Introduction： Muscle wasting is a serious complication that affects a large proportion of patients with heart failure (HF). Muscle wasting is a strong predictor of frailty and reduced survival in HF patients. Currently, standard treatments for slowing muscle loss in patients with HF are not available. The main intervention remains various types of physical activity programs. Telemonitoring is a promising strategy for improving heart failure outcomes by making it possible to monitor patients remotely. There are numerous examples of home-based exercise programs administered through telehealth services that have been beneficial for maintaining physical activity levels. These results highlight the potential utility of telehealth services for combatting sedentarism and muscle wasting among epidemic and post-epidemic phases. Objective： The purpose of this study is to evaluate the effect of a multi-component physical activity program based on home telemonitoring on patients with heart failure and muscle wasting. Methods： This study used an quasi-experimental study, two-group repeated measurement design. The experimental group received the Home-based exercise with telemonitoring and control group according to regular nursing care. Data were collected at baseline (T0), and post-tests will be conducted right after the intervention period (T1). Additionally, detraining effects will be measured 12 weeks after program cessation (T2) . Data were collected including demographic questionnaire, sarcopenia, cachexia assessment, clinical blood parameters from patient record, physical activity, loneliness, and quality of life. Scientific or Clinical Implication of the Expected Results： The study results can be used to design designated interventions and provide information for policymaking.
This study adopts an quasi-experimental study, two-group repeated-measures research design, purposive sampling, and after baseline assessment. Both the experimental group and the control group were patients included in the case management of heart failure in the hospital. The experimental group received 59 cases who participated in remote home sports, and the control group received 59 cases who received routine care for heart failure case management. In this study, G power 3.1.9.7 software was used, and ANOVA: Repeated measures, between factors was selected to calculate the sample size, and the G power program (Faul et al., 2007) was used to estimate the appropriate sample size, according to previous group and family exercise programs. Results of a study of outcomes in patients with sarcopenia (Tsekoura et al., 2017), each group required 28 participants to measure grip strength between the intervention and control groups (SD = 4.2), equivalent to an effect size of 0.316, significant level α=0.05, test force β=0.95. The estimated potential attrition rate is 30%, and 118 cases are expected to be accepted, requiring 59 subjects per Method of recruitment: Patients with mild to moderate and stable systolic heart failure who met the NYHA first-to-third definition of the New York Heart Association were first screened from the medical records, and the outpatient physicians or nurses introduced this study to potential subjects during routine return visits. After obtaining their consent, the researchers contacted them again, and after obtaining consent, they reviewed their medical records to recruit research subjects. After reviewing the acceptance and exclusion conditions, the research purpose and explanation were carried out. After the participants agreed, the study list was included in the study. on the roster for receipt. 4.How subjects consented: The outpatient physician or nurse practitioner will introduce the study to potential subjects, and after obtaining their consent, the researcher will review the medical records and then recruit the subjects, explain the research purpose and research process, inform the participants of their rights, and the subjects who are willing to participate are invited to fill in the research consent form before closing the case. Treatment effect evaluation and statistical analysis methods: The questionnaire data collected in this study were first coded and typed into the computer. After the data was checked, the data was analyzed with SPSS for Windows version 24.0, and α=.05 was used as the test for statistical significance. The data analysis methods were as follows: 1. Descriptive statistics Describe basic individual attributes in percentage, mean, standard deviation, such as gender, age, religion, education, marital status, past medical history, primary caregiver, health status, sarcopenia-related measures (calf circumference, grip strength, muscle mass, 5 times of standing up and sitting down and 6 meters walking speed) and clinical blood biochemical test values, etc., as well as the scores of physical activity, loneliness and quality of life. 2. Inferential statistics Including: group difference test: (a) Chi-square test (Chi-Squire test) to test the homogeneity of category data between the control group and the intervention group. (b) Use an independent t test to test the difference in the pretest time of the continuous variable between the control group and the intervention group? Influencing factors: (a) Independent t test (independent t test) was used for the two groups with significant differences in sociodemographic characteristics, and the cases belonged to two-category variables (gender, presence or absence of other comorbidities). ) to determine whether there is a significant difference in its relationship with blood biochemical test values, physical activity, loneliness, and quality of life. (b) One-way analysis of variance:One-way ANOVA is aimed at the two groups with significant differences in sociodemographic characteristics, and the cases belong to multi-category variables (age, education level, marriage, caregiver, occupation, health status, muscle If the F value reaches a statistically significant difference, the Sheffe test is used to analyze the differences between groups to determine whether it is related to the blood biochemical Whether there are significant differences in the relationship between test values, physical activity, loneliness, and quality of life. (c) Pearson's product moment correlation coefficients to analyze the correlation between sociodemographic characteristics and variables of blood biochemical test values, physical activity, loneliness and quality of life. Effectiveness evaluation: (a) Paired-t test was used to detect within-group nursing effectiveness indicators (measurement of sarcopenia severity and cachexia severity, blood biochemical test values, health status, physical activity, loneliness and life Quality) is there any difference between the pre-test and post-test (b)Generalized estimated equation is used to put the variables with significant differences or correlations between sociodemographic and each outcome index into the model to correct the interference on the pre-test Factors to examine the differences between the experimental group and the control group in changes in sarcopenia severity and cachexia severity, physical activity, loneliness, and quality of life outcomes. (e) Questionnaires or other research-related materials: (1) Basic data: Demographic data includes health status data, such as age, gender, occupation, income, education level, marital status, etc., which are filled in by the participants themselves; and the health status data, which is checked by the researchers from the medical records. Height, weight, number of heartbeats, systolic and diastolic blood pressure (mmHg), body mass index (BMI), medications and their quantities, surgical history, and other comorbidities within the last three months , The reason for the most recent admission, the reason for the most recent emergency, the reason for the most recent visit, the fatigue state and nutritional status, (Mini Nutritional Assessment Short-Form, MNA-SF) and other related data evaluation.
