Acupressure, Anxiety, Depression, Nurses, Psychological Distress
Conditions
Keywords
Nurses, Acupressure, Occupational stress, Burnout, professional, Resilience, psychological, Anxiety, Depression, Psychological distress
Brief summary
This measurement aims to understand the effect of self-acupressure on the Shenmen Point (神門) and (內關) points on the hand on emotional distress, anxiety, depression, stress, work fatigue and adaptability of clinical nurses. The main questions it aims to answer are: Whether acupressure can reduce emotional distress, anxiety, depression, stress and work fatigue in nursing staff. Participants will: 1\. Enforcement measures: 1. Acupressure group: Perform self-acupressure twice a day, each time for about 2 minutes, and record the acupressure records every day for 2 consecutive weeks. 2. Original method group: followed the original self-coping method. 2. After the intervention began, participants completed study questionnaires weekly for two months.
Detailed description
1. Purpose of the Study: The goal of this study is to understand emotional distress, anxiety, depression, stress, workplace fatigue, and resilience among clinical nurses, as well as the factors related to these conditions. Investigators also want to evaluate the effects of different coping methods-either by self-pressing specific acupuncture points (Shenmen and Neiguan on the hand) or using usual ways of managing stress and anxiety. 2. About the Acupressure Points: • Shenmen Point (神門): Shenmen, located on the inner wrist, is a key point on the Heart Meridian. It's often used in traditional Chinese medicine to calm the mind, ease anxiety, help with sleep problems, headaches, and emotional fatigue. • Neiguan Point (內關): Neiguan is located on the inner forearm. Pressing this point can help relieve stress, reduce bloating, calm palpitations, and improve sleep. It's often used when feeling tense or anxious. • Usual Coping Methods: If participants are assigned to this group, participants will simply continue handling stress and anxiety the way you normally do. 3. Who Can Join the Study: Participants can join this study if: * A clinical nurse aged 20 or older. * Participants screening results show a distress score of 3 or above, or a mood thermometer score of 4 or above. * Participants are willing and able to fill out questionnaires at specific times over the next two months. Participants cannot join this study if: * Participated in a similar study within the last month. * Currently work in administrative or non-patient care units (like the supply center). * Are currently pregnant. 4. Study Procedures: If participants agree to participate and sign the consent form, researchers will ask participants to fill out several questionnaires. These will cover basic info, emotional distress, mood, depression, anxiety, work stress, fatigue, and resilience. It takes about 10-20 minutes to complete. If participants distress score is 3 or higher, or your mood score is 4 or higher, participants will be randomly assigned to one of two groups: • Acupressure Group: Participants will learn how to press the Shenmen and Neiguan acupoints on their hands and press them twice a day (about 2 minutes each time) for 2 weeks. participants will also keep a simple daily log of your acupressure practice. • Usual Care Group: Participants will continue with usual ways of coping and fill out a short daily emotional self-assessment form. Investigators will check in with participants every week using the same set of questionnaires to track changes for two months. In total, participants will be asked to fill out the survey 9 times. 5. Possible Side Effects and How to Handle Them: • From the Acupressure: When pressing the Shenmen or Neiguan points, participants might feel a sensation like soreness, tingling, pressure, or slight pain-this is normal and usually tolerable. There's no research showing any harmful side effects from pressing these points. If it ever feels too uncomfortable, participants can adjust the pressure. • From Participation: The risks of participating in this study were similar to participants' normal, everyday experiences. Participants were free to stop at any time if they felt sick or uncomfortable during the study. Participants can also contact the program's emergency contacts. 6. Expected Benefits: Past research and traditional practice have shown that pressing the Shenmen and Neiguan points can help reduce anxiety, stress, insomnia, and related symptoms. While the researchers cannot guarantee that this study will help individual participants, it may help healthcare professionals understand ways to better support caregivers and could benefit others in the future. 7. The researcher's requirements for participants during the study: * Please do not participate in other similar clinical studies while participating in this clinical study. * Please fill in the daily log on time and truthfully. * Provide accurate information in the questionnaire. 8. Participants' privacy is protected: Researchers will only collect information necessary for this study. All personal data and survey responses of participants will remain confidential. The researcher will not use the participant's name or personal identification, but will assign a code to the participant so that the participant's identity remains anonymous in all records.
Interventions
BEHAVIORALAcupressure
Participants in this group will receive instruction on how to perform self-acupressure targeting two specific acupoints on the hands: Shenmen and Neiguan. The acupressure protocol includes pressing each acupoint approximately 15 times (about 30 seconds), with a total of four acupoints per session (both hands), for approximately 2 minutes per session. Pressure should be applied until a sensation of soreness, numbness, fullness, or slight pain is felt (equivalent to about 3 kg of pressure). Participants are asked to perform self-acupressure twice daily (once in the afternoon and once before bedtime, adjustable based on personal schedule) for a total of 2 weeks. They will record their practice using a daily log and continue to complete psychological and emotional outcome questionnaires weekly over a 2-month follow-up period.
Sponsors
Kaohsiung Veterans General Hospital.
