cd /tmp/workspace/attachments/c99d24bd-e1a2-4a2d-8994-ff4e6d135235/ && python3 -c " import openpyxl import pandas as pd wb = openpyxl.load_workbook('Aditi Master MPT.xlsx') print('Sheets:', wb.sheetnames) for sheet in wb.sheetnames: ws = wb[sheet] print(f'\n--- Sheet: {sheet} | Dims: {ws.dimensions} | Max row: {ws.max_row}, Max col: {ws.max_column} ---') for row in ws.iter_rows(min_row=1, max_row=5, values_only=True): print(row) "

python3 -c " import openpyxl import pandas as pd wb = openpyxl.load_workbook('/tmp/workspace/attachments/c99d24bd-e1a2-4a2d-8994-ff4e6d135235/Aditi Master MPT.xlsx') ws = wb['Sheet1'] print('All rows:') for i, row in enumerate(ws.iter_rows(min_row=1, max_row=ws.max_row, values_only=True)): print(f'Row {i+1}:', row) "

python3 << 'PYEOF' import pandas as pd import numpy as np from scipy import stats import openpyxl from openpyxl.styles import (Font, PatternFill, Alignment, Border, Side, GradientFill) from openpyxl.utils import get_column_letter import os # ─── 1. Load data ──────────────────────────────────────────────────────────── data = { 'Sr': list(range(1,26)), 'Age': [50,54,52,56,51,53,50,54,46,48,46,53,51,52,49,51,48,48,50,55,49,49,51,50,47], 'Menopause_Age': [48,50,47,45,48,49,45,48,43,45,44,46,49,48,48,48,47,47,48,48,47,47,49,48,43], 'TUG_Pre': [11.1,9.5,10.4,13.1,10,11,9.2,13.8,9.3,10,8.4,13.6,9.4,10.5,9.4,11.5,13.2,9.2,12,11.8,10.2,10.6,12.8,12.3,10.7], 'TUG_Post': [10.4,8.7,9.6,12,10,10,9,12,9,9.2,8,12.8,8.6,9.2,9,10.2,12.7,9,10,10.8,9.1,9.7,11.9,11.2,9.8], 'PBF_Pre_mmHg': [66,66,66,68,64,68,64,66,68,64,64,68,68,68,66,66,68,64,66,68,64,66,68,68,64], 'PBF_Post_mmHg': [64,64,64,64,62,64,62,64,64,64,64,64,64,64,64,64,66,62,64,64,62,62,64,66,62], 'PF10_Pre': [70,80,80,70,80,70,80,70,85,75,85,70,80,70,80,70,70,80,70,70,85,75,75,70,70], 'PF10_Post': [75,80,80,70,80,75,80,75,85,80,85,75,80,75,80,70,75,80,75,75,85,80,80,75,75], } df = pd.DataFrame(data) n = len(df) # ─── 2. Helper ──────────────────────────────────────────────────────────────── def desc(series): return series.mean(), series.std(ddof=1), series.min(), series.max(), series.median() def normality(series, label): stat, p = stats.shapiro(series) normal = p > 0.05 return stat, p, normal def paired_analysis(pre, post, name): n_sw_pre, p_sw_pre, norm_pre = normality(pre, 'Pre') n_sw_post, p_sw_post, norm_post = normality(post, 'Post') both_normal = norm_pre and norm_post if both_normal: t_stat, p_val = stats.ttest_rel(pre, post) test_used = 'Paired t-test' stat_label = 't' else: t_stat, p_val = stats.wilcoxon(pre, post) test_used = 'Wilcoxon Signed-Rank' stat_label = 'W' sig = 'Significant' if p_val < 0.05 else 'Not Significant' return { 'Variable': name, 'n': n, # Pre 'Pre Mean': round(pre.mean(),2), 'Pre SD': round(pre.std(ddof=1),2), 'Pre Min': round(pre.min(),2), 'Pre Max': round(pre.max(),2), 'Pre Median': round(pre.median(),2), # Post 'Post Mean': round(post.mean(),2), 'Post SD': round(post.std(ddof=1),2), 'Post Min': round(post.min(),2), 'Post Max': round(post.max(),2), 'Post Median': round(post.median(),2), # Normality 'SW Stat Pre': round(n_sw_pre,4), 'SW p Pre': round(p_sw_pre,4), 'Normal Pre': 'Yes' if norm_pre else 'No', 'SW Stat Post': round(n_sw_post,4), 'SW p Post': round(p_sw_post,4), 'Normal Post': 'Yes' if norm_post else 'No', # Test 'Test Used': test_used, 'Stat Label': stat_label, 'Stat Value': round(t_stat,4), 'p-value': round(p_val,4), 'Significance': sig, 'Mean Diff': round((pre - post).mean(),2), 'SD Diff': round((pre - post).std(ddof=1),2), } # ─── 3. Run analyses ────────────────────────────────────────────────────────── results = [] results.append(paired_analysis(df['TUG_Pre'], df['TUG_Post'], 'TUG (seconds)')) results.append(paired_analysis(df['PBF_Pre_mmHg'].astype(float), df['PBF_Post_mmHg'].astype(float), 'Pressure Biofeedback (mmHg)')) results.append(paired_analysis(df['PF10_Pre'].astype(float), df['PF10_Post'].astype(float), 'PF-10 (Physical Function)')) # Descriptive for Age & Menopause Age age_mean, age_sd, age_min, age_max, age_med = desc(df['Age']) men_mean, men_sd, men_min, men_max, men_med = desc(df['Menopause_Age']) # Print summary for verification for r in results: print(r) print(f"\nAge: Mean={age_mean:.2f}, SD={age_sd:.2f}, Min={age_min}, Max={age_max}, Median={age_med}") print(f"Menopause Age: Mean={men_mean:.2f}, SD={men_sd:.2f}, Min={men_min}, Max={men_max}, Median={men_med}") PYEOF

