Electronic wearable device and physical activity among US adults: An analysis of 2019 HINTS data

Examining the Relationship Between E-Cigarette Status and Wearable Device Use on Physical Activity Levels in U.S. Adults

PURPOSE

The aim of this analysis is to investigate physical activity levels amongst e-cigarette users based on their wearable device use.

DESIGN

Cross-sectional secondary data analysis using 2017 Behavioral Risk Factor Surveillance Survey (BRFSS).

SETTING

Data from the 2017 BRFSS were used.

SAMPLE

5,562 U.S. adults (age 18+).

MEASURES

Self-reported physical activity related variables from U.S. adults (age 18+).

ANALYSIS

Separate unadjusted and adjusted linear regression models were performed for each of the dependent variables using survey analysis.

RESULTS

Non-users of wearable devices and e-cigarettes account for 96.6% (95%CI [95.7, 97.6]) of the sample, whereas users of wearable devices and e-cigarettes account for 3.3% (95%CI [1.2, 5.4]) of the sample. Those who use e-cigarettes participate in almost 50% less vigorous physical activity minutes per week than nonusers, 46 (95%CI [0.43, 91.57]) and 93 (95%CI [80.59, 106.34]) minutes respectively. Individuals who use e-cigarettes and use wearable devices were found to spend significantly more time in total physical activity per week in both the unadjusted and adjusted linear regressions, P = =0.01 and P = =0.04 respectively.

CONCLUSION

The use of e-cigarettes, wearable devices, or both technologies may influence the physical activity levels of its users. Additional research is needed to better understand the association between physical activity levels and the usage of these technologies.

Assessment of ownership of smart devices and the acceptability of digital health data sharing

Smart portable devices- smartphones and smartwatches- are rapidly being adopted by the general population, which has brought forward an opportunity to use the large volumes of physiological, behavioral, and activity data continuously being collected by these devices in naturalistic settings to perform research, monitor health, and track disease. While these data can serve to revolutionize health monitoring in research and clinical care, minimal research has been conducted to understand what motivates people to use these devices and their interest and comfort in sharing the data. In this study, we aimed to characterize the ownership and usage of smart devices among patients from an expansive academic health system in the southeastern US and understand their willingness to share data collected by the smart devices. We conducted an electronic survey of participants from an online patient advisory group around smart device ownership, usage, and data sharing. Out of the 3021 members of the online patient advisory group, 1368 (45%) responded to the survey, with 871 female (64%), 826 and 390 White (60%) and Black (29%) participants, respectively, and a slight majority (52%) age 58 and older. Most of the respondents (98%) owned a smartphone and the majority (59%) owned a wearable. In this population, people who identify as female, Hispanic, and Generation Z (age 18-25), and those completing higher education and having full-time employment, were most likely to own a wearable device compared to their demographic counterparts. 50% of smart device owners were willing to share and 32% would consider sharing their smart device data for research purposes. The type of activity data they are willing to share varies by gender, age, education, and employment. Findings from this study can be used to design both equitable and cost-effective digital health studies, leveraging personally-owned smartphones and wearables in representative populations, ultimately enabling the development of equitable digital health technologies.

Social Factors and Older Adults' Use of Wearable Activity Trackers: Before and During the First Wave of the COVID-19 Pandemic

Wearable activity trackers (WAT) have shown high potential to improve health in the aging population. Evidence links various social factors with WAT use in older adults, but mainly within small samples and the prevalence of their WAT use during the COVID-19 is unknown. We reported WAT use prevalence before and during the first wave of COVID-19 and examined social factors associated with WAT use frequency using a nationally representative sample of 3302 U.S. older adults. We used Multinomial Logistic Regression to identify social factors associated with WAT use frequency. Only 10.3% of pre-COVID-19 and 10.9% of first-wave subsamples were frequent WAT users. Older adults aged 75 and above and those with low incomes were less likely to frequently use WATs. Our findings suggest socioeconomic and age disparities in WAT use among older Americans. Future studies should focus on enhancing low-income older adults' WAT adoption to enable equal access to WAT-related health benefits.