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
SINGLE (Outcomes Assessor)
Intervention model description
This is a single-center, randomized, single-blind, parallel-controlled trial. Eligible clinical nurses will be randomly assigned (1:1) to an intervention group or a control group. The intervention group will receive self-acupressure instructions on Shenmen and Neiguan points twice a day for two weeks, and record each treatment process. The control group will continue to use their usual stress and emotional distress coping strategies without any intervention. Both groups will be required to complete a validated questionnaire covering emotional distress, mood, anxiety, depression, stress, fatigue and resilience. Assessments will be conducted weekly for two months (including pre-test (baseline week 0) and follow-up for two months (weeks 1-8), a total of 9 times) to evaluate the effectiveness of self-acupressure compared with usual care.
Eligibility
Sex/Gender
ALL
Age
20 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Clinical nurses aged 20 years or older from a designated medical center. * Nurses who score ≥3 on the emotional distress thermometer or ≥4 on the Brief Symptom Rating Scale (BSRS-5) based on self-reported questionnaires. * Able and willing to complete the required study questionnaires during the 2-month study period.
Exclusion criteria
* Nurses who refuse to participate after being informed about the study or are unable to comply with the study protocol. * Nurses working in non-patient-care units (e.g., administrative departments or supply centers). * Pregnant nurses.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Depressive Symptoms | At baseline(weeks 0) and once weekly for 8 weeks(weeks 1-8) | Depressive symptoms were assessed using the Taiwanese Depression Scale, which has demonstrated strong psychometric properties in Taiwanese populations. The TDS consists of 18 items rated on a 4-point Likert scale: 0 = none or seldom (less than one day per week), 1 = sometimes (one to two days per week), 2 = often (three to four days per week), and 3 = almost always (five to seven days per week). Total scores range from 0 to 54, with higher scores indicating greater severity of depressive symptoms. The results from Lee et al. (2000) demonstrated that the TDS had excellent reliability and validity. The Cronbach's alpha coefficient was 0.90, and the area under the receiver operating characteristic (ROC) curve was 0.92. The TDS also showed good concurrent validity, with a sensitivity of 0.89 and specificity of 0.92 at a cutoff score of 19. In the present study, the TDS demonstrated excellent internal consistency, with a Cronbach's alpha of .94. |
| Psychological Distress | At baseline(weeks 0) and once weekly for 8 weeks(weeks 1-8) | Psychological distress was measured using the Brief Symptom Rating Scale-5 (BSRS-5), a validated screening tool for general psychological distress. The BSRS-5 assesses the subjective severity of the following symptoms: (1) anxiety, (2) depression, (3) hostility, (4) low self-esteem, and (5) insomnia. Each symptom was scored on a 5-point Likert scale, ranging from 0 (not at all) to 4 (extremely), with a total score ranging from 0 to 20 points. Higher scores indicate more severe psychological distress. Studies have shown that a total score of 3-4 is the optimal threshold for identifying clinically relevant distress based on receiver operating characteristic (ROC) curve analysis. The BSRS-5 showed high accuracy (AUC = 0.92) and good sensitivity (0.83) and specificity (0.86). Therefore, this study used a BSRS-5 total score ≥4 as one of the inclusion criteria to ensure that participants with at least mild psychological distress were included in the study. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Job Stress | At baseline(weeks 0) and once weekly for 8 weeks(weeks 1-8) | Perceived job stress was measured using the 14-item Work Pressure Inventory developed by Huang et al. (2017), which has demonstrated good internal consistency. The scale comprises three dimensions: low self-development, workload, and job characteristics. Each item is rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with total scores ranging from 14 to 70. Higher scores indicate greater perceived occupational stress. In the original validation study, the Cronbach's α coefficients for the three subscales were 0.81, 0.73, and 0.77, respectively. In the present study, the Work Pressure Inventory showed good overall internal consistency (Cronbach's α = .84). |
| Resilience | At baseline(weeks 0) and once weekly for 8 weeks(weeks 1-8) | This scale assesses an individual's ability to adapt and recover from stress and adversity. Each item is rated on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree), with a total score ranging from 10 to 50, with higher scores indicating greater resilience. Initial validation studies demonstrated strong psychometric properties, including good model fit in confirmatory factor analysis (GFI = 0.973) and excellent internal consistency (Cronbach's α = .91). All instruments used in this study were authorized by their original developers and have been psychometrically validated in previous studies. All scales demonstrated good to excellent internal consistency, with Cronbach's α values ranging from 0.84 to 0.95. |