Study Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
PREVENTION
Masking
SINGLE
Enrollment
118
The home telehealth physical activity training program is a telemedicine physical activity training that uses Google Meet software to communicate and supervise through webcams.
Far Eastern Memorial Hospital
Taipei, Taiwan
RECRUITINGChange from baseline in hand grip at week 12 and week 24
Measured with a Jamar Hydraulic Hand Dynamometer(kg). This hand dynamometer is ideal for routine screening of grip strength and hand function. This hand strength test also features a dual-scale readout displaying isometric grip force from 0 - 90kg.(M\<28kg; Female\<18kg for low grip strength) Change =(Week 12 score-baseline score; week 24 score-baseline score)
Time frame: baseline and week 12 and week 24
Change from baseline in The five-repetition sit-to-stand test at week 12 and week 24
The score is the amount of time (to the nearest decimal in seconds) it takes a patient to transfer from a seated to a standing position and back to sitting five times. Change =(Week 12 score-baseline score; week 24 score-baseline score)
Time frame: baseline and week 12 and week 24
Change from baseline in six-meter walking speed test at week 12 and week 24
In order to obtain accurate data, an acceleration zone and a deceleration zone of 1.5 m. Measure the time it takes the patient to actually walk six meters. The score is the amount of time (to the nearest decimal in seconds)meters are given before and after the measurement zone. Change =(Week 12 score-baseline score; week 24 score-baseline score)
Time frame: baseline and week 12 and week 24
Change from baseline in Skeletal muscle mass index (SMI) at week 12 and week 24
The SMI was calculated by dividing the limb skeletal muscle mass (kg) by the square of the height (m 2). Determined by bioelectrical impedance analysis ( for males with SMI \<7.0 kg/m2 and females with SMI \<5.7 kg/m2). Change =(Week 12 score-baseline score; week 24 score-baseline score)
Time frame: baseline and week 12 and week 24
Change from baseline in the international physical activity questionnaire - short form; IPAQ-SF at week 12 and week 24
The IPAQ-SF asks about three specific types of activity undertaken in the three domains introduced above and sitting. The specific types of activity that are assessed are walking, moderate-intensity activities, and vigorous-intensity activities; frequency (measured in days per week) and duration (time per day) are collected separately for each specific type of activity. Measure of volume of activity can be computed by weighting each type of activity by its energy requirements defined in METS (METs are multiples of the resting metabolic rate) to yield a score in METñminutes. A MET-minute is computed by multiplying the MET score by the minutes performed. Walking = 3.3 METs, Moderate PA = 4.0 METs and Vigorous PA = 8.0 METs 1. Low-intensity physical activity: \< 599 MET-min/week) 2. Moderate-intensity physical activity: 600 Met-min/week. 3. High-intensity physical activity: \>3000 Met-min/week
Time frame: baseline and week 12 and week 24
Change from baseline in (Mini Nutritional Assessment-Short Form;MNA-SF) at week 12 and week 24
Nutrition(Mini Nutritional Assessment-Short Form; MNA-SF): (1)12 to 14 points: Well-nourished (2)8 to 11 points: Potentially malnourished (3) 0 to 7 points: Malnourished Change =(Week 12 score-baseline score; week 24 score-baseline score)
Time frame: baseline and week 12 and week 24
Change from baseline in loneliness(3-item loneliness scales) at week 12 and week 24
loneliness(3-item loneliness scales) : The total score is 3 to 9 points, and a score of 6 or more is judged to be lonely. Change =(Week 12 score-baseline score; week 24 score-baseline score)
Time frame: baseline and week 12 and week 24
Change from baseline in (The 5-level EQ-5D version ;EQ-5D-5L) at week 12 and week 24
(The 5-level EQ-5D version; EQ-5D-5L): A single EQ-5D score (EQ-index) was obtained by weighting the population standard reference values established by the time trade-off (TTO). In this study, the standard reference value established by Japan, which is similar to Taiwan's public sentiment, is used to calculate and weight the scores (20) Change =(Week 12 score-baseline score; week 24 score-baseline score)
Time frame: baseline and week 12 and week 24
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