Transcultural Adaptation, Validation, Psychometric Analysis, and Interpretation of the 22-Item Thai Senior Technology Acceptance Model for Mobile Health Apps: Cross-Sectional Study DOI Creative Commons
Nida Buawangpong, Penprapa Siviroj, Kanokporn Pinyopornpanish

и другие.

JMIR Aging, Год журнала: 2025, Номер 8, С. e60156 - e60156

Опубликована: Март 11, 2025

The rapid advancement of technology has made mobile health (mHealth) a promising tool to mitigate problems, particularly among older adults. Despite the numerous benefits mHealth, assessing individual acceptance is required address specific needs people and promote their intention use mHealth. This study aims adapt validate senior model (STAM) questionnaire for mHealth in Thai context. In this cross-sectional study, we adapted original, 38-item, English version STAM using 10-point Likert scale acceptability population. We translated into forward backward translation. A total 15 adults experts completed pilot were interviewed assess its validity. items then reworded revised better comprehension cross-cultural compatibility. construct validity was evaluated by multidimensional approach, including exploratory confirmatory factor analysis nonparametric item response theory analysis. Discriminative indices consisting sensitivity, specificity, area under receiver operating characteristic (AUROC) used determine appropriate banding discriminant Internal consistency assessed Cronbach α McDonald ω coefficients. Out 1100 participants with mean age 62.3 (SD 8.8) years, 360 (32.7%) aged 45-59 740 (67.3%) 60 years older. Of 40-item questionnaire, identified 22 loadings >0.4 across 7 principal components, explaining 91.45% variance. Confirmatory confirmed that 9-dimensional sets had satisfactory fit (comparative index=0.976, Tucker-Lewis index=0.968, root square error approximation=0.043, standardized squared residual=0.044, R2 each item>0.30). score D (low≤151, moderate 152-180, high≥181) preferred as optimal 22-item cutoff based on highest sensitivity 89% (95% CI 86.1%-91.5%) AUROC 72.4% 70%-74.8%) predicting final STAM, items, exhibited remarkable internal consistency, evidenced 0.88 0.87-0.89) 0.85 0.83-0.87). For all corrected item-total correlations ranged between 0.26 0.71. demonstrated psychometric properties both reliability. potential serve practical pre-older

Язык: Английский

Patient Acceptance of Prescribed and Fully Reimbursed mHealth Apps in Germany: An UTAUT2-based Online Survey Study DOI Creative Commons
Marie Uncovska, Bettina Freitag, Sven Meister

и другие.

Journal of Medical Systems, Год журнала: 2023, Номер 47(1)

Опубликована: Янв. 27, 2023

Abstract The study aims to (1) investigate current levels of patient acceptance mHealth in Germany; (2) determine the influencing factors patients' intention use, and (3) test influence prescription reimbursement status on acceptance. Online survey with 1349 participants, which 1051 were complete included for statistical analysis, from a broad cross-section German population, addressing both users mobile health (mHealth) applications people without prior experience. SEM modeling based combination two theoretical frameworks: extended Unified Theory Acceptance Use Technology Health Protective Behavior Theories used assess Users Germany are mostly patients between ages 30 – 50 mental or endocrine conditions. General willingness use apps / DiGAs (mHealth fully reimbursed by social insurance) is high at 76%, especially if they governmentally certified, however only 27% respondents willing pay out pocket. With exception spike performance expectancy data security, lack clear differentiation apps. Perceived self-efficacy significant predictors digital interventions; age, attitude, e-literacy being key demographic predictors. A takeaway regulators, providers apps/ DiGAs, other stakeholders involved adoption importance negative beliefs early on, targeted communication around effortless usage services across age groups demographics, focus highlighting expected benefits app/ DiGA usage.

Язык: Английский

Процитировано

66
Willingness to Use Mobile Health Devices in the Post–COVID-19 Era: Nationwide Cross-sectional Study in China DOI Creative Commons
Xue Wang, Yibo Wu, Zhiyu Meng

и другие.

