Evidence map›Paper›PMID 31140445›Full record

ArticleJMIR mHealth and uHealth2019

User Preferences and Persona Design for an mHealth Intervention to Support Adherence to Cardiovascular Disease Medication in Singapore: A Multi-Method Study.

Victoria Haldane, Joel Jun Kai Koh, Aastha Srivastava, Krichelle Wei Qi Teo, Yao Guo Tan, Rui Xiang Cheng, Yi Cheng Yap, Pei-Shi Ong, Rob M Van Dam, Jie Min Foo and 5 more

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05633056 (Adaptive Evaluation of mHealth and Conventional Adherence Support Interventions to Optimize Outcome With New Treatment Regimens for Drug-resistant Tuberculosis and HIV in South Africa), which is not on this map. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
3.0field-weighted citation impact, top 8% 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.

NCT05633056 narecruitingnot on this mapstarted 2023, after this paper: background citation

Adaptive Evaluation of mHealth and Conventional Adherence Support Interventions to Optimize Outcome With New Treatment Regimens for Drug-resistant Tuberculosis and HIV in South Africa

TypeinterventionalSponsorColumbia UniversityRan2023 to 2029Enrolled360ConditionsMulti Drug Resistant Tuberculosis, HIV InfectionsArmsAdherence support intevention
3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 1 synthesis or guideline pooled it, 72 citations in OpenAlex.

  1. Pooled it
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  14. Atherosclerotic cardiovascular disease landscape in Singapore.Frontiers in cardiovascular medicine · 2024
    Review
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  19. Using Health Forum Data to Assess User Needs for mHealth App Development: A Feasibility Study on Alzheimer's Disease.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023
    Article
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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

15 authors at 3 institutions in 4 countries.

Victoria HaldaneInstitute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada.ORCID 0000-0002-8674-4099
Joel Jun Kai KohSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.ORCID 0000-0003-3488-8119
Aastha SrivastavaSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.ORCID 0000-0002-8405-5898
Krichelle Wei Qi TeoDepartment of Pharmacy, National University of Singapore, Singapore, Singapore.ORCID 0000-0002-5106-6937
Yao Guo TanDepartment of Pharmacy, National University of Singapore, Singapore, Singapore.ORCID 0000-0002-9139-6340
Rui Xiang ChengDepartment of Pharmacy, National University of Singapore, Singapore, Singapore.ORCID 0000-0003-4074-8710
Yi Cheng YapDepartment of Pharmacy, National University of Singapore, Singapore, Singapore.ORCID 0000-0002-3613-5121
Pei-Shi OngDepartment of Pharmacy, National University of Singapore, Singapore, Singapore.ORCID 0000-0003-1679-6640
Rob M Van DamSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.ORCID 0000-0002-7354-8734
Jie Min FooSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.ORCID 0000-0002-7992-9511
Falk Müller-RiemenschneiderSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.ORCID 0000-0003-1402-7477
Gerald Choon-Huat KohSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.ORCID 0000-0002-6453-6897
Pin Sym FoongSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.ORCID 0000-0002-4437-8326
Pablo PerelLondon School of Hygiene and Tropical Medicine, London, United Kingdom.ORCID 0000-0002-2342-301X
Helena Legido-QuigleySaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.ORCID 0000-0003-4113-4542
National University of Singapore · SGLondon School of Hygiene & Tropical Medicine · GBUniversity of Toronto · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe use of mobile health (mHealth) has gained popularity globally, including for its use in a variety of health interventions, particularly through short message service (SMS) text messaging. However, there are challenges to the use of mHealth, particularly among older users who have a large heterogeneity in usability and accessibility barriers when using technology.

objectiveIn order to better understand and conceptualize the diversity of users and give insight into their particular needs, we turned to persona creation. Personas are user archetypes created through data generated from multi-method inquiry with actual target users. Personas are an appropriate yet largely underutilized component of current mHealth research.

methodsLeveraging data from a multi-method study conducted in Singapore with an ethnically diverse population including Chinese, Malay, and Indian participants, we used a proforma to analyze data from the qualitative component (ie, 20 in-depth interviews) and quantitative component (ie, 100 interviewer-guided surveys). We then identified key characteristics, including technology use and preferences as well as adherence factors, to synthesize five personas reflective of persons over the age of 40 years in Singapore with atherosclerotic cardiovascular disease (ASCVD) or ASCVD risk factors, such as hypertension.

resultsWe present five personas typologized as (1) The Quiet Analog, (2) The Busy Grandparent, (3) The Socializer, (4) The Newly Diagnosed, and (5) The Hard-to-Reach. We report on four key characteristics: health care access, medication adherence, mobile phone technology usage (ie, ownership, access, and utilization), and interest in mHealth. Finally, we provide insights into how these personas may be used in the design and implementation of an mHealth intervention. Our work demonstrates how multi-method data can create biopsychosocial personas that can be used to explore and address the diversity in behaviors, preferences, and needs in user groups.

conclusionsWith wider adoption of mHealth, it is important that we consider user-centered design techniques and design thinking in order to create meaningful, patient-centered interventions for adherence to medications. Future research in this area should include greater exploration of how these five personas can be used to better understand how and when is best to deliver mHealth interventions in Singapore and beyond.

Indexed as

Drug MonitoringAgedAged, 80 and overCardiovascular DiseasesCell PhoneFemaleHumansMaleMedication AdherenceMiddle AgedQualitative ResearchSingaporeSurveys and QuestionnairesTelemedicineadherenceASCVDbiopsychosocial personaspatient perspectivespersonasqualitative

Identifiers

PMID31140445
PMCPMC6658252
OpenAlexW2903590035

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.