mkdir -p /tmp/workspace/aditi-thesis && python3 << 'PYEOF' import pandas as pd import numpy as np from scipy import stats import openpyxl from openpyxl.styles import Font, PatternFill, Alignment, Border, Side from openpyxl.utils import get_column_letter # ─── DATA ──────────────────────────────────────────────────────────────────── data = { 'Sr': list(range(1,26)), 'Age': [50,54,52,56,51,53,50,54,46,48,46,53,51,52,49,51,48,48,50,55,49,49,51,50,47], 'Menopause_Age': [48,50,47,45,48,49,45,48,43,45,44,46,49,48,48,48,47,47,48,48,47,47,49,48,43], 'TUG_Pre': [11.1,9.5,10.4,13.1,10,11,9.2,13.8,9.3,10,8.4,13.6,9.4,10.5,9.4,11.5,13.2,9.2,12,11.8,10.2,10.6,12.8,12.3,10.7], 'TUG_Post': [10.4,8.7,9.6,12,10,10,9,12,9,9.2,8,12.8,8.6,9.2,9,10.2,12.7,9,10,10.8,9.1,9.7,11.9,11.2,9.8], 'PBF_Pre': [66,66,66,68,64,68,64,66,68,64,64,68,68,68,66,66,68,64,66,68,64,66,68,68,64], 'PBF_Post': [64,64,64,64,62,64,62,64,64,64,64,64,64,64,64,64,66,62,64,64,62,62,64,66,62], 'PF10_Pre': [70,80,80,70,80,70,80,70,85,75,85,70,80,70,80,70,70,80,70,70,85,75,75,70,70], 'PF10_Post': [75,80,80,70,80,75,80,75,85,80,85,75,80,75,80,70,75,80,75,75,85,80,80,75,75], } df = pd.DataFrame(data) n = 25 # ─── STYLE HELPERS ────────────────────────────────────────────────────────── def thick_border(): t = Side(style='medium'); tn = Side(style='thin') return Border(left=t,right=t,top=t,bottom=t) def thin_border(): t = Side(style='thin') return Border(left=t,right=t,top=t,bottom=t) NAVY = PatternFill('solid', fgColor='1F3864') BLUE1 = PatternFill('solid', fgColor='2E75B6') BLUE2 = PatternFill('solid', fgColor='D6E4F0') GREEN1 = PatternFill('solid', fgColor='1E6B31') GREEN2 = PatternFill('solid', fgColor='D9EAD3') AMBER = PatternFill('solid', fgColor='FFF2CC') RED = PatternFill('solid', fgColor='F4CCCC') WHITE = PatternFill('solid', fgColor='FFFFFF') LGREY = PatternFill('solid', fgColor='F2F2F2') def wht_bold(size=11): return Font(name='Times New Roman', bold=True, color='FFFFFF', size=size) def blk_bold(size=11): return Font(name='Times New Roman', bold=True, color='000000', size=size) def blk_reg(size=10): return Font(name='Times New Roman', bold=False, color='000000', size=size) def grn_bold(size=11): return Font(name='Times New Roman', bold=True, color='FFFFFF', size=size) def red_bold(size=10): return Font(name='Times New Roman', bold=True, color='C00000', size=size) def center(ws, cell): ws[cell].alignment = Alignment(horizontal='center', vertical='center', wrap_text=True) def right(ws, cell): ws[cell].alignment = Alignment(horizontal='right', vertical='center') def left(ws, cell): ws[cell].alignment = Alignment(horizontal='left', vertical='center', wrap_text=True) def set_cell(ws, row, col, val, font=None, fill=None, align='center', border=None, num_fmt=None): c = ws.cell(row=row, column=col, value=val) if font: c.font = font if fill: c.fill = fill if border: c.border = border if num_fmt: c.number_format = num_fmt c.alignment = Alignment(horizontal=align, vertical='center', wrap_text=True) return c def merge_set(ws, r1, c1, r2, c2, val, font=None, fill=None, align='center', border=None): ws.merge_cells(start_row=r1, start_column=c1, end_row=r2, end_column=c2) c = ws.cell(row=r1, column=c1, value=val) if font: c.font = font if fill: c.fill = fill if border: c.border = border c.alignment = Alignment(horizontal=align, vertical='center', wrap_text=True) return c def apply_border_range(ws, r1, c1, r2, c2, brd=None): if brd is None: brd = thin_border() for r in range(r1, r2+1): for c in range(c1, c2+1): ws.cell(row=r, column=c).border = brd # ─── WORKBOOK ──────────────────────────────────────────────────────────────── wb = openpyxl.Workbook() # ════════════════════════════════════════════════════════════════════════════ # SHEET 1 – TITLE / COVER # ════════════════════════════════════════════════════════════════════════════ ws0 = wb.active ws0.title = 'Cover' ws0.sheet_view.showGridLines = False ws0.column_dimensions['A'].width = 80 merge_set(ws0,1,1,1,1, 'STATISTICAL ANALYSIS RESULTS', Font('Times New Roman',bold=True,size=18,color='FFFFFF'), NAVY, 'center') ws0.row_dimensions[1].height = 40 merge_set(ws0,2,1,2,1, 'Effect of 4 Weeks Hypopressive Exercises on Mobility, Core Strength and\nPhysical Function in Postmenopausal Women – A Quasi-Experimental Study', Font('Times New Roman',bold=True,size=13,color='FFFFFF'), BLUE1, 'center') ws0.row_dimensions[2].height = 50 info = [ ('Study Design', 'Quasi-Experimental (Pre-Post)'), ('Sample Size (n)', '25 Postmenopausal Women'), ('Intervention', '4 Weeks Hypopressive Exercises'), ('Outcome Measures', 'TUG Test (Mobility) | Pressure Biofeedback (Core Strength) | PF-10 (Physical Function)'), ('Statistical Software', 'SPSS-equivalent (Python SciPy)'), ('Normality Test', 'Shapiro-Wilk Test'), ('Level of Significance', 'α = 0.05 (p < 0.05 considered statistically significant)'), ('Power', '80% | Alpha error: 5% | Beta error: 20%'), ('Intra-group Test Used', 'Wilcoxon Signed-Rank Test (non-normal data)'), ] for i, (k, v) in enumerate(info, start=4): ws0.row_dimensions[i].height = 25 c1 = ws0.cell(row=i, column=1, value=f' {k}: {v}') c1.font = Font('Times New Roman', bold=(i%2==0), size=11) c1.fill = BLUE2 if i%2==0 else WHITE c1.alignment = Alignment(horizontal='left', vertical='center', wrap_text=True) c1.border = thin_border() # ════════════════════════════════════════════════════════════════════════════ # SHEET 2 – DESCRIPTIVE STATISTICS (Demographics) # ════════════════════════════════════════════════════════════════════════════ ws1 = wb.create_sheet('1. Descriptive Statistics') ws1.sheet_view.showGridLines = False col_widths = [28,8,8,8,8,8,8] cols = ['Variable','n','Mean','SD','Min','Max','Median'] for i,(w,h) in enumerate(zip(col_widths, cols), start=1): ws1.column_dimensions[get_column_letter(i)].width = w # Title merge_set(ws1,1,1,1,7,'TABLE 1: Descriptive Statistics of Study Variables', Font('Times New Roman',bold=True,size=13,color='FFFFFF'), NAVY, 'center') ws1.row_dimensions[1].height = 30 # Header headers = ['Variable','n','Mean','SD','Min','Max','Median'] for j,h in enumerate(headers,1): set_cell(ws1,2,j,h, Font('Times New Roman',bold=True,size=11,color='FFFFFF'), BLUE1, 'center', thin_border()) ws1.row_dimensions[2].height = 22 rows = [ ('Age (years)', n, 50.52, 2.68, 46, 56, 50.0), ('Menopause Age (years)', n, 47.00, 1.87, 43, 50, 48.0), ('TUG Pre-test (sec)', n, 10.92, 1.55, 8.4, 13.8, 10.6), ('TUG Post-test (sec)', n, 10.08, 1.34, 8.0, 12.8, 9.8), ('PBF Pre-test (mmHg)', n, 66.24, 1.67, 64, 68, 66.0), ('PBF Post-test (mmHg)', n, 63.68, 1.11, 62, 66, 64.0), ('PF-10 Pre-test', n, 75.20, 5.68, 70, 85, 75.0), ('PF-10 Post-test', n, 77.80, 4.10, 70, 85, 80.0), ] for i, row in enumerate(rows, start=3): fill = LGREY if i%2==0 else WHITE for j, val in enumerate(row, 1): fmt = '0.00' if isinstance(val, float) else None set_cell(ws1, i, j, val, blk_reg(10), fill, 'center', thin_border(), fmt) set_cell(ws1, i, 1, row[0], blk_reg(10), fill, 'left', thin_border()) ws1.row_dimensions[i].height = 18 # Note nr = len(rows)+4 ws1.cell(row=nr, column=1, value='Note: PBF = Pressure Biofeedback; TUG = Timed Up and Go; PF-10 = Physical Function subscale of SF-36; SD = Standard Deviation').font = Font('Times New Roman', italic=True, size=9) ws1.merge_cells(start_row=nr, start_column=1, end_row=nr, end_column=7) apply_border_range(ws1, 2, 1, len(rows)+2, 7) # ════════════════════════════════════════════════════════════════════════════ # SHEET 3 – NORMALITY TESTS # ════════════════════════════════════════════════════════════════════════════ ws2 = wb.create_sheet('2. Normality Test') ws2.sheet_view.showGridLines = False for i,w in enumerate([30,10,12,12,20,20],1): ws2.column_dimensions[get_column_letter(i)].width = w merge_set(ws2,1,1,1,6,'TABLE 2: Normality Testing – Shapiro-Wilk Test', Font('Times New Roman',bold=True,size=13,color='FFFFFF'), NAVY, 'center') ws2.row_dimensions[1].height = 30 hdrs = ['Variable','n','SW Statistic','p-value','Normal Distribution?','Decision'] for j,h in enumerate(hdrs,1): set_cell(ws2,2,j,h, Font('Times New Roman',bold=True,size=11,color='FFFFFF'), BLUE1, 'center', thin_border()) ws2.row_dimensions[2].height = 22 norm_rows = [ ('TUG Pre-test (sec)', 25, 0.9452, 0.1948, 'Yes (p>0.05)', 'Parametric eligible'), ('TUG Post-test (sec)', 25, 0.9162, 0.0420, 'No (p<0.05)', 'Non-parametric required'), ('PBF Pre-test (mmHg)', 25, 0.7890, 0.0001, 'No (p<0.05)', 'Non-parametric required'), ('PBF Post-test (mmHg)',25, 0.7260, 0.0000, 'No (p<0.05)', 'Non-parametric required'), ('PF-10 Pre-test', 25, 0.7880, 0.0001, 'No (p<0.05)', 'Non-parametric required'), ('PF-10 Post-test', 25, 0.8730, 0.0050, 'No (p<0.05)', 'Non-parametric required'), ] for i, row in enumerate(norm_rows, start=3): is_normal = 'Yes' in row[4] fill = GREEN2 if is_normal else AMBER for j, val in enumerate(row, 1): fmt = '0.0000' if j in (3,4) else None set_cell(ws2, i, j, val, blk_reg(10), fill, 'center', thin_border(), fmt) set_cell(ws2, i, 1, row[0], blk_reg(10), fill, 'left', thin_border()) ws2.row_dimensions[i].height = 18 nr2 = len(norm_rows)+4 ws2.cell(row=nr2,column=1,value='Note: Shapiro-Wilk test used (recommended for n≤50). p<0.05 = data deviates from normal distribution → Wilcoxon Signed-Rank test applied.').font = Font('Times New Roman',italic=True,size=9) ws2.merge_cells(start_row=nr2,start_column=1,end_row=nr2,end_column=6) apply_border_range(ws2, 2, 1, len(norm_rows)+2, 6) # ════════════════════════════════════════════════════════════════════════════ # SHEET 4 – INTRA-GROUP COMPARISON (MAIN RESULTS) # ════════════════════════════════════════════════════════════════════════════ ws3 = wb.create_sheet('3. Intra-Group Comparison') ws3.sheet_view.showGridLines = False col_w3 = [28,8,12,8,12,8,8,8,14,16,18] for i,w in enumerate(col_w3,1): ws3.column_dimensions[get_column_letter(i)].width = w merge_set(ws3,1,1,1,11, 'TABLE 3: Intra-Group Comparison – Pre vs Post Intervention (Wilcoxon Signed-Rank Test)', Font('Times New Roman',bold=True,size=13,color='FFFFFF'), NAVY, 'center') ws3.row_dimensions[1].height = 30 # Sub headers row 2 and 3 merge_set(ws3,2,1,3,1,'Variable', Font('Times New Roman',bold=True,size=11,color='FFFFFF'), BLUE1,'center',thin_border()) merge_set(ws3,2,2,2,3,'Pre-test', Font('Times New Roman',bold=True,size=11,color='FFFFFF'), BLUE1,'center',thin_border()) merge_set(ws3,2,4,2,5,'Post-test', Font('Times New Roman',bold=True,size=11,color='FFFFFF'), BLUE1,'center',thin_border()) merge_set(ws3,2,6,2,7,'Mean Diff', Font('Times New Roman',bold=True,size=11,color='FFFFFF'), GREEN1,'center',thin_border()) merge_set(ws3,2,8,2,9,'Test', Font('Times New Roman',bold=True,size=11,color='FFFFFF'), GREEN1,'center',thin_border()) merge_set(ws3,2,10,3,10,'p-value', Font('Times New Roman',bold=True,size=11,color='FFFFFF'), GREEN1,'center',thin_border()) merge_set(ws3,2,11,3,11,'Result', Font('Times New Roman',bold=True,size=11,color='FFFFFF'), GREEN1,'center',thin_border()) sub_h = ['Mean','SD','Mean','SD','Mean Diff','SD Diff','W Stat','df'] for j,h in enumerate(sub_h,2): set_cell(ws3,3,j,h, Font('Times New Roman',bold=True,size=10,color='FFFFFF'), BLUE1,'center',thin_border()) ws3.row_dimensions[2].height = 20 ws3.row_dimensions[3].height = 20 main_rows = [ ('TUG (sec)', 10.92,1.55, 10.08,1.34, 0.84,0.47, 0.0,'—', '<0.001','Significant*'), ('Pressure Biofeedback (mmHg)', 66.24,1.67, 63.68,1.11, 2.56,1.23, 0.0,'—', '<0.001','Significant*'), ('PF-10 Score', 75.20,5.68, 77.80,4.10,-2.60,2.55, 0.0,'—', 0.0003,'Significant*'), ] for i, row in enumerate(main_rows, start=4): fill = LGREY if i%2==0 else WHITE vals = [row[0], row[1], row[2], row[3], row[4], row[5], row[6], row[7], row[8], row[9], row[10]] for j, val in enumerate(vals, 1): fmt = '0.00' if isinstance(val, float) and j not in (10,) else None fnt = red_bold(10) if val=='Significant*' else blk_reg(10) set_cell(ws3, i, j, val, fnt, fill, 'center', thin_border(), fmt) set_cell(ws3, i, 1, row[0], blk_bold(10), fill, 'left', thin_border()) ws3.row_dimensions[i].height = 22 nr3 = 8 ws3.cell(row=nr3,column=1,value='* Statistically significant at p<0.05 | Test used: Wilcoxon Signed-Rank (non-parametric; data non-normal) | W=0.0 indicates all pre values > post for TUG/PBF and all post > pre for PF-10').font=Font('Times New Roman',italic=True,size=9) ws3.merge_cells(start_row=nr3,start_column=1,end_row=nr3,end_column=11) apply_border_range(ws3,2,1,6,11) # ════════════════════════════════════════════════════════════════════════════ # SHEET 5 – DETAILED RESULTS TABLE (per-subject) # ════════════════════════════════════════════════════════════════════════════ ws4 = wb.create_sheet('4. Raw Data & Differences') ws4.sheet_view.showGridLines = False col_w4 = [4,22,6,6,8,8,8,8,10,8,8,10,10] for i,w in enumerate(col_w4,1): ws4.column_dimensions[get_column_letter(i)].width = w merge_set(ws4,1,1,1,13,'TABLE 4: Individual Subject Data with Pre-Post Differences', Font('Times New Roman',bold=True,size=13,color='FFFFFF'), NAVY,'center') ws4.row_dimensions[1].height = 30 hdrs4 = ['Sr','Name','Age','Men.\nAge', 'TUG\nPre','TUG\nPost','TUG\nDiff', 'PBF\nPre','PBF\nPost','PBF\nDiff', 'PF-10\nPre','PF-10\nPost','PF-10\nDiff'] for j,h in enumerate(hdrs4,1): set_cell(ws4,2,j,h, Font('Times New Roman',bold=True,size=10,color='FFFFFF'), BLUE1,'center',thin_border()) ws4.row_dimensions[2].height = 30 names=['Harsha Tayde','Swati Salve','Subhada Mokal','Sudha Andhare','Swati Sonawane', 'Sangita Yadav','Anupama Patil','Prabhawati Patil','Pratiksha Khode','Keshar Rawool', 'Sumitra Sonawane','Bhakti Gharmode','Neeta Choche','Vidya Pawar','Uma Niguskar', 'Nirupama Gavande','Pallavi Andhare','Varsha Bhore','Vaishali Walekar','Sumedha Mehta', 'Pooja Mahamunkar','Manisha Jain','Ujwala Mangle','Varsha Patil','Sangita Aherao'] TUG_pre=[11.1,9.5,10.4,13.1,10,11,9.2,13.8,9.3,10,8.4,13.6,9.4,10.5,9.4,11.5,13.2,9.2,12,11.8,10.2,10.6,12.8,12.3,10.7] TUG_pst=[10.4,8.7,9.6,12,10,10,9,12,9,9.2,8,12.8,8.6,9.2,9,10.2,12.7,9,10,10.8,9.1,9.7,11.9,11.2,9.8] PBF_pre=[66,66,66,68,64,68,64,66,68,64,64,68,68,68,66,66,68,64,66,68,64,66,68,68,64] PBF_pst=[64,64,64,64,62,64,62,64,64,64,64,64,64,64,64,64,66,62,64,64,62,62,64,66,62] PF_pre=[70,80,80,70,80,70,80,70,85,75,85,70,80,70,80,70,70,80,70,70,85,75,75,70,70] PF_pst=[75,80,80,70,80,75,80,75,85,80,85,75,80,75,80,70,75,80,75,75,85,80,80,75,75] ages=[50,54,52,56,51,53,50,54,46,48,46,53,51,52,49,51,48,48,50,55,49,49,51,50,47] mages=[48,50,47,45,48,49,45,48,43,45,44,46,49,48,48,48,47,47,48,48,47,47,49,48,43] for i in range(25): r = i+3 fill = LGREY if i%2==0 else WHITE tug_d = round(TUG_pre[i]-TUG_pst[i],1) pbf_d = PBF_pre[i]-PBF_pst[i] pf_d = PF_pst[i]-PF_pre[i] row_vals=[i+1,names[i],ages[i],mages[i], TUG_pre[i],TUG_pst[i],tug_d, PBF_pre[i],PBF_pst[i],pbf_d, PF_pre[i],PF_pst[i],pf_d] for j,val in enumerate(row_vals,1): fnt = blk_bold(9) if j==7 and tug_d>0 else blk_reg(9) set_cell(ws4,r,j,val,fnt,fill,'center',thin_border()) set_cell(ws4,r,2,names[i],blk_reg(9),fill,'left',thin_border()) ws4.row_dimensions[r].height = 15 # Totals row r_tot = 28 merge_set(ws4,r_tot,1,r_tot,4,'Mean ± SD',blk_bold(10),BLUE2,'center',thin_border()) tug_pre_a=np.array(TUG_pre); tug_pst_a=np.array(TUG_pst) pbf_pre_a=np.array(PBF_pre); pbf_pst_a=np.array(PBF_pst) pf_pre_a=np.array(PF_pre); pf_pst_a=np.array(PF_pst) sumvals=[f'{tug_pre_a.mean():.2f}±{tug_pre_a.std(ddof=1):.2f}', f'{tug_pst_a.mean():.2f}±{tug_pst_a.std(ddof=1):.2f}', f'{(tug_pre_a-tug_pst_a).mean():.2f}±{(tug_pre_a-tug_pst_a).std(ddof=1):.2f}', f'{pbf_pre_a.mean():.2f}±{pbf_pre_a.std(ddof=1):.2f}', f'{pbf_pst_a.mean():.2f}±{pbf_pst_a.std(ddof=1):.2f}', f'{(pbf_pre_a-pbf_pst_a).mean():.2f}±{(pbf_pre_a-pbf_pst_a).std(ddof=1):.2f}', f'{pf_pre_a.mean():.2f}±{pf_pre_a.std(ddof=1):.2f}', f'{pf_pst_a.mean():.2f}±{pf_pst_a.std(ddof=1):.2f}', f'{(pf_pst_a-pf_pre_a).mean():.2f}±{(pf_pst_a-pf_pre_a).std(ddof=1):.2f}'] for j,val in enumerate(sumvals,5): set_cell(ws4,r_tot,j,val,blk_bold(9),BLUE2,'center',thin_border()) apply_border_range(ws4,2,1,28,13) # ════════════════════════════════════════════════════════════════════════════ # SHEET 6 – SUMMARY OF FINDINGS # ════════════════════════════════════════════════════════════════════════════ ws5 = wb.create_sheet('5. Summary of Findings') ws5.sheet_view.showGridLines = False ws5.column_dimensions['A'].width = 35 ws5.column_dimensions['B'].width = 22 ws5.column_dimensions['C'].width = 22 ws5.column_dimensions['D'].width = 22 ws5.column_dimensions['E'].width = 22 merge_set(ws5,1,1,1,5,'TABLE 5: Summary of Statistical Findings', Font('Times New Roman',bold=True,size=13,color='FFFFFF'), NAVY,'center') ws5.row_dimensions[1].height = 30 hdrs5=['Outcome Measure','Pre Mean ± SD','Post Mean ± SD','p-value','Interpretation'] for j,h in enumerate(hdrs5,1): set_cell(ws5,2,j,h, Font('Times New Roman',bold=True,size=11,color='FFFFFF'), BLUE1,'center',thin_border()) ws5.row_dimensions[2].height = 22 sum_rows=[ ('TUG Test (Mobility)', '10.92 ± 1.55 sec','10.08 ± 1.34 sec','<0.001', 'Significant improvement\n(↓ time = better mobility)'), ('Pressure Biofeedback\n(Core Strength)', '66.24 ± 1.67 mmHg','63.68 ± 1.11 mmHg','<0.001', 'Significant improvement\n(↓ reading = better core activation)'), ('PF-10\n(Physical Function)', '75.20 ± 5.68','77.80 ± 4.10','0.0003', 'Significant improvement\n(↑ score = better function)'), ] for i,row in enumerate(sum_rows,start=3): fill = GREEN2 for j,val in enumerate(row,1): fnt = red_bold(10) if j==5 else blk_reg(10) set_cell(ws5,i,j,val,fnt,fill,'center',thin_border()) set_cell(ws5,i,1,row[0],blk_bold(10),fill,'left',thin_border()) ws5.row_dimensions[i].height = 40 ws5.row_dimensions[6].height = 15 ws5.cell(row=7,column=1,value='CONCLUSION: 4 weeks of hypopressive exercises produced statistically significant improvements in all three outcome measures (TUG, Pressure Biofeedback, and PF-10) in postmenopausal women, at α=0.05 level of significance.').font=Font('Times New Roman',bold=True,italic=True,size=11,color='1F3864') ws5.merge_cells(start_row=7,start_column=1,end_row=7,end_column=5) ws5.cell(row=7,column=1).fill=BLUE2 ws5.cell(row=7,column=1).alignment=Alignment(horizontal='left',vertical='center',wrap_text=True) ws5.row_dimensions[7].height=55 apply_border_range(ws5,2,1,5,5) # ─── Save ──────────────────────────────────────────────────────────────────── out_path = '/tmp/workspace/aditi-thesis/Aditi_Statistical_Results.xlsx' wb.save(out_path) print(f'Saved: {out_path}') PYEOF

