A content analysis of precede-proceed constructs in stress management mobile apps

A narrative review of anxiety regulation in PhD students based on Green model

To gain a better understanding of the factors that contribute to anxiety among PhD students and the reasons for poor regulation in the current situation, this paper analyses the existing literature on anxiety among PhD students using Green's model. It also compares and evaluates various methods of regulating anxiety. The literature review to extract information on the causes and levels of anxiety, methods and outcomes of anxiety intervention and regulation, and to make comparisons. The study reveals that the phenomenon of PhD students' anxiety has intensified globally in recent years, especially after the end of the epidemic. PhD students experience anxiety due to research pressure, economic pressure, future development, and interpersonal pressure. The main influencing factors are currently the relationship with the supervisor, development prospects, social support, and peer comparison. Among the stress relief methods, the regulation of self-relaxation was found to have better effects on mild anxiety, such as positive thinking, meditation, yoga and physical exercise can be helpful for emotion release then help focus on problem solved. Whereas severe anxiety may require institutional and pharmacological support, also including using psychological therapy such as behavioral cognitive therapy and systematic desensitization methods. For university, competence to provide course-assisted guidance, such as writing groups, peer support, and time management, is also important. Academic communities should pay attention to the guidance on academic fairness. However, PhD students are often unaware of the resources available to them for coping with stress and may not take the initiative to seek psychological counseling or institutional assistance. Therefore, PhD students should receive support from various sources, be guided to express their thoughts, and receive additional education and academic assistance to manage stress. This will enhance their confidence and aid in improving their scientific research.

mSexHealth: An Overview of Mobile Sexual Health Applications

The aim of this study was to examine the existing mobile applications focused on sexual health and analyze the included content and therapeutic techniques. Three databases with mobile applications were searched and 47 applications met the criteria. More applications have been developed for men, most of them included content for erectile dysfunction, and only one for vaginismus. Most apps included sexuality tips and Kegel exercises, and only one technique for working on thoughts and emotions. In conclusion, a number of mobile applications have been developed to enhance sexual functioning, but scientific verification of their effectiveness has been completely lacking.

Insights into mobile health application market via a content analysis of marketplace data with machine learning

BACKGROUND

Despite the benefits offered by an abundance of health applications promoted on app marketplaces (e.g., Google Play Store), the wide adoption of mobile health and e-health apps is yet to come.

OBJECTIVE

This study aims to investigate the current landscape of smartphone apps that focus on improving and sustaining health and wellbeing. Understanding the categories that popular apps focus on and the relevant features provided to users, which lead to higher user scores and downloads will offer insights to enable higher adoption in the general populace. This study on 1,000 mobile health applications aims to shed light on the reasons why particular apps are liked and adopted while many are not.

METHODS

User-generated data (i.e. review scores) and company-generated data (i.e. app descriptions) were collected from app marketplaces and manually coded and categorized by two researchers. For analysis, Artificial Neural Networks, Random Forest and Naïve Bayes Artificial Intelligence algorithms were used.

RESULTS

The analysis led to features that attracted more download behavior and higher user scores. The findings suggest that apps that mention a privacy policy or provide videos in description lead to higher user scores, whereas free apps with in-app purchase possibilities, social networking and sharing features and feedback mechanisms lead to higher number of downloads. Moreover, differences in user scores and the total number of downloads are detected in distinct subcategories of mobile health apps.

CONCLUSION

This study contributes to the current knowledge of m-health application use by reviewing mobile health applications using content analysis and machine learning algorithms. The content analysis adds significant value by providing classification, keywords and factors that influence download behavior and user scores in a m-health context.

How Do Apps Work? An Analysis of Physical Activity App Users' Perceptions of Behavior Change Mechanisms

Background

Physical activity apps are commonly used to increase levels of activity and health status. To date, the focus of research has been to determine the potential of apps to influence behavior, to ascertain the efficacy of a limited number of apps to change behavior, and to identify the characteristics of apps that users prefer.

Objective

The purpose of this study was to identify the mechanisms by which the use of physical activity apps may influence the users’ physical activity behavior.

Methods

This study used a cross-sectional survey of users of health-related physical activity apps during the past 6 months. An electronic survey was created in Qualtrics’ Web-based survey software and deployed on Amazon Mechanical Turk. Individuals who had used at least one physical activity app in the past 6 months were eligible to respond. The final sample comprised 207 adults living in the United States. 86.0% (178/207) of respondents were between the ages of 26 and 54 years, with 51.2% (106/207) of respondents being female. Behavior change theory informed the creation of 20 survey items relating to the mechanisms of behavior change. Respondents also reported about engagement with the apps, app likeability, and physical activity behavior.

Results

Respondents reported that using a physical activity app in the past 6 months resulted in a change in their attitudes, beliefs, perceptions, and motivation. Engagement with the app (P<.001), frequency of app use (P=.03), and app price (P=.01) were related to the reported impact of the behavior change theory or mechanisms of change. The mechanisms of change were associated with self-reported physical activity behaviors (P<.001).

Conclusions

The findings from this study provide an overview of the mechanisms by which apps may impact behavior. App developers may wish to incorporate these mechanisms in an effort to increase impact. Practitioners should consider the extent to which behavior change theory is integrated into a particular app when they consider making recommendations to others wishing to increase levels of physical activity.