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The mHealth (mHealth) industry is a huge global market, but the interruption or continuation of mHealth is a major issue affecting its positive outcomes. So far, the results of studies of influencing factors are not consistent. Thus, studies on the combined effects of influencing factors on the intention to continue mHealth are limited. Therefore, this study aims to systematically analyze quantitative studies of mHealth continued use intentions and examine the combined effect of each direct and indirect contributor. Until October 2021, eight bibliographic databases were searched. We included 58 peer-reviewed studies on influencing factors and intentions to continue using mHealth. Among the 19 factors directly influencing continuing intention, 15 were significant, attitude (β = 0.450; 95% CI: 0.135, 0.683), satisfaction (β = 0.406; 95% CI: 0.292, 0.509), health energy (β = 0.359 ; 95% CI: 0.204, 0.497), perceived utility (β = 0.343; 95% CI: 0.280, 0.403), and perceived health quality of life (β = 0.315, 95% CI: 0.211, 0.412). Readiness. There was high heterogeneity between studies, so we performed subgroup analyzes to examine the moderating effect of different characteristics on the influence effect. Geographic region, user type, mHealth type, user age, and publication year significantly moderated influential relationships such as trust and persistent intent. Therefore, mHealth developers must develop personalized strategies to promote continuous use based on user characteristics.
The mHealth (mHealth) industry is a huge global market worth over US$50 billion and is rapidly evolving as a primary healthcare management tool with a wide range of products and an expanding user base, but it is also facing new challenges. Mobile health (mHealth) is defined by the World Health Organization (WHO) as “medical and public health practices supported by mobile devices such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices. ” 1 It can improve the capability and efficiency of medical services, monitor patients’ daily activities and physical performance, 2 improve personal health status by influencing personal health behavior and avoiding health risks, 3 reduce 63% hospital readmission and health care costs. disease prevention33 % .4 With these benefits, mHealth technologies and services represent the future of the healthcare industry.The global mHealth market will be $51.635 billion in 2020 with an expected CAGR of 25% and will reach $225.765 billion in 2026 5 There are currently more than 300,000 mHealth apps on the market, representing about 40% of smartphone users using mHealth apps 6 The coronavirus disease pandemic has led to increased use of mHealth: a survey showed that during During the pandemic, mHealth app downloads increased by 25%. 7 Despite the rapid growth of mHealth and the widespread acceptance of its use, the main challenge for this type of technology is whether users will abandon or continue to use it. 8
Sustainability is important for mHealth because initial short-term use of mHealth services is unlikely to achieve long-term health management goals and improve user health. Moreover, short-term use is not conducive to the survival of mHealth services and technologies in the market. In this context, Bhattacherji pointed out that the long-term success of information technology or information systems does not depend on initial implementation, but on persistence. 9 In a clinical trial setting, most health management services based on mHealth interventions should be used for more than 3 weeks to show effective results. 10 However, a large, observational, real-world study found that of the 189,770 users who downloaded the mHealth app, only 2.58% were active users for at least a week11; similarly, the rest after entering the service less than two times. 12 Thus, while mHealth has shown positive results in clinical trial settings, there is limited evidence for its authenticity—global benefits due to poor persistence.
Perseverance also goes a long way for mHealth companies. Previous research has shown that 30% of mobile health apps are deleted within a month of being downloaded. 6 Despite the size of the mHealth app market, only 7% of mHealth apps have more than 50,000 monthly active users, while 62% of apps report less than 1,000 monthly active users (active users are defined as users who use the app). at least once a month). 13 In 2020, app makers lost an average of $57,000 per month due to deletions. 14 Thus, improving the sustainability of mHealth services in the real world is a prerequisite for effective health management and the promotion of such technologies.
