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Yang M, Al Mamun A, Gao J, Rahman MK, Salameh AA, Alam SS. Predicting m-health acceptance from the perspective of unified theory of acceptance and use of technology. Sci Rep 2024; 14:339. [PMID: 38172184 PMCID: PMC10764358 DOI: 10.1038/s41598-023-50436-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 12/19/2023] [Indexed: 01/05/2024] Open
Abstract
Addressing the growing popularity of mobile health (m-Health) technology in the health industry, the current study examined consumers' intention and behaviour related to the usage of digital applications based on the unified theory of acceptance and use of technology (UTAUT). In particular, this study quantitatively assessed the moderating role of perceived product value and mediating role of intention to use m-Health application among Indonesians. This study adopted a cross-sectional design and collected quantitative data from conveniently selected respondents through an online survey, which involved 2068 Telegram users in Indonesia. All data were subjected to the analysis of partial least square- structural equation modeling (PLS-SEM). The obtained results demonstrated the moderating effect of perceived product value on the relationship between intention to use m-Health application (m-health app) and actual usage of m-Health app and the mediating effects of intention to use m-Health app on the relationships of perceived critical mass, perceived usefulness, perceived convenience, perceived technology accuracy, and perceived privacy protection on actual usage of m-Health app. However, the intention to use m-Health app did not mediate the influence of health consciousness and health motivation on the actual usage of m-Health app. Overall, this study's findings on the significance of intention to use m-Health app and perceived product value based on the UTAUT framework serve as insightful guideline to expand the usage of m-Health app among consumers.
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Affiliation(s)
- Marvello Yang
- Faculty of Entrepreneurship, Institute of Technology and Business Sabda Setia Pontianak, Kota Pontianak, Kalimantan Barat, 78121, Indonesia
| | - Abdullah Al Mamun
- UKM - Graduate School of Business, Universiti Kebangsaan, Malaysia, UKM, 43600, Bangi, Selangor Darul Ehsan, Malaysia.
| | - Jingzu Gao
- UKM - Graduate School of Business, Universiti Kebangsaan, Malaysia, UKM, 43600, Bangi, Selangor Darul Ehsan, Malaysia
| | - Muhammad Khalilur Rahman
- Faculty of Entrepreneurship and Business, Universiti Malaysia Kelantan, Pengkalan Chepa, Malaysia
- Angkasa-Umk Research Academy, Universiti Malaysia Kelantan, Pengkalan Chepa, Malaysia
| | - Anas A Salameh
- College of Business Administration, Prince Sattam Bin Abdulaziz University, 11942, Al-Kharj, Saudi Arabia
| | - Syed Shah Alam
- UKM - Graduate School of Business, Universiti Kebangsaan, Malaysia, UKM, 43600, Bangi, Selangor Darul Ehsan, Malaysia
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Zheng S, Edney SM, Goh CH, Tai BC, Mair JL, Castro O, Salamanca-Sanabria A, Kowatsch T, van Dam RM, Müller-Riemenschneider F. Effectiveness of holistic mobile health interventions on diet, and physical, and mental health outcomes: a systematic review and meta-analysis. EClinicalMedicine 2023; 66:102309. [PMID: 38053536 PMCID: PMC10694579 DOI: 10.1016/j.eclinm.2023.102309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 10/16/2023] [Accepted: 10/26/2023] [Indexed: 12/07/2023] Open
Abstract
Background Good physical and mental health are essential for healthy ageing. Holistic mobile health (mHealth) interventions-including at least three components: physical activity, diet, and mental health-could support both physical and mental health and be scaled to the population level. This review aims to describe the characteristics of holistic mHealth interventions and their effects on related behavioural and health outcomes among adults from the general population. Methods In this systematic review and meta-analysis, we searched MEDLINE, Embase, Cochrane Central Register of Controlled Trials, PsycINFO, Scopus, China National Knowledge Infrastructure, and Google Scholar (first 200 records). The initial search covered January 1, 2011, to April 13, 2022, and an updated search extended from April 13, 2022 to August 30, 2023. Randomised controlled trials (RCTs) and non-randomised studies of interventions (NRSIs) were included if they (i) were delivered via mHealth technologies, (ii) included content on physical activity, diet, and mental health, and (iii) targeted adults (≥18 years old) from the general