The present study is a descriptive cross-sectional study. In this study, the sample size was calculated on the basis of a pilot study conducted with 30 nurses, which produced a mean acceptance score of 4.055 (SD 0.722). With a confidence level of 95% and an accuracy estimate (d) defined as a conservative fraction of the SD (d = 0.124 × 0.722), consistent with established methodological guidelines for achieving high precision in continuous data [20,21], Cochran formula was used to calculate the required sample size, resulting in a total of 250 participants (Equation 1).

The inclusion criteria included Iranian citizenship, a university degree in nursing, no known psychological disorders based on self-reports (such as mood disorders or anxiety disorders), at least 6 months of clinical experience, current employment in clinical roles in inpatient departments, and consent to participate in the study. Refusal to complete the data collection instruments was also considered as an exclusion criterion. A stratified random sample by department was conducted among eligible nurses in different clinical wards of hospitals affiliated with Kashan University of Medical Sciences. This sampling process was conducted over 2 months, from August to October 2022.

Equation 1 shows the Cochrane formula for sample size.

After approval from the Research Council and the Ethics Committee of Kashan University of Medical Sciences, the first author of the article approached the nursing offices of 5 hospitals (Shahid Beheshti, Naghavi, Matini, Kargarnejad, and Seyed Al Shohada Hospitals) with an introductory letter. After the study procedure was explained and permission from hospital management was secured, a comprehensive list was compiled detailing all clinical departments, including the names and total numbers of active nursing staff in each unit. To ensure proportional representation in line with the inclusion criteria and the target sample size, a proportionate number of eligible nurses from each department was then selected via simple random sampling. After consent was obtained from the head nurses, the first author of the article visited the departments at the beginning of each shift in accordance with the work schedules of the selected nurses. After providing consent, the selected nurses were instructed to complete the data collection instruments. The instruments were collected at the end of each shift. If the scales were not completed within the specified time frame, an agreement was reached with the respective nurse regarding the time of submission. In cases where the selected sample was unavailable or uncooperative, another nurse from the same department was randomly selected. All data collection instruments were completed by the nurses themselves and were based on self-reports.

In the present study, data were collected via the probable predictors of acceptance of MHAs by nurses using the Nurses’ Mobile Health Device Acceptance Scale (NMHDA-S) [8] (Multimedia Appendix 1).

The probable predictors of acceptance of mobile health programs, according to the nurses’ questionnaire, are divided into 4 sections:

This questionnaire was developed in Persian by the researchers of the present article on the basis of a comprehensive literature review, including relevant articles and questionnaires, as well as a survey of nursing and health information management professionals. The qualitative content validity was confirmed by 10 faculty members from Kashan University of Medical Sciences. The reliability of the questionnaire was evaluated via the test-retest method with a group of 20 nurses over a 1-week interval. The degree of agreement between the 2 measurements was determined by calculating the intraclass correlation coefficient for quantitative variables (such as knowledge of MHAs) and the κ statistic for categorical variables (such as familiarity factors with MHAs). Notably, all the responses regarding personal and professional variables remained consistent across both assessments. The intraclass correlation coefficients for the quantitative variables were found to exceed 0.95, whereas the κ statistics for the categorical variables surpassed 0.97.

The NMHDA-S was used to evaluate the acceptance of MHAs among nurses. This scale was originally developed in Persian on the basis of the constructs of the ECM and was psychometrically evaluated by Mirabootalebi et al [8] in 2024. The NMHDA-S consists of 33 items categorized into 5 subscales: usage and performance (10 items), social influence (7 items), perceived ease of use (5 items), effectiveness (7 items), and new technology anxiety (4 items). The respondents rated each item on a 7-point Likert-type scale. To determine the total acceptance score, the mean of all the items is calculated, resulting in a continuous score ranging from 1 to 7, where higher values indicate greater acceptance. Subscale scores are derived via the same mean-based procedure. The scale’s construct validity has been confirmed, and it demonstrates high internal consistency, with Cronbach α and McDonald ω coefficients of 0.938 and 0.953, respectively. These indices were estimated to be above 0.88 for all the subscales [8].

The data were analyzed using SPSS version 16. Acceptance scores were rated on a 7-point scale (1 to 7), and the 95% CI was calculated for the target population. Skewness and kurtosis indices were used to assess the data distribution, with a range of ±2 indicating a normal distribution. To prioritize the scale factors, a stepwise multiple linear regression analysis was performed to determine the contributions of various factors to acceptance, including usage and performance, social influence, perceived ease of use, effectiveness, and new technology anxiety.