| Emotional Distress | At baseline(weeks 0) and once weekly for 8 weeks(weeks 1-8) | Emotional distress was measured using the Distress Thermometer (DT), a single self-report screening instrument with a score range of 0 (no distress) to 10 (extreme distress), with higher scores indicating greater emotional distress. The DT demonstrated good psychometric properties in validation studies, with sensitivities ranging from 0.50 to 1.00 (median = 0.83) and specificities ranging from 0.36 to 0.98 (median = 0.68). This study used a DT cutoff score of ≥3 as the inclusion criterion. Other studies have shown that the optimal DT cutoff score varies across settings, typically ranging from 3 to 5, while thresholds of ≥4 or ≥5 are commonly used in clinical practice. The use of a score of ≥3 in this study was intended to maximize sensitivity and minimize the risk of underidentifying caregivers considered at high risk for psychological distress. |
| Occupational Burnout | At baseline(weeks 0) and once weekly for 8 weeks(weeks 1-8) | Occupational burnout was assessed using the Chinese version of the Copenhagen Burnout Inventory (CBI), which has demonstrated good psychometric properties in Taiwanese populations. The scale consists of four subscales: personal burnout, work-related burnout, client-related burnout, and overcommitment to work. Each item is rated on a five-point frequency scale: always (100), often (75), sometimes (50), rarely (25), and never (0). Subscale scores are calculated as the average of the items within each domain, ranging from 0 to 100, with higher scores indicating more severe occupational burnout. The original validation study reported Cronbach's α values above 0.84 across all subscales. In the present study, the scale demonstrated excellent internal consistency (Cronbach's α = .95). |
| Anxiety | At baseline(weeks 0) and once weekly for 8 weeks(weeks 1-8) | Anxiety levels were assessed using the state subscale of the State-Trait Anxiety Inventory (STAI-S). Studies have confirmed the multidimensional factor structure of the Chinese version of the scale and demonstrated good psychometric properties, including adequate convergent and discriminant validity. In this sample, the STAI-S demonstrated excellent internal consistency (Cronbach's α = .95). The STAI-S consists of 20 items that assess anxiety-related feelings, thoughts, and behaviors at the time of assessment. Each item is rated on a 4-point Likert scale ranging from 1 (not at all) to 4 (very much). Items 1, 2, 5, 8, 10, 11, 15, 16, 19, and 20 are reverse-scored. The total score ranges from 20 to 80, with scores between 20 and 39 indicating mild anxiety, 40 to 59 indicating moderate anxiety, and 60 to 80 indicating severe anxiety. |
Countries
Taiwan
Participant flow
Recruitment details
Clinical nurses aged ≥20 years were pre-screened with a questionnaire, and those with an emotional distress score ≥3 (0-10 points) or an emotional thermometer score ≥4 after screening were included (this was the week 0 of enrollment).
Pre-assignment details
Among those who met the criteria of emotional distress score ≥ 3 points (0-10 points) or emotional thermometer score ≥ 4 points, 160 subjects were selected and randomly divided into a control group or an acupoint massage group for an 8-week follow-up (the acupoint massage group was required to press the Shenmen and Neiguan acupoints on their own in the first two weeks, and the control group received no intervention measures), and filled out questionnaires every week for post-test follow-up.
Participants by arm
| Arm | Count |
|---|---|
| Control Group Care continued in the original manner without any intervention. | 80 |
| Intervention Group (Acupressure) Participants perform self-acupressure on the Shenmen and Neiguan points twice daily for 2 weeks. | 80 |
| Total | 160 |
Baseline characteristics
| Characteristic | Intervention Group (Acupressure) | Total | Control Group |
|---|---|---|---|
| Age, Continuous | 30.44 years STANDARD_DEVIATION 10.03 | 32.59 years STANDARD_DEVIATION 11.281 | 34.45 years STANDARD_DEVIATION 12.04 |
| Race and Ethnicity Not Collected | — | 0 Participants | — |
| Region of Enrollment Taiwan | 80 Participants | 160 Participants | 80 Participants |
| Sex: Female, Male Female | 74 Participants | 151 Participants | 77 Participants |
| Sex: Female, Male Male | 6 Participants | 9 Participants | 3 Participants |
Adverse events
| Event type | EG000 affected / at risk | EG001 affected / at risk |
|---|---|---|
| deaths Total, all-cause mortality | 0 / 80 | 0 / 80 |
| other Total, other adverse events | 0 / 80 | 0 / 80 |
| serious Total, serious adverse events | 0 / 80 | 0 / 80 |
Outcome results
Primary
Depressive Symptoms
Depressive symptoms were assessed using the Taiwanese Depression Scale, which has demonstrated strong psychometric properties in Taiwanese populations. The TDS consists of 18 items rated on a 4-point Likert scale: 0 = none or seldom (less than one day per week), 1 = sometimes (one to two days per week), 2 = often (three to four days per week), and 3 = almost always (five to seven days per week). Total scores range from 0 to 54, with higher scores indicating greater severity of depressive symptoms. The results from Lee et al. (2000) demonstrated that the TDS had excellent reliability and validity. The Cronbach's alpha coefficient was 0.90, and the area under the receiver operating characteristic (ROC) curve was 0.92. The TDS also showed good concurrent validity, with a sensitivity of 0.89 and specificity of 0.92 at a cutoff score of 19. In the present study, the TDS demonstrated excellent internal consistency, with a Cronbach's alpha of .94.
