-weighted citation impact, top 5% of its field
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

16 citing papers in PubMed, 27 citations in OpenAlex.

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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

6 authors at 3 institutions in 2 countries.

Yiyang ShengNetwellCASALA, Dundalk Institute of Technology, Dundalk, Co. Louth, Ireland.ORCID https://orcid.org/0000-0001-9140-7903
Julie DoyleNetwellCASALA, Dundalk Institute of Technology, Dundalk, Co. Louth, Ireland.ORCID https://orcid.org/0000-0003-4017-6329
Raymond BondSchool of Computing, Ulster University, Jordanstown, UK.
Rajesh JaiswalSchool of Computing, Dundalk Institute of Technology, Dundalk, Co. Louth, Ireland.
Shane GavinNetwellCASALA, Dundalk Institute of Technology, Dundalk, Co. Louth, Ireland.
John DinsmoreTrinity Centre for Practice and Healthcare Innovation, School of Nursing and Midwifery, Trinity College Dublin, Dublin, Ireland.
Dundalk Institute of Technology · IETrinity College Dublin · IEUniversity of Ulster · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Ageing populations are resulting in higher prevalence of people with multiple chronic conditions (multimorbidity). Digital health platforms have great potential to support self-management of multimorbidity, increasing a person's awareness of their health and well-being, supporting a better understanding of diseases and encouraging behaviour change. However, little research has explored the long-term engagement of older adults with such digital interventions. Methods: The aim of this study is to analyse how 60 older adults with multimorbidity engaged with digital symptom and well-being monitoring through a digital health platform over a period of approximately 12 months. Data analysis focused on user retention, frequency of monitoring, intervals in monitoring and patterns of daily engagement. Results: Our findings show that the overall engagement with the digital health platform was high, with more than 80% of participants using the technology devices for over 200 days. The submission frequency for symptom parameters (e.g. blood glucose (BG), blood pressure (BP), etc.) was between three and four times per week which was higher than that of self-report (2.24) and weight (2.84). Submissions of exercise (6.12) and sleep (5.67) were more frequent. The majority of interactions happened in the morning time. The most common time of submission for symptom parameters was 10 am, whereas 8 am was the most common time for weight measurements. Conclusions: The findings indicate the patterns of engagement of older adults with complex chronic diseases with digital home-based self-management systems.

Indexed as

Ageingchronic diseasedigital healthengagementmultimorbidityself-management

Identifiers

PMID36171962
PMCPMC9511338
OpenAlexW4296881202

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.