The effects of the factors were analyzed in 2 phases to identify predictors. In the first phase, univariate analyses, including independent t tests, one-way ANOVA, and Pearson correlation coefficients, were conducted to assess the relationship between each factor and MHA acceptance. Variables with a significance level of <0.20 were subsequently entered into a stepwise multiple linear regression model. To ensure the validity of the regression results, key assumptions were rigorously evaluated. Multicollinearity was assessed using variance inflation factors (VIFs) and tolerance indices, while autocorrelation among the residuals was examined using the Durbin-Watson statistic. The normality and homoscedasticity of the residuals were evaluated using objective statistical measures—specifically, skewness and kurtosis values within ±2 were considered indicative of normal distributions. This analysis was further supported by visual inspection of residual histograms and normal probability (P-P) plots. For all analyses, a P value of <.05 was considered statistically significant.

All necessary approvals were obtained from the Research Council (number 039, dated 2022) and the Ethics Committee of Kashan University of Medical Sciences (code 40138). The first author explained the study to each nurse individually in a quiet, private, and comfortable room within the department. The key points discussed included the purpose of the study, the expected actions required for participation, the estimated time needed to complete the instruments, measures to ensure the confidentiality of the data, the absence of negative consequences if they choose not to participate in the study, the individuals and departments overseeing the study, and their right to withdraw from participation at any time. After these explanations, each nurse provided verbal consent and then signed a written informed consent form.

Among the 306 eligible nurses, 250 (81.7%) participated in the study, resulting in the analysis of 250 samples. The mean age of the participants was 35.624 (SD 7.582) years. Table 1 presents the personal and professional information, knowledge of MHAs, history of familiarity with MHAs, and usage of MHAs. The mean knowledge score of MHAs was calculated to be 2.976 (SD 1.210) on a 7-point scale (1 to 7).

The mean acceptance score for using MHAs in nursing was 4.207 (SD 0.740; on a scale of 1 to 7), indicating a moderate level of acceptance among clinical nurses. This rate was estimated to be 4.207 (95% CI 4.115-4.299) within the target population. All quantitative variables in the current study had a normal distribution based on the criteria included in the methodology (skewness and kurtosis within ±2). Stepwise multiple linear regression was used to prioritize the subscales of the acceptance scale. The results revealed that the contributions of each subscale, including usage and performance, social influence, perceived ease of use, effectiveness, and new technology anxiety, to explaining the variations in acceptance ratings were 1.9%, 5.5%, 12.6%, 76.9%, and 3.1%, respectively. Consequently, effectiveness proved to be the most important subscale.

A significant univariate association was found between acceptance scores and several factors, including gender (P=.03), education level (P=.001), observations of health care professionals or acquaintances using MHAs (P<.001), participation in relevant courses (P=.002), encouragement to use MHAs by health care professionals (P<.001), instances of patient inquiries about MHAs (P=.002), the presence of individuals with educational or professional backgrounds in information technology or related fields among first-degree relatives or close friends (P<.001), professional (P=.001), and personal use (P=.03), duration of daily internet use (P=.009), interest in participating in relevant educational programs (P<.001), willingness to engage with topics about new technologies (P<.001), and knowledge of MHAs (P=.002; Tables 2 and 3).

A stepwise multiple linear regression was performed to further examine the relationships between the independent variables and acceptance scores. The VIFs for all the variables were <5, and their tolerances exceeded 0.10, indicating an absence of multicollinearity. The Durbin-Watson statistic was 1.943, which falls within the acceptable range of 1.5 to 2.5. Before conducting the final regression analysis, the underlying assumptions were thoroughly verified. The Durbin-Watson statistic was within the acceptable range, confirming the absence of autocorrelation. The VIF values indicated no multicollinearity. For the regression residuals, both skewness and kurtosis were within the acceptable range of ±2, suggesting normality. Additionally, examination of residual histograms and normal P-P plots further confirmed that the assumptions of normality and homoscedasticity were fully satisfied. Consequently, the assumptions of multiple linear regression were satisfied, confirming the validity of the model. The results show that the simultaneous inclusion of 4 variables in the model is significant (F_4,245=18.308, P<.001) and explains 23% of the observed variance in acceptance scores. These variables include interest in participating in relevant educational programs, encouragement to use MHAs by health care professionals, gender, and duration of daily internet use. The variable with the greatest influence is interest in participating in relevant educational programs, which accounts for 17.6% of the observed changes. Interest in participating in relevant educational programs, encouragement by health care professionals to use MHAs, and duration of daily internet use positively influence acceptance. In addition, being a man is associated with greater acceptance (Table 4).

