Presentation
DH5 - Exploring the Impact of Wearable Device Usage on Self-Efficacy in Health Management: A Consumers’ Viewpoint
DescriptionTitle
Exploring the Impact of Wearable Device Usage on Self-Efficacy in Health Management: A Consumers’ Viewpoint
Background and introduction
In the digital era, wearable devices, such as smartwatches, smart rings, fitness wristbands, , have become pivotal tools for health monitoring and management. Indeed, many wearables enable real-time health surveillance and intricate data analysis to individualize health feedback to end users. Some wearable manufacturers even take a step further with patient monitoring and telehealth initiatives, allowing healthcare professionals to track adherence to medical regimens.
Moreover, the advent of wearable technology has paved the way for seamless integration of health data into Electronic Health Records (EHRs), ensuring a continuous and collaborative approach towards patient care. Such features empower individuals to adopt a more proactive and informed attitude toward their health decisions. Importantly, the real time feedback mechanisms supported by many wearable devices may amplify patients’ confidence and assurance by making health data visualization accessible and facilitating easy sharing of health data with healthcare professionals. An implicit benefit of these wearable health devices is their potential to boost an individual's confidence in managing their health, commonly referred to as health self-efficacy, more informed decision-making and personalized health goals in their health management and lifestyle.
However, navigating the pathway from wearable device usage to enhanced health self-efficacy remains under-explored, particularly considering the nuanced interplay of psychosocial variables and human factors variables woven into the domains of personal cognition in healthcare, technological literacy, and emotional state. In this exploration, our study sharpens its focus on three pivotal mediators: confidence in personal health management, understanding of medical statistics, and the potential impact of social isolation. These factors collectively pose an intriguing question in wearable-based healthcare: How does frequent wearable device usage interact with these mediating variables to influence an individual's self-efficacy in managing their health?
This research aims to unravel the impact of wearable device usage on health self-efficacy, exploring the mediating roles of confidence in health management, understanding of medical statistics, and social isolation. The findings are crucial for enhancing wearable device design and functionality, ensuring they effectively support users in their health management journeys.
Method
This exploration leverages data from the Health Information National Trends Survey 6 (HINTS 6), conducted between March 7 and November 8, 2022, which aimed to accumulate responses from approximately 7,000 adults. The target demographic of HINTS encompasses civilian, non-institutionalized adults aged 18 or older residing in the United States, providing a robust and diverse substrate for investigating national trends in health information and behavior.
Our research methodology first excluded respondents who had not used an electronic wearable device (e.g., Fitbit, Apple Watch, or Garmin Vivofit) for health or activity monitoring in the past 12 months. Further, these included individuals were queried: "In the past month, how often did you use a wearable device to track your health?" (which included a scale of usage frequency ranging from 'Every day' to 'I did not use a wearable device in the past month.' Upon this preliminary categorization, we excluded invalid and missing data, ultimately retaining a total of 1,790 data for analysis.
Then we leverages the Hayes Process Model to explore the intricate, potentially mediating relationships between the frequency of wearable health device usage and confidence in health self-management, intertwined with critical psychosocial and human factors variables. Demographic variables such as age, gender, education level, and income are also included as control variables. We hypothesize that pathways linking wearable device usage and health self-efficacy may be mediated by confidence in managing health, social isolation, and comprehension of medical statistics. Then, 5,000 bootstrap samples are employed for the stability and credibility of the mediation analysis.
Preliminary results:
Descriptive analyses were first employed to comprehend the general trends and distributions within the variables of interest. The participant pool is primarily female, comprising about 65% of respondents. A significant portion is aged between 31 and 45, while approximately 61% have achieved at least a college degree. Notably, a considerable number of participants are in higher income brackets. These demographics provide a foundation for exploring correlations with health management confidence in subsequent analyses. Subsequently, Chi-Square tests were utilized to decipher the potential associations between demographic variables and confidence in health management. A majority of participants expressed high levels of confidence in managing their health, predominantly leaning towards the “Completely confident" and "Very confident" categories. The demographic distributions within the sample were notably diverse, allowing for an inclusive exploration across varied demographic groups. Upon deploying the Chi-Square test, a highly significant association was unearthed between income and confidence in health management (p < 0.001), suggesting that income levels may play a pivotal role in shaping individuals’ confidence in managing health. Moreover, educational attainment also exhibited a significant association with health management confidence (p = 0.0039), pointing towards a potential influence of educational background on health confidence. Conversely, age and gender did not showcase significant associations with confidence in health management, indicating a consistent level of health management confidence across different age groups and genders.
