Characteristics of and Factors Influencing College Nursing Students' Willingness to Utilize mHealth for Health Promotion

Factors Affecting Medical Students’ Continuance Intention to Use Mobile Health Applications

Background

With the dramatic growth in smartphones, mobile health applications (apps) in the field of healthcare or medicine, which are characterized by strong operability, flexibility and interactivity, provide a supplementary approach to medical learning. The aims of this study were to awaken medical students to pay more attention to the learning function of mobile health app and gain deeper insight into our understanding of the factors influencing medical students’ mobile health apps continuance intention for enhancing practical utilization.

Methods

A cross-sectional survey was conducted with a self-designed questionnaire administered to medical students at Chongqing Medical University. Data were collected from 450 participants from March to April 2019. SPSS V.25 was used for the descriptive statistical analysis of the results. Smart PLS 3.2.7 was used to construct a structural equation modelling framework to estimate the influencing factors of medical students’ continuance intention to use mobile health apps.

Results

The external characteristics of mobile health apps had a positive influence on perceived ease of use (β = 0.378, P < 0.001). Perceived ease of use had a positive impact on perceived usefulness (β = 0.573, P < 0.001). Perceived ease of use (β = 0.195, P = 0.011), perceived usefulness (β = 0.450, P < 0.001) and subjective norms (β = 0.255, P < 0.001) had a positive impact on attitude towards using mobile health apps. Additionally, perceived usefulness (β = 0.202, P < 0.001) and attitude (β = 0.730, P < 0.001) had a remarkable influence on continuance intention. Furthermore, the mediating effect of attitude between perceived usefulness and continuance intention was significant (β = 0.329, P < 0.001).

Conclusion

Both perceived usefulness and attitude have a significant effect on medical students’ mobile health apps continuance intention. So, we suggest apps providers and developers evoking and maintaining users’ interest in mobile health apps and perfecting apps’ features from various aspect. Also, more attention should be paid on social relationship to introduce medical students to familiarize mobile health apps and strengthen adhesiveness of medical students.

A multi-method analytical approach to predicting young adults' intention to invest in mHealth during the COVID-19 pandemic

Mobile-based health (mHealth) systems are proving to be a popular alternative to the traditional visits to healthcare providers. They can also be useful and effective in fighting the spread of infectious diseases, such as the COVID-19 pandemic. Even though young adults are the most prevalent mHealth user group, the relevant literature has overlooked their intention to invest in and use mHealth services. This study aims to investigate the predictors that influence young adults' intention to invest in mHealth (IINmH), particularly during the COVID-19 crisis, by designing a research methodology that incorporates both the health belief model (HBM) and the expectation-confirmation model (ECM). As an expansion of the integrated HBM-ECM model, this study proposes two additional predictors: mobile Internet speed and mobile Internet cost. A multi-method analytical approach, including partial least squares structural equation modelling (PLS-SEM), fuzzy-set qualitative comparative analysis (fsQCA), and machine learning (ML), was utilised together with a sample dataset of 558 respondents. The dataset-about young adults in Bangladesh with an experience of using mHealth-was obtained through a structured questionnaire to examine the complex causal relationships of the integrated model. The findings from PLS-SEM indicate that value-for-money, mobile Internet cost, health motivation, and confirmation of services all have a substantial impact on young adults' IINmH during the COVID-19 pandemic. At the same time, the fsQCA results indicate that a combination of predictors, instead of any individual predictor, had a significant impact on predicting IINmH. Among ML methods, the XGBoost classifier outperformed other classifiers in predicting the IINmH, which was then used to perform sensitivity analysis to determine the relevance of features. We expect this multi-method analytical approach to make a significant contribution to the mHealth domain as well as the broad information systems literature.

A Qualitative Exploration of Desired mHealth App Mechanisms Related to Daily Life Influences for College Nursing Students

Introduction

Mobile health (mHealth) apps are digital health tools that allow for the delivery and access to vital health information, support, and encouragement needed to foster positive behavior change. Designing and developing mHealth solutions based on daily life influences for nursing students is imperative to establishing healthier physical and mental health habits.

Methods

Multiple focus groups (n=10) were conducted, and a questionnaire (n=11) was administered to undergraduate students in the professional nursing component. Themed analysis of focus-group data was conducted along with descriptive analysis of the questionnaire.

Results

All participants stated it has been more difficult to maintain a healthy lifestyle since beginning the nursing program. This deterioration can be attributed to three key areas: mental health needs/support, rigor of nursing school, and decline in positive health choices. Participants stated they would use an mHealth app designed specifically for nursing students to combat deterioration of their health.

Conclusions

The results of this study (100% positive response rate) reveal mHealth applications might be a powerful tool in helping nursing students transform their physical and mental health. It appears that if an mHealth application is created with the specific "must-haves" of nursing students then we might experience a positive shift in health behaviors for nursing students, which will hopefully transcend into their careers as nurses.

Research on the Intelligent Display of Cultural Relics in Smart Museums Based on Intelligently Optimized Digital Images

The smart museum is a new platform for the appreciation and display of cultural relics across time and space. In the era of 3D scanning technology, computer technology, and network technology, it is necessary to deeply study the smarter and more perfect forms of smart museums. This article, through the analysis of the characteristics of the display level of the smart museum, tries to create a new humanized and intelligent display of cultural relics.

Perceptions of Mobile Health Apps and Features to Support Psychosocial Well-being Among Frontline Health Care Workers Involved in the COVID-19 Pandemic Response: Qualitative Study

BACKGROUND

Frontline health care workers are experiencing a myriad of physical and psychosocial challenges amid the COVID-19 pandemic. There is growing recognition that digital technologies have the potential to improve the well-being of frontline workers. However, there has been limited development of wellness interventions using mobile health (mHealth) technology. More importantly, little research has been conducted on how frontline workers perceive mHealth-based support to promote their well-being.

OBJECTIVE

This study aimed to explore frontline workers' experience of conventional psychological wellness programs and their perceptions of the usefulness of mHealth apps and features for promoting well-being. It also sought to identify factors that could potentially influence uptake and retention of an mHealth-based wellness program.

METHODS

We conducted semistructured interviews using purposive sampling with frontline workers involved in the COVID-19 response. Various visual materials, collated from existing mHealth app features, were presented to facilitate discussion. Interviews were audio-recorded and transcribed verbatim. Thematic analysis based on grounded theory was undertaken. Themes were subsequently mapped to key nudge strategies-those commonly used for mHealth development-to assess participants' preferences for particular features and their reasoning.

RESULTS

A total of 42 frontline workers participated in 12 one-on-one interviews or focus group discussions. Frontline workers generally had a limited ability to identify their own psychological problems and liked the reminders functionality of the app to track their mood over time. A personalized goal-setting feature (ie, tailoring) and in-app resources were generally valued, while frequent coaching and messages (ie, framing) were seen as a distraction. The majority of participants desired a built-in chat function with a counselor (ie, guidance) for reasons of accessibility and protection of privacy. Very few participants appreciated a gamification function. Frontline workers commonly reported the need for ongoing social support and desired access to an in-app peer support community (ie, social influence). There were, however, concerns regarding potential risks from virtual peer interactions. Intrinsic motivational factors, mHealth app technicality, and tangible rewards were identified as critical for uptake and retention.

CONCLUSIONS

Our study highlights the potential of mHealth apps with relevant features to be used as wellness tools by frontline health care workers. Future work should focus on developing a nonintrusive and personalized mHealth app with in-app counseling, peer support to improve well-being, and tangible and extrinsic rewards to foster continued use.