Journal of Medical Internet Research, Год журнала: 2023, Номер 25, С. e44225 - e44225

Опубликована: Янв. 31, 2023

Background Despite the increased development and use of mobile health (mHealth) devices during COVID-19 pandemic, there is little knowledge willingness Chinese people to mHealth key factors associated with their in post–COVID-19 era. Therefore, a more comprehensive multiangle investigation required. Objective We aimed probe attitudes regarding analyze possible associations between attitude some based on socioecological model. Methods A survey was conducted using quota sampling recruit participants from 148 cities China June 20 August 31, 2022. Data were analyzed multiple stepwise regression examine devices. Standardized coefficients (β) 95% CIs calculated regression. Results The contained collection 21,916 questionnaires 21,897 valid questionnaires, 99.91% effective response rate. median score era 70 points scale 0 100. Multiple results showed that female gender (β=.03, CI 1.04-2.35), openness personality trait (β=.05, 0.53-0.96), higher household per capita monthly income 0.77-2.24), commercial insurance (β=.04, 1.77-3.47) In addition, high scores literacy (β=.13, 0.53-0.68), self-reported rating (β=.22, 0.24-0.27), social support (β=.08, 0.40-0.61), family 0.03-0.16), neighbor relations (β=.12, 2.09-2.63), status (β=.07, 1.19-1.69) likely Conclusions On basis theoretical framework model, this study identified specifically These findings provide reference information for research, development, promotion, application future

Язык: Английский

Процитировано

26
Applying the UTAUT2 framework to patients’ attitudes toward healthcare task shifting with artificial intelligence DOI Creative Commons
Weiting Huang,

Wen Chong Ong,

Mark Kei Fong Wong

и другие.

BMC Health Services Research, Год журнала: 2024, Номер 24(1)

Опубликована: Апрель 11, 2024

Abstract Background Increasing patient loads, healthcare inflation and ageing population have put pressure on the system. Artificial intelligence machine learning innovations can aid in task shifting to help systems remain efficient cost effective. To gain an understanding of patients’ acceptance toward such with AI, this study adapted Unified Theory Acceptance Use Technology 2 (UTAUT2), looking at performance effort expectancy, facilitating conditions, social influence, hedonic motivation behavioural intention. Methods This was a cross-sectional which took place between September 2021 June 2022 National Heart Centre, Singapore. One hundred patients, aged ≥ 21 years least one heart failure symptom (pedal oedema, New York Association II-III limitation, orthopnoea, breathlessness), who presented cardiac imaging laboratory for physician-ordered clinical echocardiogram, underwent both echocardiogram by skilled sonographers experience novice guided AI technologies. They were then given survey looked above-mentioned constructs using UTAUT2 framework. Results Significant, direct, positive effects all behavioral intention accepting AI-novice combination found. Facilitating expectancy top 3 constructs. The analysis moderating variables, age, gender education levels, found no impact Conclusions These results are important stakeholders changemakers as policymakers, governments, physicians, insurance companies, they design adoption strategies ensure successful engagement focusing factors affecting technologies used shifting.

Язык: Английский

Процитировано

10
Mobile Apps in Digital Health: Patient Expectations and Factors Influencing Patient Acceptance – a Literature Review DOI
Uwe Radtke, Atilla Wohllebe

Опубликована: Янв. 6, 2025

With the digitalization of healthcare, mobile apps are also becoming increasingly relevant. This narrative literature review examines general expectations and requirements patients for health based on scientific studies from last 10 years (2014-2024), identifies specific functions features summarizes acceptance factors. The results show that should primarily serve success treatment. Specifically desired include data entry automated tracking, reminder alerting, personalization customization, education information as well sharing connectivity. Perceived usefulness ease use among most important Personal support healthcare professionals plays an role in some contexts. Various further research directions discussed. Future could example focus question how affect user satisfaction loyalty once they have been implemented.

Язык: Английский

Процитировано

2
Clinical perspectives on AI integration: assessing readiness and training needs among healthcare practitioners DOI Creative Commons

Tinotenda J. Masawi,

Edward Miller, Daniel Rees

и другие.

Journal of Decision System, Год журнала: 2025, Номер 34(1)

Опубликована: Янв. 2, 2025

Язык: Английский

Процитировано

1
Fitness Apps's purchase behaviour: Amalgamation of Stimulus-Organism-Behaviour-Consequence framework (S–O–B–C) and the innovation resistance theory (IRT) DOI
Debarun Chakraborty,

Hari Babu Singu,

Smruti Patre

и другие.

