Feasibility and outcomes of a community-based,mobile health system-monitored lifestyle intervention in chronic stroke: A pilot study

Abstract

BACKGROUND:

People with disabilities face challenges in daily life during the COVID-19 pandemic, including limited access to care, exposure to lifestyle-related diseases, and difficulties in regular exercise. Therefore, it is important to establish health safety nets using Information and Communication Technology (ICT) in communities.

OBJECTIVE:

This study aimed to develop an m-Health-based personalized lifestyle intervention algorithm targeting high-risk groups of lifestyle-related diseases (including hypertension, diabetes, and obesity) among people with hemiplegic disabilities, and to verify its feasibility.

METHODS:

Six people at a high risk of lifestyle-related diseases participated in an 8-week lifestyle intervention using a wearable device and the S-Health program. The self-health management areas included walking, moderate-intensity exercise, weight, blood pressure, blood sugar, diet, calorie intake, heart rate, sobriety, no smoking. Health, physical, psychological, and social changes were measured before and after the study.

RESULTS:

The intervention had a positive impact on the participants’ health, with statistically significant differences found in fasting blood glucose, highest systolic blood pressure, grip strength, and motor function assessment. Quality of life, health-related quality of life, and self-efficacy improved post-intervention.

CONCLUSION:

Our findings can be used as preliminary evidence for establishing m-Health-based health safety net systems for people with disabilities who live in communities.

Keywords

Community stroke m-Health lifestyle interventions feasibility

1. Introduction

During the COVID-19 pandemic, people with disabilities have faced challenges such as restricted access to medical care and difficulty in acquiring health-related information due to social distancing. These issues have exacerbated existing health problems and put them at risk for new diseases, particularly lifestyle-related conditions such as diabetes, hypertension, and obesity [1]. Accordingly, people with disabilities are 1.1 $\sim$ 2.4 times more likely to develop diabetes and hypertension, and 2.8 $\sim$ 3.3 times more likely to develop obesity than people with no disabilities [2, 3].

Generally, people with disabilities have poorer health conditions and are more susceptible to diseases than people without disabilities, resulting in the early onset of chronic diseases and frequent secondary functional disorders. Various forms of preventive management and health promotion related to overall health, such as continuous monitoring of chronic diseases, rehabilitation treatment in communities, and prevention of lifestyle-related diseases, should be supported [4].

A health safety net refers to a range of institutional strategies aimed at protecting individuals and communities from various health threats that may arise at different stages of life or during a pandemic. It is important to improve access to healthcare services for the underserved individuals in communities, such as people with disabilities [5, 6]. In addition, a proper service delivery system is necessary when establishing a health safety net for the disabled. The World Health Organization (WHO) reported that limited resources could be overcome on the supplier side, and structural obstacles and behavioral limitations can be overcome on the consumer side [7]. Hence, it is crucial to develop a mobile health (m-Health) system that utilizes Information and Communications Technology (ICT), a key component of the fourth industrial revolution. The m-Health system provides non-face-to-face monitoring and health management services to ensure feasibility healthcare delivery [8].

After a stroke, an average of 2.38 health-related events occur, increasing the risk of recurrent stroke or secondary medical complications [9]. The risk factors for stroke include hypertension, diabetes, heart disease, environmental factors, nutritional factors, alcohol consumption, tobacco use, education, lifestyle and behavior, air pollution, suffering from depression, higher weight, and lack of physical activity [10]. Long-term bed rest and lack of activities after stroke were reported to increase the prevalence of obesity, hypertension, and diabetes [11, 12, 13]. Therefore, there is a need for evidence-based strategic interventions that focus on lifestyle changes to improve health, particularly in relation to lifestyle-related diseases such as obesity, hypertension, and diabetes after a stroke [14].

Lifestyle encompasses various factors such as personal values, attitudes, and culture. It refers to the consistency shown in past experiences, problem-solving methods, and a forward-looking attitude [15]. The lifestyle intervention method is a strategy that motivates the individual to recognize problems in his/her health life and establish a practical plan to improve them [16].

For the prevention of major diseases in modern society, such as heart disease, stroke, cancer, depression, and kidney disease [17], a variety of lifestyle factors need to be modified, including weight control, proper nutrition, regular exercise, smoking cessation, stress reduction, creation of a social support system, and leisure activities [18]. It is noteworthy that self-efficacy resulting from these interventions is a very important factor influencing the behavioral change in the early stages, as it acts as a motivating factor for future success, reinforces the intention of planning a healthy lifestyle and ultimately supports a change towards a healthier lifestyle [19, 20].