Electronic Wearable Device Use for Physical Activity in Older Adults: A Qualitative Study

Innovative solutions to help older adults increase physical activity are critically important. In this qualitative study, we explored older adults' acceptance, capability, and experiences of using three different types of electronic wearable devices over a period of 4-24 weeks for self-monitoring and promoting physical activity. We conducted 23 semistructured interviews with older adults who participated in three physical activity intervention studies. Two researchers analyzed the data using NVivo version 12, applying a directed content analysis that was partially guided by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Six themes emerged: (1) device learning, (2) hedonic motivation, (3) habit and adherence, (4) facilitating conditions, (5) effort expectancy, and (6) performance expectancy. Although most older adults (95.8%) from this study were first-time users, they reflected positive experiences and generally enjoyed using electronic wearable devices. Participants reported issues related to electronic wearable device functionalities that can be improved to better enhance user experience and motivate increased physical activity. Future research should explore the role of electronic wearable devices in older adults' physical activity with an emphasis on behavioral change over time.

Telehealth Utilization During the COVID-19 Pandemic Among People With Functional Disabilities in the U.S.: A Preliminary Analysis of National Health Interview Survey 2020 Data

Introduction

Telehealth has been widely promoted and adopted at multiple levels in the U.S. healthcare system during the COVID-19 pandemic. However, this rapid expansion of telehealth services may have further exacerbated health inequities among marginalized groups.

Methods

Using the 2020 National Health Interview Survey, this study compared patterns of telehealth use between people with functional disabilities and people without disabilities during the first year of the pandemic.

Results

In the multivariable-adjusted logistic regression models, respondents with moderate disabilities were significantly more likely to report telehealth use, not pandemic related (OR=1.25, 95% CI=1.03, 1.52) and telehealth use, pandemic related (OR=1.43, 95% CI=1.28, 1.60) than people without disabilities. Similarly, respondents with severe disabilities were significantly more likely to report telehealth use, not pandemic related (OR=1.46, 95% CI=1.07, 2.00) and telehealth use, pandemic related (OR=2.06, 95% CI=1.72, 2.46). In addition, telehealth use varied by the number of limitations and disability type.

Conclusions

People with functional disabilities were more likely to report telehealth use than people without disabilities. Furthermore, these associations strengthened with increasing disability severity and number of limitations while varying by disability type. Additional studies are warranted to explore ways of providing patient-centered telehealth to responsively meet various healthcare needs of people with functional disabilities and improve their health outcomes.

Association Between Wearable Device Use and Levels of Physical Activity Among Older Adults in the US: Evidence From the 2019-2020 Health Information National Trends Survey

Objective To examine the relationship between electronic wearable device (WD) use and physical activity (PA) levels among older adults in the US. Methods Data were pooled from 3310 older adults from the 2019 and 2020 Health Information National Trends Survey. The explanatory variable was WD use, and the outcomes were weekly PA levels, resistance training, and sedentary time. Logistic regression was conducted to investigate the association between WD use and the reported outcome variables. Separate logistic models were also fitted to explore the relationship between WD use and physical activity outcomes among a subgroup of older adults with chronic conditions. Results A total of 14.4% of older adults reported WD use. Older adults who use WD were more likely to meet national guidelines for weekly levels of PA (odds ratio (OR) 1.60, 95% confidence intervals (CI) (1.10, 2.32); p = 0.015) and resistance strength training (OR 1.54, 95% CI (1.14, 2.09); p = 0.005) when compared with their counterparts not using WD. After restricting the analysis to those with chronic conditions only, WD use was only associated with a higher level of weekly strength training (OR 1.68, 95% CI 1.19, 2.38; p = 0.004). Conclusion  WD use may be associated with increased physical activity among older adults, including those with chronic health conditions. Further studies are needed to examine the factors influencing the adoption and sustained use of WD in older adults.