Time frame: At baseline(weeks 0) and once weekly for 8 weeks(weeks 1-8)
Population: After the pre-test questionnaire assessment (baseline week 0), the subjects (control group/intervention group) will be followed up with a weekly questionnaire survey for eight weeks (weeks 1 to 8) after the start of the program. The depression questionnaire will obtain data for a total of nine weeks (baseline week 0 and follow-up weeks 1 to 8) and conduct data analysis.
| Arm | Measure | Group | Value (MEAN) | Dispersion |
|---|---|---|---|---|
| Control Group | Depressive Symptoms | Tracking Week 2 | 11.70 score on a scale | Standard Deviation 7.51 |
| Control Group | Depressive Symptoms | Tracking Week 6 | 12.66 score on a scale | Standard Deviation 9.63 |
| Control Group | Depressive Symptoms | Tracking Week 4 | 13.35 score on a scale | Standard Deviation 8.39 |
| Control Group | Depressive Symptoms | Tracking Week 7 | 12.05 score on a scale | Standard Deviation 9.24 |
| Control Group | Depressive Symptoms | Tracking Week 3 | 12.05 score on a scale | Standard Deviation 8.22 |
| Control Group | Depressive Symptoms | Tracking Week 8 | 12.31 score on a scale | Standard Deviation 10.64 |
| Control Group | Depressive Symptoms | Tracking Week 5 | 12.54 score on a scale | Standard Deviation 8.84 |
| Control Group | Depressive Symptoms | Tracking Week 0 | 13.03 score on a scale | Standard Deviation 7.37 |
| Control Group | Depressive Symptoms | Tracking Week 1 | 11.76 score on a scale | Standard Deviation 7.2 |
| Intervention Group (Acupressure) | Depressive Symptoms | Tracking Week 0 | 13.00 score on a scale | Standard Deviation 7.33 |
| Intervention Group (Acupressure) | Depressive Symptoms | Tracking Week 1 | 11.55 score on a scale | Standard Deviation 6.76 |
| Intervention Group (Acupressure) | Depressive Symptoms | Tracking Week 2 | 10.84 score on a scale | Standard Deviation 8.12 |
| Intervention Group (Acupressure) | Depressive Symptoms | Tracking Week 3 | 10.86 score on a scale | Standard Deviation 7.51 |
| Intervention Group (Acupressure) | Depressive Symptoms | Tracking Week 4 | 10.59 score on a scale | Standard Deviation 9.02 |
| Intervention Group (Acupressure) | Depressive Symptoms | Tracking Week 5 | 10.95 score on a scale | Standard Deviation 8.98 |
| Intervention Group (Acupressure) | Depressive Symptoms | Tracking Week 6 | 10.93 score on a scale | Standard Deviation 9.22 |
| Intervention Group (Acupressure) | Depressive Symptoms | Tracking Week 7 | 10.16 score on a scale | Standard Deviation 8.19 |
| Intervention Group (Acupressure) | Depressive Symptoms | Tracking Week 8 | 10.51 score on a scale | Standard Deviation 8.62 |
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.837Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.428Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.269Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.015Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.163Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.14Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.079Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.151Generalized Estimating Equations, GEE
Primary
Psychological Distress
Psychological distress was measured using the Brief Symptom Rating Scale-5 (BSRS-5), a validated screening tool for general psychological distress. The BSRS-5 assesses the subjective severity of the following symptoms: (1) anxiety, (2) depression, (3) hostility, (4) low self-esteem, and (5) insomnia. Each symptom was scored on a 5-point Likert scale, ranging from 0 (not at all) to 4 (extremely), with a total score ranging from 0 to 20 points. Higher scores indicate more severe psychological distress. Studies have shown that a total score of 3-4 is the optimal threshold for identifying clinically relevant distress based on receiver operating characteristic (ROC) curve analysis. The BSRS-5 showed high accuracy (AUC = 0.92) and good sensitivity (0.83) and specificity (0.86). Therefore, this study used a BSRS-5 total score ≥4 as one of the inclusion criteria to ensure that participants with at least mild psychological distress were included in the study.
Time frame: At baseline(weeks 0) and once weekly for 8 weeks(weeks 1-8)
Population: After the pre-test questionnaire assessment (baseline week 0), the subjects (control group/intervention group) will be followed up with weekly questionnaires for eight weeks (weeks 1 to 8) after the start of the program, of which the Mood Thermometer (Brief Symptoms Rating Scale, BSRS-5) questionnaire will obtain data for a total of nine weeks (baseline week 0 and follow-up weeks 1 to 8) and conduct data analysis.