Lifestyle factors after a stroke include smoking cessation, reduction in alcohol consumption, proper blood sugar control, healthy eating, steady exercise using a pedometer, maintaining appropriate weight, and having a positive mindset [10]. While several studies have focused on lifestyle modifications and risk factor management for stroke prevention, most of these studies have primarily aimed at preventing secondary stroke and managing health after a stroke. To overcome these limitations, attempts are being made to utilize m-Health. For example, m-Health systems like Masterstroke and StrokeCoach were developed to provide education on self-management of chronic diseases and dietary and exercise management in people with disabilities who suffered from a stroke, with a focus on secondary stroke prevention. These systems were feasibility in improving the knowledge of secondary stroke prevention, functional balance, eating habits, and quality of life [21, 22]. Ultimately, they were shown to positively affect lifestyle changes by modifying problematic behaviors better than traditional lifestyle intervention. However, interventions applied through m-Health based on the behavioral change theory have shown limitations in terms of direct monitoring and the quality and quantity of healthcare provided. Therefore, efforts to develop m-Health programs that can induce behavioral changes and stimulate patients’ activity are needed. In this context, to prevent secondary strokes and manage health in the community, it is necessary to continuously improve and disseminate m-Health programs that induce lifestyle changes by monitoring blood sugar, blood pressure, weight, diet, and physical activity and providing health missions for behavioral changes. The development and validation of such programs are crucial to ensure the feasibility and quality of healthcare provided to stroke patients [21, 22, 23, 24, 25].

Therefore, this study applied an 8-week m-Health-based program that monitored parameters such as blood sugar, blood pressure, weight, and physical activity, and provided health challenges for behavioral change in the high-risk group of lifestyle-related diseases among people with hemiplegic disabilities in communities. The feasibility of the intervention was verified by analyzing the health challenges practice and achievement rates, health, physical, cognitive, and social changes, and user satisfaction before and after the program.

2. Materials and methods

2.1 The m-Health system

This study monitored lifestyles of people with stroke hemiplegia living in communities using Samsung Electronics’ S-Health, a highly reliable and valid m-Health system, and provided lifestyle interventions aimed at stimulating behavioral changes [21, 22, 23, 24, 25]. S-Health recorded and collected information on exercise, daily activity, exercise intensity, and stress index for the assessment of basic health. Moreover, walking, running, moving distances, calorie consumption, and activity times were recorded, and sleep habits were analyzed. Lastly, the system was linked to ICT-based smart bands, blood glucose meters, blood pressure monitors, and weight scales, enabling integrated monitoring of various parameters such as heart rate, blood sugar, blood pressure, and weight. S-Health not only tracked and charted daily activities for systematic management but also managed individual health conditions by integrating and analyzing the collected health data and setting tailor-made lifestyle goals for the participants.

2.2 Study subjects

The subjects of this study were six people with chronic hemiplegic disabilities living in communities, who had at least one high-risk factor for lifestyle-related diseases, i.e., diabetes, high blood pressure, or obesity (fasting blood sugar $>$ 100, blood pressure: highest $>$ 120, lowest $>$ 80, body mass index: $>$ 25). The inclusion criteria were as follows: 1) those who could participate in moderate-intensity exercise according to the American College of Sports Medicine (ACSM) standards; 2) those aged 19 or older; and 3) those who could grasp objects using all fingers and walk independently. The exclusion criteria were as follows: 1) those who did not understand the purpose of this study; 2) those with serious life-threatening diseases such as cardiovascular conditions except for stroke; 3) those who participated in other programs within the last three months; 4) pregnant women or women who could be pregnant.

This study was conducted following the Declaration of Helsinki and approved by the Rehabilitation Hospital Institutional Review Board of the National Rehabilitation Center. Before the start of the intervention, the purpose and characteristics of the intervention were explained to all subjects, and each of them signed a consent form for voluntary participation. The general characteristics of the subjects are shown in Table 1.

Table 1
Characteristic of participants

Characteristic ( $n=$ 6)	M $\pm$ SD or $n$ (%) frequency
Age (yr)	66.00 $\pm$ 7.04
Gender
(male)	3 (50.0)
(female)	3 (50.0)
Occupation
(yes)	1 (16.7)
(no)	5 (83.3)
Type of disability
(hemiplegia, Lt)	3 (50.0)
(hemiplegia, Rt)	3 (50.0)
Degree of disability
(Severe)	3 (50.0)
(Mild)	3 (50.0)
Registration duration of disability
( $\sim$ 10yr)	1 (16.7)
( $\sim$ 20yr)	5 (83.3)
Lifestyle disease management group
Diabetic ( $>$ 100)	6 (100.0)
Hypertension ( $>$ 120)	6 (100.0)
Obesity (BMI $>$ 25)	4 (66.7)

