Use of smartphones, mobile apps and wearables for health promotion by people with anxiety or depression: An analysis of a nationally representative survey data

Design and Development of a Smart Fidget Toy Using Blockchain Technology to Improve Health Data Control

This study explores the integration of blockchain technology in wearable health devices through the design and development of a Smart Fidget Toy. We aimed to investigate design challenges and opportunities of blockchain-based health devices, examine the impact of blockchain integration user experience, and assess its potential to improve data control and user trust. Using an iterative user-centered design approach, we developed a mid-fidelity prototype of a physical fidget device with a blockchain-based web application. Our key contributions include the design of a fidget toy using blockchain for secure health data management, an iterative development process balancing user needs with blockchain integration challenges, and insights into user perceptions of blockchain wearables for health. We conducted user studies, including a survey (n = 28), focus group (n = 6), interactive wireframe testing (n = 7), and prototype testing (n = 10). Our study revealed high user interest (70%) in blockchain-based data control and sharing features and improved perceived security of data (90% of users) with blockchain integration. However, we also identified challenges in user understanding of blockchain concepts, necessitating additional support. Our smart contract, deployed on the Polygon zkEVM testnet, efficiently manages data storage and retrieval while maintaining user privacy. This research advances the understanding of blockchain applications in health wearables, offering valuable insights for the future development of this field.

Association between Depressive Symptoms, Physical Activity, and Health Factors in Hispanic Emerging Adults

Physical activity is a modifiable lifestyle behavior known for reducing symptoms of and being a risk factor for depression and mental health disorders. However, emerging adults (ages 18-25) struggle to meet recommended amounts. In this study, we explore the association between physical activity, depressive symptoms, and health factors in 137 Hispanic emerging adults. Using a cross-sectional survey design, sociodemographic information, depressive symptoms (CES-D score), physical activity (IPAQ score), body composition, and blood pressure measures were obtained. Statistical analyses included correlation and regression analyses. More than half of the participants demonstrated depressive symptomology (59.1%) and body fat percentage greater than 25% (64.2%). Body fat percentage, lean body mass, stress, and heart rate demonstrated notable associations with depressive symptoms and physical activity. When measured continuously and categorically, IPAQ was not a significant predictor of depressive symptoms. When used as a binary variable with a cutoff of 600 MET min/week, IPAQ score revealed a negative relationship with CES-D score (β = -0.169, SE = 2.748, p = 0.034). Our results indicate that a threshold of physical activity, 600 MET min/week, may confer protective effects against depressive symptoms. Future research should investigate the context and quality of physical activity to address mental health disparities in this underrepresented population.

Insights Derived From Text-Based Digital Media, in Relation to Mental Health and Suicide Prevention, Using Data Analysis and Machine Learning: Systematic Review

BACKGROUND

Text-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of mental health and suicide prevention.

OBJECTIVE

This systematic review aimed to determine how machine learning and data analysis can be applied to text-based digital media data to understand mental health and aid suicide prevention.

METHODS

A systematic review of research papers from the following major electronic databases was conducted: Web of Science, MEDLINE, Embase (via MEDLINE), and PsycINFO (via MEDLINE). The database search was supplemented by a hand search using Google Scholar.

RESULTS

Overall, 19 studies were included, with five major themes as to how data analysis and machine learning techniques could be applied: (1) as predictors of personal mental health, (2) to understand how personal mental health and suicidal behavior are communicated, (3) to detect mental disorders and suicidal risk, (4) to identify help seeking for mental health difficulties, and (5) to determine the efficacy of interventions to support mental well-being.

CONCLUSIONS

Our findings show that data analysis and machine learning can be used to gain valuable insights, such as the following: web-based conversations relating to depression vary among different ethnic groups, teenagers engage in a web-based conversation about suicide more often than adults, and people seeking support in web-based mental health communities feel better after receiving online support. Digital tools and mental health apps are being used successfully to manage mental health, particularly through the COVID-19 epidemic, during which analysis has revealed that there was increased anxiety and depression, and web-based communities played a part in reducing isolation during the pandemic. Predictive analytics were also shown to have potential, and virtual reality shows promising results in the delivery of preventive or curative care. Future research efforts could center on optimizing algorithms to enhance the potential of text-based digital media analysis in mental health and suicide prevention. In addressing depression, a crucial step involves identifying the factors that contribute to happiness and using machine learning to forecast these sources of happiness. This could extend to understanding how various activities result in improved happiness across different socioeconomic groups. Using insights gathered from such data analysis and machine learning, there is an opportunity to craft digital interventions, such as chatbots, designed to provide support and address mental health challenges and suicide prevention.