When controlling for other variables, the average acceptance score for MHAs among female nurses was 0.226 points lower than that of male nurses. In contrast, nurses who received encouragement from health care professionals to use MHAs experienced an average increase of 0.235 points in their acceptance scores. For each 1-unit increase in interest in participating in related educational programs, the acceptance score increased by an average of 0.310 points. Additionally, each extra hour of daily internet usage corresponds to an average increase of 0.086 points in the acceptance score. In this model, the unstandardized coefficients (B) represent the absolute change in the dependent variable per unit change in the independent variable, measured in the original units of the independent variable. The standardized coefficients (β) are dimensionless values that normalize the variables by their SDs, allowing them to be assessed on a common scale. While B quantifies the absolute magnitude of change, β is used to interpret the relative importance and compare the predictive power of the independent variables, regardless of their measurement units. According to the β coefficients in Table 4, interest in participating in relevant educational programs has the highest relative explanatory power in the model.

The present study aimed to investigate the acceptance of MHAs and associated factors among clinical nurses in Iran. The principal findings indicate moderate acceptance of MHAs among the surveyed nurses, with a mean score of 4.207 (SD 0.740) out of 7.

A study conducted by Shanmugapriya et al [22] in India reported that 71.6% of nursing students strongly agreed, 27.8% agreed, and only 0.6% expressed no opinion regarding their acceptance of MHAs. In addition, Kürtüncü et al [7] reported a mean total score of 124.69 (SD 15.98; ranging from 0‐128) for Turkish nurses on the MHAS acceptance model, indicating a high level of acceptance in this group. The researchers explained that the reason for the high level of acceptance among Turkish nurses was related to their high use of smartphones [7]. In Iran, the acceptance of MHAs among nurses was found to be moderate, possibly due to the lack of necessary infrastructure, absence of established guidelines, and insufficient knowledge [6]. In simpler terms, nurses are still not sufficiently equipped for digital transformation in health care and face several challenges, including limited digital skills, restrictions on smartphone usage, and financial constraints. By examining these specific behavioral factors in a resource-limited context such as Iran, this study goes beyond incremental findings to provide a comprehensive understanding of the unique challenges of digital health adoption in developing health care systems. It is recommended that incentives be offered to nurses to help offset the costs of using their personal smartphones for clinical tasks [23].

The findings of the current study suggest that the dimensions of “effectiveness” and “perceived ease of use” contribute significantly to the variations in acceptance scores. In contrast, “usage and performance” was responsible for the least variation in these scores. These results are consistent with those of previous studies. Hsiao and Chen [19] highlighted the impact of maturity and perceived usefulness on technology acceptance. Recent evidence on the adoption of health care technology indicates that perceived usefulness and perceived ease of use play significant roles in technology acceptance and the intention to use these innovations [24]. Additionally, recent studies focusing on nurses and health care professionals emphasize that perceived usefulness is an essential factor influencing both technology acceptance and the intention to use it [24,25]. In a review paper, Nezamdoust et al [26] identified 8 key themes influencing nurses’ use of MHAs: ease of use, usefulness, security and privacy, feasibility and performance, design and user interface, effectiveness, infrastructure, adaptability, and social norms. The findings of this and other studies indicate that perceived usefulness, perceived ease of use, and maturity are important determinants of clinical nurses’ acceptance of MHAs. Owing to living in the information and technology era and recognizing the benefits of using MHAs, nurses are increasingly inclined to use similar systems. Therefore, it is clear that perceived ease of use and usefulness play important roles in the acceptance of MHAs and ultimately encourage their increased or continued use.