Intention to continue use is an important driver of long-term use, defined as the user’s desire and tendency to continue using mHealth15. Therefore, the number of topical studies of factors and mechanisms that influence the intention of continuous use of mHealth services is gradually increasing. Different types of mHealth services have been explored, such as preventive health care or chronic disease management applications,16,17 wearables18 and telemedicine. 19 The relevant literature is growing, and results vary by theory, model, and reported population. 20,21,22,23 For example, Paré et al. In an effort to examine willingness to continue using wearables for health management, perceived ease of use was found to have a significant positive effect on willingness to continue using (β = 0.33, P < 0.01)20. In contrast, Cho and Li found that the effect was not statistically significant (β = 0.10, P = 0.15). 21 This conflicting evidence has made it difficult for researchers to understand the mechanisms behind sustainable mHealth intentions. Therefore, it is necessary to comprehensively judge the factors affecting the continuity of the work of mobile medical services.
While systematic reviews of the sustainability of mHealth services or information systems have been published, there have been no quantitative and comprehensive meta-analyses of factors that influence intention to continue using mHealth. Khalil et al. A systematic review of intentions for the future use of the mHealth application was conducted, with a comprehensive description of the theoretical models used in the studies and the distribution of influencing factors. However, due to only a descriptive review, the paradoxical effect-effect phenomenon cannot be explained6. A meta-analysis combines the quantitative results of several empirical studies to comprehensively assess the overall influence of influencing factors, which is more rigorous than a meta-analysis. Descriptive systematic reviews and provision of information. 24 Frank et al. A meta-analysis of the factors influencing the intention of continuous use of an information system was conducted, and after integrating the influencing effects, five optimal influencing factors were identified. 25 However, the use cases for information systems in Frank et al. Difference from mHealth services. Similarly, user perception is also different. Thus, the results of Frank et al. The reasons for continuing to use mHealth services cannot be fully explained.
To address the above limitations, this study focused on mHealth users, both the general population and patients with medical conditions. We aim to summarize the effect of direct influencers on user willingness to continue using, and then use direct influencers as intermediate variables to integrate the influence of indirect factors to examine the combined impact of all influencers. In addition, through meta-analysis, we identified the main factors influencing the intention to continue. On this basis, a subgroup analysis was performed to explore the moderating effect of study design and population on the exposure relationship to provide a basis for developing a personalized strategy for the continued use of mHealth.
The study selection process and literature search results are shown in Figure 1. Using our search strategy, we found 1030 articles in eight databases. After removing duplicate articles, 470 articles remained. After additional exclusion criteria were applied, a total of 58 cross-sectional studies were included in our analysis, including 19 factors directly influencing persistence readiness, which were examined more than 3 times.
Using the preferred reporting items for systematic reviews and meta-analyses as a screening process, a total of 58 studies were included.
Table 1 summarizes the characteristics of these 58 studies. The three countries with the highest research activity were China (25 studies), South Korea (7 studies), and the United States (6 studies). The most commonly used theories and models are Information System Continuity Expectation Validation Model (ECM-ISC, 15 times), Technology Adoption Model (TAM, 9 times), and Unified Technology Acceptance and Use Theory (UTAUT, 6 times). . All studies used self-reports of current intentions and perceptions of use, which were collected through questionnaires. Study quality scores ranged from 4 to 16 with a mean SD of 12.43 (2.50) (Supplementary Table 1). The Kendall Consistency Index is used to assess the agreement between investigators’ estimates, which is 0.971, indicating very strong agreement.
(1) Directly influencing factors of constant readiness. Figure 2 shows that among the 19 direct influences on intention to continue, the combined effect of four factors was not significant (social influence (0.098 [95% CI, -0.029, 0.222], P = 0.068, I2 = 81.14%; convenience, conditional) (0.169 [95% CI, -0.144, 0.452], P = 0.086, I2 = 82.45%); Perceived risk (-0.065 [95% CI, -0.302, 0.180], P = 0.259, I2 = 64.33%) Engagement (0.536 [95% CI, -0.817, 0.982], P = 0.140, I2 = 99.13%) The five most studied factors were satisfaction (26 times), perceived usefulness (24 times) , perceived ease of using sex (12 times), trust (11 times), and social influence (8 times). In terms of the size of the combined effect coefficients, the top five factors that have a direct impact on the willingness to continue are attitudes (0.450 [95% CI , 0.135 , 0.683], P < 0.001, I2 = 92.34%), satisfaction (0.406 [95% CI, 0.292, 0.509], P < 0.001, I2 = 95.25%), health promotion (0.359 [95% CI, 0.204, 0.497] , P<0.001, I2 = 15.90%), perceived perceived utility (0.343 [95% CI, 0.280, 0.403], P < 0.001, I2 = 86.26%), and perceived health quality of life (0.315 [95% CI, 0.211, 0.412], P < 0.001, I2 = 0.00%) was considered as the main factor influencing the willingness to continue.