| Arm | Measure | Group | Value (MEAN) | Dispersion |
|---|---|---|---|---|
| Control Group | Psychological Distress | Tracking Week 2 | 5.54 score on a scale | Standard Deviation 3.12 |
| Control Group | Psychological Distress | Tracking Week 6 | 4.99 score on a scale | Standard Deviation 3.8 |
| Control Group | Psychological Distress | Tracking Week 4 | 5.53 score on a scale | Standard Deviation 3.67 |
| Control Group | Psychological Distress | Tracking Week 7 | 5.09 score on a scale | Standard Deviation 3.85 |
| Control Group | Psychological Distress | Tracking Week 3 | 5.05 score on a scale | Standard Deviation 3.03 |
| Control Group | Psychological Distress | Tracking Week 8 | 5.05 score on a scale | Standard Deviation 4.15 |
| Control Group | Psychological Distress | Tracking Week 5 | 5.28 score on a scale | Standard Deviation 3.81 |
| Control Group | Psychological Distress | Tracking Week 0 | 6.53 score on a scale | Standard Deviation 3.36 |
| Control Group | Psychological Distress | Tracking Week 1 | 5.65 score on a scale | Standard Deviation 3.42 |
| Intervention Group (Acupressure) | Psychological Distress | Tracking Week 0 | 6.41 score on a scale | Standard Deviation 3.28 |
| Intervention Group (Acupressure) | Psychological Distress | Tracking Week 1 | 5.54 score on a scale | Standard Deviation 3.32 |
| Intervention Group (Acupressure) | Psychological Distress | Tracking Week 2 | 5.31 score on a scale | Standard Deviation 3.56 |
| Intervention Group (Acupressure) | Psychological Distress | Tracking Week 3 | 4.6 score on a scale | Standard Deviation 3.23 |
| Intervention Group (Acupressure) | Psychological Distress | Tracking Week 4 | 4.66 score on a scale | Standard Deviation 3.44 |
| Intervention Group (Acupressure) | Psychological Distress | Tracking Week 5 | 4.81 score on a scale | Standard Deviation 3.42 |
| Intervention Group (Acupressure) | Psychological Distress | Tracking Week 6 | 4.48 score on a scale | Standard Deviation 3.39 |
| Intervention Group (Acupressure) | Psychological Distress | Tracking Week 7 | 4.55 score on a scale | Standard Deviation 3.26 |
| Intervention Group (Acupressure) | Psychological Distress | Tracking Week 8 | 4.40 score on a scale | Standard Deviation 3.18 |
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.63Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.608Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.244Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.052Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.229Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.184Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.148Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.143Generalized Estimating Equations, GEE
Secondary
Anxiety
Anxiety levels were assessed using the state subscale of the State-Trait Anxiety Inventory (STAI-S). Studies have confirmed the multidimensional factor structure of the Chinese version of the scale and demonstrated good psychometric properties, including adequate convergent and discriminant validity. In this sample, the STAI-S demonstrated excellent internal consistency (Cronbach's α = .95). The STAI-S consists of 20 items that assess anxiety-related feelings, thoughts, and behaviors at the time of assessment. Each item is rated on a 4-point Likert scale ranging from 1 (not at all) to 4 (very much). Items 1, 2, 5, 8, 10, 11, 15, 16, 19, and 20 are reverse-scored. The total score ranges from 20 to 80, with scores between 20 and 39 indicating mild anxiety, 40 to 59 indicating moderate anxiety, and 60 to 80 indicating severe anxiety.
Time frame: At baseline(weeks 0) and once weekly for 8 weeks(weeks 1-8)
Population: After the pre-test questionnaire assessment (baseline week 0), the subjects (control group/intervention group) will be followed up with weekly questionnaires for eight weeks (weeks 1 to 8) after the start of the program. The anxiety questionnaire will obtain data for a total of nine weeks (baseline week 0 and follow-up weeks 1 to 8) and conduct data analysis.
| Arm | Measure | Group | Value (MEAN) | Dispersion |
|---|---|---|---|---|
| Control Group | Anxiety | Tracking Week 2 | 44.64 score on a scale | Standard Deviation 10.09 |
| Control Group | Anxiety | Tracking Week 6 | 45.01 score on a scale | Standard Deviation 11.29 |
| Control Group | Anxiety | Tracking Week 4 | 45.00 score on a scale | Standard Deviation 10.38 |
| Control Group | Anxiety | Tracking Week 7 | 45.49 score on a scale | Standard Deviation 11.97 |
| Control Group | Anxiety | Tracking Week 3 | 43.93 score on a scale | Standard Deviation 10.73 |
| Control Group | Anxiety | Tracking Week 8 | 45.18 score on a scale | Standard Deviation 13.12 |
| Control Group | Anxiety | Tracking Week 5 | 44.58 score on a scale | Standard Deviation 10.49 |
| Control Group | Anxiety | Tracking Week 0 | 46.15 score on a scale | Standard Deviation 9.2 |
| Control Group | Anxiety | Tracking Week 1 | 45.18 score on a scale | Standard Deviation 10.76 |
| Intervention Group (Acupressure) | Anxiety | Tracking Week 0 | 45.41 score on a scale | Standard Deviation 9.96 |
| Intervention Group (Acupressure) | Anxiety | Tracking Week 1 | 44.19 score on a scale | Standard Deviation 11.52 |
| Intervention Group (Acupressure) | Anxiety | Tracking Week 2 | 43.36 score on a scale | Standard Deviation 11.36 |
| Intervention Group (Acupressure) | Anxiety | Tracking Week 3 | 41.78 score on a scale | Standard Deviation 11.72 |
| Intervention Group (Acupressure) | Anxiety | Tracking Week 4 | 42.04 score on a scale | Standard Deviation 12.42 |
| Intervention Group (Acupressure) | Anxiety | Tracking Week 5 | 42.98 score on a scale | Standard Deviation 12.82 |
| Intervention Group (Acupressure) | Anxiety | Tracking Week 6 | 42.65 score on a scale | Standard Deviation 13.54 |
| Intervention Group (Acupressure) | Anxiety | Tracking Week 7 | 44.14 score on a scale | Standard Deviation 13 |
| Intervention Group (Acupressure) | Anxiety | Tracking Week 8 | 41.61 score on a scale | Standard Deviation 12.83 |
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.857Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.684Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.333Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.09Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.545Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.251Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.701Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.08Generalized Estimating Equations, GEE
Secondary
Emotional Distress
Emotional distress was measured using the Distress Thermometer (DT), a single self-report screening instrument with a score range of 0 (no distress) to 10 (extreme distress), with higher scores indicating greater emotional distress. The DT demonstrated good psychometric properties in validation studies, with sensitivities ranging from 0.50 to 1.00 (median = 0.83) and specificities ranging from 0.36 to 0.98 (median = 0.68). This study used a DT cutoff score of ≥3 as the inclusion criterion. Other studies have shown that the optimal DT cutoff score varies across settings, typically ranging from 3 to 5, while thresholds of ≥4 or ≥5 are commonly used in clinical practice. The use of a score of ≥3 in this study was intended to maximize sensitivity and minimize the risk of underidentifying caregivers considered at high risk for psychological distress.