In the present study, “usage and performance” made the smallest contribution to explaining the difference in caregiver acceptance of MHAs. Consistent with these findings, Hsiao and Chen [19] concluded that individual habits do not significantly influence the acceptance or performance of MHAs. In addition, Sezgin et al [16] reported no significant relationships between outcome acceptance, habits, self-efficacy, expected performance, creativity, and community influence on behavioral intentions. Chen et al [27] also concluded that security risk has a negative effect on the intention to use. However, some studies support the rejection of the hypothesis of the present study. Recent findings indicate that privacy and performance risks negatively impact trust and the intention to adopt digital health technologies, suggesting that perceived risk reduces acceptance [24]. Studies by Lwoga and Lwoga [13] and also Chopdar et al [14] have emphasized the influence of perceived security risk. The factors influencing usage and performance include personal habits, security risk, and social approval. The underutilization of MHAs can be attributed to several challenges, such as the absence of robust policies, lack of practical and user-friendly apps, and nurses’ unawareness of the potential benefits of MHAs. These issues likely play an important role in the resulting usage scores.

On the basis of the results of the present study, “interest in participating in relevant educational programs,” “encouragement to use MHAs by health care professionals,” “gender,” and “duration of internet use per day” were identified as factors influencing nurses’ acceptance of using MHAs in their nursing practice. The greatest contribution was “interest in participating in relevant educational programs,” which was significantly positively correlated with the acceptance rate.

Kaya et al [28] reported a significant association between in-service training and changes in nurses’ attitudes toward themselves, computers, and information technology devices. This technology requires training and attention to organizational and environmental factors to increase technology anxiety among nurses. In addition, it is important to create a supportive organizational culture that promotes technical skills and provides adequate time and resources for learning [29,30]. In a 2019 study [31], 69.5% of nurses could not use technology effectively and correctly. One of the main reasons for this is nurses’ lack of computer skills. Addressing this issue through continuous education for nurses can help bridge the generational and educational divide, ultimately alleviating their anxiety about using new technologies. Lifelong education for nurses plays a critical role in improving their acceptance of technology, especially in the face of constant changes in patient care, advancements in knowledge, and evolving practices and policies. In addition, supervisor and peer support can encourage nurses to embrace information technology by actively promoting MHA, effectively integrating it into clinical practice, and recognizing the resulting performance improvements.

The present study revealed that encouraging the use of MHAs among health care professionals has a positive and significant effect on acceptance rates. Recent evidence indicates that social factors significantly influence users’ intention to adopt technology, whereas attitudes remain a key contributor in these contexts [32]. Recent studies have shown that social influence significantly predicts the intention to use emerging technologies [33]. The findings of the study by Alasmari and Zhang [34] are consistent with those of the present study. The acceptance of MHAs by nurses is significantly influenced by various factors, including use by nursing colleagues, other health care professionals, and care managers. For example, colleagues who integrate smartphones into their clinical practice can be role models for nurses and encourage them to use similar technologies. In addition, the positive experiences and satisfaction of colleagues and managers with MHAs can help reduce nurses’ technology-related anxiety and reservations and thus increase their overall acceptance of these tools. In particular, sharing positive experiences with experts can be a strong motivator for nurses. When selecting MHAs, nurses usually consult more experienced colleagues, often giving considerable weight to their recommendations. Consequently, a positive view from peers can significantly increase nurses’ acceptance of technologies. When colleagues endorse specific technologies, this promotes a sense of approval among health care professionals. Conversely, peer resistance to MHAs can have a negative effect on acceptance and usage.

In the statistical model outlined in the present study, the “duration of daily internet usage” is found to have a positive effect on the acceptance score. Shanmugapriya et al [22] reported that there is a statistically significant correlation between the acceptance of MHAs and the time spent on nursing studies (internet-based learning). A 2020 study [35] identified several significant predictors of acceptance of MHAs among patients with cancer, including age, education level, and access to the internet. In addition, the duration of daily internet use increased among nurses using smartphones, which in turn impacted their acceptance of these apps. Smartphones provide easy access to the internet and allow nurses to access important online resources, such as treatment and medication guidelines, directly from their workplace. This capability enhances the ease of access and utilization of digital information in the clinical setting. In addition, smartphones facilitate communication with colleagues, physicians, and patients via various platforms, such as email, messaging applications, and telemedicine services.