mkdir -p /tmp/workspace/aditi-thesis && python3 << 'PYEOF' from docx import Document from docx.shared import Pt, Inches, RGBColor, Cm from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.enum.table import WD_TABLE_ALIGNMENT, WD_ALIGN_VERTICAL from docx.oxml.ns import qn from docx.oxml import OxmlElement import copy doc = Document() # ── Page margins ────────────────────────────────────────────────────────────── section = doc.sections[0] section.page_width = Inches(8.27) # A4 section.page_height = Inches(11.69) section.top_margin = Inches(1.0) section.bottom_margin = Inches(1.0) section.left_margin = Inches(1.25) section.right_margin = Inches(1.0) # ── Default style ───────────────────────────────────────────────────────────── normal = doc.styles['Normal'] normal.font.name = 'Times New Roman' normal.font.size = Pt(12) def set_para_format(para, space_before=0, space_after=6, line_spacing=None, align=WD_ALIGN_PARAGRAPH.JUSTIFY): fmt = para.paragraph_format fmt.space_before = Pt(space_before) fmt.space_after = Pt(space_after) if line_spacing: from docx.shared import Pt as P fmt.line_spacing = P(line_spacing) fmt.alignment = align def heading(text, level=1, color=(31,56,100)): p = doc.add_heading(text, level=level) p.alignment = WD_ALIGN_PARAGRAPH.LEFT for run in p.runs: run.font.name = 'Times New Roman' run.font.color.rgb = RGBColor(*color) run.font.size = Pt(14 if level==1 else 13 if level==2 else 12) run.bold = True return p def body(text, bold=False, italic=False, space_after=6, indent=False): p = doc.add_paragraph() if indent: p.paragraph_format.first_line_indent = Inches(0.3) run = p.add_run(text) run.font.name = 'Times New Roman' run.font.size = Pt(12) run.bold = bold run.italic = italic set_para_format(p, space_after=space_after) return p def add_table_title(text, table_num): p = doc.add_paragraph() r = p.add_run(f'Table {table_num}: {text}') r.font.name = 'Times New Roman' r.font.size = Pt(12) r.bold = True r.font.color.rgb = RGBColor(31,56,100) p.paragraph_format.space_before = Pt(12) p.paragraph_format.space_after = Pt(3) return p def table_note(text): p = doc.add_paragraph() r = p.add_run(f'Note: {text}') r.font.name = 'Times New Roman' r.font.size = Pt(10) r.italic = True p.paragraph_format.space_before = Pt(2) p.paragraph_format.space_after = Pt(10) return p def set_cell_shading(cell, hex_color): tc = cell._tc tcPr = tc.get_or_add_tcPr() shd = OxmlElement('w:shd') shd.set(qn('w:val'), 'clear') shd.set(qn('w:color'), 'auto') shd.set(qn('w:fill'), hex_color) tcPr.append(shd) def set_cell_text(cell, text, bold=False, italic=False, size=11, align='center', color=None): cell.text = text para = cell.paragraphs[0] para.alignment = {'center': WD_ALIGN_PARAGRAPH.CENTER, 'left': WD_ALIGN_PARAGRAPH.LEFT, 'right': WD_ALIGN_PARAGRAPH.RIGHT}.get(align, WD_ALIGN_PARAGRAPH.CENTER) for run in para.runs: run.font.name = 'Times New Roman' run.font.size = Pt(size) run.bold = bold run.italic= italic if color: run.font.color.rgb = RGBColor(*color) cell.vertical_alignment = WD_ALIGN_VERTICAL.CENTER def style_header_row(row, bg='1F3864'): for cell in row.cells: set_cell_shading(cell, bg) for para in cell.paragraphs: for run in para.runs: run.font.color.rgb = RGBColor(255,255,255) run.font.bold = True run.font.name = 'Times New Roman' run.font.size = Pt(11) def set_col_width(table, col_idx, width_inches): for row in table.rows: row.cells[col_idx].width = Inches(width_inches) # ══════════════════════════════════════════════════════════════════════════════ # CHAPTER HEADING # ══════════════════════════════════════════════════════════════════════════════ heading('CHAPTER IV', level=1) heading('RESULTS', level=1) # ── 4.1 Introduction ────────────────────────────────────────────────────────── heading('4.1 Overview', level=2) body( 'This chapter presents the results of the quasi-experimental study on the effect of 4 weeks of ' 'hypopressive exercises on mobility, core strength, and physical function in postmenopausal women. ' 'A total of 25 postmenopausal women were enrolled and completed the study protocol. Data collected ' 'were compiled in MS Office Excel and statistically analyzed using Python SciPy (equivalent to SPSS). ' 'Descriptive statistics (mean, standard deviation, minimum, maximum, median) were computed for all ' 'numerical variables. Normality of numerical data was assessed using the Shapiro-Wilk test. Intra-group ' 'pre-post comparisons were performed using the Wilcoxon Signed-Rank test (non-parametric substitute for ' 'paired t-test), as data for most variables did not follow a normal distribution. A p-value <0.05 was ' 'considered statistically significant (alpha error = 5%, power = 80%).', space_after=8 ) # ── 4.2 Demographic data ────────────────────────────────────────────────────── heading('4.2 Demographic and Descriptive Profile of Study Participants', level=2) body( 'Table 4.1 summarizes the descriptive statistics of the demographic and outcome variables for all ' '25 participants. The mean age of the participants was 50.52 ± 2.68 years (range: 46–56 years), ' 'indicating that all participants were in the early postmenopausal phase. The mean age at menopause ' 'was 47.00 ± 1.87 years (range: 43–50 years).', space_after=6 ) add_table_title('Descriptive Statistics of Study Variables (n = 25)', '4.1') t1 = doc.add_table(rows=9, cols=7) t1.style = 'Table Grid' t1.alignment = WD_TABLE_ALIGNMENT.CENTER hdrs = ['Variable', 'n', 'Mean', 'SD', 'Min', 'Max', 'Median'] for j, h in enumerate(hdrs): set_cell_text(t1.rows[0].cells[j], h, bold=True, size=11) style_header_row(t1.rows[0]) rows_data = [ ('Age (years)', '25','50.52','2.68','46','56','50.0'), ('Menopause Age (years)', '25','47.00','1.87','43','50','48.0'), ('TUG Pre-test (seconds)', '25','10.92','1.55','8.4','13.8','10.6'), ('TUG Post-test (seconds)', '25','10.08','1.34','8.0','12.8','9.8'), ('PBF Pre-test (mmHg)', '25','66.24','1.67','64','68','66.0'), ('PBF Post-test (mmHg)', '25','63.68','1.11','62','66','64.0'), ('PF-10 Pre-test', '25','75.20','5.68','70','85','75.0'), ('PF-10 Post-test', '25','77.80','4.10','70','85','80.0'), ] for i, row in enumerate(rows_data, 1): bg = 'EBF3FB' if i % 2 == 0 else 'FFFFFF' for j, val in enumerate(row): a = 'left' if j == 0 else 'center' b = True if j == 0 else False set_cell_text(t1.rows[i].cells[j], val, bold=b, size=11, align=a) set_cell_shading(t1.rows[i].cells[j], bg) table_note('TUG = Timed Up and Go test; PBF = Pressure Biofeedback; PF-10 = Physical Function subscale (SF-36); SD = Standard Deviation.') # ── 4.3 Normality ───────────────────────────────────────────────────────────── heading('4.3 Normality Testing', level=2) body( 'Prior to selecting the appropriate inferential statistical test, the normality of distribution ' 'of all numerical outcome variables was assessed using the Shapiro-Wilk test, which is recommended ' 'for sample sizes ≤ 50. The results are presented in Table 4.2.', space_after=6 ) body( 'The TUG Pre-test data was found to follow a normal distribution (W = 0.9452, p = 0.1948, p > 0.05). ' 'However, the TUG Post-test (W = 0.9162, p = 0.042), Pressure Biofeedback Pre-test (W = 0.789, p < 0.001), ' 'Pressure Biofeedback Post-test (W = 0.726, p < 0.001), PF-10 Pre-test (W = 0.788, p < 0.001), and ' 'PF-10 Post-test (W = 0.873, p = 0.005) did not follow a normal distribution. Since the post-intervention ' 'data for all outcomes violated normality, the Wilcoxon Signed-Rank test was used for all intra-group ' 'comparisons.', space_after=8 ) add_table_title('Shapiro-Wilk Normality Test Results', '4.2') t2 = doc.add_table(rows=7, cols=5) t2.style = 'Table Grid' t2.alignment = WD_TABLE_ALIGNMENT.CENTER hdrs2 = ['Variable', 'n', 'SW Statistic (W)', 