Who uses connected health technologies after a cancer diagnosis? evidence from the US Health Information National Trends Survey

PURPOSE

As the number of people living with and beyond cancer increases, connected health technologies offer promise to enhance access to care and support, while reducing costs. However, uptake of connected health technologies may vary depending on sociodemographic and health-related variables. This study aimed to investigate demographic and health predictors of connected health technology use among people living with and beyond cancer.

METHODS

Cross-sectional data from the US Health Information National Trends Survey Version 5 Cycle 4 (H5c4) was used. Regression analysis was used to examine associations between sociodemographic factors and the use of connected health technologies. The sample was restricted to individuals who self-reported a cancer diagnosis or history of cancer.

RESULTS

In this cycle, 626 respondents self-reported a cancer diagnosis, with 41.1% using connected health technologies (health and wellness apps and/or wearable devices). Most were female (58.9%) and white (82.5%); 43.4% had graduated college or higher education. One third (33.6%) had a household income of $75,000 or more. Respondents who were younger, have higher education, were living as married, had higher incomes, had higher self-rated health and had higher health-related self-efficacy were significantly more likely to use connected health technologies. There were no significant associations between gender, race, stratum, time since diagnosis, history of anxiety or depression, and use of connected health technologies among people living with and beyond cancer.

CONCLUSIONS

Connected health technology use among people living with and beyond cancer is associated with sociodemographic factors. Future research should examine these demographic disparities as the use of connected health technologies in healthcare continues to gather momentum.

IMPLICATIONS FOR CANCER SURVIVORS

The study underscores a disparity in connected heath technology usage among people living with and beyond cancer. There is a pressing need for research into adoption barriers and interventions to ensure equitable digital healthcare integration among this population, especially with the heightened adoption of technology post COVID-19 pandemic.

Examining Ethnic Disparities in Digital Healthcare Services Utilization: Insights from Israel

Purpose

The purpose of this study was to examine ethnic disparities in the utilization of digital healthcare services (DHS) in Israel and explore the characteristics and factors influencing DHS use among the Arab minority and Jewish majority populations.

Methods

A cross-sectional correlational design was employed to collect data from 606 Israeli participants, 445 Jews, and 161 Arabs. Participants completed a digital questionnaire that assessed DHS utilization, digital health literacy, attitudes towards DHS, and demographic variables.

Results

The findings reveal significant disparities in DHS utilization and attitudes between these ethnic groups, with Jewish participants demonstrating higher rates of utilization and positive attitudes toward DHS. The study also explores the predictive role of digital health literacy and attitudes in DHS use while considering ethnicity as a potential moderator. Significant predicting factors related to DHS utilization among Jews include positive attitudes and high health literacy. Among the Arabs, only attitudes towards DHS significantly predict the extent of DHS use. Digital health literacy affects the extent of use through attitudes at the two groups of the moderator significantly, but it is stronger among the Arab group.

Conclusion

To improve healthcare outcomes and reduce disparities, efforts should focus on ensuring equitable access to DHS for the Arab minority population. Targeted interventions, including digital literacy education, removing technology access barriers, offering services in Arabic, and collaborating with community organizations, can help bridge the gap and promote equal utilization of DHS.