Time frame: At baseline(weeks 0) and once weekly for 8 weeks(weeks 1-8)
Population: After the pre-test questionnaire assessment (baseline week 0), the subjects (control group/intervention group) will be followed up with weekly questionnaires for eight weeks (weeks 1 to 8) after the start of the program. A total of nine weeks (baseline week 0 and follow-up weeks 1 to 8) of Distress Thermometer questionnaire data will be obtained and analyzed.
| Arm | Measure | Group | Value (MEAN) | Dispersion |
|---|---|---|---|---|
| Control Group | Emotional Distress | Tracking Week 2 | 3.59 score on a scale | Standard Deviation 1.93 |
| Control Group | Emotional Distress | Tracking Week 6 | 3.38 score on a scale | Standard Deviation 1.88 |
| Control Group | Emotional Distress | Tracking Week 4 | 3.48 score on a scale | Standard Deviation 2.02 |
| Control Group | Emotional Distress | Tracking Week 7 | 2.93 score on a scale | Standard Deviation 1.71 |
| Control Group | Emotional Distress | Tracking Week 3 | 3.41 score on a scale | Standard Deviation 1.95 |
| Control Group | Emotional Distress | Tracking Week 8 | 3.26 score on a scale | Standard Deviation 1.83 |
| Control Group | Emotional Distress | Tracking Week 5 | 3.35 score on a scale | Standard Deviation 1.97 |
| Control Group | Emotional Distress | Tracking Week 0 | 4.01 score on a scale | Standard Deviation 1.9 |
| Control Group | Emotional Distress | Tracking Week 1 | 3.95 score on a scale | Standard Deviation 1.95 |
| Intervention Group (Acupressure) | Emotional Distress | Tracking Week 0 | 3.94 score on a scale | Standard Deviation 2 |
| Intervention Group (Acupressure) | Emotional Distress | Tracking Week 1 | 3.84 score on a scale | Standard Deviation 1.91 |
| Intervention Group (Acupressure) | Emotional Distress | Tracking Week 2 | 3.43 score on a scale | Standard Deviation 1.84 |
| Intervention Group (Acupressure) | Emotional Distress | Tracking Week 3 | 3.18 score on a scale | Standard Deviation 1.68 |
| Intervention Group (Acupressure) | Emotional Distress | Tracking Week 4 | 3.09 score on a scale | Standard Deviation 1.83 |
| Intervention Group (Acupressure) | Emotional Distress | Tracking Week 5 | 3.28 score on a scale | Standard Deviation 1.97 |
| Intervention Group (Acupressure) | Emotional Distress | Tracking Week 6 | 3.2 score on a scale | Standard Deviation 2.07 |
| Intervention Group (Acupressure) | Emotional Distress | Tracking Week 7 | 3.30 score on a scale | Standard Deviation 2.01 |
| Intervention Group (Acupressure) | Emotional Distress | Tracking Week 8 | 3.23 score on a scale | Standard Deviation 2.07 |
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.913Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.802Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.605Generalized Estimating Equations, GEE
Comparison: Generalized Estimating Equations were used to analyze repeated measures data and to assess the interaction effect between group and time. Potential interfering factors such as age and years of work experience were controlled during the analysis, and the effect size (Cohen's d) and its 95% confidence interval were calculated to assist in interpreting the clinical significance. Statistical analysis was performed using SPSS version XX or R version XX, and the significance level was set at p \< 0.05.p-value: 0.35Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 1Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.744Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.202Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.918Generalized Estimating Equations, GEE
Secondary
Job Stress
Perceived job stress was measured using the 14-item Work Pressure Inventory developed by Huang et al. (2017), which has demonstrated good internal consistency. The scale comprises three dimensions: low self-development, workload, and job characteristics. Each item is rated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree), with total scores ranging from 14 to 70. Higher scores indicate greater perceived occupational stress. In the original validation study, the Cronbach's α coefficients for the three subscales were 0.81, 0.73, and 0.77, respectively. In the present study, the Work Pressure Inventory showed good overall internal consistency (Cronbach's α = .84).