Exploring the Impact of Wearable Device Usage on Self-Efficacy in Health Management: A Consumers’ Viewpoint
Background and introduction
In the digital era, wearable devices, such as smartwatches, smart rings, fitness wristbands, , have become pivotal tools for health monitoring and management. Indeed, many wearables enable real-time health surveillance and intricate data analysis to individualize health feedback to end users. Some wearable manufacturers even take a step further with patient monitoring and telehealth initiatives, allowing healthcare professionals to track adherence to medical regimens.
Moreover, the advent of wearable technology has paved the way for seamless integration of health data into Electronic Health Records (EHRs), ensuring a continuous and collaborative approach towards patient care. Such features empower individuals to adopt a more proactive and informed attitude toward their health decisions. Importantly, the real time feedback mechanisms supported by many wearable devices may amplify patients’ confidence and assurance by making health data visualization accessible and facilitating easy sharing of health data with healthcare professionals. An implicit benefit of these wearable health devices is their potential to boost an individual's confidence in managing their health, commonly referred to as health self-efficacy, more informed decision-making and personalized health goals in their health management and lifestyle.
However, navigating the pathway from wearable device usage to enhanced health self-efficacy remains under-explored, particularly considering the nuanced interplay of psychosocial variables and human factors variables woven into the domains of personal cognition in healthcare, technological literacy, and emotional state. In this exploration, our study sharpens its focus on three pivotal mediators: confidence in personal health management, understanding of medical statistics, and the potential impact of social isolation. These factors collectively pose an intriguing question in wearable-based healthcare: How does frequent wearable device usage interact with these mediating variables to influence an individual's self-efficacy in managing their health?
This research aims to unravel the impact of wearable device usage on health self-efficacy, exploring the mediating roles of confidence in health management, understanding of medical statistics, and social isolation. The findings are crucial for enhancing wearable device design and functionality, ensuring they effectively support users in their health management journeys.
Method
This exploration leverages data from the Health Information National Trends Survey 6 (HINTS 6), conducted between March 7 and November 8, 2022, which aimed to accumulate responses from approximately 7,000 adults. The target demographic of HINTS encompasses civilian, non-institutionalized adults aged 18 or older residing in the United States, providing a robust and diverse substrate for investigating national trends in health information and behavior.
Our research methodology first excluded respondents who had not used an electronic wearable device (e.g., Fitbit, Apple Watch, or Garmin Vivofit) for health or activity monitoring in the past 12 months. Further, these included individuals were queried: "In the past month, how often did you use a wearable device to track your health?" (which included a scale of usage frequency ranging from 'Every day' to 'I did not use a wearable device in the past month.' Upon this preliminary categorization, we excluded invalid and missing data, ultimately retaining a total of 1,790 data for analysis.
Then we leverages the Hayes Process Model to explore the intricate, potentially mediating relationships between the frequency of wearable health device usage and confidence in health self-management, intertwined with critical psychosocial and human factors variables. Demographic variables such as age, gender, education level, and income are also included as control variables. We hypothesize that pathways linking wearable device usage and health self-efficacy may be mediated by confidence in managing health, social isolation, and comprehension of medical statistics. Then, 5,000 bootstrap samples are employed for the stability and credibility of the mediation analysis.
Preliminary results:
Descriptive analyses were first employed to comprehend the general trends and distributions within the variables of interest. The participant pool is primarily female, comprising about 65% of respondents. A significant portion is aged between 31 and 45, while approximately 61% have achieved at least a college degree. Notably, a considerable number of participants are in higher income brackets. These demographics provide a foundation for exploring correlations with health management confidence in subsequent analyses. Subsequently, Chi-Square tests were utilized to decipher the potential associations between demographic variables and confidence in health management. A majority of participants expressed high levels of confidence in managing their health, predominantly leaning towards the “Completely confident" and "Very confident" categories. The demographic distributions within the sample were notably diverse, allowing for an inclusive exploration across varied demographic groups. Upon deploying the Chi-Square test, a highly significant association was unearthed between income and confidence in health management (p < 0.001), suggesting that income levels may play a pivotal role in shaping individuals’ confidence in managing health. Moreover, educational attainment also exhibited a significant association with health management confidence (p = 0.0039), pointing towards a potential influence of educational background on health confidence. Conversely, age and gender did not showcase significant associations with confidence in health management, indicating a consistent level of health management confidence across different age groups and genders.
Event Type
Poster Presentation
TimeMonday, March 254:45pm - 6:15pm CDT
LocationSalon C
Digital Health
Simulation and Education
Hospital Environments
Medical and Drug Delivery Devices
Patient Safety Research and Initiatives