The data show a forest diagram of effect relationships included in the meta-analysis (28 effect relationships). P: p-value of the combined coefficient significance test for the influence of the independent construct on the dependent construct.
(2) Influencing factors of intermediate factors. We also combined and analyzed the influencing factors of satisfaction, perceived usefulness, and perceived ease of use to explore factors that indirectly influence the intention to continue using. All four independent satisfaction variables were significant; the most studied factor was perceived utility (14 times), and service quality had the largest combined effect ratio (0.623 [95% CI, 0.223, 0.843], P < 0.001, I2 = 98.20% ). All pooled effects of the four independent perceived utility variables were significant, confirmation was the most studied contributory factor (12 times), and pooled confirmation ratio was the largest (0.660 [95% CI, 0.488, 0.783], P < 0.001, I2 = 98.23 %). The cumulative effect of validation on perceived ease of use is significant. Effect estimates and confidence intervals for individual studies and overall combined results are illustrated using forest plots, as shown in the supplementary figure. A1-A28 and Supplementary Tables A1-A28. On fig. 2 shows that most of the studies were highly heterogeneous. On fig. 3 is a path diagram of all impact relationships with significant combined effects.
Draw a path map of all influence relationships with significant combined effect, including the impact of 15 direct influence relationships and 9 indirect influence relationships on continuation intention. Note: CC = combined factor.
The results of our subgroup analysis, stratified by study design and population, are summarized in Tables 2-6. Table 2 shows that the relationships between trust and intent to continue using, social influence and intent to continue using, validation and satisfaction, and perceived ease of use and perceived usefulness vary significantly by geographic region. The shock effect of developing countries or regions is stronger than that of developed countries or regions.
Relationships between satisfaction and intention to continue using, trust and intention to continue using, social influence and intention to continue using, and confirmation and perceived utility varied significantly by type of user (Table 3). Compared to general users, the satisfaction and trust of the patient group did not significantly affect the willingness to continue using. Compared with the patient group, social influence in the community group did not significantly affect the willingness to continue treatment. The effect of validation on perceived utility was stronger in the public user group than in the patient group.
The mHealth type significantly changed the associations between perceived utility and sustainable intention, and social influence and sustainable intention (Table 4). Compared to disease-focused mHealth and teleconsultations, the impact of social influence on persistence intention was not significant in health promotion-prevention-focused mHealth. In the mHealth group of applications for disease management and teleconsultations, the coefficient of perceived usefulness on the intention to continue working was the largest.
The age of the study population significantly changed the relationship between satisfaction and the desire to continue (Table 5). The effect of satisfaction rate on willingness to continue is greatest among older people.
The relationship between trust and persisting intentions varied significantly depending on the year of publication (Table 6). The impact of trust on lingering intentions is noticeable only in recent years (2016-2021). However, the quality of the study did not significantly affect the results (Supplementary Table 2).
Funnel plots and Egger’s linear regression tests found little evidence of publication bias (Supplementary Figures B1–B28).