Time frame: At baseline(weeks 0) and once weekly for 8 weeks(weeks 1-8)
Population: After the pre-test questionnaire assessment (baseline week 0), the subjects (control group/intervention group) will be followed up with weekly questionnaires for eight weeks (weeks 1 to 8) after the start of the program. The Nurse Stress questionnaire will obtain data for a total of nine weeks (baseline week 0 and follow-up weeks 1 to 8) and conduct data analysis.
| Arm | Measure | Group | Value (MEAN) | Dispersion |
|---|---|---|---|---|
| Control Group | Job Stress | Tracking Week 2 | 45.98 score on a scale | Standard Deviation 6.35 |
| Control Group | Job Stress | Tracking Week 6 | 45.26 score on a scale | Standard Deviation 6.81 |
| Control Group | Job Stress | Tracking Week 4 | 45.66 score on a scale | Standard Deviation 7.02 |
| Control Group | Job Stress | Tracking Week 7 | 45.53 score on a scale | Standard Deviation 7.09 |
| Control Group | Job Stress | Tracking Week 3 | 45.64 score on a scale | Standard Deviation 6.52 |
| Control Group | Job Stress | Tracking Week 8 | 45.93 score on a scale | Standard Deviation 7.23 |
| Control Group | Job Stress | Tracking Week 5 | 45.95 score on a scale | Standard Deviation 7.28 |
| Control Group | Job Stress | Tracking Week 0 | 45.55 score on a scale | Standard Deviation 6.13 |
| Control Group | Job Stress | Tracking Week 1 | 45.80 score on a scale | Standard Deviation 6.29 |
| Intervention Group (Acupressure) | Job Stress | Tracking Week 0 | 45.31 score on a scale | Standard Deviation 7.79 |
| Intervention Group (Acupressure) | Job Stress | Tracking Week 1 | 45.10 score on a scale | Standard Deviation 6.87 |
| Intervention Group (Acupressure) | Job Stress | Tracking Week 2 | 45.63 score on a scale | Standard Deviation 7.14 |
| Intervention Group (Acupressure) | Job Stress | Tracking Week 3 | 44.90 score on a scale | Standard Deviation 8.33 |
| Intervention Group (Acupressure) | Job Stress | Tracking Week 4 | 44.74 score on a scale | Standard Deviation 7.71 |
| Intervention Group (Acupressure) | Job Stress | Tracking Week 5 | 44.89 score on a scale | Standard Deviation 8.08 |
| Intervention Group (Acupressure) | Job Stress | Tracking Week 6 | 45.29 score on a scale | Standard Deviation 7.58 |
| Intervention Group (Acupressure) | Job Stress | Tracking Week 7 | 45.18 score on a scale | Standard Deviation 8.3 |
| Intervention Group (Acupressure) | Job Stress | Tracking Week 8 | 44.89 score on a scale | Standard Deviation 8.11 |
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.636Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.909Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.627Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.474Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.378Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.768Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.909Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.449Generalized Estimating Equations, GEE
Secondary
Occupational Burnout
Occupational burnout was assessed using the Chinese version of the Copenhagen Burnout Inventory (CBI), which has demonstrated good psychometric properties in Taiwanese populations. The scale consists of four subscales: personal burnout, work-related burnout, client-related burnout, and overcommitment to work. Each item is rated on a five-point frequency scale: always (100), often (75), sometimes (50), rarely (25), and never (0). Subscale scores are calculated as the average of the items within each domain, ranging from 0 to 100, with higher scores indicating more severe occupational burnout. The original validation study reported Cronbach's α values above 0.84 across all subscales. In the present study, the scale demonstrated excellent internal consistency (Cronbach's α = .95).
Time frame: At baseline(weeks 0) and once weekly for 8 weeks(weeks 1-8)
Population: After the pre-test questionnaire assessment (baseline week 0), the subjects (control group/intervention group) will be followed up with weekly questionnaires for eight weeks (weeks 1 to 8) after the start of the program. The workplace fatigue questionnaire will obtain data for a total of nine weeks (baseline week 0 and follow-up weeks 1 to 8) and conduct data analysis.