2.3 Study procedures

Following the pre-assessment of fasting blood sugar, blood pressure, body mass index (BMI), height, weight, grip strength, motor function (using Motor Function Assessment Scale (K-MAS)), depression (PHQ-9), self-efficacy, Self-Management Confidence, Quality of Life (EQ-5D), and Health-related Quality of Life (EQ-VAS), education was given to the subjects on how to use the bands, blood glucose meters, sphygmomanometers, and weight scales linked to the m-Health system-based S-Health app [23, 24, 25]. Then, life-log data on weight, BMI, blood sugar, blood pressure, and the number of steps were collected in the S-Health system using a dedicated device for eight weeks. The S-Health app was also used to register dietary, exercise, drinking, smoking and daily risk records manually [23, 24, 25]. Furthermore, tailor-made challenges were designed for each participant, aimed at stimulating the behavioral change for improved lifestyle habits. The challenges included walking, moderate or high-intensity exercise, achieving the recommended daily calorie intake, calorie consumption versus calorie intake-achieving 500 calories or more, controlling the amount of daily sugar and sodium intake, abstinence from alcohol, and smoking cessation. In the 4th week of the intervention, in-person performance checks of m-Health-based monitoring and lifestyle interventions were conducted. After the 8-week lifestyle intervention, a post-test was conducted to analyze changes before and after, similar to the pre-test. Additionally, participant satisfaction with the program was evaluated at the end of the intervention (Fig. 1).

Table 2
The lifestyle intervention’s target subjects, number, and achievement goals

Lifestyle intervention	Subject	Period	Formula calculator
1. Walking	Diabetes, hypertension, obesity	1 time/day	Existence/nonexistence
2. Exercise for at least 150 minutes of medium strength or 75 minutes of high strength	Diabetes, hypertension, obesity	2 times/week	Moderate intensity: less than 64–75% of maximum heart rate High intensity: 75% of maximum heart rate or higher $\divideontimes$ Maximum heart rate $=$ 220-age
3. Achieve recommended daily intake calories	Diabetes, hypertension, obesity	1 time/day	Standard weight ((height-100) $\times$ 0.9) $\times$ activity index^*
4. Calorie intake – Calorie consumption $=-$ 500 or more	Obesity	1 time/day	Calorie consumption formula
5. Achieving daily intake of sugar	Diabetes, obesity	1 time/day	Daily calorie intake 10% $\downarrow$ 50 g $\downarrow$
6. Achieving daily intake of sodium	Diabetes, hypertension,	1 time/day	2000 mg $\downarrow$
7. Abstinence from alcohol	Diabetes, hypertension,	1 time/day	30 g $\downarrow$ /day 2cups $\downarrow$ /day
8. No smoking	Diabetes, hypertension,	1 time/day	Existence/nonexistence

Source: Korean Diabetes Association. ^* activity index.

•

Low activity (sitting mainly or doing only light daily movements), 25.

•

Normal activity (regular life), 30–35.

•

High activity (physical labor, etc.), 40.

^* Calculation formula for calories consumption.

•

Men: {( $-$ 55.0969 $+$ (0.6309x heart rate) $+$ (0.1988x weight) $+$ (0.2017x age)}/4.184 $\times$ 60x Exercise Time.

•

Female: {( $-$ 20.4022 $+$ (0.4472x heart rate) $-$ (0.1263x weight) $+$ (0.074x age)}/4.184 $\times$ 60x Exercise Time.

Figure 1.

Research process.

2.4 Lifestyle interventions and subject monitoring

2.4.1 Lifestyle interventions

Lifestyle interventions for diabetes, hypertension, and obesity were selected based on previous studies [1, 4, 14, 21, 22, 23, 24, 25]. The lifestyle interventions selected for the study included methods for regularly self-managing lifestyle-related diseases and promoting behavioral changes. According to the subject’s lifestyle-related disease, tailored interventions for blood sugar, blood pressure, weight, number of steps, diet, exercise, abstinence from alcohol, and smoking cessation were provided.

To confirm the healthy lifestyle practice rates according to lifestyle interventions in this study, the practice of a healthy lifestyle and achieving health goals according to each intervention was monitored and analyzed. The rate of healthy living and achievement of health goals were analyzed Table 2 presents information on the target subjects, the number of individuals, and the goals achieved by the lifestyle interventions. This study applied the concept of m-Health-based lifestyle intervention and monitoring to monitor healthy life practices and adherence to health challenges. Accordingly, life-log data were collected through lifestyle monitoring of people with chronic hemiplegic disabilities utilizing a smartwatc band (Galaxy Fit, Samsung Electronics, Korea), a blood glucose meter (CareSense N Premier, iSense Korea), a blood pressure monitor (Boryeong, A&D, Korea), and weight scale (Yunmai S Color 2, Korea) linked with S-Health (Samsung Electronics, Korea). Diet, exercise, drinking, smoking and daily risk were recorded in the S-Health in a self-reported manner.

In the case of diabetes, life-log data were collected through bands, blood glucose meters, and weight scales, and diet-, exercise-, drinking-, and smoking-related information were recorded in S-Health in a self-reported manner. In the case of hypertension, life-log data were collected through bands, blood pressure monitors, and weight scales, and diet-, exercise-, drinking-, and smoking-related information were self-recorded in S-Health. Finally, in the case of obesity, life-log data were collected through bands and weight scales, and diet and exercise data were recorded in S-Health. Table 3 shows the number of data collected and indicators according to diabetes, obesity, and hypertension.