This study revealed that men had a greater level of acceptance. A 2019 study [34] revealed that men tended to use mobile learning technologies more strongly than women did. In addition, a study by Nunes et al [36] confirmed the significant influence of men on the acceptance of MHAs. In this context, Al-Azawei [37] pointed to gender difference as a moderator in the relationship between the model constructs. However, in this study, this moderating effect was found to be minimal with respect to the relationship between e-learning self-efficacy and learning management system acceptance. Self-efficacy had a stronger effect on the intention to use learning management system in men than in women, suggesting that men have relatively higher levels of acceptance [37]. However, a 2024 study [38] examining students’ intentions to use online learning through educational technology applications based on the UTAUT model revealed a significant influence of women on acceptance. Recent evidence suggests that both gender and age can affect the relationship between self-efficacy and technology adoption, with self-efficacy remaining a crucial factor in users’ intentions to adopt technology [39]. Furthermore, the findings indicate that specific computer effectiveness has a more pronounced effect on perceived ease of use than does general remaining efficacy. Among cofactors, general computer self-efficacy emerged as a significant predictor of perceived usefulness for both men and women. Additionally, while perceptions of general computer self-efficacy were lower, it still had a notable influence on the intention to use technology. Conversely, specific computer self-efficacy demonstrated a stronger indirect effect on behavioral intentions among women, although both genders reported similar levels of self-efficacy [40]. The results can serve as a reference for hospitals and clinical training organizations. When integrating nursing technologies into nurses’ clinical practice, it is important to consider user characteristics and relevant moderators when specific groups are targeted through MHAs. Studies suggest that men are more at ease with and acclimatized to technology than women are. In addition, societal expectations around gender roles can influence how people perceive and interact with technology. In a country with cultural characteristics similar to those of Iran, it seems that women’s greater anxiety about new technology and men’s greater belief in their self-efficacy contribute to these results. Further research is needed, as there is a lack of robust evidence in this area for different societies. Analyzing the underlying causes in Iranian society highlights the need for qualitative approaches.

Importantly, several factors may contribute to the discrepancies between the results of the current study and those of previous related research. These factors include the timing and location of the studies, the type of statistical tests, the presence or absence of established national guidelines, differences in the relevant target groups, and the specific characteristics of those populations.

The substantial sample size and the ability to conduct multiple analyses are notable strengths of the current study. Several factors may play important roles for health care professionals, including task mobility, individual performance, technological maturity [19], self-efficacy, responsiveness [41], organizational support, environmental and organizational influences, adaptability [17], and acceptance of MHA; however, these factors were not explored in the current study. In this context, the final multiple linear regression model (Table 4) explained only 23% of the variance in the acceptance score, indicating that 77% of the variance is explained by other variables not included in the model. Consequently, the interpretation of these results should be approached with more caution, as the identified predictors provide only a partial explanation of MHAS acceptance. Furthermore, the timing of sampling during the COVID-19 crisis disrupted the usual structure of hospital departments, changed the composition of human resources, and significantly increased the occupational stress among nurses in this setting [42]. As a result, the unwillingness of a considerable number of nurses to participate in the study probably affected the generalizability of the findings, which is recognized as a limitation of this study. It is recommended that a study be conducted under normal conditions, free from the constraints imposed by pandemic crises, to address the limitations of the current study. In addition, conducting a qualitative study with a grounded theory approach would be valuable for an in-depth investigation of the factors influencing the acceptance of MHAs by nurses in different societies, including Iran.

This study has several limitations. First, the data were collected concurrently with the COVID-19 crisis, during which the nursing staff experienced a high workload and occupational stress [42], potentially affecting participation and responses. A substantial proportion of eligible nurses were unwilling to cooperate, which may have introduced response bias. Furthermore, the participants’ knowledge of MHAs was assessed using a single-item question, which may not have captured the full multidimensionality of this construct. It is strongly recommended that future studies evaluate this variable via more appropriate, comprehensive, and highly precise measurement tools.

The present study revealed moderate acceptance of MHAs among nurses in hospitals affiliated with Kashan University of Medical Sciences. The key factors that positively and significantly contribute to the acceptance rate include “interest in participating in relevant educational programs,” “encouragement from health care professionals to use MHAs,” and “duration of daily internet use.” In addition, the acceptance rate is significantly higher among men than women. Facilitating interest in participation in relevant training courses, alongside the provision of adequate internet access and the implementation of incentive policies targeting all nursing professionals, especially women, may increase the acceptance of MHAs in the delivery of care. Therefore, health policy makers and nursing managers should recognize the importance of this issue when designing continuing education programs for nurses, formulating policies related to incentive structures, and establishing supportive infrastructure in hospitals. By implementing effective strategies, they can improve the acceptance of MHAs among nurses in the clinical setting.