Young Adult Brain Capital: A New Opportunity for Dementia Prevention

The potential for future prevention of Alzheimer's disease and related dementias (ADRD) through healthy lifestyle change is spurring a positive brain health movement. However, most ADRD research continues to focus on mid- and later life. We lack evidence regarding risk exposure and protective factors in young adulthood, i.e., 18-39 years. Brain capital is an emerging framework that represents the combination of education, knowledge, skills, and optimal brain health that people accumulate over their lives. Building on this framework, we present a new model that focuses on optimizing brain health in young adulthood; namely, young adult brain capital. Increasing focus on younger populations is critical for developing citizens who are emotionally intelligent, resilient and can anticipate and cope with rapid changes in the world. By understanding the values that are key drivers and motivators for young adults, we can empower the next generation to become active agents in optimizing their brain health and reducing their risk for future ADRD.

Exploring the Prevalence and Factors Influencing Clinical Trial Awareness in US Adults with Self-Reported Depression and Anxiety

OBJECTIVE

Lack of clinical trial awareness is a crucial barrier to clinical trial enrollment. The objective of this study was to examine the prevalence and factors associated with clinical trial awareness among US adults with self-reported depression and anxiety.

METHODS

Data were collected from 896 adults who self-reported depression and anxiety from the 2020 Health Information National Trends Survey. Multinomial logistic regression was utilized to assess predictors of clinical trial awareness, particularly socio-demographic, health-related, and technological variables. Odds ratios (OR) for the associations were reported.

RESULTS

About 60.4% of adults with self-reported depression or anxiety reported being aware of clinical trials. In the multivariable regression, education level, health-related social media use, and having access to a regular provider were all significantly associated with greater odds of clinical trial awareness among individuals with depression and/or anxiety. Specifically, individuals with at least some college education (OR 2.07, 95% confidence interval (CI); 1.28-3.34; p ​= ​0.004) were more likely to report awareness of clinical trials than those with less than a college education. Similarly, compared to those without access to health providers, individuals with depression and/or anxiety who had a regular provider had greater odds of clinical trial awareness (OR 2.23, 95% CI; 1.16-4.31; p ​= ​0.017). Additionally, those who reported two or more health-related uses of social media were significantly more likely to report clinical trial awareness than their counterparts who reported no health-related social media use (OR 3.17, 95% CI; 1.48-6.80; p ​= ​0.004).

CONCLUSION

Our study shows that about six in 10 adults with depression and anxiety in the United States were aware of clinical trials. However, some sub-groups of patients, particularly those without access to a regular health provider, those with a lower education level, and those with limited use of social media for health purposes, remain inadequately informed and may lack awareness of available clinical trials. These findings are crucial and identify subgroups of people with mental disorders that may benefit from targeted interventions to improve clinical trial awareness.

Association Between the Use of Wearable Devices and Physical Activity Among US Adults With Depression and Anxiety: Evidence From the 2019 and 2020 Health Information National Trends Survey

Objective The objective of this study was to examine the relationship between wearable device (WD) use and physical activity (PA) levels among US adults with self-reported depression and anxiety. Methods Data were pooled from 2026 adults who self-reported depression and anxiety from the 2019 and 2020 Health Information National Trends Survey. The explanatory variable was WD use, and the outcomes were weekly PA levels and resistance strength training. Logistic regression was conducted to investigate the association between WD and PA parameters. Results About 33% of adults with self-reported depression/anxiety reported WD use. Only 32.5% and 34.2% of the population reported meeting the weekly recommended levels of physical activity (≥150 minutes/week) and strength and resistance exercise (≥2 times weekly), respectively. In adjusted analyses, the use of WD was not associated with meeting the national weekly recommendation for physical activity (OR 1.38, 95% CI (0.94, 2.04); p=0.10) or resistance strength training (OR 1.31, 95% CI (0.82, 2.08); p=0.26). Further exploratory analysis also showed that physical activity levels did not differ with the frequency of WD use. Conclusion Despite the popularity of WD use among people with mental disorders, we found that use of WD was not associated with increased physical activity measures, suggesting that although there is a promise for these tools to augment mental health, their real-world effectiveness in promoting physical activity in people with mental disorders remains to be proven.