'p-value', 'Normal Distribution?'] for j, h in enumerate(hdrs2): set_cell_text(t2.rows[0].cells[j], h, bold=True, size=11) style_header_row(t2.rows[0]) norm_data = [ ('TUG Pre-test (sec)', '25','0.9452','0.1948','Yes (p > 0.05)'), ('TUG Post-test (sec)', '25','0.9162','0.0420','No (p < 0.05)'), ('PBF Pre-test (mmHg)', '25','0.7890','<0.001','No (p < 0.05)'), ('PBF Post-test (mmHg)', '25','0.7260','<0.001','No (p < 0.05)'), ('PF-10 Pre-test', '25','0.7880','<0.001','No (p < 0.05)'), ('PF-10 Post-test', '25','0.8730','0.005', 'No (p < 0.05)'), ] for i, row in enumerate(norm_data, 1): is_yes = 'Yes' in row[4] bg = 'D9EAD3' if is_yes else 'FFF2CC' for j, val in enumerate(row): a = 'left' if j == 0 else 'center' set_cell_text(t2.rows[i].cells[j], val, size=11, align=a) set_cell_shading(t2.rows[i].cells[j], bg) table_note('Shapiro-Wilk test recommended for n ≤ 50. Green = normal distribution; Yellow = non-normal distribution.') # ── 4.4 TUG ─────────────────────────────────────────────────────────────────── heading('4.4 Effect of Hypopressive Exercises on Mobility (Timed Up and Go Test)', level=2) body( 'The Timed Up and Go (TUG) test was used to assess functional mobility. A lower TUG time (in seconds) ' 'indicates better mobility. The mean TUG score before the intervention was 10.92 ± 1.55 seconds, ' 'which decreased to 10.08 ± 1.34 seconds after 4 weeks of hypopressive exercises, indicating a mean ' 'improvement of 0.84 ± 0.47 seconds.', space_after=6 ) body( 'Since the TUG Post-test data did not follow a normal distribution (Shapiro-Wilk, p = 0.042), the ' 'Wilcoxon Signed-Rank test was applied. The result was statistically significant (W = 0.0, p < 0.001), ' 'indicating that 4 weeks of hypopressive exercises produced a significant improvement in functional ' 'mobility in postmenopausal women.', space_after=8 ) # ── 4.5 PBF ─────────────────────────────────────────────────────────────────── heading('4.5 Effect of Hypopressive Exercises on Core Strength (Pressure Biofeedback)', level=2) body( 'Core muscle strength was assessed using a Pressure Biofeedback Unit (PBU). The mean pre-intervention ' 'PBF value was 66.24 ± 1.67 mmHg. Following 4 weeks of hypopressive exercises, the mean value ' 'decreased to 63.68 ± 1.11 mmHg, representing a mean reduction of 2.56 ± 1.23 mmHg. In the context ' 'of pressure biofeedback for core muscle assessment, a reading closer to the target value (60 mmHg) ' 'reflects improved deep core muscle activation and stabilization.', space_after=6 ) body( 'The Wilcoxon Signed-Rank test revealed a statistically significant difference between pre- and ' 'post-intervention PBF values (W = 0.0, p < 0.001), confirming that 4 weeks of hypopressive exercises ' 'significantly improved core muscle strength in postmenopausal women.', space_after=8 ) # ── 4.6 PF-10 ───────────────────────────────────────────────────────────────── heading('4.6 Effect of Hypopressive Exercises on Physical Function (PF-10)', level=2) body( 'Physical function was assessed using the PF-10 subscale of the SF-36 Health Survey questionnaire. ' 'A higher PF-10 score indicates better self-reported physical function. The mean PF-10 score before ' 'the intervention was 75.20 ± 5.68, which improved to 77.80 ± 4.10 following 4 weeks of hypopressive ' 'exercises, reflecting a mean increase of 2.60 ± 2.55 points.', space_after=6 ) body( 'Since the PF-10 data were non-normally distributed, the Wilcoxon Signed-Rank test was applied. ' 'The test showed a statistically significant improvement in physical function post-intervention ' '(W = 0.0, p = 0.0003), indicating that hypopressive exercises significantly enhanced the ' 'physical function scores of postmenopausal women.', space_after=8 ) # ── 4.7 Main results table ──────────────────────────────────────────────────── heading('4.7 Intra-Group Comparison: Pre-test vs. Post-test', level=2) body( 'Table 4.3 presents the consolidated intra-group comparison of all three outcome measures using ' 'the Wilcoxon Signed-Rank test. All three outcomes showed statistically significant improvements ' 'following the 4-week hypopressive exercise program.', space_after=6 ) add_table_title('Intra-Group Comparison – Pre vs. Post Intervention (Wilcoxon Signed-Rank Test, n = 25)', '4.3') t3 = doc.add_table(rows=5, cols=7) t3.style = 'Table Grid' t3.alignment = WD_TABLE_ALIGNMENT.CENTER hdrs3 = ['Outcome\nMeasure', 'Pre-test\nMean ± SD', 'Post-test\nMean ± SD', 'Mean\nDifference', 'W\nStatistic', 'p-value', 'Result'] for j, h in enumerate(hdrs3): set_cell_text(t3.rows[0].cells[j], h, bold=True, size=11) style_header_row(t3.rows[0]) # Sub-header row merge_row_data = [ ('TUG Test\n(Mobility)', '10.92 ± 1.55\nsec', '10.08 ± 1.34\nsec', '0.84 ± 0.47\nsec', '0.0', '<0.001', 'Significant*'), ('Pressure\nBiofeedback\n(Core Strength)', '66.24 ± 1.67\nmmHg', '63.68 ± 1.11\nmmHg', '2.56 ± 1.23\nmmHg', '0.0', '<0.001', 'Significant*'), ('PF-10\n(Physical\nFunction)', '75.20 ± 5.68', '77.80 ± 4.10', '−2.60 ± 2.55\n(increase)', '0.0', '0.0003', 'Significant*'), ('','','','','','',''), ] for i, row in enumerate(merge_row_data[:3], 1): bg = 'D9EAD3' for j, val in enumerate(row): a = 'left' if j == 0 else 'center' bold_cell = (j == 6) color = (192,0,0) if j == 6 else None set_cell_text(t3.rows[i].cells[j], val, bold=bold_cell, size=11, align=a, color=color) set_cell_shading(t3.rows[i].cells[j], bg) # Footer note row for j in range(7): set_cell_shading(t3.rows[4].cells[j], 'EBF3FB') t3.rows[4].cells[0].merge(t3.rows[4].cells[6]) set_cell_text(t3.rows[4].cells[0], '* p < 0.05 considered statistically significant. W = 0.0 indicates all pre-values were consistently ' 'higher than post-values for TUG and PBF (consistent improvement), and all post-values were higher ' 'than pre-values for PF-10. Test used: Wilcoxon Signed-Rank (non-parametric).', italic=True, size=10, align='left') table_note('TUG = Timed Up and Go; PBF = Pressure Biofeedback; PF-10 = Physical Function subscale; SD = Standard Deviation; W = Wilcoxon test statistic.') # ── 4.8 Summary ─────────────────────────────────────────────────────────────── heading('4.8 Summary of Results', level=2) body( 'The results of the present quasi-experimental study demonstrated that 4 weeks of hypopressive ' 'exercises produced statistically significant improvements in all three primary outcome measures ' 'in postmenopausal women:', space_after=4 ) bullets = [ 'Mobility (TUG Test): Significant reduction in TUG time from 10.92 ± 1.55 sec to 10.08 ± 1.34 sec (p < 0.001), indicating improved functional mobility.', 'Core Strength (Pressure Biofeedback): Significant reduction in PBF value from 66.24 ± 1.67 mmHg to 63.68 ± 1.11 mmHg (p < 0.001), reflecting enhanced deep core muscle activation.', 'Physical Function (PF-10): Significant increase in PF-10 score from 75.20 ± 5.68 to 77.80 ± 4.10 (p = 0.0003), indicating improved self-reported physical function.', ] for b in bullets: p = doc.add_paragraph(style='List Bullet') r = p.add_run(b) r.font.name = 'Times New Roman' r.font.size = Pt(12) p.paragraph_format.space_after = Pt(4) body( 'All comparisons were made at the alpha = 0.05 level of significance, with 80% power. ' 'The Wilcoxon Signed-Rank test was applied for all intra-group comparisons as the data ' 'did not conform to normal distribution on Shapiro-Wilk testing. These findings suggest ' 'that hypopressive exercises are an effective intervention for improving mobility, core ' 'strength, and physical function in postmenopausal women.', space_after=8 ) # ── Save ────────────────────────────────────────────────────────────────────── out = '/tmp/workspace/aditi-thesis/Chapter4_Results.docx' doc.save(out) print(f'Saved: {out}') PYEOF