| Arm | Measure | Group | Value (MEAN) | Dispersion |
|---|---|---|---|---|
| Control Group | Occupational Burnout | Tracking Week 5 | 48.33 score on a scale | Standard Deviation 17.21 |
| Control Group | Occupational Burnout | Tracking Week 6 | 48.47 score on a scale | Standard Deviation 18.48 |
| Control Group | Occupational Burnout | Tracking Week 3 | 46.53 score on a scale | Standard Deviation 14.19 |
| Control Group | Occupational Burnout | Tracking Week 7 | 48.60 score on a scale | Standard Deviation 17.46 |
| Control Group | Occupational Burnout | Tracking Week 2 | 48.60 score on a scale | Standard Deviation 14.92 |
| Control Group | Occupational Burnout | Tracking Week 8 | 48.74 score on a scale | Standard Deviation 18.8 |
| Control Group | Occupational Burnout | Tracking Week 4 | 46.90 score on a scale | Standard Deviation 17.19 |
| Control Group | Occupational Burnout | Tracking Week 0 | 49.97 score on a scale | Standard Deviation 15.19 |
| Control Group | Occupational Burnout | Tracking Week 1 | 48.20 score on a scale | Standard Deviation 14.77 |
| Intervention Group (Acupressure) | Occupational Burnout | Tracking Week 0 | 49.79 score on a scale | Standard Deviation 15.74 |
| Intervention Group (Acupressure) | Occupational Burnout | Tracking Week 1 | 49.05 score on a scale | Standard Deviation 15.64 |
| Intervention Group (Acupressure) | Occupational Burnout | Tracking Week 2 | 48.44 score on a scale | Standard Deviation 17.48 |
| Intervention Group (Acupressure) | Occupational Burnout | Tracking Week 3 | 46.46 score on a scale | Standard Deviation 17.86 |
| Intervention Group (Acupressure) | Occupational Burnout | Tracking Week 4 | 46.86 score on a scale | Standard Deviation 17.4 |
| Intervention Group (Acupressure) | Occupational Burnout | Tracking Week 6 | 46.62 score on a scale | Standard Deviation 17.94 |
| Intervention Group (Acupressure) | Occupational Burnout | Tracking Week 7 | 46.53 score on a scale | Standard Deviation 19.45 |
| Intervention Group (Acupressure) | Occupational Burnout | Tracking Week 8 | 46.24 score on a scale | Standard Deviation 19.66 |
| Intervention Group (Acupressure) | Occupational Burnout | Tracking Week 5 | 47.43 score on a scale | Standard Deviation 17.67 |
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.593Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.994Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.959Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.946Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.715Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.422Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.379Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.302Generalized Estimating Equations, GEE
Secondary
Resilience
This scale assesses an individual's ability to adapt and recover from stress and adversity. Each item is rated on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree), with a total score ranging from 10 to 50, with higher scores indicating greater resilience. Initial validation studies demonstrated strong psychometric properties, including good model fit in confirmatory factor analysis (GFI = 0.973) and excellent internal consistency (Cronbach's α = .91). All instruments used in this study were authorized by their original developers and have been psychometrically validated in previous studies. All scales demonstrated good to excellent internal consistency, with Cronbach's α values ranging from 0.84 to 0.95.
Time frame: At baseline(weeks 0) and once weekly for 8 weeks(weeks 1-8)
Population: After the pre-test questionnaire assessment (baseline week 0), the subjects (control group/intervention group) will be followed up with weekly questionnaires for eight weeks (weeks 1 to 8) after the start of the program, of which the Resilience questionnaire will obtain data for a total of nine weeks (baseline week 0 and follow-up weeks 1 to 8) and conduct data analysis.
| Arm | Measure | Group | Value (MEAN) | Dispersion |
|---|---|---|---|---|
| Control Group | Resilience | Tracking Week 2 | 38.83 score on a scale | Standard Deviation 5.04 |
| Control Group | Resilience | Tracking Week 6 | 39.09 score on a scale | Standard Deviation 5.6 |
| Control Group | Resilience | Tracking Week 4 | 39.40 score on a scale | Standard Deviation 4.65 |
| Control Group | Resilience | Tracking Week 7 | 39.16 score on a scale | Standard Deviation 5.36 |
| Control Group | Resilience | Tracking Week 3 | 38.86 score on a scale | Standard Deviation 5.08 |
| Control Group | Resilience | Tracking Week 8 | 39.59 score on a scale | Standard Deviation 5.24 |
| Control Group | Resilience | Tracking Week 5 | 39.30 score on a scale | Standard Deviation 4.55 |
| Control Group | Resilience | Tracking Week 0 | 38.70 score on a scale | Standard Deviation 4.84 |
| Control Group | Resilience | Tracking Week 1 | 38.64 score on a scale | Standard Deviation 4.64 |
| Intervention Group (Acupressure) | Resilience | Tracking Week 0 | 40.00 score on a scale | Standard Deviation 4.94 |
| Intervention Group (Acupressure) | Resilience | Tracking Week 1 | 39.18 score on a scale | Standard Deviation 5.22 |
| Intervention Group (Acupressure) | Resilience | Tracking Week 2 | 39.62 score on a scale | Standard Deviation 4.87 |
| Intervention Group (Acupressure) | Resilience | Tracking Week 3 | 39.63 score on a scale | Standard Deviation 4.81 |
| Intervention Group (Acupressure) | Resilience | Tracking Week 4 | 40.15 score on a scale | Standard Deviation 5.64 |
| Intervention Group (Acupressure) | Resilience | Tracking Week 5 | 40.21 score on a scale | Standard Deviation 5.49 |
| Intervention Group (Acupressure) | Resilience | Tracking Week 6 | 40.19 score on a scale | Standard Deviation 5.38 |
| Intervention Group (Acupressure) | Resilience | Tracking Week 7 | 40.24 score on a scale | Standard Deviation 5.48 |
| Intervention Group (Acupressure) | Resilience | Tracking Week 8 | 40.33 score on a scale | Standard Deviation 5.7 |
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.333Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.273Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.424Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.482Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.627Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.806Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.786Generalized Estimating Equations, GEE
Comparison: Generalized estimating equations (GEE) were chosen over repeated-measures analysis of variance or generalized linear mixed models because they provide estimates of the population mean, are robust to correlated error specifications, can accommodate missing data under the MAR/MCAR assumptions, and do not require sphericity. All tests were two-tailed, with α = 0.05.p-value: 0.497Generalized Estimating Equations, GEE