Table 3
The number of data collected and indicators according to diabetes, obesity, and hypertension

Measurement & recording methods			Period	Collected date
Life-log data	S-health		3 times/day	Calorie intake
measurement			2 times/week	Moderate intensity exercise
	Smart band		All the time	Number of steps, step time, displacement (km), calorie consumption (kcal), heart rate
	Smart blood glucose meter		2 times/week	Fasting blood sugar (mg/DL)
	Smart blood pressure monitor		1 time/day	Systolic blood pressure (mmHg) diastolic blood pressure (mmHg) mean blood pressure (mmHg) pulse rate (times)
	Smart scale		1 time/day	Weight (kg), body mass index (BMI), body fat percentage (%)
Health life	Drinking history	Self-reported record	1 time/day	Alcohol consumption
record	Smoking history	Self-reported record	1 time/day	Amount smoked

Table 4

Healthy living practice rate

	Min	Max	M $\pm$ SD
Blood pressure	62.5	98.2	88.7	$\pm$ 13.6
Blood sugar	81.3	125.0	104.2	$\pm$ 16.6
Weight	78.6	98.2	88.1	$\pm$ 8.6
Walking	85.7	100	94.9	$\pm$ 7.2
Food diary
Breakfast	17.9	98.2	80.7	$\pm$ 31.1
Lunch	17.9	96.4	80.4	$\pm$ 30.9
Dinner	17.9	96.4	78.3	$\pm$ 29.8
Snacks	0	98.2	27.7	$\pm$ 37.7
Calorie intake	12.5	98.2	64.0	$\pm$ 33.9
Calories consumed	92.9	100	98.5	$\pm$ 2.9
Moderate intensity exercise	100	100	100
Heart rate	42.9	100	82.1	$\pm$ 23.4
Abstinence form alcohol	82.1	100	97.0	$\pm$ 7.3
No smoking	100	100	100

M: mean, SD: standard deviation.

Table 5

Healthy goal attainment rate

	Min	Max	M $\pm$ SD
4,400 steps	62.5	98.2	88.7	$\pm$ 13.6
Target steps individual steps goal	81.3	125.0	104.2	$\pm$ 16.6
Calorie intake $<$ Calorie consumption	78.6	98.2	88.1	$\pm$ 8.6
Target calories individual calories goal	85.7	100	94.9	$\pm$ 7.2

M: mean, SD: standard deviation.

2.4.2 Assessment tool

To determine the feasibility of m-Health-based lifestyle intervention and monitoring, the subjects’ physical, psychological, and social function were measured before and after the application of m-Health-based lifestyle intervention and monitoring. Physical changes were measured using the Motor Assessment Scale (MAS) developed by Janet H. Carr to evaluate the functional recovery of stroke patients and adapted by Yuri Cha et al. [26, 27].

Psychological changes were measured using 1) the Patient Health Questionnaire-9 (PHQ-9) developed by Kroenke et al. and adapted by Seung-jin Park [28, 29]; 2) the Self-efficacy scale developed by Shere et al. based on Bandura’s theory, adapted and modified by A-young Kim, and used by Park Jong-il [30, 31]; and 3) Self-Management Confidence Scale from the Intensive Health Management Intervention Book, Chapter on Hypertension, Tailored Health Management Project by the Korea Health Promotion Foundation [32].

To examine social changes and the quality of life, the following tools were used: 1) Korean EQ-5D EQVAS and 2) EQVAS Paper version of the EUROQOL group [33, 34].

2.4.3 Data analysis

The data on healthy lifestyle practice rates, goal achievement rates, and feasibility of lifestyle intervention were analyzed to calculate the mean and standard deviation of all dependent variables using the SPSS software, version 21.0. It is difficult to assume a normal distribution in the population; the pre-post mean difference and the change amount were tested through the Wilcoxon signed-rank test, a non-parametric statistical analysis method corresponding to the paired $t$ -test in this study. The statistical significance level ( $\alpha$ ) was set at 0.05.

3. Results

3.1 Healthy lifestyle monitoring for lifestyle-related disease management

As part of the lifestyle-related disease monitoring intervention, the following parameters and records were measured or registered: blood pressure (daily), blood sugar (twice a week), weight (daily), walking (daily), meal content (breakfast, lunch, dinner, snack) (daily), calorie intake (daily), calorie consumption (daily), moderate-intensity exercise (twice a week), heart rate (twice a week), abstinence from alcohol (daily), smoking cessation (daily). As a result of the monitoring, the rates of healthy lifestyle practice were 88.7% for blood pressure, 104.2% for blood sugar, 88.1% for weight, 94.9% for walking, 80.7% for breakfast, 80.4% for lunch, 78.3% for dinner, 27.7% for a snack, 64.0% for calorie intake, and 98.5% for calorie consumption.

3.2 Health goal achievement rates

The health goal achievement rates were determined by the number of daily steps and calories. On average, 77.2% of the recommended steps for older adults (4,400 steps) were achieved, and 60.2% of the individual target steps (8,000 steps) were achieved. The achievement rates for health goals were 45.5% on average when the calorie intake was less than the calories consumed and 22.3% based on the individual recommended calorie intake (average 1,784 kcal).