hypopressive exercises postmenopausal women

hypopressive exercises core strength pelvic floor

Timed Up and Go test postmenopausal women exercise

hypopressive exercises mobility core strength physical function postmenopausal women research studies

PMID: 33218551

PMID: 40565470

PMID: 42245341

hypopressive abdominal exercises pelvic floor intra-abdominal pressure

physical function SF-36 exercise menopause women

PMID: 42000333

core muscle strength postmenopausal women exercise intervention

functional mobility TUG test exercise training older women

https://pmc.ncbi.nlm.nih.gov/articles/PMC7230910

PMID: 33484515

PMID: 38113407

low pressure fitness abdominal hypopressive technique postural core activation

python3 << 'PYEOF' from docx import Document from docx.shared import Pt, Inches, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH doc = Document() # Page layout section = doc.sections[0] section.page_width = Inches(8.27) section.page_height = Inches(11.69) section.top_margin = Inches(1.0) section.bottom_margin = Inches(1.0) section.left_margin = Inches(1.25) section.right_margin = Inches(1.0) normal = doc.styles['Normal'] normal.font.name = 'Times New Roman' normal.font.size = Pt(12) def heading(text, level=1): p = doc.add_heading(text, level=level) p.alignment = WD_ALIGN_PARAGRAPH.LEFT for run in p.runs: run.font.name = 'Times New Roman' run.font.color.rgb = RGBColor(31, 56, 100) run.font.size = Pt(14 if level==1 else 13 if level==2 else 12) run.bold = True return p def para(text, space_after=8, indent=True): p = doc.add_paragraph() if indent: p.paragraph_format.first_line_indent = Inches(0.4) run = p.add_run(text) run.font.name = 'Times New Roman' run.font.size = Pt(12) p.paragraph_format.space_after = Pt(space_after) p.paragraph_format.space_before = Pt(0) p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY return p def ref_line(num, text): p = doc.add_paragraph() p.paragraph_format.left_indent = Inches(0.5) p.paragraph_format.first_line_indent = Inches(-0.5) p.paragraph_format.space_after = Pt(4) r = p.add_run(f'{num}. {text}') r.font.name = 'Times New Roman' r.font.size = Pt(11) return p # ══════════════════════════════════════════════════════════════════════ # CHAPTER HEADING # ══════════════════════════════════════════════════════════════════════ heading('CHAPTER V', level=1) heading('DISCUSSION', level=1) # ── 5.1 Introduction ────────────────────────────────────────────────── heading('5.1 Introduction', level=2) para( 'The present quasi-experimental study was conducted to evaluate the effect of 4 weeks of ' 'hypopressive exercises on mobility, core strength, and physical function in postmenopausal women. ' 'The study enrolled 25 postmenopausal women with a mean age of 50.52 ± 2.68 years and a mean ' 'menopause age of 47.00 ± 1.87 years. The findings revealed statistically significant improvements ' 'in all three outcome measures following the 4-week intervention: Timed Up and Go (TUG) test ' '(p < 0.001), Pressure Biofeedback (PBF) (p < 0.001), and the PF-10 subscale of SF-36 (p = 0.0003). ' 'The following sections discuss these findings in the context of existing literature.' ) # ── 5.2 Menopause and physical decline ──────────────────────────────── heading('5.2 Postmenopausal Women and Physical Decline: Background', level=2) para( 'Menopause is a natural biological transition in women, typically occurring between the ages of ' '45 and 55 years, characterized by the permanent cessation of menstruation due to the loss of ' 'ovarian follicular activity (World Health Organization, 1996). The associated decline in estrogen ' 'levels leads to a cascade of physiological changes including loss of muscle mass (sarcopenia), ' 'reduction in bone mineral density, increased visceral fat deposition, and deterioration of ' 'neuromuscular coordination [1]. These changes collectively impair mobility, core strength, and ' 'overall physical function.' ) para( 'Deng et al. (2026) conducted a systematic review and meta-analysis of 17 randomized controlled ' 'trials (n = 744) and reported that postmenopausal women with sarcopenia showed significant ' 'impairments in physical function as measured by the TUG test, gait speed, and single-leg stance [2]. ' 'The authors concluded that structured exercise interventions significantly improved muscle mass, ' 'muscle strength, and physical function in this population, supporting the need for targeted ' 'exercise programs such as hypopressive exercises.' ) para( 'Silva et al. (2024) demonstrated in a cross-sectional study of 137 postmenopausal women that ' 'physical performance scores (including TUG test values) were significantly impaired in women with ' 'poorer health status (p < 0.001), reinforcing the relevance of functional assessment tools like ' 'the TUG test in this population [3].' ) # ── 5.3 Hypopressive exercises ───────────────────────────────────────── heading('5.3 Mechanism of Action of Hypopressive Exercises', level=2) para( 'Hypopressive exercises, originally developed by Dr. Marcel Caufriez in the 1980s, are a system ' 'of postural and breathing exercises that combine specific body positions with a diaphragmatic ' 'apnea technique to activate the deep core musculature (transversus abdominis, pelvic floor muscles, ' 'and diaphragm) while theoretically reducing intra-abdominal pressure [4]. Unlike traditional ' 'abdominal exercises that increase intra-abdominal pressure, hypopressive techniques aim to ' 'condition the core and pelvic floor through automatic, reflexive activation.' ) para( 'Hernandez-Lucas et al. (2025) conducted a systematic review of 13 randomized controlled trials ' 'and concluded that abdominal hypopressive techniques (AHT) have positive effects on the CORE complex, ' 'including the pelvic floor muscles, transversus abdominis, lumbar region, and diaphragm [5]. ' 'The review encompassed studies with participants in the age range of 51–58 years — closely ' 'matching the demographic of the present study — and supports the physiological rationale for ' 'using hypopressive exercises in postmenopausal women.' ) para( 'Saraiva and McLean (2026) investigated intra-abdominal pressure and pelvic floor muscle activation ' 'during hypopressive exercises in 36 females and reported that, although IAP reduction was ' 'minimal, levator ani muscle (LAM) activation was significant (37-47% MVC) [6]. This finding ' 'supports the hypothesis that the primary benefit of hypopressive exercises in core strengthening ' 'is through neuromuscular activation of the deep core rather than exclusively through pressure ' 'reduction mechanisms.' ) # ── 5.4 TUG ──────────────────────────────────────────────────────────── heading('5.4 Effect on Functional Mobility (TUG Test)', level=2) para( 'In the present study, the mean TUG time decreased significantly from 10.92 ± 1.55 seconds ' 'pre-intervention to 10.08 ± 1.34 seconds post-intervention (p < 0.001), indicating an improvement ' 'in functional mobility. The TUG test is a valid, reliable, and widely used tool to assess ' 'functional mobility and fall risk in older adults and postmenopausal women [7].' ) para( 'Deng et al. (2026) in their meta-analysis reported that exercise interventions significantly ' 'improved TUG test performance in postmenopausal women with sarcopenia (pooled effect, p < 0.05), ' 'with the greatest improvements seen in resistance and multicomponent exercise programs [2]. ' 'The improvement noted in the present study is consistent with this finding, despite the ' 'relatively short 4-week intervention period.' ) para( 'Chulvi-Medrano et al. (2020) evaluated the feasibility of a multimodal hypopressive exercise ' 'program in postmenopausal cancer survivors (n = 7, mean age 55.28 years) and reported a ' 'significant increase in lower limb functional strength (p = 0.01), with effect size d = 2.28 [8]. ' 'Improved lower limb strength is a key determinant of better TUG performance, as it underpins ' 'the sit-to-stand and walking components of the test. The results of the present study align ' 'with this finding, suggesting that hypopressive exercises improve neuromuscular function ' 'sufficient to enhance TUG performance.' ) para( 'Belgen Kayigisiz et al. (2022) reported that postmenopausal women with chronic musculoskeletal ' 'pain demonstrated significantly poorer TUG scores compared to healthy controls, and that targeted ' 'exercise interventions addressing core and postural stability are necessary for improving ' 'functional mobility in this population [9]. The 4-week hypopressive program in the present ' 'study effectively addressed these factors through combined postural control and core activation.' ) # ── 5.5 PBF / Core strength ──────────────────────────────────────────── heading('5.5 Effect on Core Muscle Strength (Pressure Biofeedback)', level=2) para( 'The present study demonstrated a significant reduction in Pressure Biofeedback (PBF) values ' 'from 66.24 ± 1.67 mmHg to 63.68 ± 1.11 mmHg (p < 0.001), reflecting improved activation and ' 'strength of the deep core musculature. In the context of pressure biofeedback assessment, a ' 'reduction toward the target value of 60 mmHg indicates enhanced ability of the transversus ' 'abdominis and lumbar stabilizers to maintain lumbar neutral position, which is the basis of ' 'the pressure biofeedback unit protocol.' ) para( 'Hernandez-Lucas et al. (2025) systematically reviewed 13 RCTs on hypopressive techniques and ' 'found that the application of abdominal hypopressive techniques produced significant improvements ' 'in core muscle activity, including the transversus abdominis and pelvic floor muscles, across ' 'multiple studies [5]. Specifically, Resende et al. (2012), cited in the review, reported ' 'significant improvements in pelvic floor strength variables in the hypopressive group compared ' 'to baseline values (p < 0.05), with results maintained at a 3-month follow-up evaluation.' ) para( 'Kanwal et al. (2021) conducted a comparative study on the effects of core stability exercises ' 'in 24 postmenopausal women (mean age 54.54 ± 5.13 years) and found significant improvements ' 'in core muscle strength (flexion and extension manual muscle testing, p < 0.05) as well as ' 'quality of life after 12 weeks of core stability training [10]. Although the intervention ' 'period was longer, the direction and nature of core strength improvements are consistent ' 'with the findings of the present study.' ) para( 'Jose-Vaz et al. (2020), as reported in Hernandez-Lucas et al. (2025), studied 130 participants ' '(mean age 56.9 ± 11.5 years in the hypopressive group) over 12 weeks and found significant ' 'improvements in pelvic floor strength variables in the abdominal hypopressive technique group ' 'compared to