Association between Pain and Frequent Physical Exercise among Adults in the United States: A Cross-Sectional Database Study

Pain affects over 20% of United States adults, and less than 50% of United States adults participate in frequent physical exercise. This cross-sectional database study included 13,758 United States adults aged >18 years from the 2020 Medical Expenditure Panel Survey (MEPS) and analyzed the association between severity of pain (independent variable) and frequent physical exercise (dependent variable), adjusting for demographic, economic, limitation, and health variables using multivariable logistic regression. The study showed 50.3% of adults report frequently exercising. Only 37.1% of adults reported experiencing pain of any degree, with a majority of them experiencing little pain. In the adjusted model, extreme pain vs. none, quite a bit of pain vs. none, Hispanic vs. non-Hispanic ethnicity, having a functional limitation vs. no limitation, and being overweight/obese vs. not being obese/overweight were associated with lower odds of reporting doing frequent physical exercise. Meanwhile, being ≥65 or 40-64 vs. 18-39 years of age, male vs. female, white vs. not white race, private or public vs. no health coverage, and good vs. poor general health were associated with greater odds of reporting doing frequent physical exercise. These variables associated with frequent physical exercise should be considered in future work when designing health interventions.

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.

From intention to action: The factors affecting health data sharing intention and action

INTRODUCTION

Effective prevention and treatment of diseases requires utilization of health-related lifestyle data, which has thus become increasingly important. According to some studies, participants were willing to share their health data for use in medical care and research. Although intention does not always accurately reflect action, few studies have examined the question of whether data-sharing intention leads to data-sharing action.

OBJECTIVE

The aim of this study was to examine the extent of actualizing data-sharing intention to data-sharing action and to identify the factors that influence data-sharing intention and action.

METHODS

A web-based survey of members of a university examined the data-sharing intention and issues of concern when making decisions on data sharing. The participants were asked to deposit their armband data for use in research at the end of the survey. A comparison of data-sharing intention and action in relation to the participants' characteristics was performed. Factors having a significant effect on data-sharing intention and action were identified using logistic regressions.

RESULTS

Of 386 participants, 294 expressed willingness to share health data. However, only 73 participants deposited their armband data. The primary reason for refusal to deposit armband data was the inconvenience of the data transfer process (56.3%). Appropriate compensation had a significant effect on data-sharing intention (OR: 3.3, CI: 1.86-5.75) and action (OR: 2.8, CI: 1.14-8.21). The compensation for data sharing (OR:2.8, CI:1.14-8.21) and familiarity with data (OR:3.1, CI:1.36-8.21) were significant predictors of data sharing action, however, data-sharing intention was not (OR: 1.5, CI:0.65-3.72).

CONCLUSION

Despite expressing willingness to share their health data, the participants' intention was not actualized to data-sharing behavior for depositing armband data. Implementation of a streamlined data transfer process and providing appropriate compensation might facilitate data-sharing. These findings could be useful in development of strategies to facilitate sharing and reuse of health data.

AccNet24: A deep learning framework for classifying 24-hour activity behaviours from wrist-worn accelerometer data under free-living environments

OBJECTIVE

Although machine learning techniques have been repeatedly used for activity prediction from wearable devices, accurate classification of 24-hour activity behaviour categories from accelerometry data remains a challenge. We developed and validated a deep learning-based framework for classifying 24-hour activity behaviours from wrist-worn accelerometers.

METHODS

Using an openly available dataset with free-living wrist-based raw accelerometry data from 151 participants (aged 18-91 years), we developed a deep learning framework named AccNet24 to classify 24-hour activity behaviours. First, the acceleration signal (x, y, and z-axes) was segmented into 30-second nonoverlapping windows, and signal-to-image conversion was performed for each segment. Deep features were automatically extracted from the signal images using transfer learning and transformed into a lower-dimensional feature space. These transformed features were then employed to classify the activity behaviours as sleep, sedentary behaviour, and light-intensity (LPA) and moderate-to-vigorous physical activity (MVPA) using a bidirectional long short-term memory (BiLSTM) recurrent neural network. AccNet24 was trained and validated with data from 101 and 25 randomly selected participants and tested with the remaining unseen 25 participants. We also extracted 112 hand-crafted time and frequency domain features from 30-second windows and used them as inputs to five commonly used machine learning classifiers, including random forest, support vector machines, artificial neural networks, decision tree, and naïve Bayes to classify the 24-hour activity behaviour categories.