Table 6
Results of feasibility

Outcome measures	Pre-test		Post-test		z-score	$p$ -value
Health
Systolic blood pressure (mmHg)	135.83	$\pm$ 18.58	123.67	$\pm$ 11.50	$-$ 2.201	0.028^*
Diastolic blood pressure (mmHg)	82.00	$\pm$ 9.84	81.33	$\pm$ 9.83	$-$ 0.315	0.752
Fasting blood sugar test (mg/dL)	121.67	$\pm$ 15.55	106.67	$\pm$ 11.29	$-$ 1.782	0.075
Physical
Weight (kg)	65.33	$\pm$ 11.59	65.28	$\pm$ 11.25	$-$ 0.105	0.916
Body mass index (BMI)	26.19	$\pm$ 3.74	26.20	$\pm$ 3.82	$-$ 0.105	0.917
Dominant grip (Sitting)	20.35	$\pm$ 6.24	26.02	$\pm$ 8.27	$-$ 2.207	0.027^*
Thirteen grip (Sitting)	14.10	$\pm$ 11.34	12.70	$\pm$ 10.22	$-$ 0.524	0.600
Dominant grip (Standing)	22.10	$\pm$ 5.99	22.53	$\pm$ 9.32	$-$ 1.572	0.116
Thirteen grip (Standing)	15.13	$\pm$ 11.51	13.98	$\pm$ 10.11	$-$ 0.106	0.916
Motor assessment scale (MAS)	33.67	$\pm$ 6.95	36.00	$\pm$ 5.73	$-$ 2.070	0.038^*
Modified Barthel Index (MBI)	98.33	$\pm$ 2.07	98.33	$\pm$ 2.07	0.000	1.000
Psychological
Patient health questionnaire (PHQ-9)	1.83	$\pm$ 1.17	4.33	$\pm$ 4.50	$-$ 1.214	0.225
Self-efficacy	57.67	$\pm$ 3.93	58.00	$\pm$ 2.53	$-$ 0.631	0.528
– Competence	17.67	$\pm$ 4.32	18.33	$\pm$ 3.93	$-$ 0.406	0.684
– Confidence	20.00	$\pm$ 2.28	19.67	$\pm$ 3.33	$-$ 0.271	0.786
– Driving force	16.00	$\pm$ 1.79	16.00	$\pm$ 1.55	0.000	1.000
Self-management confidence	41.67	$\pm$ 3.93	44.33	$\pm$ 3.27	$-$ 1.761	0.078
Social ability
EuroQol 5 dimension (EQ-5D)	0.73	$\pm$ 0.09	0.85	$\pm$ 0.19	1.483	0.138
EuroQol Visual Analogue Scale (EQ-VAS)	61.67	$\pm$ 18.62	78.33	$\pm$ 9.83	1.633	0.102

M: mean, SD: standard deviation.

3.3 Satisfaction

The satisfaction level with the lifestyle intervention was, on average, 4.46 points. The overall satisfaction was 4.50 points. Service process satisfaction was 4.47 points, service outcome satisfaction was 4.37 points, service experience satisfaction was 4.50 points, and service accessibility satisfaction was 4.44 points. For the services, the results showed a high level of satisfaction with 3 points (moderate) or higher in all categories.

3.4 Results of feasibility

The feasibility of lifestyle interventions was identified in four areas, namely health, physical, psychological, and social changes. There were six subjects in the final analysis. After using the health management self-service through the healthcare app for eight weeks, fasting blood sugar (121.67 $\pm$ 15.55 $\rightarrow$ 106.67 $\pm$ 11.29), confidence domain of self-efficacy (20.00 $\pm$ 2.28 $\rightarrow$ 19.67 $\pm$ 3.33), confidence in self-management (41.67 $\pm$ 3.93 $\rightarrow$ 44.33 $\pm$ 3.27), quality of life (EQ-5D) (0.73 $\pm$ 0.09 $\rightarrow$ 0.85 $\pm$ 0.19), and health-related quality of life (EQ-VAS) (61.67 $\pm$ 18.62 $\rightarrow$ 78.33 $\pm$ 9.83) showed a tendency to improve compared to the pre-test, even though the differences were not statistically significant. In addition, there were statistically significant differences in the highest systolic blood pressure (135.83 $\pm$ 18.58 $\rightarrow$ 123.67 $\pm$ 11.50), dominant grip strength (in sitting position) (20.35 $\pm$ 6.24 $\rightarrow$ 26.02 $\pm$ 8.27), and motor function (33.67 $\pm$ 6.95 $\rightarrow$ 36.00 $\pm$ 5.73).