According to the magnitude of the net effect ratios, the five main factors influencing willingness to continue using mHealth are attitudes, satisfaction, health empowerment, perceived usefulness, and perceived quality of life. Attitudes towards mHealth refers to the positive or negative attitudes of users toward mHealth services or devices. 26 People seek cognitive coordination between perception and behavior that results in attitudes toward specific behaviors that influence persistent intentions. 27 However, users have different attitudes towards the same mHealth service, and exploring ways to improve user attitudes is an issue that should be explored in the future. User satisfaction refers to the user’s overall assessment of the performance of a product. 28. Users evaluate satisfaction by comparing the actual service or product with their expectations of the service or product before use. 22 High satisfaction means that the products or services exceed user expectations, so they are more likely to continue using them. In many theories and models, such as ECM-ISC and TAM, satisfaction is an important leading variable of continuous intention and also the most studied factor in the study of mobile continuous medical intention. This suggests that the positive impact of satisfaction on lasting intentions is widely recognized. Health empowerment is an effective strategy for promoting personal health, and self-management is an important aspect of health promotion. 29 More specifically, health empowerment is about exercising patient control over their own health and being aware of the resources available in health care. Through the mobile health service system. 30 Compared to traditional offline health management and disease control methods, mHealth has the advantages of lower cost, greater time efficiency and easier access. When using mHealth services, if patients feel that they can effectively manage their health and control their health status, this will increase the likelihood of continuing mHealth and provide cost and time savings. Perceived usefulness refers to the degree to which people believe that using a particular system will improve their performance. 28. Mobile health is a tool or service with obvious utilitarian properties that users can use to find health management services or to prevent and treat diseases. control. The question of whether mHealth can improve the efficiency and effectiveness of managing their health is at the forefront of users’ attention, so high perceived usefulness is an important factor in encouraging users’ intention to continue using. Health-perceived quality of life refers to the outcomes obtained by users through interaction with mHealth and can be defined as overall health and well-being. 31 Continuous self-management of their health is not easy, and users need a regulatory mechanism that includes goal setting, monitoring and feedback. 32 Healthy life quality perception is a kind of feedback received by users on health management behavior. Positive feedback about the results will encourage users to keep setting goals. When this regulatory mechanism works well, users will spontaneously perform actions aimed at continuous health management. Therefore, the perceived quality of healthy life is an important factor in improving the continued readiness of users.
In summary, among the five factors that have the greatest influence on the intention to continue using mHealth, two factors are related to the function of mHealth and the quality of services (the extent to which mHealth serves its actual purpose and the extent to which users’ health improves). ) governance has a positive effect on intent to continue treatment), and two factors are associated with complex user attitudes towards mHealth (comprehensive user emotional state and an overall assessment of the impact of mHealth on intent to continue). Finally, health empowerment, closely linked to health governance, has a significant impact on continued mHealth readiness. It is also a unique factor influencing user behavior in the field of mHealth that is not mentioned in the ongoing review of information systems and online technologies25,33 and is an important finding of our study.
A meta-analysis can clarify whether a particular influencing factor has a significant effect on willingness to continue, and a subgroup analysis can further explore whether a particular influencing factor has a stronger effect on a certain type of research subject and find significant differences in impact. same factor Adjusted different functions. We find that the level of economic development of a country or region has a significant moderating effect on the effect. Validation in developing countries or regions had a greater effect on satisfaction than in developed countries or regions ((0.606 [95% CI, 0.336, 0.784], P < 0.001, I2 = 96.55%) compared with (0.365 [95% CI, 0.336, 0.784], P < 0.001, I2 = 96.55%) 0.281, 0.443], P < 0.001, I2 = 60.29%), indicating that users in developing countries consider the effectiveness of mHealth when deciding whether to continue using it, whether it reaches the expected level. Similarly, in developing countries or regions, the effect of perceived ease of use on perceived usefulness was increased ((0.653 [95% CI, 0.413, 0.808], P < 0.001, I2 = 86.93%) compared with (0.183 [95% CI , 0.413, 0.808] CI, 0.083, 0.280], P < 0.001, I2 = 41.56%) Developed countries Users in countries or regions report that the perceived ease of use of mHealth is relatively high. The impact on their perceived usefulness is weak. the effect of trust on persistent intentions only has a significant effect in developing countries or regions ((0.353 [95% CI, 0.200, 0.489], P < 0.001, I2 = 90.13%) and (0.121 [95% CI], -0.398, 0.581], P = 0.477, I2 = 93.67%)), which may be due to the fact that science and technology develop rapidly and tend to use more advanced technologies.34 However, mHealth technology is still in its early stages. stages of development in developing countries, and user acceptance of Abundant health care services is still in its infancy, with more to come. We anticipate that once users have trust in mHealth services, they will be more likely to continue using them.