RESULTS

Using the same training, validation, and test data and window size, the classification accuracy of AccNet24 outperformed the accuracy of the other five machine learning classification algorithms by 16%-30% on unseen data.

CONCLUSION

AccNet24, relying on signal-to-image conversion, deep feature extraction, and BiLSTM achieved consistently high accuracy (>95 %) in classifying the 24-hour activity behaviour categories as sleep, sedentary, LPA, and MVPA. The next generation accelerometry analytics may rely on deep learning techniques for activity prediction.

Nutritional status, health risk behaviors, and eating habits are correlated with physical activity and exercise of brazilian older hypertensive adults: a cross-sectional study

BACKGROUND

Nutritional status, health risk behaviors, eating habits, and other comorbidities (such as diabetes) may be associated with recommended amounts of physical activity (PA) and exercise (EX) in healthy older adults. However, these associations are still unclear for older hypertensive adults, who require greater care from health professionals. The purpose of this study was to associate the nutritional status, health risk behaviors, eating habits, and the presence of diabetes with recommended amounts of physical activity and exercise practice of older hypertensive adults.

METHODS

Ten thousand seven hundred eighty-nine older hypertensive adults (70.9 ± 7.4 years) from the VIGITEL telephone survey were classified according to PA levels (insufficiently active/sufficiently active) and EX practice (non-practitioners/practitioners). Binary logistic regression was used to observe the odds ratio (OR) between independent variables (nutritional status [body mass index], sociodemographic characteristics [age/sex/years of study], risk behaviors [screen time/alcohol/tobacco consumption], eating habits [minimally/ultra-processed foods consumption score], and the presence of diabetes) with recommended amounts of PA/EX (dependent variable).

RESULTS

Highest nutritional status (OR_PA = 0.975 [95%-CI: 0.965 - 0.985]; OR_EX = 0.981[95%-CI: 0.972 - 0.991]), age (OR_PA = 0.955 [95%-CI: 0.949 - 0.961]; OR_EX = 0.980[95%-CI: 0.975 - 0.986]), screen time (OR_PA = 0.909[95%-CI: 0.835 - 0.990]), alcohol consumption (OR_PA = 0.683[95%-CI: 0.621 - 0.758]; OR_EX = 0.702[95%-CI: 0.637 - 0.779]), tobacco (OR_PA = 0.601 [95%-CI: 0.492 - 0.736]; OR_EX = 0.464[95%-CI: 0.384 - 0.562]) ultra-processed foods consumption score (OR_PA = 0.896[95%-CI: 0.871 - 0.921]; OR_EX = 0.886[95%-CI: 0.863 - 0.909]) and having diabetes (OR_PA = 0.780[95%-CI: 0.708 - 0.859]; OR_EX = 0.831[95%-CI: 0.759 - 0.909]) reduced the odds of being sufficiently active/practicing exercise (p < 0.05). Male sex (OR_PA = 1.633[95%-CI: 1.491 - 1.789]; OR_EX = 1.247[95%-CI: 1.140 - 1.363]), years of study (OR_PA = 1.026[95%-CI: 1.018 - 1.035]; OR_EX = 1.050[95%-CI: 1.041 - 1.058]), and minimally processed foods consumption score increased the odds of being sufficiently active/practicing exercise (OR_PA = 1.132[95%-CI: 1.109 - 1.155]; OR_EX = 1.167[95%-CI: 1.145 - 1.191], respectively; p < 0.05).

CONCLUSION

Nutritional status, health risk behaviors, eating habits, and the presence of diabetes were associated with the odds of older hypertensive adults complying with PA and EX recommendations. The results may help health professionals understand how these factors are associated with the changes of older hypertensive adults participating in physical activity and exercise.

Association Between Health Information‒Seeking Behavior on YouTube and Physical Activity Among U.S. Adults: Results From Health Information Trends Survey 2020

Introduction

Although physical activity has many health benefits, 45.8% of U.S. adults did not meet the WHO recommendation in 2018. Delivering health-related content, particularly physical activity, through YouTube may help to overcome some barriers, such as lack of access to resources. This study aimed to examine the association between watching health-related information on YouTube and increased levels of physical activity among U.S. adults.

