Feedback on Physical Activity Through a Wearable Device Connected to a Mobile Phone App in Patients With Metabolic Syndrome: Pilot Study

The Paradox of Digital Health: Why Middle-Aged Adults Outperform Young Adults in Health Management Utilization via Technology

Background/Objectives: Globally, life expectancy has been increasing with South Korea focusing on improving health to enhance quality of life. The COVID-19 pandemic further emphasized the need for digital transformation in healthcare, accelerating digital health adoption. This study explores the digital divide between 'Digital Natives (20-39 Y)' and 'Digital Immigrants (40-69 Y)', focusing on digital device usage and confidence. Methods: This study utilized national survey data from the Digital Health Literacy Survey Results and Policy Implications, focusing on differences in digital device use and confidence between young adults (20-39 Y) and middle-aged adults (40-69 Y). The participants comprised 1000 adults aged 20 to 69 in the Republic of Korea. Respondents were queried about their use of digital health tools, such as wearable devices and mobile apps. Confidence in using digital systems and managing health via digital tools was assessed using a five-point Likert scale. Results: The findings indicated that while young adults have lower rates of using digital devices for healthcare, they exhibit higher confidence in using such devices. In contrast, middle-aged adults, despite having lower confidence, report higher usage of digital devices for healthcare purposes. Conclusions: This study explored differences in digital confidence and healthcare usage between age groups and aimed to propose effective health management strategies based on digital accessibility.

User Engagement With mHealth Interventions to Promote Treatment Adherence and Self-Management in People With Chronic Health Conditions: Systematic Review

BACKGROUND

There are numerous mobile health (mHealth) interventions for treatment adherence and self-management; yet, little is known about user engagement or interaction with these technologies.

OBJECTIVE

This systematic review aimed to answer the following questions: (1) How is user engagement defined and measured in studies of mHealth interventions to promote adherence to prescribed medical or health regimens or self-management among people living with a health condition? (2) To what degree are patients engaging with these mHealth interventions? (3) What is the association between user engagement with mHealth interventions and adherence or self-management outcomes? (4) How often is user engagement a research end point?

METHODS

Scientific database (Ovid MEDLINE, Embase, Web of Science, PsycINFO, and CINAHL) search results (2016-2021) were screened for inclusion and exclusion criteria. Data were extracted in a standardized electronic form. No risk-of-bias assessment was conducted because this review aimed to characterize user engagement measurement rather than certainty in primary study results. The results were synthesized descriptively and thematically.

RESULTS

A total of 292 studies were included for data extraction. The median number of participants per study was 77 (IQR 34-164). Most of the mHealth interventions were evaluated in nonrandomized studies (157/292, 53.8%), involved people with diabetes (51/292, 17.5%), targeted medication adherence (98/292, 33.6%), and comprised apps (220/292, 75.3%). The principal findings were as follows: (1) >60 unique terms were used to define user engagement; "use" (102/292, 34.9%) and "engagement" (94/292, 32.2%) were the most common; (2) a total of 11 distinct user engagement measurement approaches were identified; the use of objective user log-in data from an app or web portal (160/292, 54.8%) was the most common; (3) although engagement was inconsistently evaluated, most of the studies (99/195, 50.8%) reported >1 level of engagement due to the use of multiple measurement methods or analyses, decreased engagement across time (76/99, 77%), and results and conclusions suggesting that higher engagement was associated with positive adherence or self-management (60/103, 58.3%); and (4) user engagement was a research end point in only 19.2% (56/292) of the studies.

CONCLUSIONS

The results revealed major limitations in the literature reviewed, including significant variability in how user engagement is defined, a tendency to rely on user log-in data over other measurements, and critical gaps in how user engagement is evaluated (infrequently evaluated over time or in relation to adherence or self-management outcomes and rarely considered a research end point). Recommendations are outlined in response to our findings with the goal of improving research rigor in this area.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42022289693; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022289693.