The type of user has a significant deterrent effect on the influence effect. The effect of satisfaction on intention to continue was only significant among the population ((0.442 [95% CI, 0.321, 0.550], P < 0.001, I2 = 96.27%) compared with (0.346 [95% CI, -0.120, 0.687], P = 0.054, I2 = 96.47%)), which indicates that the population is more likely to continue using mHealth when their satisfaction is high. The effect of confirmation on perceived utility was significantly stronger in the general user group ((0.711 [95% CI, 0.455, 0.859], P < 0.001, I2 = 98.20%) compared with (0.387 [95% CI, -0.084, 0.716 ], P < 0.001, I2 = 90.33%), indicating that when their expectations are met, they are more likely to find mHealth beneficial Compared to patients in need of medical treatment, the expectations of healthy people are relatively easy to confirm. In contrast, the effect of social influence on the intention to continue was only significant in patients than in healthy controls ((0.248 [95% CI, -0.012, 0.477], P < 0.001, I2 = 57.77%) vs (-0.012). [ 95% CI, -0.065, 0.040], P = 0.515, I2 = 0.00%)), indicating that patients are more likely to continue using mHealth if their social environment supports the use of mHealth technology. services and tools, so developers of mHealth technologies need to target their users accordingly.
The mHealth type had a significant moderating effect on the influence effect. In m-health focused on disease management and teleconsultations, the influence of perceived usefulness and social influence on the intention to continue treatment is greatly enhanced. This phenomenon is due to the fact that the target users of this type of mobile medical care are patients, and the main purpose of their use is remote counseling or disease management. When mHealth meets its practical needs, users find it useful and are more likely to continue using it.
The age of the user has a significant deterrent effect on the influence effect. The overall effect ratio for willingness to continue treatment was highest among older adults (0.761 [95% CI, 0.352, 0.926], P < 0.001, I2 = 97.91%) and lowest among age groups (0.284 [95% CI], 0.221, 0.345). ], P < 0.001, I2 = 82.49%), indicating that when users are satisfied with mHealth, older users are more likely to continue using mHealth than users of other age groups. These results provide a theoretical basis for creating personalized mHealth design. MHealth services can be designed to provide various features that personalize the experience according to the age of the user, such as focusing on studying the impact on older user satisfaction.
An analysis of the moderating effect of the year of publication reveals a trend in the impact factor. The effect of trust on persistent intention was not significant in previously published studies, but showed significant effects in recently published studies ((0.220 [95% CI, -0.288, 0.632], P = 0.170, I2 = 92.30%) vs. (0.304) [95% CI, 0.090, 0.491], P = 0.001, I2 = 88.74]. This finding can be explained by increasing user confidence in mHealth services. As technology advances, users are gradually focusing on technology-related security issues.
According to the results of the study, the factors that have the greatest influence on the willingness to continue mHealth can be divided into three aspects: the function of mHealth and quality of services, the comprehensive experience of mHealth users, and the expansion of health opportunities. Therefore, mHealth developers can improve the design in three ways. First, efforts must be made to improve the effectiveness of mHealth in promoting health and treating disease. For example, proposed medical interventions should be evidence-based and significantly improve the health of users. The realization by users that mHealth is useful for managing their health is an important basis for further use. Second, developers can use user-generated content to collect user ratings of various features in the app’s experience to remove feature limitations and improve user satisfaction. 35 Finally, health empowerment has a significant positive impact on continued use intent, indicating that users not only need mHealth to significantly improve their health status, but also hope that mHealth can improve their ability to manage their health. Therefore, mHealth should provide adequate health education, improve users’ health literacy, allow users to make decisions about managing their own health, and fully participate in managing their own health.