Methods

Using the U.S. national cross-sectional survey-Health Information National Trends Survey 2020 (n=3,865), we conducted a multivariable logistic regression on obtaining 150 minutes of at least moderate-intensity physical activity per week (WHO guidelines) by watching health-related information on YouTube, controlling for demographics (age, sex, race/ethnicity), socioeconomics (income, education level, insurance coverage, employment), current use of cigarettes and e-cigarettes, use of electronic wearable devices (e.g., Fitbit), self-reported health status, BMI, and the presence of chronic conditions (e.g., diabetes, heart disease, cancer) and depression or anxiety disorders.

Results

Overall, 40.8% (weighted) of respondents reported using YouTube to watch health-related videos, and 39.2% reported meeting the WHO-recommended physical activity level. After controlling for covariates, adults who reported watching health-related videos on YouTube in the past 12 months (versus not watching) were 1.33 times more likely to do 150 minutes or more of moderate physical activity a week (AOR=1.33; 95% CI=1.01, 1.76).

Conclusions

This study suggests that adults who view health-related YouTube videos may be more likely to meet the WHO-recommended level of physical activity. This finding could inform future behavioral interventions using online video platforms to promote physical activity.

Wearable Activity Tracker Use and Physical Activity Among Informal Caregivers in the United States: Quantitative Study

BACKGROUND

With an increase in aging population and chronic medical conditions in the United States, the role of informal caregivers has become paramount as they engage in the care of their loved ones. Mounting evidence suggests that such responsibilities place substantial burden on informal caregivers and can negatively impact their health. New wearable health and activity trackers (wearables) are increasingly being used to facilitate and monitor healthy behaviors and to improve health outcomes. Although prior studies have examined the efficacy of wearables in improving health and well-being in the general population, little is known about their benefits among informal caregivers.

OBJECTIVE

This study aimed to examine the association between use of wearables and levels of physical activity (PA) among informal caregivers in the United States.

METHODS

We used data from the National Cancer Institute's Health Information National Trends Survey 5 (cycle 3, 2019 and cycle 4, 2020) for a nationally representative sample of 1273 community-dwelling informal caregivers-aged ≥18 years, 60% (757/1273) female, 75.7% (990/1273) had some college or more in education, and 67.3% (885/1273) had ≥1 chronic medical condition-in the United States. Using jackknife replicate weights, a multivariable logistic regression was fit to assess an independent association between the use of wearables and a binary outcome: meeting or not meeting the current World Health Organization's recommendation of PA for adults (≥150 minutes of at least moderate-intensity PA per week).

RESULTS

More than one-third (466/1273, 37.8%) of the informal caregivers met the recommendations for adult PA. However, those who reported using wearables (390/1273, 31.7%) had slightly higher odds of meeting PA recommendations (adjusted odds ratios 1.1, 95% CI 1.04-1.77; P=.04) compared with those who did not use wearables.

CONCLUSIONS

The results demonstrated a positive association between the use of wearables and levels of PA among informal caregivers in the United States. Therefore, efforts to incorporate wearable technology into the development of health-promoting programs or interventions for informal caregivers could potentially improve their health and well-being. However, any such effort should address the disparities in access to innovative digital technologies, including wearables, to promote health equity. Future longitudinal studies are required to further support the current findings of this study.

Comparison of Participation in Strength Activity Between Wearable Device Users and Nonusers: 2017 Behavioral Risk Factors Surveillance System

BACKGROUND

There are many benefits associated with engaging in strength physical activity. Many studies did not examine the engagement of strength activity among wearable device users. This study aimed to examine the association between wearable device usage and engagement of strength activity in free-living settings using nationally representative data.

METHODS

A total of 8250 adult wearable device users and nonusers from 8 states of the 2017 Behavioral Risk Factors Surveillance System were included in analysis. Multiple regression models were performed to determine the association between the dependent variables of strength activities and the independent variable of wearable devices.