Physical Activity Pattern of Adults With Metabolic Syndrome Risk Factors: Time-Series Cluster Analysis

Background

Physical activity plays a crucial role in maintaining a healthy lifestyle, and wrist-worn wearables, such as smartwatches and smart bands, have become popular tools for measuring activity levels in daily life. However, studies on physical activity using wearable devices have limitations; for example, these studies often rely on a single device model or use improper clustering methods to analyze the wearable data that are extracted from wearable devices.

Objective

This study aimed to identify methods suitable for analyzing wearable data and determining daily physical activity patterns. This study also explored the association between these physical activity patterns and health risk factors.

Methods

People aged >30 years who had metabolic syndrome risk factors and were using their own wrist-worn devices were included in this study. We collected personal health data through a web-based survey and measured physical activity levels using wrist-worn wearables over the course of 1 week. The Time-Series Anytime Density Peak (TADPole) clustering method, which is a novel time-series method proposed recently, was used to identify the physical activity patterns of study participants. Additionally, we defined physical activity pattern groups based on the similarity of physical activity patterns between weekdays and weekends. We used the χ2 or Fisher exact test for categorical variables and the 2-tailed t test for numerical variables to find significant differences between physical activity pattern groups. Logistic regression models were used to analyze the relationship between activity patterns and health risk factors.

Results

A total of 47 participants were included in the analysis, generating a total of 329 person-days of data. We identified 2 different types of physical activity patterns (early bird pattern and night owl pattern) for weekdays and weekends. The physical activity levels of early birds were less than that of night owls on both weekdays and weekends. Additionally, participants were categorized into stable and shifting groups based on the similarity of physical activity patterns between weekdays and weekends. The physical activity pattern groups showed significant differences depending on age (P=.004) and daily energy expenditure (P<.001 for weekdays; P=.003 for weekends). Logistic regression analysis revealed a significant association between older age (≥40 y) and shifting physical activity patterns (odds ratio 8.68, 95% CI 1.95-48.85; P=.007).

Conclusions

This study overcomes the limitations of previous studies by using various models of wrist-worn wearables and a novel time-series clustering method. Our findings suggested that age significantly influenced physical activity patterns. It also suggests a potential role of the TADPole clustering method in the analysis of large and multidimensional data, such as wearable data.

Effect of exercise intervention using mobile healthcare on blood lipid level and health-related physical fitness in obese women: a randomized controlled trial

PURPOSE

This study aimed to evaluate the effects of 12 weeks of non-face-to-face exercise intervention using mobile health (mHealth) on blood lipid levels and health-related physical fitness in obese women.

METHODS

Thirty obese women (aged: 39.40 ± 11.07 years, percent body fat: 37.05 ± 5.15%) were enrolled, and all completed the study. Non-face-to-face exercises were performed for 12 weeks using a mHealth and smart tracker (Charge 4, Fitbit, USA). Participants were randomly assigned to an experimental (EXP) or control (CON) group. The 12-week exercise program using mHealth included resistance (twice a week for 60 min), aerobics (five times a week for 50 min), and flexibility (five times a week for 10 min).

RESULTS

The results showed that high-density lipoprotein cholesterol (Post - Pre: 9.07 mg·dL-1, p < 0.001) and ratio of low-density to high-density lipoprotein cholesterol (Post - Pre: -0.71 mg·dL-1, p < 0.05) significantly changed during the intervention period in EXP. There were significant increases in sit-ups (Post - Pre: 7.73 numbers, p < 0.001), grip strength (Post - Pre: 2.92 kg, p < 0.001), and sit and reach (Post - Pre: 2.51 cm, p < 0.01) in EXP.

CONCLUSION

Non-face-to-face exercise using mHealth for 12 weeks improved blood lipid levels and health-related physical fitness; therefore, it can help improve compliance through self-monitoring and lifestyle changes by increasing physical activity.

Nonlinear, Multicomponent Physical Exercise With Heart Rate Variability-Guided Prescription in Women With Breast Cancer During Treatment: Feasibility and Preliminary Results (ATOPE Study)

OBJECTIVE

The purpose of this study was to examine the feasibility, safety, adherence, and preliminary efficacy of the ATOPE program during radiotherapy (RT) or chemotherapy (CT) for women with breast cancer.