Subgroup analysis showed that geographic region, type of user, and type of mHealth significantly affected the results. Therefore, developers should clarify the target audience of mHealth and highlight the countries and regions in which it is distributed. First, trust has a significant impact on the intention to continue using only in developing countries, and the impact of perceived ease of use on perceived usefulness increases significantly in developing countries. This result may be related to the immaturity of the development of mHealth technologies in developing countries and, as a result, uncertainty among users. Developers should improve the usability of mHealth in developing countries, simplify the human-computer interaction process, fully demonstrate the design and functionality of mHealth, improve the security of mHealth, and increase user trust and perceived ease of use. Second, developers should pay attention to the difference between the user type and the mHealth type. The effect of social influence on willingness to continue treatment was significant only in patient populations and the use of mHealth for disease management and teleconsultations. Therefore, mHealth for patients must take steps to increase the social impact of products. For example, mHealth can provide an online community for communication between patients, increasing users’ chances of gaining social influence. On the contrary, operators may consider promoting mHealth among healthcare professionals. Once accredited, healthcare professionals can support their patients in continuing to use mHealth to improve their health.
This study has some limitations. First, although we searched the literature as exhaustively as possible, some studies were not found due to limitations in the literature database. However, we searched eight databases from interdisciplinary fields and developed a search strategy that took into account the multiple meanings of the search terms. Therefore, we consider our study to be representative. Second, while this study confirmed the moderating effect of a country or region’s level of economic development, user type, mHealth type, user age, and graduation time on the impact effect, characteristics that have a moderating effect, such as cultural background, income, gender, literacy in health issues, the user’s ethnicity and occupation may not have been determined. Due to the lack of completeness of the included literature, the influence of the above factors was not analyzed, so future studies should investigate these features.
The continuation of mHealth plays an important role in building a model of self-management of their health and empowering users, and is also key to the survival of mHealth companies in the market. A comprehensive assessment of the factors influencing and limiting the intention to continue mHealth is essential to develop improvement strategies. In summary, this study analyzed 58 quantitative studies of intention to continue mHealth services and examined the pooled effect of 28 intervention effects via meta-analysis. The results show that the current mHealth continuation intention research framework tends to be comprehensive and diversified; ECM-ISC, TAM and UTAUT are widely used theoretical models, among which ECM-ISC is the most widely accepted. In addition, according to the size of the aggregated effect ratios, the five main factors influencing the willingness to continue health mobility are attitudes, satisfaction, health empowerment, perceived usefulness, and perceived quality of life. Health empowerment is a specific factor in health information behavior of users that may attract the attention of scientists and developers in the future. Subgroup analysis results showed that geographic region, user type, mHealth type, user age, and time of publication of the study had a significant moderating effect on some effects. Therefore, researchers and developers should take into account the countries or regions where mHealth is used, accurately define the user characteristics of target users, and develop personalized mHealth services to increase the sustainability of mHealth and, ultimately, to create a sustainable model of self-management of their health.
We followed the Guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to conduct and report on elements of a systematic review (Supplementary Table 3). A systematic literature search was conducted for cross-sectional studies up to October 8, 2021 in the following databases: PubMed, Embase, WOS Core Collection, CINAHL, Scopus, PsycInfo, EI, and ACM. The search formula has three parts: “mHealth”, “continuation” and “intention”. The search has no language restrictions. We also searched and reviewed the references cited in the relevant reports found for any additional research. Detailed search formulas are shown in Supplementary Table 4.
Studies meeting the following criteria were included in the current meta-analysis: (1) Full text was available. For articles that do not have full texts provided by archives and publishers, we contact the authors of the articles through ResearchGate (a research social networking site). The full text was considered unavailable if there was no response to the full text request by the end of the data analysis. (2) The main result of the study was the willingness to continue. Intention to continue use is the desire and inclination of the user to continue using mHealth. 15 This variable is measured using a self-administered questionnaire, usually on a 5- or 7-point Likert scale, for example, “I intend to continue using the mHealth app that I have been using rather than stop using it.” (3) The type of study is quantitative. (4) Research objects are products or services related to mHealth. WHO defines mHealth as “medical practices and public health practices supported by mobile devices such as mobile phones, patient monitoring devices, PDAs and other wireless devices.” 1 Thus, the mHealth chosen in this study must meet two characteristics: the product or service must be delivered using mobile devices, and the services provided by mHealth are in the healthcare category. Participants in the study were mHealth users, including the general population for prevention and patients for disease management. Exclusion criteria were as follows: (1) studies that did not show regression coefficients between variables (study methods were not based on correlation analysis or regression analysis); (2) reviews, letters, reviews, editorials; (3) studies that were not published during the peer review process. Three researchers (Tong Wang, Jun Liang, and Mingfu Nuo) independently reviewed the titles and abstracts and then assessed the full text of each potentially relevant article.