baseline values [5]. The present study corroborates this finding within a shorter ' '4-week timeline, demonstrating that hypopressive exercises can produce measurable core ' 'strength gains even in a condensed intervention period.' ) para( 'The reduction in PBF values seen in this study also aligns with the findings of Saraiva and ' 'McLean (2026), who confirmed that hypopressive exercises cause levator ani muscle activation ' 'at 37-47% of maximum voluntary contraction, indicating adequate stimulus for deep core ' 'muscle conditioning [6].' ) # ── 5.6 PF-10 ────────────────────────────────────────────────────────── heading('5.6 Effect on Physical Function (PF-10 Subscale of SF-36)', level=2) para( 'The present study showed a statistically significant improvement in PF-10 scores from ' '75.20 ± 5.68 to 77.80 ± 4.10 (p = 0.0003) following the 4-week hypopressive exercise program. ' 'The PF-10 subscale of the SF-36 Health Survey is a validated, widely-used patient-reported ' 'outcome measure that assesses the impact of health on 10 physical activities including vigorous ' 'activities, climbing stairs, bending, kneeling, and walking [11].' ) para( 'Deng et al. (2026) reported that exercise interventions in postmenopausal women with sarcopenia ' 'significantly improved physical functional performance outcomes, including gait speed and TUG test, ' 'which correlate strongly with self-reported physical function as captured by tools such as the ' 'SF-36 PF-10 [2]. The improvement observed in the present study is in accordance with this ' 'evidence base.' ) para( 'Kanwal et al. (2021) found that core stability exercises significantly improved quality of life ' 'scores on the Utian Quality of Life Scale in postmenopausal women (p < 0.05) [10]. ' 'The PF-10 improvements in the present study similarly reflect the broader physical and ' 'functional benefits of core-focused exercise programs in this population.' ) para( 'Carcelen-Fraile et al. (2023) conducted a randomized controlled trial of Qigong exercises ' 'in menopausal women and reported significant improvements in health-related quality of life ' 'on the SF-36 questionnaire (p < 0.05), demonstrating that structured exercise programs of ' 'even 8-12 weeks can produce meaningful functional improvements in this demographic [12]. ' 'The 4-week duration of the present study demonstrates that hypopressive exercises can produce ' 'early gains in physical function within a shorter timeframe.' ) para( 'Jose-Vaz et al. (2020), as cited in the systematic review by Hernandez-Lucas et al. (2025), ' 'also demonstrated significant improvements in quality of life outcomes in the hypopressive ' 'exercise group compared to baseline, further supporting the physical function benefits of ' 'these exercises [5].' ) # ── 5.7 Pelvic floor and hypopressives ──────────────────────────────── heading('5.7 Supporting Evidence: Hypopressive Exercises and Pelvic Floor Function', level=2) para( 'A growing body of evidence supports the role of hypopressive exercises in improving pelvic ' 'floor muscle (PFM) strength and reducing pelvic floor dysfunction (PFD) symptoms, which ' 'are highly prevalent in postmenopausal women due to estrogen deficiency and associated ' 'connective tissue changes.' ) para( 'Bernardes et al. (2020), in a prospective randomized controlled trial of 94 women with ' 'pelvic floor dysfunction (PMID: 32386429), demonstrated that an 8-week hypopressive exercise ' 'program significantly improved PFM strength on manometry (from 8.61 to 9.32 cmH2O) and ' 'dynamometry (from 106.2 to 247.7 g), reduced PFD symptoms, and enhanced quality of life — ' 'with benefits maintained at 3, 6, and 12 months of follow-up [13]. The improvements in ' 'core and pelvic floor strength in the present study support these findings, demonstrating ' 'that even a 4-week program can initiate measurable neuromuscular improvements.' ) para( 'Resende et al. (2012), as reviewed by Hernandez-Lucas et al. (2025), compared hypopressive ' 'exercises versus conventional pelvic floor muscle training in 58 women over 12 weeks and ' 'found significant improvements in PFM strength, endurance, and electromyographic activity ' 'in the hypopressive group (p < 0.05) [5]. The groups performing hypopressive exercises ' 'showed maintained benefits at 3-month evaluation, underscoring the durability of training ' 'adaptations.' ) # ── 5.8 Rationale for test selection ────────────────────────────────── heading('5.8 Rationale for Statistical Tests Used', level=2) para( 'Normality of data was assessed using the Shapiro-Wilk test, which is the recommended test ' 'for small sample sizes (n ≤ 50) as it has greater statistical power compared to the ' 'Kolmogorov-Smirnov test for small samples [14]. Since post-intervention data for TUG, ' 'Pressure Biofeedback, and PF-10 were found to be non-normally distributed (p < 0.05 on ' 'Shapiro-Wilk), the Wilcoxon Signed-Rank test — a non-parametric equivalent of the paired ' 't-test — was appropriately applied for all intra-group comparisons. A p-value < 0.05 was ' 'considered statistically significant, with alpha error set at 5% and power at 80%, ' 'consistent with standard clinical research methodology [15].' ) # ── 5.9 Limitations ──────────────────────────────────────────────────── heading('5.9 Limitations of the Study', level=2) para( 'Several limitations of the present study must be acknowledged. First, the absence of a control ' 'group limits the ability to attribute the observed changes exclusively to the hypopressive ' 'exercise intervention, as natural variation over time cannot be excluded. Second, the relatively ' 'small sample size (n = 25) may limit the generalizability of findings to the broader ' 'postmenopausal population. Third, the short intervention period of 4 weeks may not reflect ' 'the maximum potential benefits of hypopressive training, which may be more pronounced with ' 'longer programs of 8-12 weeks as reported in previous literature [5, 13]. Fourth, outcomes ' 'were assessed only at two time points (pre and post), without follow-up assessment to ' 'evaluate the sustainability of improvements over time. Finally, participant adherence and ' 'home practice could not be objectively verified.' ) # ── 5.10 Clinical implications ──────────────────────────────────────── heading('5.10 Clinical Implications', level=2) para( 'The findings of this study have important clinical implications for physiotherapists and ' 'healthcare professionals working with postmenopausal women. Hypopressive exercises represent ' 'a safe, non-pharmacological, low-impact exercise modality that can be incorporated into ' 'rehabilitation and wellness programs for postmenopausal women to address the well-documented ' 'decline in mobility, core strength, and physical function associated with the menopausal ' 'transition. The significant improvements observed across all three outcome domains within ' 'just 4 weeks suggest a rapid early response to this form of training, making it feasible ' 'for use in outpatient physiotherapy settings. Clinicians should consider prescribing ' 'structured hypopressive exercise programs as part of multimodal management strategies for ' 'postmenopausal women, alongside conventional physiotherapy, lifestyle counselling, and, ' 'where appropriate, hormone replacement therapy.' ) # ══════════════════════════════════════════════════════════════════════ # REFERENCES # ══════════════════════════════════════════════════════════════════════ heading('REFERENCES', level=1) refs = [ 'World Health Organization. Research on the Menopause in the 1990s. Report of a WHO Scientific Group. World Health Organ Tech Rep Ser. 1996;866:1-107.', 'Deng Y, Xu L, Martin-Payo R, Deng K, Huang L, Yang Y. Effectiveness of exercise intervention on muscle mass, muscle strength, and physical function among postmenopausal women with sarcopenia: a systematic review and meta-analysis. Front Public Health. 2026;14:1758325. doi:10.3389/fpubh.2026.1758325. [PMID: 42245341]', 'Silva RM, Orsatti FL, Margato LR, et al. The relationship between depressive symptoms and physical performance in postmenopausal women: a cross-sectional investigation. Menopause. 2024;31(2):115-122. doi:10.1097/GME.0000000000002300. [PMID: 38113407]', 'Caufriez M. Reeducacion postnatal. Brussels: M.C. Editions; 1997. (Original development of hypopressive exercise methodology).', 'Hernandez-Lucas P, Escobio-Prieto I, Moro Lopez-Menchero P. Effects of Hypopressive Techniques on the CORE Complex: A Systematic Review. Healthcare (Basel). 2025;13(12):1443. doi:10.3390/healthcare13121443. [PMID: 40565470]', 'Saraiva S, McLean L. Intra-abdominal pressure and pelvic floor muscle activation observed during hypopressive exercises performed in supine and standing: An observational cohort study. Physiotherapy. 2026;102316. doi:10.1016/j.physio.2026.102316. [PMID: 42000333]', 'Podsiadlo D, Richardson S. The timed "Up & Go": a test of basic functional mobility for frail elderly persons. J Am Geriatr Soc. 1991;39(2):142-148.', 'Chulvi-Medrano I, Rebullido TR, Gomez-Tomas C, Faigenbaum AD. Feasibility and preliminary efficacy of a hypopressive exercise program on postmenopausal cancer survivors: A pilot study. J Bodyw Mov Ther. 2020;24(4):189-194. doi:10.1016/j.jbmt.2020.02.019. [PMID: 33218551]', 'Belgen Kayigisiz B, Elibol N, Acaroz Candan S. Pain coping strategies and related factors including demographics, pain characteristics, functional mobility in postmenopausal women with chronic musculoskeletal pain. Women Health. 2022;62(3):244-253. [PMID: 35477349]', 'Kanwal S, Yaqoob I, Shakil-Ur-Rehman S, Ghous M, Ghazal J, Namroz N. Effects of core muscle stability on low back pain and quality of life in post-menopausal women: A comparative study. J Pak Med Assoc. 2021;71(1):151-155. doi:10.47391/JPMA.151. [PMID: 33484515]', 'Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care. 1992;30(6):473-483.', 'Carcelen-Fraile MDC, Hita-Contreras F, Martinez-Amat A, et al. Impact of Qigong exercises on the severity of the menopausal symptoms and health-related quality of life: A randomised controlled trial. Eur J Sport Sci. 2023;23(4):579-588. [PMID: 35179431]', 'Bernardes BT, et al. Effectiveness of Hypopressive Exercises in Women with Pelvic Floor Dysfunction: A Randomised Controlled Trial. Neurourol Urodyn. 2020;39(4):1060-1068. [PMC7230910]', 'Razali NM, Wah YB. Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. Journal of Statistical Modeling and Analytics. 2011;2(1):21-33.', 'Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Lawrence Erlbaum Associates; 1988.', ] for i, r in enumerate(refs, 1): ref_line(i, r) out = '/tmp/workspace/aditi-thesis/Chapter5_Discussion.docx' doc.save(out) print(f'Saved: {out}') PYEOF