Journal of Retailing and Consumer Services, Год журнала: 2022, Номер 67, С. 103033 - 103033

Опубликована: Июнь 3, 2022

Язык: Английский

Процитировано

38
Factors influencing the acceptance of telemedicine in the Philippines DOI
Ardvin Kester S. Ong, Yoshiki B. Kurata,

Sophia Alessandra D.G. Castro

и другие.

Technology in Society, Год журнала: 2022, Номер 70, С. 102040 - 102040

Опубликована: Июнь 16, 2022

Язык: Английский

Процитировано

37
Social Acceptance of Mobile Health among Young Adults in Japan: An Extension of the UTAUT Model DOI Open Access
Jianfei Cao,

Karin Kurata,

Yeongjoo Lim

и другие.

International Journal of Environmental Research and Public Health, Год журнала: 2022, Номер 19(22), С. 15156 - 15156

Опубликована: Ноя. 17, 2022

The unprecedented development of information and communication technologies has opened up immense possibilities in the field health care. Mobile (mHealth) is gaining increasing attention as an important technology for solving health-related problems. Although a high rate smartphone usage among young people Japan been identified, management not high. As Japanese youth are potential users mHealth, it necessary to explore theories that influence behavioral intention adopt mHealth. This study conducted questionnaire survey university collected 233 valuable responses. was adapted extended from unified theory acceptance use (UTAUT) model measure eight constructs: consciousness, social influence, facilitation conditions, perceived risk, trust, performance expectancy, effort intention. Structural equation modeling used hypothesis testing. We found expectancy directly influenced Health consciousness indirectly through trust expectancy. Facilitation conditions makes vital theoretical contribution policymakers product developers further diffusion mHealth Japan.

Язык: Английский

Процитировано

34
Investigating retailing customers' adoption of augmented reality apps: integrating the unified theory of acceptance and use of technology (UTAUT2) and task-technology fit (TTF) DOI
Mohamed A. Khashan, Mohamed M. Elsotouhy,

Thamir Hamad Alasker

и другие.

Marketing Intelligence & Planning, Год журнала: 2023, Номер 41(5), С. 613 - 629

Опубликована: Июнь 29, 2023

Purpose Since the advent of augmented reality (AR) technology, “Smart Retailing” has become dominant business model in retail sector. Therefore, comprehending dynamics AR adoption is essential if retailers are to successfully encourage customers embrace this extremely innovative form technology. As a result, authors propose and evaluate more comprehensive model, consisting task-technology fit (TTF) unified theory acceptance use technology (UTUAT2) models, for low-income countries. Design/methodology/approach The present research uses variance-based partial least squares structural equation modeling (PLS-SEM) using WarpPLS.7 examine 398 responses from Egyptian consumers. Findings TTF, performance expectancy (PE), effort (EE), social influence (SI), facilitating condition (FC), hedonic motivation (HM) customer innovativeness (CI) positively affect shoppers' behavioral intentions (BI) adopt Apps retail, while perceived risk (PR) negatively affects BI. Originality/value current study first investigate determinants BI toward context UTAUT2 TTF models.

Язык: Английский

Процитировано

22
Extending the Privacy Calculus to the mHealth Domain: Survey Study on the Intention to Use mHealth Apps in Germany DOI Creative Commons
Niklas von Kalckreuth, Markus A. Feufel

JMIR Human Factors, Год журнала: 2023, Номер 10, С. e45503 - e45503

Опубликована: Июнь 21, 2023

With the increasing digitalization of health sector, more and mobile (mHealth) apps are coming to market continuously collect process sensitive data for benefit patients providers. These technologies open up new opportunities make care system efficient save costs but also pose potential threats such as loss or finances.This study aims present an empirical review adaptation extended privacy calculus model mHealth domain understand what factors influence intended usage technologies.A survey was conducted empirically validate our model, using a case vignette cover story. Data were collected from 250 German participants analyzed covariance-based structural equation model.The explains R2=79.3% variance in intention use. The 3 main (social norms, attitude privacy, perceived control over personal data) influenced use apps, albeit partially indirectly. is driven by benefits technology, trust provider, social norms. Privacy concerns have no bearing on has large inhibiting effect benefits, well provider. Perceived clearly dispels supports relationship between user provider.Based calculus, domain-specific better than previous, general models. findings allow providers improve their products increase targeting specific groups.

Язык: Английский

Процитировано

15