RESULTS

Wearable device users were 1.26 (95% confidence interval, 1.01-1.81) times the odds of nonusers in engaging in strength activity. Users also had higher odds of meeting both the strength and aerobic physical activity guidelines than nonusers (odds ratio = 1.49; 95% confidence interval, 1.07-2.06; adjusted odds ratio = 1.43; 95% confidence interval, 1.02-2.00). No associations were found between wearable device utilization and frequency of strength activity per week.

CONCLUSION

Wearable device users were more likely to engage in strength activity than nonusers. However, additional studies are needed to determine the effectiveness of wearable devices in promoting strength activity.

Stepping up with GGIR: Validity of step cadence derived from wrist-worn research-grade accelerometers using the verisense step count algorithm

The Verisense Step Count Algorithm facilitates generation of steps from wrist-worn accelerometers. Based on preliminary evidence suggesting a proportional bias with overestimation at low steps/day, but underestimation at high steps/day, the algorithm parameters have been revised. We aimed to establish validity of the original and revised algorithms relative to waist-worn ActiGraph step cadence. We also assessed whether step cadence was similar across accelerometer brand and wrist. Ninety-eight participants (age: 58.6±11.1 y) undertook six walks (~500 m hard path) at different speeds (cadence: 92.9±9.5-127.9±8.7 steps/min) while wearing three accelerometers on each wrist (Axivity, GENEActiv, ActiGraph) and an ActiGraph on the waist. Of these, 24 participants also undertook one run (~1000 m). Mean bias for the original algorithm was -21 to -26.1 steps/min (95% limits of agreement (LoA) ~±65 steps/min) and mean absolute percentage error (MAPE) 17-22%. This was unevenly distributed with increasing error as speed increased. Mean bias and 95%LoA were halved with the revised algorithm parameters (~-10 to -12 steps/min, 95%LoA ~30 steps/min, MAPE ~10-12%). Performance was similar across brand and wrist. The revised step algorithm provides a more valid measure of step cadence than the original, with MAPE similar to recently reported wrist-wear summary MAPE (7-11%).

At the Crossroads! Time to Start Taking Smartwatches Seriously

Patients have demonstrated a growing interest in using wearable devices, particularly smartwatches, to monitor and improve their cardiovascular wellness. Wearable devices are now one of the fastest growing sectors of the technology industry, and big technology companies, such as Apple (Apple Watch), Google (Fitbit), and Samsung (Galaxy), have engineered smartwatch features that are capable of monitoring biometrics, such as heart rhythm, heart rate, blood pressure, and sleep. These devices hold significant potential to impact the relation between cardiologists and their patients, but concerns exist about device trustworthiness to detect pertinent data points and deliver alerts with accuracy. How these devices' features will interplay with cardiologists' workflow has also yet to be defined and requires thoughtful implementation. Furthermore, the success of smartwatches as medical devices is dependent on patients' continuous use. Keeping patients engaged with their devices through leveraging behavioral factors may lead to achieving and optimizing healthcare goals. Socioeconomic disparities and privacy concerns are other barriers in the path forward. Cardiovascular professional societies are uniquely poised to help impact how these devices are eventually accepted and used in everyday practice. In conclusion, engagement and collaboration with big tech companies will help guide how this market grows.

Veteran and Staff Experience from a Pilot Program of Health Care System-Distributed Wearable Devices and Data Sharing

OBJECTIVE

 The growing trend to use wearable devices to track activity and health data has the potential to positively impact the patient experience with their health care at home and with their care team. As part of a pilot program, the U.S. Department of Veterans Affairs (VA) distributed Fitbits to Veterans through four VA medical centers. Our objective was to assess the program from both Veterans' and clinicians' viewpoints. Specifically, we aimed to understand barriers to Fitbit setup and use for Veterans, including syncing devices with a VA mobile application (app) to share data, and assess the perceived value of the device functions and ability to share information from the Fitbit with their care team. In addition, we explored the clinicians' perspective, including how they expected to use the patient-generated health data (PGHD).

METHODS

 We performed semi-structured interviews with 26 Veterans and 16 VA clinicians to assess the program. Responses to each question were summarized in order of frequency of occurrence across participants and audited by an independent analyst for accuracy.

