Evidence map›Paper›PMID 36461017›Full record

ArticleBMC medical informatics and decision making2022

Usability evaluation of mHealth apps for elderly individuals: a scoping review.

Qiuyi Wang, Jing Liu, Lanshu Zhou, Jing Tian, Xuemei Chen, Wei Zhang, He Wang, Wanqiong Zhou, Yitian Gao

Abstract readScoping Review
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 2 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

52 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

9 authors.

Qiuyi Wang *Clinical Nursing Department, Naval Medical University, 800 Xiang Yin Road, Yangpu District, Shanghai, 200433, China.
Jing Liu *Clinical Nursing Department, Naval Medical University, 800 Xiang Yin Road, Yangpu District, Shanghai, 200433, China.
Lanshu ZhouClinical Nursing Department, Naval Medical University, 800 Xiang Yin Road, Yangpu District, Shanghai, 200433, China. zhoulanshu@hotmail.com.
Jing TianClinical Nursing Department, Naval Medical University, 800 Xiang Yin Road, Yangpu District, Shanghai, 200433, China.
Xuemei ChenClinical Nursing Department, Naval Medical University, 800 Xiang Yin Road, Yangpu District, Shanghai, 200433, China.
Wei ZhangClinical Nursing Department, Naval Medical University, 800 Xiang Yin Road, Yangpu District, Shanghai, 200433, China.
He WangClinical Nursing Department, Naval Medical University, 800 Xiang Yin Road, Yangpu District, Shanghai, 200433, China.
Wanqiong ZhouClinical Nursing Department, Naval Medical University, 800 Xiang Yin Road, Yangpu District, Shanghai, 200433, China.
Yitian GaoClinical Nursing Department, Naval Medical University, 800 Xiang Yin Road, Yangpu District, Shanghai, 200433, China.

Funding

Shanghai Science and Technology Committee under Grant 16XD1403200
6 · The paper itself

Abstract

backgroundUsability is a key factor affecting the acceptance of mobile health applications (mHealth apps) for elderly individuals, but traditional usability evaluation methods may not be suitable for use in this population because of aging barriers. The objectives of this study were to identify, explore, and summarize the current state of the literature on the usability evaluation of mHealth apps for older adults and to incorporate these methods into the appropriate evaluation stage.

methodsElectronic searches were conducted in 10 databases. Inclusion criteria were articles focused on the usability evaluation of mHealth apps designed for older adults. The included studies were classified according to the mHealth app usability evaluation framework, and the suitability of evaluation methods for use among the elderly was analyzed.

resultsNinety-six articles met the inclusion criteria. Research activity increased steeply after 2013 (n = 92). Satisfaction (n = 74) and learnability (n = 60) were the most frequently evaluated critical measures, while memorability (n = 13) was the least evaluated. The ratios of satisfaction, learnability, operability, and understandability measures were significantly related to the different stages of evaluation (P < 0.05). The methods used for usability evaluation were questionnaire (n = 68), interview (n = 36), concurrent thinking aloud (n = 25), performance metrics (n = 25), behavioral observation log (n = 14), screen recording (n = 3), eye tracking (n = 1), retrospective thinking aloud (n = 1), and feedback log (n = 1). Thirty-two studies developed their own evaluation tool to assess unique design features for elderly individuals.

conclusionIn the past five years, the number of studies in the field of usability evaluation of mHealth apps for the elderly has increased rapidly. The mHealth apps are often used as an auxiliary means of self-management to help the elderly manage their wellness and disease. According to the three stages of the mHealth app usability evaluation framework, the critical measures and evaluation methods are inconsistent. Future research should focus on selecting specific critical measures relevant to aging characteristics and adapting usability evaluation methods to elderly individuals by improving traditional tools, introducing automated evaluation tools and optimizing evaluation processes.

Indexed as

Mobile ApplicationsSelf-ManagementTelemedicineAgedAgingHumansRetrospective StudiesApplicationsElderlyHuman factor designmHealthUsability

Identifiers

PMID36461017
PMCPMC9717549

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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.