METHODS

This single-blind, pretest-posttest feasibility study included 38 women with breast cancer at the beginning of their treatment. The ATOPE program consisted of 12 to 18 sessions of a multimodal physical exercise program, prescribed based on daily heart rate variability and clinimetric assessments using the ATOPE+ mHealth system. Overall health was assessed with quality of life, autonomous balance, and body composition, whereas health-related fitness was measured through functional capacity, physical activity levels, and upper and lower limb strength.

RESULTS

The rates of recruitment, retention, and adherence were 52.35, 73.68, and 84.37%, respectively, and the satisfaction rating was 9.2 out of a possible 10 points. The perceived health status change score was 3.83 points, scored on a -5 to 5 point scale. No adverse effects were found. Compliance results showed that the ATOPE+ mHealth system was used on 73.38% of the days, and the Fitbit bracelet (Google, Mountain View, CA, USA) was used on 84.91% of the days. Women stayed physically active 55% of days. Regarding preliminary results, for overall health, the percentage of body fat in the RT group decreased by 1.93%, whereas it increased by 5.03% in the CT group. Lower limb strength increased in the RT group, specifically knee extensor isometric strength (6.07%), isokinetic knee flexors 180 degree/second (1.53%), and isokinetic knee extensors 300 degree/second (4.53%), in contrast with the reductions found in the CT group (11.07, 18.67, and 14.89%, respectively).

CONCLUSION

The ATOPE program, through nonlinear prescription based on daily monitoring with the ATOPE+ mHealth system, is feasible and safe for application during breast cancer treatment. The results suggest that the overall health can be maintained or even improved regarding most variables.

IMPACT

This study focused on the feasibility, safety, and completion of a physical therapist-led program at early diagnosis for adults with breast cancer. The multimodal, supervised, tailored, nonlinear physical exercise program is feasible and safe, showed a good completion rate, and was able to prevent the quality-of-life deficits that are often triggered by systemic breast cancer treatment. This study highlights the importance of daily morning assessments using the ATOPE+ mHealth system in patients with breast cancer to prescribe nonlinear physical exercise.

Effects of a Mobile-Health Exercise Intervention on Body Composition, Vascular Function, and Autonomic Nervous System Function in Obese Women: A Randomized Controlled Trial

Purpose

The present study verified the effect of an m-health exercise intervention using a 12-week exercise program on body composition, vascular function, and the ANS.

Patients and Methods

Thirty obese adult women participants were randomized (n = 15 each) into the experimental (EXP) group, those who performed mobile-health (m-health) exercises using a wearable device (Charge 4, Fitbit, USA) and AI-fit web page, or the control (CON) group, those who continued their daily activities as before. Muscle function, cardiorespiratory endurance, and flexibility were assessed during the exercise program using the AI-fit web page and wearable device. The EXP group participated in exercise interventions using the m-health system for 12 weeks, while the CON group was encouraged to maintain their normal daily routines. Body composition, vascular function, and autonomic nervous system (ANS) were evaluated before and after the intervention.

Results

Significant decreases were noted in fat mass (Post - Pre: -1.47 kg; p < 0.001) and percent body fat (Post - Pre: -2.11%; p < 0.05). Flow-mediated dilatation (Post - Pre: 2.63%; p < 0.001) was significantly increased, and brachial-ankle pulse wave velocity (Post - Pre: -91.49 cm·sec^-1; p < 0.01) was significantly decreased. RMSSD (Post - Pre: 10.43 ms; p < 0.01), NN50 (Post-Pre: 24.04; p < 0.05), pNN50 (Post - Pre: 7.70%; p < 0.05) and HF (Post-Pre: 179.60 ms²; p < 0.05) increased significantly.

Conclusion

In conclusion, m-health exercise interventions using AI fit and wearable devices are effective in preventing obesity and improving vascular function, and ANS.