4. Discussion

This study aimed to develop a personalized lifestyle intervention algorithm using m-Health technology for the high-risk group of lifestyle-related diseases (diabetes, hypertension, and obesity) among people with hemiplegic disabilities in communities. The study evaluated the feasibility of this tailored lifestyle intervention protocol designed specifically for people with disabilities. Through a wearable device and S-Health app, tailored online health care for eight weeks and once a month in-person counseling was provided. Thorough education on the use of the health management device and application was given before the study to encourage active participation in the program. Six individuals with a high risk of lifestyle-related diseases (diabetes, hypertension, and obesity) among people with hemiplegia living in communities were included. No one dropped out, and there were no adverse reactions. The satisfaction level on the lifestyle intervention was, on average, 4.46 points. The overall satisfaction was 4.50 points. Service process satisfaction was 4.47 points, service outcome satisfaction was 4.37 points, service experience satisfaction was 4.50 points, and service accessibility satisfaction was 4.44 points. For the services, the results showed a high level of satisfaction with 3 points (moderate) or higher in all categories. The study verified the safety and satisfaction of offering a personalized lifestyle intervention based on m-Health technology to people with stroke hemiplegia residing in communities where maintaining a healthy lifestyle is challenging In a review to examine the feasibility of digital intervention services for people with disabilities in communities, positive satisfaction was reported when assistive technology evaluation, diagnostic evaluation, rehabilitation service, and counseling using ICT-based m-Health were provided, like in this study [1, 7, 21, 22, 23, 24, 25, 35, 36, 37].

Healthy lifestyle practices for managing lifestyle-related diseases introduced here included monitoring of blood pressure (daily), blood sugar (twice a week), weight (daily), walking (daily), meal content (breakfast, lunch, dinner, snack) (daily), calorie intake (daily), calorie consumption (daily), moderate-intensity exercise (twice a week), heart rate (twice a week), abstinence from alcohol (daily), smoking cessation (daily) [1, 4, 8, 10, 14, 19]. As a result, the healthy life practice rates were 88.7% for blood pressure, 104.2% for blood sugar, 88.1% for weight, 94.9% for walking, 80.7% for breakfast, 80.4% for lunch, 78.3% for dinner, 27.7% for snacks, 64.0% for calorie intake, and 98.5% for calorie consumption. The number of steps used in this study was based on a previous report by Lee et al. [38], which identified 4,400 steps as a meaningful target for older adults. On average, 77.2% of the participants achieved this target during the intervention. On average, 60.2% of the target number of steps (an average of 8,000 steps) were achieved. On average, 45.5% achievement was observed in the calorie consumed vs. calorie intake goal, while 22.3% achievement was observed in the target recommended calorie goal (average of 1,784 kcal for the metabolism). This suggests that physical limitations and care support conditions posed as barriers for people with disabilities in communities to maintain a healthy lifestyle, including difficulties in daily activities such as walking and eating.

Previous studies have shown that the risk of stroke can be greatly reduced with an active lifestyle, smoking cessation, and healthy eating habits. However, the main obstacles are unhealthy lifestyles and eating habits. It is important to continuously educate patients about the benefits of a healthy lifestyle and eating habits to encourage their active participation in such programs. To add, the diet tracking in this study relied on self-reported manual recording, which had limitations in accurately measuring and evaluating the diet and inducing behavioral changes in eating habits. Artificial intelligence technology that automatically records types of food and calories using an app by taking photos of food could help overcome this limitation and induce lifestyle changes more efficiently [1, 4, 14, 21, 22, 23, 24, 25].

Feasibility of the lifestyle interventions was identified in four areas: health, physical, psychological, and social. After eight weeks of intervention, fasting blood sugar, confidence domain of self-efficacy, confidence in self-management, quality of life (EQ-5D), and health-related quality of life were not significantly different but showed a tendency to improve. In addition, systolic blood pressure, dominant grip strength (sitting position), and motor function were significantly improved. A prior study also demonstrated that lifestyle interventions had a positive impact on health and physical function, as well as on patient satisfaction [21, 22].

The Masterstroke, a stroke prevention program, was used to provide education on self-management of chronic diseases and diet and exercise management focusing on secondary stroke prevention for nine weeks to stroke patients with disabilities in communities utilizing the m-Health system. The intervention resulted in improved knowledge of secondary stroke prevention, functional balance, healthier eating habits, and better quality of life [21].

The StrokeCoach stroke survivor program aimed to promote healthy lifestyles among stroke survivors in the community setting by providing community-based digital interventions for six months. The program aimed to remove risk factors and provide lifestyle coaching seven times to help stroke survivors manage their health. Even after the end of the program, positive effects on continuous lifestyle improvement were maintained. Ultimately, digital interventions, including m-Health, positively affected lifestyle changes by modifying problematic behaviors better than traditional methods [22]. Hence, this study is noteworthy as it designed a personalized lifestyle intervention protocol that emphasizes monitoring and managing the quality of lifestyle interventions for stroke hemiplegic individuals in communities facing challenges in maintaining healthy habits [25]. Furthermore, the feasibility of the intervention was assessed based on previous research, making it an important contribution to the field. However, additional human and physical support is still needed to overcome difficulties in daily life for people with stroke hemiplegia in communities and to actively promote healthy living habits in these patients.

