Drivers of Mobile Health Acceptance and Use From the Patient Perspective: Survey Study and Quantitative Model Development

Salgado, Tânia; Tavares, Jorge; Oliveira, Tiago

doi:10.2196/17588

Cited by 47 publications

(56 citation statements)

References 47 publications

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“…Salgado et al [ 48 ] recently reviewed various studies about mobile technology solutions to address health care challenges. Following their review, they suggested that the presence of a chronic health condition predicates an impact on the acceptance of mHealth technology.…”

Section: Introductionmentioning

confidence: 99%

“…Perceived ease of use is the subjective belief that the product, when used, does not require significant physical or mental effort. The higher these two constructs are rated, the greater the intention to use the product [ 46 , 48 ]. Paying attention to these constructs in an app’s design can encourage use of these technological tools.…”

Section: Introductionmentioning

confidence: 99%

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Supporting Management of Noncommunicable Diseases With Mobile Health (mHealth) Apps: Experimental Study

Kela¹,

Eytam²,

Katz³

2022

JMIR Hum Factors

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Background Noncommunicable diseases (NCDs) are the leading global health problem in this century and are the principal causes of death and health care spending worldwide. Mobile health (mHealth) apps can help manage and prevent NCDs if people are willing to use them as supportive tools. Still, many people are reluctant to adopt these technologies. Implementing new apps could result in earlier intervention for many health conditions, preventing more serious complications. Objective This research project aimed to test the factors that facilitate the adoption of mHealth apps by users with NCDs. We focused on determining, first, what user interface (UI) qualities and complexity levels appeal to users in evaluating mHealth apps. We also wanted to determine whether people prefer that the data collected by an mHealth app be analyzed using a physician or an artificial intelligence (AI) algorithm. The contribution of this work is both theoretical and practical. We examined users’ considerations when adopting mHealth apps that promote healthy lifestyles and helped them manage their NCDs. Our results can also help direct mHealth app UI designers to focus on the most appealing aspects of our findings. Methods A total of 347 respondents volunteered to rate 3 models of mHealth apps based on 16 items that measured instrumentality, aesthetics, and symbolism. Respondents rated each model after reading 1 of 2 different scenarios. In one scenario, a physician analyzed the data, whereas, in the other, the data were analyzed by an AI algorithm. These scenarios tested the degree of trust people placed in AI algorithms versus the “human touch” of a human physician regarding analyzing data collected by an mHealth app. Results As shown by the responses, the involvement of a human physician in the application had a significant effect (P<.001) on the perceived instrumentality of the simple model. The complex model with more controls was rated significantly more aesthetic when associated with a physician performing data analysis rather than an AI algorithm (P=.03). Conclusions Generally, when participants found a human touch in the mHealth app (connection to a human physician who they assumed would analyze their data), they judged the app more favorably. Simple models were evaluated more positively than complex ones, and aesthetics and symbolism were salient predictors of preference. These trends suggest that designers and developers of mHealth apps should keep the designs simple and pay special attention to aesthetics and symbolic value.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Supporting Management of Noncommunicable Diseases With Mobile Health (mHealth) Apps: Experimental Study

Kela¹,

Eytam²,

Katz³

2022

JMIR Hum Factors

View full text Add to dashboard Cite

show abstract

“…Moreover, the COVID-19 outbreak has had extensive consequences for the provision of health care. This holds especially for older patients whose health care usage is likely to be higher while the pandemic makes it more critical for them to stay home, as age significantly determines the clinical features and prognosis of COVID-19 [10][11][12]. Through their rapid uptake, mobile health (mHealth) technologies enable health care without the need for face-to-face contact [10,13,14].…”

Section: Introductionmentioning

confidence: 99%

Relationship Between Perceived Risks of Using mHealth Applications and the Intention to Use Them Among Older Adults in the Netherlands: Cross-sectional Study