Exploring the Role of Wearable Electronic Medical Devices in Improving Cardiovascular Risk Factors and Outcomes Among Adults: A Systematic Review

There is a developing trend of using wearable electronic devices as health aides, spurred on by telecommunication companies as fitness devices and marketed as such. They have been shown to count steps, pulse, and record arrhythmias, doubling as communication devices and prompting healthcare providers in some instances. We sought to determine if there was a direct correlation between device use and increased physical activity as recommended by the World Health Organization, or if this physical activity increase was only marginal at best. In addition, we sought to investigate if there were additional benefits to using these devices besides increased self-awareness of health. This systematic review used Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Keywords for searching articles centered around cardiovascular disease, wearable electronic devices, and their synonyms. Most of the data were obtained from PubMed, although other contributing databases were used, including ResearchGate, Science.gov, ScienceDirect, and PubMed Medical Subject Headings database. Only full-text articles were used. We identified 62 articles that met our search criteria but narrowed them down to 19 following qualitative assessment. Increased physical activity was found to be the one parameter that stood out by way of benefit from the device. Other findings, such as reduced waist circumference, obesity, glycated hemoglobin, and lipid levels, shared mixed results. At this time, we do not have a definition of what duration of device use is deemed standard for health. We have no consensus on which devices are superior health-wise. Our study, however, indicates that these devices, used with adequate health professional supervision, have a role to play in motivation and increased physical activity, enough to cause impactful gains in cardiovascular health.

The Impact of Wearable Devices on Physical Activity for Chronic Disease Patients: Findings from the 2019 Health Information National Trends Survey

BACKGROUND

Wearable devices are shown to be an advanced tool for chronic disease management, but their impacts on physical activity remain uninvestigated. This study aims to examine the effect of wearable devices on physical activity in general people and chronic patients.

METHODS

Our sample was from the third cycle of the fifth iteration of the Health Information National Trends Survey (HINTS), which includes a total of 5438 residents. Genetic matching was used to evaluate the effect of wearable devices on physical activity in different populations.

RESULTS

(1) Both using wearable devices and using them with high frequency will improve physical activity for the whole population. (2) Wearable devices may have greater positive effects on physical activity for chronic patients. (3) Especially in patients with hypertension, high-frequency use of wearable devices can significantly improve the duration and frequency of physical activity.

CONCLUSIONS

Wearable devices lead to more physical activity, and the benefit is more noticeable for chronic patients, particularly those with hypertension.

Ambulatory and successive home-based heart rate targeted aerobic training improves arterial parameters: a follow-up study in people with metabolic syndrome

BACKGROUND

Studies demonstrated that outpatient aerobic exercise programs (aEP) can significantly decrease aortic stiffness in people with metabolic syndrome (MetS). There is some limited data that remotely supervised home-based aEP can also improve arterial stiffness in this population. We aimed to evaluate the changes in the arterial wall parameters after the 2-month ambulatory supervised aEP followed by the 6-month home-based aEP with and without targeting of heart rate (HR) by electrocardiogram (ECG) in people with MetS.

METHODS

In this prospective study (ClinicalTrials.gov identifier: NCT05592704) 132 MetS subjects (mean age 52.44 ± 6.26 years, 54.55% female) were evaluated. At first, all subjects participated in the 2-month ambulatory supervised aEP, which consisted of 40 individual aerobic training sessions on a cycle ergometer 5 times/week for 40 min and received the recommendations for home-based training. Then the study (n = 66) and the control (n = 66) groups participated in the 6-month home-based aEP, but only the study group subjects targeted their HR using ECG monitor connected to the smartphone during workouts. Arterial stiffness parameters and carotid artery intima-media thickness (cIMT) were evaluated in all participants at baseline and after 8 months.

RESULTS

After 8 months, carotid-femoral pulse wave velocity (c-f PWV) significantly reduced in both groups (-12.22% in the study group vs. -7.85% in the control group, all p < .001) without a significant between-group difference (p = 0.144). A significant improvement of carotid-radial pulse wave velocity (c-r PWV) was observed only in the study group (-11.37%, p < .001, d = -0.671) with significant between-group difference (p < .001). The reduction of c-r PWV after 8 months of aEP occurred when c-r PWV at baseline was in the 2nd quartile (>7.90 m/s). A significant decrease of 3.32% in cIMT was present only in the study group (p = .032, d = -0.288).