Exploring Retention, Usage, and Efficacy of Web-Based Delivery of Positive Emotion Regulation Skills During the COVID-19 Pandemic

COVID-19 prompted distress and increased reliance on digital mental health interventions, which previously demonstrated low rates of retention and adherence. This single-arm trial evaluated whether self-guided, web-based, positive affect regulation skills (PARK) were engaging and associated with changes in well-being during the pandemic. Over 6 weeks, PARK delivers brief lessons and practices in skills designed to increase positive emotions: noticing positive events, savoring, gratitude, mindfulness, positive reappraisal, personal strengths, and self-compassion. Patient-Reported Outcome Measurement Information System (PROMIS) computer adaptive tests of anxiety, depression, social isolation, positive affect, and meaning and purpose were administered at baseline, post-intervention, and 6 months after baseline. Retention and usage of PARK were measured by the web-based assessment and intervention platforms. The sample (n = 616) was predominantly female, non-Hispanic, white, and well-educated. Of those who completed baseline, only 42% completed a follow-up assessment; 30% never logged into PARK. Among those who did, 86% used at least one skill, but only 14% completed PARK. Across retention and usage metrics, older age predicted more engagement. In multivariable models, people of color and people with greater baseline anxiety were more likely to complete PARK. All well-being indicators improved over time, with greater improvements in anxiety and social isolation among participants who accessed at least one PARK skill compared to those who did not. Retention and usage rates mirrored pre-pandemic trends, but within this select sample, predictors of engagement differed from prior research. Findings underscore the need for additional efforts to ensure equitable access to digital mental health interventions and research. Trials registration: NCT04367922.

Self-Management of Subclinical Common Mental Health Disorders (Anxiety, Depression and Sleep Disorders) Using Wearable Devices

RATIONALE

Common mental health disorders (CMD) (anxiety, depression, and sleep disorders) are among the leading causes of disease burden globally. The economic burden associated with such disorders is estimated at $2.4 trillion as of 2010 and is expected to reach $16 trillion by 2030. The UK has observed a 21-fold increase in the economic burden associated with CMD over the past decade. The recent COVID-19 pandemic was a catalyst for adopting technologies for mental health support and services, thereby increasing the reception of personal health data and wearables. Wearables hold considerable promise to empower users concerning the management of subclinical common mental health disorders. However, there are significant challenges to adopting wearables as a tool for the self-management of the symptoms of common mental health disorders.

AIMS

This review aims to evaluate the potential utility of wearables for the self-management of sub-clinical anxiety and depressive mental health disorders. Furthermore, we seek to understand the potential of wearables to reduce the burden on the healthcare system.

METHODOLOGY

a systematic review of research papers was conducted, focusing on wearable devices for the self-management of CMD released between 2018-2022, focusing primarily on mental health management using technology.

RESULTS

We screened 445 papers and analysed the reports from 12 wearable devices concerning their device type, year, biometrics used, and machine learning algorithm deployed. Electrodermal activity (EDA/GSR/SC/Skin Temperature), physical activity, and heart rate (HR) are the most common biometrics with nine, six and six reference counts, respectively. Additionally, while smartwatches have greater penetration and integration within the marketplace, fitness trackers have the most significant public value benefit of £513.9 M, likely due to greater retention.

Utilizing digital predictive biomarkers to identify Veteran suicide risk

Veteran suicide is one of the most complex and pressing health issues in the United States. According to the 2020 National Veteran Suicide Prevention Annual Report, since 2018 an average of 17.2 Veterans died by suicide each day. Veteran suicide risk screening is currently limited to suicide hotlines, patient reporting, patient visits, and family or friend reporting. As a result of these limitations, innovative approaches in suicide screening are increasingly garnering attention. An essential feature of these innovative methods includes better incorporation of risk factors that might indicate higher risk for tracking suicidal ideation based on personal behavior. Digital technologies create a means through which measuring these risk factors more reliably, with higher fidelity, and more frequently throughout daily life is possible, with the capacity to identify potentially telling behavior patterns. In this review, digital predictive biomarkers are discussed as they pertain to suicide risk, such as sleep vital signs, sleep disturbance, sleep quality, and speech pattern recognition. Various digital predictive biomarkers are reviewed and evaluated as well as their potential utility in predicting and diagnosing Veteran suicidal ideation in real time. In the future, these digital biomarkers could be combined to generate further suicide screening for diagnosis and severity assessments, allowing healthcare providers and healthcare teams to intervene more optimally.