5. Conclusions

This study showed that the m-Health-based tailored lifestyle intervention program developed to prevent and manage lifestyle-related diseases in people with disabilities in communities was feasible in managing lifestyle-related diseases (diabetes, hypertension, and obesity). It is expected that the results of this study will be used as preliminary evidence for establishing m-Health-based health safety net systems for people with disabilities in community settings in the future. However, the small sample size of this study limits the generalization of the results to larger populations. Therefore, examining the feasibility of the m-Health-based lifestyle intervention with larger sample sizes in larger cohorts will be necessary to establish its feasibility more widely. Moreover, developing m-Health-based lifestyle intervention protocols according to various types of disabilities will be required. In addition, since eight weeks may not be enough to achieve desirable long-term behavioral changes, follow-up studies on the long-term provision of this protocol and its feasibility are needed. Finally, due to a significant health information gap between people with and without disabilities, m-Health-based health management systems may not fully reflect the characteristics of people with disabilities. Therefore, it is essential to develop and test various lifestyle intervention protocols that cater to different types of disabilities, involve a larger number of subjects, longer intervention periods, and various service providing methods (in-person and online) to determine their feasibility.

Funding

This research was funded by the Rehabilitation Institute, Korea National Rehabilitation Center (No. 2022-H-2).

Data availability statement

The datasets generated and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.

Footnotes

Acknowledgments

The authors have no acknowledgments.

Conflict of interest

The authors declare that they have no conflict of interest.

References

Kim

Bae

Y-H

Kim

Lee

, editors. Formative Usability Evaluation of a Three-Way Digital Healthcare System for the People with Disabilities and Their Caregivers: A Cross-Sectional Study. Healthcare; 2022. Multidisciplinary Digital Publishing Institute.

Wong

Backholer

Gearon

Harding

Freak-Poli

Stevenson

, et al. Diabetes and risk of physical disability in adults: A systematic review and meta-analysis. The Lancet Diabetes & Endocrinology. 2013; 1(2): 106-14.

Flygare Wallén

Ljunggren

Carlsson

Pettersson

Wändell

. High prevalence of diabetes mellitus, hypertension and obesity among persons with a recorded diagnosis of intellectual disability or autism spectrum disorder. Journal of Intellectual Disability Research. 2018; 62(4): 269-80.

Towfighi

Cheng

Hill

Barry

Lee

Valle

, et al. Results of a pilot trial of a lifestyle intervention for stroke survivors: Healthy eating and lifestyle after stroke. Journal of Stroke and Cerebrovascular Diseases. 2020; 29(12): 105323.

BAEK . An Analysis of the Policy Formation Process of ‘Health, Medical, Welfare Network’ based on Kingdon’s Policy Streams Framework: the case of 301 network in Seoul: Department of Health Policy and Management The Graduate School of Public Health Seoul National University. 2020.

Shin

, editor. Health care safety net in Korea: issues and policy options. Health Welf Policy Forum. 2009.

Organization

. mHelath. Use of appropriate digital technologies for public health. 2018.

Center

KNR

. A study on the development of an on-tact digital healthcare system based on the health safety net for the people with disabilities in the era of COVID-19. 2021.

Ostwald

Wasserman

Davis

. Medications, comorbidities, and medical complications in stroke survivors: The CAReS study. Rehabilitation Nursing. 2006; 31(1): 10-4.

10.

Galimanis

Mono

M-L

Arnold

Nedeltchev

Mattle

. Lifestyle and stroke risk: A review. Current Opinion in Neurology. 2009; 22(1): 60-8.

11.

Virani

Alonso

Benjamin

Bittencourt

Callaway

Carson

, et al. American heart association council on epidemiology and prevention statistics committee and stroke statistics subcommittee. Heart Disease and Stroke Statistics-2020 Update: A Report from the American Heart Association Circulation. 2020; 141(9): e139-e596.

12.

Kumar

Selim

Caplan

. Medical complications after stroke. The Lancet Neurology. 2010; 9(1): 105-18.

13.

Billinger

Coughenour

MacKay-Lyons

Ivey

. Reduced cardiorespiratory fitness after stroke: biological consequences and exercise-induced adaptations. Stroke research and treatment. 2012; 2012.

14.

Driver

McShan

Swank

Grobe

Calhoun

Bailey

, et al. Creating an appropriate adaptation of a healthy lifestyle intervention for people after stroke. Brain Injury. 2020; 34(11): 1497-503.

15.

Mollborn

Lawrence

Saint Onge

. Contributions and challenges in health lifestyles research. Journal of Health and Social Behavior. 2021; 62(3): 388-403.

16.

Dunn

Andersen

Jakicic

. Lifestyle physical activity interventions: History, short-and long-term effects, and recommendations. American Journal of Preventive Medicine. 1998; 15(4): 398-412.

17.

Stürmer

Hasselbach

Amelang

. Personality, lifestyle, and risk of cardiovascular disease and cancer: Follow-up of population based cohort. Bmj. 2006; 332(7554): 1359.

18.