CONCLUSIONS

The combination of 2-month ambulatory supervised aEP and successive 6-month home-based aEP targeted by HR monitoring using ECG improved arterial properties in MetS subjects more than the same combination without HR targeting, leading to the greater reduction of c-r PWV and cIMT.

Design, development, utility and usability testing of the EMPOWER-SUSTAIN Self-Management Mobile App© among primary care physicians and patients with metabolic syndrome

Objective

This study aimed to design, develop, assess and refine the EMPOWER-SUSTAIN Self-Management Mobile App© among primary care physicians (PCP) and patients with metabolic syndrome (MetS) in primary care.

Methodology

Using the software-development-life-cycle (SDLC) iterative model, storyboard and wireframe were drafted; and a mock prototype was designed to illustrate the content and function graphically. Subsequently, a working prototype was developed. Qualitative studies using the 'think-aloud' and cognitive-task-analysis methods were conducted for the utility and usability testing. Topic guide was based on the 10-Nielsen's-Heuristic-Principles. Utility testing was conducted among PCP in which they 'thought-aloud' while performing tasks using the mobile app. Usability testing was conducted among MetS patients after they were given the app for 3 weeks. They 'thought-aloud' while performing tasks using the app. Interviews were audio- and video-recorded, and transcribed verbatim. Thematic content analysis was performed.

Result

Seven PCP and nine patients participated in the utility and usability testing, respectively. Six themes (efficiency of use, user control and freedom, appearance and aesthetic features, clinical content, error prevention, and help and documentation) emerged. PCP found the mobile app attractive and relevant sections were easy to find. They suggested adding 'zoom/swipe' functions and some parts needed bigger fonts. Patients commented that the app was user-friendly, has nice interface, and straightforward language. It helped them understand their health better. Based on these findings, the mobile app was refined.

Conclusion

This app was produced using a robust SDLC method to increase users' satisfaction and sustainability of its use. It could potentially improve self-management behaviour among MetS patients in primary care.

Effective Prevention and Management Tools for Metabolic Syndrome Based on Digital Health-Based Lifestyle Interventions Using Healthcare Devices

Digital health-based lifestyle interventions (e.g., mobile applications, short messaging service, wearable devices, social media, and interactive websites) are widely used to manage metabolic syndrome (MetS). This study aimed to confirm the utility of self-care for prevention or management of MetS. We recruited 106 participants with one or more MetS risk factors from December 2019 to September 2020. Participants were provided five healthcare devices and applications. Characteristics were compared at baseline and follow-up to examine changes in risk factors, engagement, persistence, and physical activity (analyzed through device use frequency and lifestyle interventions performed). Participants with 1-2 MetS risk factors showed statistically significant reductions in waist circumference (WC) and blood pressure (BP). Participants with ≥3 MetS risk factors showed statistically significant reductions in risk factors including weight, body mass index, WC, BP, and fasting blood sugar (FBS). The prevention and improvement groups used more healthcare devices than the other groups. Smartwatch was the most frequently used device (5 times/week), and physical activity logged more than 7000 steps/week. WC, BP, and FBS of the improvement group were reduced by more than 40%. Based on engagement, persistence, and physical activity, digital health-based lifestyle interventions could be helpful for MetS prevention and management.

Effectiveness of App-Based Intervention to Improve Health Status of Sedentary Middle-Aged Males and Females

BACKGROUND

Adherence to a nutritional program and physical activity are the fundamental aspects of treatment for weight loss and associated problems. Previous research has shown that self-monitoring using a mobile device improves self-management.

METHODS

A total of 35 subjects (40.6 ± 9.24 years) participated in the study. During the control period (3 months), they received physical exercise guidelines and a personalized nutritional program, with the aim of promoting health status. In the experimental period (3 months), there was also a connection between the physical world (health care processes) and the digital world (app). All participants had their body composition and cardiovascular variables measured. They also underwent calcaneal densitometry to determine bone quality. Descriptive statistics, correlations and analysis of variance were performed (by a researcher who was not involved in the data collection) to study the changes between before and after interventions, as well as to make a comparison between treatments.