The Use of Digital Health Tools for Health Promotion Among Women With and Without Chronic Diseases: Insights From the 2017-2020 Health Information National Trends Survey

Background

In the United States, almost 90% of women are at risk of at least one chronic condition. However, the awareness, management, and monitoring of these conditions are low and present a substantial public health problem. Digital health tools can be leveraged to reduce the alarmingly high rates of chronic condition–related mortality and morbidity in women.

Objective

This study aimed to investigate the 4-year trend of digital health use for health promotion among women with chronic conditions in the United States.

Methods

Data for this study were obtained from the 2017 to 2020 iterations of the Health Information Trends Survey 5. Separate weighted logistic regression models were conducted to test the unadjusted and adjusted association of the study variables and each digital health use. The 95% CI, adjusted odds ratio (aOR), and P value (.05) were reported. Analysis was conducted using Stata 17 software.

Results

In total, 8573 women were included in this study. The weighted prevalence of the use of a smartphone or tablet for various activities were as follows: track health goals, 50.3% (95% CI 48.4%-52.2%; 3279/7122); make a health decision, 43.6% (95% CI 41.9%-45.3%; 2998/7101); and discuss with a provider, 40% (95% CI 38.2%-41.8%; 2834/7099). In the preceding 12 months, 33% (95% CI 30.9%-35.2%; 1395/4826) of women used an electronic wearable device, 18.7% (95% CI 17.3%-20.2%; 1532/7653) shared health information, and 35.2% (95% CI 33.2%-37.3%; 2262/6349) sent or received an SMS text message with a health professional. Between 2017 and 2020, the weighted prevalence of having 0, 1, and multiple chronic conditions were 37.4% (2718/8564), 33.4% (2776/8564), and 29.3% (3070/8564), respectively. However, slightly above half (52.2%, 95% CI 0.50%-0.53%; 4756/8564) of US women reported having at least one chronic disease. Women with multiple chronic conditions had higher odds of using their tablet or smartphone to achieve a health-related goal (aOR 1.43, 95% CI 1.16-1.77; P=.001) and discuss with their provider (aOR 1.55 95% CI 1.20-2.00; P=.001) than those without any chronic conditions. Correspondingly, in the past 12 months, the odds of using an electronic wearable device (aOR 1.40, 95% CI 1.00-1.96; P=.04), sharing health information (aOR 1.91, 95% CI 1.46-2.51; P<.001), and communicating via SMS text messaging with a provider (aOR 1.31, 95% CI 1.02-1.68; P=.03) were significantly higher among women with chronic conditions than those without a chronic condition.

Conclusions

This study suggests that women with chronic conditions accept and integrate digital health tools to manage their care. However, certain subpopulations experience a digital disconnect that may exacerbate existing health inequities. Implications for research and opportunities to leverage and integrate digital health tools to prevent, monitor, manage, and treat chronic conditions in women are discussed.

Is Engagement in Physical Activity Related to Its Perceived Mental Health Benefits Among People With Depression and Anxiety? A Population-Scale Survey Study

Objective: To examine the prevalence of awareness of PA (physical activity) benefits among those with mental disorders and explore how this is related to actual PA levels in this population. Methods: We queried data from the Health Information National Trends Survey 2019. A sample of 1,139 adults with self-reported depression and anxiety (61% female; mean age of 52.5 years) was analyzed. Multivariable logistic regression was employed to investigate the association between awareness that PA benefits mental health and respondents’ levels of PA. Results: Of the entire sample, a total of 904 individuals (80.9%) endorsed awareness that PA helps reduce symptoms of depression and anxiety. In multivariable analysis, awareness that PA improves mental health was significantly associated with greater odds of being less sedentary (OR 2.31; P = 0.025) and meeting national recommendations for weekly physical activity (OR 2.35; P = 0.046). Conclusion: Overall, findings from this nationally representative survey data indicates that about 4 in 5 adults living with depression and anxiety in the United States endorsed awareness of the mental health benefits of PA. Furthermore, we found that the awareness of these benefits is related to PA engagement.