RESULTS

 Our findings reveal that despite setup challenges, there is support for the use of Fitbits to engage Veterans and help manage their health. Clinicians believed there were benefits for having Veterans use the Fitbits and expected to use the PGHD in a variety of ways as part of the Veterans' care plans, including monitoring progress toward health behavior goals. Veterans were overwhelmingly enthusiastic about using the Fitbits; this enthusiasm seems to extend beyond the 3 month "novelty period."

CONCLUSION

 The pilot program for distributing Fitbits to Veterans appears to be successful from both Veterans' and clinicians' perspectives and suggests that expanded use of wearable devices should be considered. Future studies will need to carefully consider how to incorporate the PGHD into the electronic health record and clinical workflow.

The Effects of mHealth-Based Gamification Interventions on Participation in Physical Activity: Systematic Review

Background

It is well known that regular physical exercise has associated benefits; yet, participation remains suboptimal. Mobile health (mHealth) has become an indispensable medium to deliver behavior change interventions, and there is a growing interest in the gamification apps in mHealth to promote physical activity (PA) participation. Gamification could use game design elements (such as points, leaderboards, and progress bars), and it has the potential to increase motivation for PA and engagement. However, mHealth-based gamification interventions are still emerging, and little is known about the application status and efficacy of such interventions.

Objective

This systematic review aims to investigate gamification apps in mHealth for improving PA levels and simultaneously summarize the impact of gamification interventions on PA participation.

Methods

We searched PubMed, Scopus, Web of Science, Embase, CINAHL (EBSCO host), and IEEE Xplore from inception to December 20, 2020. Original empirical research exploring the effects of gamification interventions on PA participation was included. The papers described at least one outcome regarding exercise or PA participation, which could be subjective self-report or objective indicator measurement. Of note, we excluded studies about serious games or full-fledged games.

Results

Of 2944 studies identified from the database search, 50 (1.69%) were included, and the information was synthesized. The review revealed that gamification of PA had been applied to various population groups and broadly distributed among young people but less distributed among older adults and patients with a disease. Most of the studies (30/50, 60%) combined gamification with wearable devices to improve PA behavior change, and 50% (25/50) of the studies used theories or principles for designing gamified PA interventions. The most frequently used game elements were goal-setting, followed by progress bars, rewards, points, and feedback. This review demonstrated that gamification interventions could increase PA participation; however, the results were mixed, and modest changes were attained, which could be attributed to the heterogeneity across studies.

Conclusions

Overall, this study provides an overview of the existing empirical research in PA gamification interventions and provides evidence for the efficacy of gamification in enhancing PA participation. High-quality empirical studies are needed in the future to assess the efficacy of a combination of gamification and wearable activity devices to promote PA, and further exploration is needed to investigate the optimal implementation of these features of game elements and theories to enhance PA participation.

Frequent and diverse use of electronic health records in the United States: A trend analysis of national surveys

Objective

Considering the increasing integration of electronic health records (EHRs) into medical practice by healthcare organizations, it is especially pertinent to understand its actual usage by the general public in recent years. This study aims to explore factors associated with the frequency and diversity of EHR usage in the United States over time.

Methods

We analyzed three iterations (2017, 2018, and 2019) of the Health Information National Trends Survey (HINTS). HINTS is a national cross-sectional survey conducted by the National Cancer Institute to document attitudes and perceptions about health information access and use among American adults.

Results

Both frequency and diversity of EHR usage have slightly increased across the years. However, its overall usage still remained low. Three technology-related enablers (access to digital devices, access to the Internet, and perceived usefulness of EHRs) were positively related to EHR usage in all three iterations. In addition, perceived health status was a constant and negative predictor of EHR usage over years. Doctor–patient communication was positively associated with the frequency of EHR usage in two survey waves.

Conclusions

More initiatives to increase EHR usage in the United States are needed. We advocate for providing affordable Internet access and smartphone to underserved populations; in medical encounters, doctors should have more patient-centered communication, introduce the benefits of EHRs to patients, and promote EHR adoption in terms of frequency and diversity.

