Socio-demographic digital divide in mHealth app use among clients at outpatient departments in Inner Mongolia, China: a cross-sectional study (Preprint)

Using Wearable Technologies to Monitor Physical Activity and Exercise in Patients: A Narrative Review

ABSTRACT

With an increasing prevalence of sedentary lifestyles driving a prominent public health crisis, digital health tools such as wearable technologies are revolutionizing clinicians' ability to track physical activity and exercise. Despite their ubiquity in the consumer market, these technologies have not yet been fully incorporated into clinical practice. Though these tools promise efficacy and accessibility, a careful review of the current literature is important to understand the challenges and future promise of clinical implementation. Important considerations of implementation include health maintenance and disease prevention, ease of use by patients and providers, incorporation into the electronic health record, cost considerations, safety, privacy, and ethical considerations. This narrative review describes the recent literature on the implementation of wearable technologies in the prescription of physical activity and exercise. Application of these technologies is promising for this field's future.

Sociodemographic factors and health digital divide among urban residents: Evidence from a population-based survey in China

Background

The deep integration of digital technology and healthcare services has propelled the healthcare system into the era of digital health. However, vulnerable populations in the field of information technology, they face challenges in benefiting from the digital dividends brought by digital health, leading to the emerging phenomenon of the "health digital divide."

Methods

This study utilized the sample of 3547 urban from the 2021 Chinese Social Survey data for analysis. Models were constructed with digital access divide, digital usage divide, and digital outcome divide for urban residents, and structural equation modeling was implemented for analysis.

Results

The impact β coefficients (95% CI) of urban residents' digital access on the frequency of digital use, internet healthcare utilization, and patient experience were (β = 0.737, P < 0.001), (β = 0.047, P < 0.05), and (β = 0.079, P < 0.001), respectively. Urban elderly groups were at a disadvantage in digital access and usage (β = -0.007, β = -0.024, and β = -0.004), as well as those with lower educational levels (β = 0.109, β = 0.162, and β = 0.045). However, these two factors did not have a significant direct impact on the patient experience in urban areas.

Conclusions

The health digital divide of urban residents exhibits a cascading effect, primarily manifested in the digital access and usage divide. To bridge health digital divide among urban residents, efforts must be made to improve digital access and usage among the elderly and those with lower educational levels.

Informing Patient-Provider Engagement for Shared Decision Making Through Mobile Health Applications

Objective

The study utilized a cross-sectional dataset to identify demographic and health factors associated with patient utilization of mHealth applications for engaging with healthcare providers. The focus was on adults with chronic health conditions as the primary app user group. The goal was to reveal specific barriers and facilitators to app adoption among smartphone users, with the aim of highlighting opportunities for upgrades that promote patient empowerment as a prerequisite for shared decision-making (SDM).

Methods

Data from the Health Information National Trends Survey (HINTS 5, Cycle 4, 2020) with 3865 respondents (≥18 years old) stratified analyses and weighted logistic regression were used.

Results

The study found that individuals having a wellness app on a smartphone increased the likelihood (OR 2.68, CI: 2.02-3.56, p-value < 0.0001) of discussing health conditions with providers. Furthermore, individuals with multiple chronic health conditions were more likely (OR 1.93, CI 1.26-2.95, p-value < 0.01) to use apps to use mobile health applications to engage with healthcare providers. Other significant variables affecting app usage such as race, marital status, and educational level.

Conclusion

Due to difficulties obtaining in-person healthcare, the COVID-19 epidemic forced a swift deployment of mHealth technologies. Even in the absence of a crisis, mobile health applications continue to be crucial for improving patient-provider engagement and developing novel approaches to healthcare delivery. During the pandemic, people with numerous chronic diseases used apps to stay in touch with doctors and maintain their reliance on these platforms. Nonetheless, different smartphone users continue to use mHealth application in different ways. The findings revealing barriers in mHealth app adoption among certain patient subgroups suggest opportunities for developers, in collaboration with users and providers, to enhance inclusion and acceptability when upgrading mHealth application platforms.