population or those at risk of non-communicable diseases (NCDs) or mental disorders. Studies were excluded if they targeted pregnant women (due to distinct physiological responses), individuals with pre-existing NCDs or mental disorders (to emphasise prevention), or primarily utilised web, email, or structured phone support (to focus on mobile technologies without exclusive human support). Data (summary data from published reports) extraction and risk-of-bias assessment were completed by two reviewers using a standard template and Cochrane risk-of-bias tools, respectively. Narrative syntheses were conducted for all studies, and random-effects models were used in the meta-analyses to estimate the pooled effect of interventions for outcomes with comparable data in the RCTs. The study was registered in PROSPERO, CRD42022315166. Findings After screening 5488 identified records, 34 studies (25 RCTs and 9 pre-post NRSIs) reported in 43 articles with 5691 participants (mean age 39 years, SD 12.5) were included. Most (91.2%, n = 31/34) were conducted in high-income countries. The median intervention duration was 3 months, and only 23.5% (n = 8/34) of studies reported follow-up data. Mobile applications, short-message services, and mobile device-compatible websites were the most common mHealth delivery modes; 47.1% (n = 16/34) studies used multiple mHealth delivery modes. Of 15 studies reporting on weight change, 9 showed significant reductions (6 targeted on individuals with overweight or obesity), and in 10 studies reporting perceived stress levels, 4 found significant reductions (all targeted on general adults). In the meta-analysis, holistic mHealth interventions were associated with significant weight loss (9 RCTs; mean difference -1.70 kg, 95% CI -2.45 to -0.95; I2 = 89.00%) and a significant reduction in perceived stress levels (6 RCTs; standardised mean difference [SMD] -0.32; 95% CI -0.52 to -0.12; I2 = 14.52%). There were no significant intervention effects on self-reported moderate-to-vigorous physical activity (5 RCTs; SMD 0.21; 95%CI -0.25 to 0.67; I2 = 74.28%) or diet quality scores (5 RCTs; SMD 0.21; 95%CI -0.47 to 0.65; I2 = 62.27%). All NRSIs were labelled as having a serious risk of bias overall; 56% (n = 14/25) of RCTs were classified as having some concerns, and the others as having a high risk of bias. Interpretation Findings from identified studies suggest that holistic mHealth interventions may aid reductions in weight and in perceived stress levels, with small to medium effect sizes. The observed effects on diet quality scores and self-reported moderate-to-vigorous physical activity were less clear and require more research. High-quality RCTs with longer follow-up durations are needed to provide more robust evidence. To promote population health, future research should focus on vulnerable populations and those in middle- and low-income countries. Optimal combinations of delivery modes and components to improve efficacy and sustain long-term effects should also be explored. Funding National Research Foundation, Prime Minister's Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) Programme and Physical Activity and Nutrition Determinants in Asia (PANDA) Research Programme.
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Affiliation(s)
- Shenglin Zheng
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Sarah Martine Edney
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Chin Hao Goh
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Bee Choo Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore
| | - Jacqueline Louise Mair
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Oscar Castro
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
| | - Alicia Salamanca-Sanabria
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A∗STAR), Singapore
| | - Tobias Kowatsch
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore
- Institute for Implementation Science in Health Care, University of Zürich, Zürich, Switzerland
- School of Medicine, University of St. Gallen, St. Gallen, Switzerland
- Centre for Digital Health Interventions, Department of Management, Technology and Economics ETH Zürich, Zürich, Switzerland
| | - Rob M. van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Departments of Exercise and Nutrition Sciences and Epidemiology, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA
| | - Falk Müller-Riemenschneider
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore
- Digital Health Centre, Berlin Institute of Health, Charité – Universitätsmedizin Berlin, Berlin, Germany
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