The data was extracted using standard data collection forms. From each included study, we extracted the following information: first author, year of publication, article title, mHealth type, user type and age characteristics, sample size, country or region where the study was conducted, statistical methods, explanatory variables and dependent variables Variables, coefficients regressions, P-values, and other statistical measures. In some of the included studies, the association hypothesis was based on related theories in psychology or behavior, so we collected theories or models on which the hypothesis was based. Two independent researchers (Wang Tong and Nuo Mingfu) performed the data extraction process and any disagreements were resolved through group discussion.
The quality of the included studies was assessed by two investigators (Tong Wang and Jun Liang) according to the JBI PACES36 checklist for analytical cross-sectional studies.
In this meta-analysis, the pooled regression coefficient with 95% confidence interval was the primary outcome. According to Sarkar et al.,37 the effect relationships included in a meta-analysis should be tested at least three times across different studies. Statistical analyzes were made of the relationship between influencing factors tested at least 3 times in the included studies and willingness to continue. Path analysis is commonly used in the study of intent to continue, to examine the direct and indirect effects of factors influencing intent to continue. Therefore, we not only synthesized the factors of direct influence of readiness to continue, but also synthesized the factors of influence of its main factors of direct influence, and also analyzed variables with indirect influence. Taking the three most significant influencing factors of continuation intention as mediating factors, the integrated effect of the relationship between influencing and mediating factors is analyzed. We calculated pooled regression coefficients and their corresponding 95% CIs using a random effects model24 that can account for within-study and between-study variability. In addition, we applied an inverse variance weighting method with an additive component of inter-study variance in a random effects model based on DerSimonian-Laird estimates to combine effect sizes. Since many variables are included in studies, most of which are subjective perceptions of users, the definitions and measurements of all variables differ in each study. We group the same constructs from the same theory or model into the same variables. For the same structure that goes back to different theories or does not have a theoretical basis, researchers distinguish by definitions and objects of measurement. A definition with many occurrences is used as a standard, and concepts other than the standard definition are treated as different variables and not integrated. Disagreements are resolved through group discussion.
The included studies were analyzed for risk of bias using funnel plots and the Egger test. Heterogeneity between studies was assessed using I2 (<50%, 50-74%, and >75% were low, moderate, and high heterogeneity, respectively).
To test the significance of differences in regression coefficients and the possible influence of residual confounders, we conducted a subgroup analysis for possible sources of heterogeneity, including geographic location, user type, mobile health type, user age, and study quality. First, some studies have shown that the economic development of a country or region has a deterrent effect on the use of information systems25, so we divided the study into economically developed and developing groups according to the countries or regions studied. and others. User type and eHealth application have been found to have a significant deterrent effect on their acceptance. Based on their classification of user types, we divided the studies into populations and patients (a subgroup analysis of healthcare professionals was not performed due to the small number of studies involving healthcare professionals as mHealth users). 24 Users of mHealth are mainly divided into healthy people and sick people. 24 Depending on the target users, mHealth can be divided into two categories: 1. Health promotion and prevention, 2. Disease treatment and teleconsultations. 24,38 In addition, wearable health monitoring devices are an important category of mHealth based primarily on device functionality. Thus, this study divides mHealth into three subgroups: 1. Health promotion and prevention, 2. Disease management and remote counseling, 3. Wearable devices. Finally, some studies classified participants as college students39 or older people ≥60 years old32, so we analyzed the age characteristics of the participants and divided the studies into the following categories: , older people (≥60 years old) and all age groups. In addition, these studies were divided into two groups according to the year of publication: early (2010-2015) and recent (2016-2021).
We performed all statistical analyzes using SPSS version 24 statistical software and 1.140 meta-essences and all P-values were two-tailed with a significance level of 0.05. This study has not been registered.
For more information on study design, see the Nature study abstract linked to this article.
The authors declare that all data included in this study is available in the article and its supplementary information files.
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Post time: Mar-10-2023