Consumer preference to utilise a mobile health app: A stated preference experiment

Lim, David; Norman, Richard; Robinson, Suzanne

doi:10.1371/journal.pone.0229546

Cited by 20 publications

(21 citation statements)

References 37 publications

(31 reference statements)

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“…Some assessment criteria that may facilitate the evaluation of the value domain were identified, such as questions related to an app’s usefulness in improving patients’ quality of life, 20 improve monitoring and management of disease, 20 , 21 and facilitate healthcare service appointments. 22 …”

Section: Resultsmentioning

confidence: 99%

Scoping review: Development and assessment of evaluation frameworks of mobile health apps for recommendations to consumers

Hensher

Cooper

Dona

et al. 2021

Journal of the American Medical Informatics Association

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Objective The study sought to review the different assessment items that have been used within existing health app evaluation frameworks aimed at individual, clinician, or organizational users, and to analyze the scoring and evaluation methods used in these frameworks. Materials and Methods We searched multiple bibliographic databases and conducted backward searches of reference lists, using search terms that were synonyms of “health apps,” “evaluation,” and “frameworks.” The review covered publications from 2011 to April 2020. Studies on health app evaluation frameworks and studies that elaborated on the scaling and scoring mechanisms applied in such frameworks were included. Results Ten common domains were identified across general health app evaluation frameworks. A list of 430 assessment criteria was compiled across 97 identified studies. The most frequently used scaling mechanism was a 5-point Likert scale. Most studies have adopted summary statistics to generate the total scoring of each app, and the most popular approach taken was the calculation of mean or average scores. Other frameworks did not use any scaling or scoring mechanism and adopted criteria-based, pictorial, or descriptive approaches, or “threshold” filter. Discussion There is wide variance in the approaches to evaluating health apps within published frameworks, and this variance leads to ongoing uncertainty in how to evaluate health apps. Conclusions A new evaluation framework is needed that can integrate the full range of evaluative criteria within one structure, and provide summative guidance on health app rating, to support individual app users, clinicians, and health organizations in choosing or recommending the best health app.

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Section: Resultsmentioning

confidence: 99%

Scoping review: Development and assessment of evaluation frameworks of mobile health apps for recommendations to consumers

Hensher

Cooper

Dona

et al. 2021

Journal of the American Medical Informatics Association

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show abstract

“…Perceptions of functionality, performance, trustworthiness, ease of use (e.g., interface, time required to learn etc. ), and certain privacy concerns that are influential in usage [ 31 ] may be overcome by application developers through the careful design of mobile apps and communication in mobile marketplaces [ 32 ]. For instance, it has been shown that informational content, organizational attributes, technology-related features, and user control factors influence the trustworthiness of m-health applications [ 33 ].…”

Section: Background and Related Workmentioning

confidence: 99%

Insights into mobile health application market via a content analysis of marketplace data with machine learning

Aydın

Silahtaroğlu

2021

PLoS ONE

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Background Despite the benefits offered by an abundance of health applications promoted on app marketplaces (e.g., Google Play Store), the wide adoption of mobile health and e-health apps is yet to come. Objective This study aims to investigate the current landscape of smartphone apps that focus on improving and sustaining health and wellbeing. Understanding the categories that popular apps focus on and the relevant features provided to users, which lead to higher user scores and downloads will offer insights to enable higher adoption in the general populace. This study on 1,000 mobile health applications aims to shed light on the reasons why particular apps are liked and adopted while many are not. Methods User-generated data (i.e. review scores) and company-generated data (i.e. app descriptions) were collected from app marketplaces and manually coded and categorized by two researchers. For analysis, Artificial Neural Networks, Random Forest and Naïve Bayes Artificial Intelligence algorithms were used. Results The analysis led to features that attracted more download behavior and higher user scores. The findings suggest that apps that mention a privacy policy or provide videos in description lead to higher user scores, whereas free apps with in-app purchase possibilities, social networking and sharing features and feedback mechanisms lead to higher number of downloads. Moreover, differences in user scores and the total number of downloads are detected in distinct subcategories of mobile health apps. Conclusion This study contributes to the current knowledge of m-health application use by reviewing mobile health applications using content analysis and machine learning algorithms. The content analysis adds significant value by providing classification, keywords and factors that influence download behavior and user scores in a m-health context.

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“…We segmented literature into three preference-elicitation categories based on the preference study design: (I) experimental preference methods (DCE, conjoint analysis, (60), and one used a take-it-or leave conjoint analysis (62).…”

Section: Preference Elicitation Approachesmentioning

confidence: 99%

“…Sub-group and heterogeneity analysis conducted in many studies revealed that demographic characteristics such as age (47,54,62,65,66,70,71,74,78,86,87,97,101), race (60,102), gender (51,62,86,97), education (62,86,87,101), income (46,70) and proximity to care (46,79) were associated with patient preferences for HIT. Younger patients and higher income patients generally placed higher utility on HIT services.…”

Section: Preferences For Hit Vary Based On Patient Characteristicsmentioning

confidence: 99%

Patient preferences for health information technologies: a systematic review

Crossnohere¹,

Weiss²,

Hyman³

et al. 2021

J Hosp Manag Health Policy

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Background: Advances in patient-facing health information technology (HIT) promise to improve health care delivery and patient outcomes. Low utilization of HIT suggests that the preferences of patients may not be adequately guiding the development of these technologies. This systematic review offers an assessment of published evidence regarding patient preferences for HIT.Methods: Articles addressing preferences for HIT from patient and other end-user groups published up through 2020 were identified from PubMed, Web of Science, Scopus and via hand searching. Articles that used quantitative stated-preference methods to explore preferences for HIT were eligible for inclusion.Studies that explored attitudes towards HIT without eliciting trade-offs were excluded. Critical appraisal of study quality was conducted using the PREFS checklist and quality criteria identified by the US Food and Drug Administration including heterogeneity analysis and patient engagement in study design. We conducted thematic analysis of the main preference findings from each study to synthesize patient and enduser preferences for HIT. The review was not registered and authors received no funding to conduct the review.Results: The search yielded 7,299 unique articles, 59 of which were ultimately included in the review.Studies explored preferences for telemedicine (n=30), patient portals (n=12), mHealth (n=9) or multiple HITs (n=8). Preference elicitation methods included direct elicitation (n=26), discrete-choice experiments (n=13), conjoint analysis (n=6), contingent valuation (n=5), and ranking exercises (n=9). Studies had a mean PREFS score of 3.51 out of 5. Forty-two studies conducted preference heterogeneity analysis and only 20 included patients in study design. Thematic meta-analysis indicated that patients prefer HIT that is convenient and lower cost, but does not sacrifice quality, and preferences varied by demographic features such as age as well as depending on the type of health information being communicated.Conclusions: Patient and end-users have distinct preferences for the use of HIT in their medical care.It is timely that researchers and healthcare administrators consider these preferences for HIT given its rapid uptake amidst the COVID-19 pandemic. Although this literature demonstrates that patients can be engaged as participants in preference studies to identify meaningful aspects of HIT, the field was limited in its inclusion of patients in the design of such studies. Future development of HIT should be guided by highquality preference research that integrates patients in all stages in the design and implementation of HIT.

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Consumer preference to utilise a mobile health app: A stated preference experiment

Cited by 20 publications

References 37 publications

Scoping review: Development and assessment of evaluation frameworks of mobile health apps for recommendations to consumers

Scoping review: Development and assessment of evaluation frameworks of mobile health apps for recommendations to consumers

Insights into mobile health application market via a content analysis of marketplace data with machine learning

Patient preferences for health information technologies: a systematic review

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