RESULTS

The use of an app, in which there exist a prediction of the evolution, messages of results and advice, among others, mediated by the assistance of dietitians/nutritionists and sports scientists, had a positive impact on the improvement of health parameters, showing significant differences in all variables except troponin.

CONCLUSIONS

The combination of healthy habits with the use of the app provided benefits, improving health.

The influence of wearables on healthcare outcomes in chronic disease: a systematic review (Preprint)

Background

Chronic diseases contribute to high rates of disability and mortality. Patient engagement in chronic disease self-management is an essential component of chronic disease models of health care. Wearables provide patient-centered health data in real time, which can help inform self-management decision-making. Despite the perceived benefits of wearables in improving chronic disease self-management, their influence on health care outcomes remains poorly understood.

Objective

This review aimed to examine the influence of wearables on health care outcomes in individuals with chronic diseases through a systematic review of the literature.

Methods

A narrative systematic review was conducted by searching 6 databases for randomized and observational studies published between January 1, 2016, and July 1, 2021, that included the use of a wearable intervention in a chronic disease group to assess its impact on a predefined outcome measure. These outcomes were defined as any influence on the patient or clinician experience, cost-effectiveness, or health care outcomes as a result of the wearable intervention. Data from the included studies were extracted based on 6 key themes, which formed the basis for a narrative qualitative synthesis. All outcomes were mapped against each component of the Quadruple Aim of health care. The guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement were followed in this study.

Results

A total of 30 articles were included; studies reported 2446 participants (mean age: range 10.1-74.4 years), and the influence of 14 types of wearables on 18 chronic diseases was presented. The most studied chronic diseases were type 2 diabetes (4/30, 13%), Parkinson disease (3/30, 10%), and chronic lower back pain (3/30, 10%). The results were mixed when assessing the impact on a predefined primary outcome, with 50% (15/30) of studies finding a positive influence on the studied outcome and 50% (15/30) demonstrating a nil effect. There was a positive effect of 3D virtual reality systems on chronic pain in 7% (2/30) of studies that evaluated 2 distinct chronic pain syndromes. Mixed results were observed in influencing exercise capacity; weight; and biomarkers of disease, such as hemoglobin A_1c, in diabetes. In total, 155 outcomes were studied. Most (139/155, 89.7%) addressed the health care outcomes component. This included pain (11/155, 7.5%), quality of life (7/155, 4.8%), and physical function (5/155, 3.4%). Approximately 7.7% (12/155) of outcome measures represented the patient experience component, with 1.3% (2/155) addressing the clinician experience and cost.

Conclusions

Given their popularity and capability, wearables may play an integral role in chronic disease management. However, further research is required to generate a strong evidence base for safe and effective implementation.

Trial Registration

PROSPERO International Prospective Register of Systematic Reviews CRD42021244562; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=244562

A Technology-Mediated Interventional Approach to the Prevention of Metabolic Syndrome: A Systematic Review and Meta-Analysis

BACKGROUND

Technology-mediated interventions help overcome barriers to program delivery and spread metabolic syndrome prevention programs on a large scale. A meta-analysis was performed to evaluate the impact of these technology-mediated interventions on metabolic syndrome prevention.

METHODS

In this meta-analysis, from 30 January 2018, three databases were searched to evaluate interventions using techniques to propagate diet and exercise lifestyle programs for adult patients with metabolic syndrome or metabolic risk.

RESULTS

Search results found 535 citations. Of these, 18 studies met the inclusion criteria analyzed in this article. The median duration of intervention was 4 months and the follow-up period ranged from 1.5 to 30 months. The standardized mean difference (SMD) between the two groups was waist circumference -0.35 (95% CI -0.54, -0.15), triglyceride -0.14 (95% CI -0.26, -0.03), fasting blood glucose -0.31 (95% CI -0.42, -0.19), body weight -1.34 (95% CI -2.04, -0.64), and body mass index -1.36 (95% CI -2.21, -0.51). There was no publication bias in this study.

CONCLUSION

Technology-mediated intervention improved clinically important metabolic syndrome related indicators such as excess body fat around the waist, fasting glucose, and body mass index. These interventions will play an important role in the dissemination of metabolic syndrome prevention programs.