Willingness to Share Data From Wearable Health and Activity Trackers: Analysis of the 2019 Health Information National Trends Survey Data

BACKGROUND

Sharing data from wearable health and activity trackers (wearables) with others may improve the health and behavioral outcomes of wearable users by generating social support and improving their ability to manage their health. Investigating individual factors that influence US adults' willingness to share wearable data with different types of individuals may provide insights about the population subgroups that are most or least likely to benefit from wearable interventions. Specifically, it is necessary to identify digital health behaviors potentially associated with willingness to share wearable data given that the use of and engagement with various technologies may broadly influence web-based health information-sharing behaviors.

OBJECTIVE

This study aims to identify sociodemographic, health, and digital health behavior correlates of US adults' willingness to share wearable data with health care providers and family or friends.

METHODS

Data for the analytic sample (N=1300) were obtained from the 2019 Health Information National Trends Survey of the National Cancer Institute. Digital health behavior measures included frequency of wearable device use, use of smartphones or tablets to help communicate with providers, use of social networking sites to share health information, and participation in a web-based health community. Multivariable logistic regression analysis of weighted data examined the associations between digital health behaviors and willingness to share wearable device data, controlling for sociodemographics and health-related characteristics.

RESULTS

Most US adults reported willingness to share wearable data with providers (81.86%) and with family or friends (69.51%). Those who reported higher health self-efficacy (odds ratio [OR] 1.97, 95% CI 1.11-3.51), higher level of trust in providers as a source of health information (OR 1.98, 95% CI 1.12-3.49), and higher level of physical activity (OR 2.00, 95% CI 1.21-3.31) had greater odds of willingness to share data with providers. In addition, those with a higher frequency of wearable use (OR 2.15, 95% CI 1.35-3.43) and those who reported use of smartphones or tablets to help communicate with providers (OR 1.99, 95% CI 1.09-3.63) had greater odds of willingness to share data with providers. Only higher level of physical activity was associated with greater odds of willingness to share wearable data with family or friends (OR 1.70, 95% CI 1.02-2.84). Sociodemographic factors were not significantly associated with willingness to share wearable data.

CONCLUSIONS

The findings of this study suggest that, among US adult wearable users, behavior-related factors, rather than sociodemographic characteristics, are key drivers of willingness to share health information obtained from wearables with others. Moreover, behavioral correlates of willingness to share wearable data are unique to the type of recipient (ie, providers vs family or friends). Future studies could use these findings to inform the development of interventions that aim to improve the use of patient-generated data from wearable devices in health care settings.

Exploring the Association Between Electronic Wearable Device Use and Levels of Physical Activity Among Individuals With Depression and Anxiety: A Population Level Study

Aim: The present study aimed to investigate the cross-sectional association between self-reported use of electronic wearable devices (EWDs) and the levels of physical activity among a representative sample of adults with depression and anxiety in the United States. Methods: For this cross-sectional study, data were pooled from the Health Information National Trends Survey 2019. A sample of 1,139 adults with self-reported depression and anxiety (60.9% women; mean age of 52.5 years) was analyzed. The levels of physical activity and prevalence of EWD utilization were self-reported. The chi-square tests were used to compare individual characteristics through the use of EWDs. Multivariable logistic regression was employed to investigate the association between EWDs and physical activity levels while adjusting for sociodemographic and health-related factors. Results: From the 1,139 adults with self-reported depression and anxiety, 261 (weighted percentage 28.1%) endorsed using EWD in the last year. After adjusting for covariates, the use of EWDs was only significantly associated with a higher odds of reporting intention to lose weight (OR 2.12; 95% CI 1.04, 4.35; p = 0.04). We found no association between the use of EWDs and meeting the national weekly recommendation for physical activity or resistance/strength exercise training. Conclusion: About three in 10 adults suffering from depression and anxiety in the United States reported using EWDs in the last year. The current study findings indicate that among people living with mental illness, EWD use is associated with higher odds of weight loss intent suggesting that EWDs may serve as an opening for the clinical interactions around physical health through identifying patients primed for behavior change. Further large-scale studies using randomized trial designs are needed to examine the causal relationships between EWDs and the physical activity of people with mental health conditions.