Kolb

Martin

. Environmental/lifestyle factors in the pathogenesis and prevention of type 2 diabetes. BMC Medicine. 2017; 15(1): 1-11.

19.

Ashford

Edmunds

French

. What is the best way to change self-efficacy to promote lifestyle and recreational physical activity? A systematic review with meta-analysis. British Journal of Health Psychology. 2010; 15(2): 265-88.

20.

Amiri

Chaman

Khosravi

. The relationship between health-promoting lifestyle and its related factors with self-efficacy and well-being of students. Osong Public Health and Research Perspectives. 2019; 10(4): 221.

21.

White

Bynon

Marquez

Sweetapple

Pollack

. Masterstroke: A pilot group stroke prevention program for community dwelling stroke survivors. Disability and Rehabilitation. 2013; 35(11): 931-8.

22.

Sakakibara

Lear

Barr

Benavente

Goldsmith

Silverberg

, et al. Development of a chronic disease management program for stroke survivors using intervention mapping: the stroke coach. Archives of Physical Medicine and Rehabilitation. 2017; 98(6): 1195-202.

23.

Baek

. Mobile-based digital healthcare platforms: SmartWellness. Archives of Design Research. 2021; 34(1): 101-112.

24.

Wen

Zhang

Liu

Lei

. Evaluating the consistency of current mainstream wearable devices in health monitoring: A comparison under free-living conditions. Journal of Medical Internet Research. 2017; 19(3): e68.

25.

Sakakibara

Lear

Barr

Goldsmith

Schneeberg

Silverberg

, et al. Telehealth coaching to improve self-management for secondary prevention after stroke: A randomized controlled trial of Stroke Coach. International Journal of Stroke. 2022; 17(4): 455-64.

26.

Carr

Shepherd

Nordholm

Lynne

. Investigation of a new motor assessment scale for stroke patients. Physical Therapy. 1985; 65(2): 175-80.

27.

Cha

Jung

Chung

. Reliability of the Korean version of motor assessment scale in patients with stroke. Journal of Special Education & Rehabilitation Science. 2013; 52: 279-93.

28.

Kroenke

Spitzer

Williams

. The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine. 2001; 16(9): 606-13.

29.

Park

S-J

Choi

H-R

Choi

J-H

Kim

K-W

Hong

J-P

. Reliability and validity of the Korean version of the Patient Health Questionnaire-9 (PHQ-9). Anxiety and Mood. 2010; 6(2): 119-24.

30.

Sherer

Maddux

Mercandante

Prentice-Dunn

Jacobs

Rogers

. The self-efficacy scale: Construction and validation. Psychological Reports. 1982; 51(2): 663-71.

31.

Park

. The modeling effects of spiritual well-being on self-efficacy and job satisfaction of the social workers: seoul christian university. 2008.

32.

Foundation

KHP

. Customized home visit health management project Intensive health management intervention book High blood pressure edition. 2011.

33.

EuroQoL. EQ-5D 2022 [Available from: http://www.euroqol.org/eq-5d/eq-5d-products/eq-5d-5l-translations.html.

34.

EuroQoL. EQ-VAS 2022 [Available from: https://euroqol.org/eq-5d-instruments/eq-5d-3l-translations.html.

35.

Evron

Schwartz

Sajina

Grosman-Rimon

Dudkiewicz

. A digital exercise and augmented reality training system improved mobility among stroke patients: A randomized control trial. Technology and Health Care. 2024; 32(1): 89-101.

36.

Tang

Seetharam

TG.b

Vignesh

. Internet of Things-assisted intelligent monitoring model to analyze the physical health condition. Technology and Health Care. 2021; 29(6): 1355-1369.

37.

Shankar

Santhosh Kumar

B.c

. Fog-Internet of things-assisted multi-sensor intelligent monitoring model to analyze the physical health condition. Technology and Health Care. 2021; 29(6): 1319-1337.

38.

Lee

I-M

Shiroma

Kamada

Bassett

Matthews

Buring

. Association of step volume and intensity with all-cause mortality in older women. JAMA Internal Medicine. 2019; 179(8): 1105-12.

Feasibility and outcomes of a community-based,mobile health system-monitored lifestyle intervention in chronic stroke: A pilot study

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Materials and methods

2.1 The m-Health system

2.2 Study subjects

Table 1 Characteristic of participants

Table 2 The lifestyle intervention’s target subjects, number, and achievement goals

2.4.1 Lifestyle interventions

Table 3 The number of data collected and indicators according to diabetes, obesity, and hypertension

2.4.3 Data analysis

3. Results

3.1 Healthy lifestyle monitoring for lifestyle-related disease management

3.2 Health goal achievement rates

Table 6 Results of feasibility

3.4 Results of feasibility

4. Discussion

5. Conclusions

Funding

Data availability statement

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Characteristic of participants

Table 2
The lifestyle intervention’s target subjects, number, and achievement goals

Table 3
The number of data collected and indicators according to diabetes, obesity, and hypertension

Table 6
Results of feasibility