Large-data contextualizations of social determinant associations in pediatric head and neck cancers

PURPOSE OF REVIEW

Prior investigations in social determinants of health (SDoH) and their impact on pediatric head and neck cancers are limited by the narrow scope of cancer types and SDoH being studied while lacking inquiry on the interrelational contribution of varied SDoH in real-world contexts. The purpose of this review is to discuss the current research tackling these shortcomings of SDoH-based studies in head and neck cancer and to discuss means of applying these findings in prospective initiatives and implementations.

RECENT FINDINGS

Through leveraging contemporary, large-data analyses measuring diverse social vulnerabilities, several studies have identified comprehensive delineations of which social disparities contribute the largest quantifiable impact on the care of head and neck cancer patients. Progressing from prior SDoH-based research of the decade, these studies contextualize the effect of social vulnerabilities and have laid the foundations to begin addressing these issues in the complex, modern-day environment of interrelatedsocial factors.

SUMMARY

Social determinants of health markedly affect pediatric head and neck cancer care and prognosis in complex and surprising ways. Modern-day tools and analyses derived from large-data techniques have unveiled the quantifiable underpinnings of how SDoH impact these pathologies.

Use Patterns of Smartphone Apps and Wearable Devices Supporting Physical Activity and Exercise: Large-Scale Cross-Sectional Survey

Background

Physical inactivity is a global health issue, and mobile health (mHealth) apps are expected to play an important role in promoting physical activity. Empirical studies have demonstrated the efficacy and efficiency of app-based interventions, and an increasing number of apps with more functions and richer content have been released. Regardless of the success of mHealth apps, there are important evidence gaps in the literature; that is, it is largely unknown who uses what app functions and which functions are associated with physical activity.

Objective

This study aims to investigate the use patterns of apps and wearables supporting physical activity and exercise in a Japanese-speaking community sample.

Methods

We recruited 20,573 web-based panelists who completed questionnaires concerning demographics, regular physical activity levels, and use of apps and wearables supporting physical activity. Participants who indicated that they were using a physical activity app or wearable were presented with a list of app functions (eg, sensor information, goal setting, journaling, and reward), among which they selected any functions they used.

Results

Approximately one-quarter (n=4465) of the sample was identified as app users and showed similar demographic characteristics to samples documented in the literature; that is, compared with app nonusers, app users were younger (odds ratio [OR] 0.57, 95% CI 0.50-0.65), were more likely to be men (OR 0.83, 95% CI 0.77-0.90), had higher BMI scores (OR 1.02, 95% CI 1.01-1.03), had higher levels of education (university or above; OR 1.528, 95% CI 1.19-1.99), were more likely to have a child (OR 1.16, 95% CI 1.05-1.28) and job (OR 1.28, 95% CI 1.17-1.40), and had a higher household income (OR 1.40, 95% CI 1.21-1.62). Our results revealed unique associations between demographic variables and specific app functions. For example, sensor information, journaling, and GPS were more frequently used by men than women (ORs <0.84). Another important finding is that people used a median of 2 (IQR 1-4) different functions within an app, and the most common pattern was to use sensor information (ie, self-monitoring) and one other function such as goal setting or reminders.

Conclusions

Regardless of the current trend in app development toward multifunctionality, our findings highlight the importance of app simplicity. A set of two functions (more precisely, self-monitoring and one other function) might be the minimum that can be accepted by most users. In addition, the identified individual differences will help developers and stakeholders pave the way for the personalization of app functions.

Analysis of Mobile App-Based Mental Health Solutions for College Students: A Rapid Review

BACKGROUND

College students are one of the most susceptible age groups to mental health problems. With the growing popularity of mobile health (mHealth), there is an increasing need to investigate its implications for mental health solutions. This review evaluates mHealth interventions for addressing mental health problems among college students.

METHODS

An online database search was conducted. Articles were required to focus on the impact of mHealth intervention on student mental health. Fifteen of the 487 articles, initially pulled from the search query, were included in the review.

RESULTS

The review identified three primary aspects of mental health: depression, anxiety, and stress. Research that found statistically significant improvements following mHealth intervention involved study durations between four and eight weeks, daily app use, guided lessons using cognitive behavioral therapy, acceptance and commitment therapy, and meditation. The review's findings show that future work must address the concern of digital divide, gender and sex differences, and have larger sample sizes.

CONCLUSIONS

There is potential to improve depressive symptoms and other similar mental health problems among college students via mobile app interventions. However, actions must be taken to improve barriers to communication and better reach the younger generations.