Klaver¹,

Klundert²,

Broek³

et al. 2021

JMIR Mhealth Uhealth

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Background Considering the increasing demand for health services by older people and the ongoing COVID-19 pandemic, digital health is commonly viewed to offer a pathway to provide safe and affordable health services for older adults, thus enabling self-management of their health while health care systems are struggling. However, several factors cause older people to be particularly reluctant to adopt digital health technologies such as mobile health (mHealth) tools. In addition to previously studied technology acceptance factors, those related to perceived risks of mHealth use (eg, leakage of sensitive information or receiving incorrect health recommendations) may further diminish mHealth adoption by older adults. Objective The aim of this study was to explore the relationship between perceived risks of using mHealth applications and the intention to use these applications among older adults. Methods We designed a cross-sectional study wherein a questionnaire was used to collect data from participants aged 65 years and older in the Netherlands. Perceived risk was divided into four constructs: privacy risk, performance risk, legal concern, and trust. Linear regression analyses were performed to determine the associations between these perceived risk constructs and the intention to use mHealth applications. Results Linear regression per perceived risk factor showed that each of the four constructs is significantly associated with the intention to use mobile medical applications among older adults (adjusted for age, sex, education, and health status). Performance risk (β=–.266; P=<.001), legal concern (β=–.125; P=.007), and privacy risk (β=–.100; P=.03) were found to be negatively correlated to intention to use mHealth applications, whereas trust (β=.352; P=<.001) was found to be positively correlated to the intention to use mHealth applications. Conclusions Performance risk, legal concern, and privacy risk as perceived by older adults may substantially and significantly decrease their intention to use mHealth applications. Trust may significantly and positively affect this intention. Health care professionals, designers of mHealth applications, and policy makers can use these findings to diminish performance risks, and tailor campaigns and applications to address legal and privacy concerns and promote mHealth uptake and health care access for older adults, especially during the COVID-19 pandemic.

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“…12 The intent to use mHealth technologies was critical for its acceptance by patients. [33][34][35][36] Both intrinsic (eg, sex, age, education level, self-efficacy, usage experience, and health perception) and extrinsic (eg, social influence and facilitating conditions) factors were associated with the intent to use mHealth technologies. [33][34][35][36] The findings of our study were based on the perspectives of PHC providers on patients' use and acceptance, which does not reflect all aspects of personal factors.…”

Section: Dovepressmentioning

confidence: 99%

“…[33][34][35][36] Both intrinsic (eg, sex, age, education level, self-efficacy, usage experience, and health perception) and extrinsic (eg, social influence and facilitating conditions) factors were associated with the intent to use mHealth technologies. [33][34][35][36] The findings of our study were based on the perspectives of PHC providers on patients' use and acceptance, which does not reflect all aspects of personal factors. According to our results, it is unlikely that patients will use the application for PA counselling without support from caregivers and PHC providers.…”

Section: Dovepressmentioning

confidence: 99%

Challenges of Implementing an mHealth Application for Personalized Physical Activity Counselling in Primary Health Care: A Qualitative Study

Wattanapisit¹,

Amaek²,

Wattanapisit³

et al. 2021

IJGM

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Introduction: A mobile health (mHealth) technology has the potential to facilitate personalized physical activity (PA) counselling. We aimed to explore the feasibility and challenges of implementing a newly developed mHealth application (PAC app) for personalized PA counselling. Material and Methods: A qualitative design employed a descriptive phenomenology approach. Data were collected through focus group discussions (FGDs) with primary health care (PHC) providers and were analyzed using a deductive thematic approach. Results: A total of 16 participants participated in four FGDs. Four major themes were found: application for personalized PA counselling, barriers to the use of the application by providers, patient involvement, and impact on PHC services. Discussion: The results showed that the new mHealth application can potentially facilitate PA counselling. However, its use in PHC settings requires an understanding of the context of service delivery; the challenges faced by providers and patients and effects on services must be considered. Conclusion:Future research should focus on the long-term use of PAC app and its impact on behavioral and health outcomes.

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Drivers of Mobile Health Acceptance and Use From the Patient Perspective: Survey Study and Quantitative Model Development

Cited by 47 publications

References 47 publications

Supporting Management of Noncommunicable Diseases With Mobile Health (mHealth) Apps: Experimental Study

Supporting Management of Noncommunicable Diseases With Mobile Health (mHealth) Apps: Experimental Study

Relationship Between Perceived Risks of Using mHealth Applications and the Intention to Use Them Among Older Adults in the Netherlands: Cross-sectional Study

Challenges of Implementing an mHealth Application for Personalized Physical Activity Counselling in Primary Health Care: A Qualitative Study

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