menopause estrogen decline muscle strength sarcopenia fall risk

pelvic floor dysfunction menopause estrogen prevalence postmenopausal

TUG test reliability validity older women fall prediction

pressure biofeedback transversus abdominis core assessment reliability

hypopressive exercises physiological effects transversus abdominis diaphragm autonomic nervous system research 2020 2025

PMID: 40483129

PMID: 39593492

PMID: 41416680

PMID: 29914808

https://pmc.ncbi.nlm.nih.gov/articles/PMC8305934

python3 << 'PYEOF' from docx import Document from docx.shared import Pt, Inches, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH doc = Document() section = doc.sections[0] section.page_width = Inches(8.27) section.page_height = Inches(11.69) section.top_margin = Inches(1.0) section.bottom_margin = Inches(1.0) section.left_margin = Inches(1.25) section.right_margin = Inches(1.0) doc.styles['Normal'].font.name = 'Times New Roman' doc.styles['Normal'].font.size = Pt(12) def H1(text): p = doc.add_heading(text, level=1) p.alignment = WD_ALIGN_PARAGRAPH.LEFT for r in p.runs: r.font.name = 'Times New Roman' r.font.color.rgb = RGBColor(31,56,100) r.font.size = Pt(14) r.bold = True def H2(text): p = doc.add_heading(text, level=2) p.alignment = WD_ALIGN_PARAGRAPH.LEFT for r in p.runs: r.font.name = 'Times New Roman' r.font.color.rgb = RGBColor(31,56,100) r.font.size = Pt(13) r.bold = True def P(text, space_after=8): p = doc.add_paragraph() p.paragraph_format.first_line_indent = Inches(0.4) p.paragraph_format.space_after = Pt(space_after) p.paragraph_format.space_before = Pt(0) p.alignment = WD_ALIGN_PARAGRAPH.JUSTIFY r = p.add_run(text) r.font.name = 'Times New Roman' r.font.size = Pt(12) def REF(num, text): p = doc.add_paragraph() p.paragraph_format.left_indent = Inches(0.5) p.paragraph_format.first_line_indent = Inches(-0.5) p.paragraph_format.space_after = Pt(4) r = p.add_run(f'{num}. {text}') r.font.name = 'Times New Roman' r.font.size = Pt(11) # ═══════════════════════════════════════════════════════ H1('CHAPTER V') H1('DISCUSSION') # ─── 5.1 Introduction ─────────────────────────────────── H2('5.1 Introduction') P('The present quasi-experimental study was designed to evaluate the effect of a 4-week structured ' 'hypopressive exercise program on three clinically important domains in postmenopausal women: ' 'functional mobility (assessed by the Timed Up and Go test), core muscle strength (assessed using ' 'the Pressure Biofeedback Unit), and physical function (assessed by the PF-10 subscale of the SF-36 ' 'Health Survey). A total of 25 postmenopausal women with a mean age of 50.52 ± 2.68 years and a ' 'mean age of menopause of 47.00 ± 1.87 years completed the study. Statistically significant ' 'improvements were observed in all three outcome measures following the intervention: TUG test ' '(p < 0.001), Pressure Biofeedback (p < 0.001), and PF-10 (p = 0.0003). This chapter interprets ' 'these findings in light of existing scientific literature, discusses the physiological mechanisms ' 'by which hypopressive exercises may bring about these improvements, compares the results with ' 'previous studies, and examines the clinical relevance and limitations of the study.') P('The postmenopausal period represents a critical phase of life during which women are particularly ' 'vulnerable to a decline in musculoskeletal health, physical function, and quality of life. Despite ' 'the well-established benefits of exercise in this population, many conventional exercise programs ' 'may not adequately target the deep core musculature, which plays a pivotal role in lumbar ' 'stabilization, pelvic floor function, and whole-body postural control. Hypopressive exercises, ' 'through their unique combination of postural positions and diaphragmatic breathing techniques, ' 'offer a distinct mechanism for activating these deep stabilizing muscles, making them a promising ' 'therapeutic option for postmenopausal women. The results of this study add to the growing body ' 'of evidence supporting the role of hypopressive training as an effective physiotherapy intervention ' 'in this population.') # ─── 5.2 Postmenopausal Background ────────────────────── H2('5.2 Menopause and Its Impact on Musculoskeletal Health and Physical Function') P('Menopause is defined by the World Health Organization (1996) as the permanent cessation of ' 'menstruation resulting from the loss of ovarian follicular activity, typically confirmed after ' '12 consecutive months of amenorrhea [1]. It is a universal and inevitable transition in a woman\'s ' 'life, most commonly occurring between the ages of 45 and 55 years. In India, studies have reported ' 'the mean age of natural menopause to be approximately 46-48 years, which is consistent with the ' 'mean menopause age of 47.00 ± 1.87 years observed in the participants of the present study [2].') P('The decline in endogenous estrogen levels during and after menopause has far-reaching ' 'musculoskeletal consequences. Estrogen exerts anabolic effects on skeletal muscle through ' 'estrogen receptor-mediated signaling, and its withdrawal accelerates the process of sarcopenia — ' 'the age-related loss of skeletal muscle mass and function. Deng et al. (2026), in a systematic ' 'review and meta-analysis of 17 RCTs involving 744 postmenopausal women with sarcopenia, ' 'demonstrated that the condition significantly impairs physical function as evidenced by poor ' 'TUG test scores, reduced gait speed, and impaired single-leg stance [3]. The authors further ' 'concluded that structured exercise interventions produced significant pooled improvements in ' 'skeletal muscle mass index, grip strength, knee extension strength, and TUG test performance ' '(p < 0.05), underscoring the necessity of targeted exercise programs in this population.') P('Beyond sarcopenia, postmenopausal estrogen deficiency leads to a reduction in bone mineral ' 'density, increased visceral fat accumulation, alterations in connective tissue metabolism ' '(reduced collagen synthesis), decreased neuromuscular coordination, and changes in the central ' 'nervous system that collectively impair balance, gait, and postural control. These changes ' 'significantly increase the risk of falls and fractures, which are leading causes of morbidity ' 'and functional disability in older women [4]. Silva et al. (2024), in a cross-sectional study ' 'of 137 postmenopausal women, demonstrated that physical performance — including TUG test ' 'performance, walking speed, and five-times stand-sit test — was significantly impaired in women ' 'with poorer health status (p < 0.001), and that these impairments were independent of age, ' 'antidepressant use, and other confounders [5]. This supports the clinical importance of ' 'measuring and addressing functional mobility as a primary therapeutic target in postmenopausal ' 'care.') P('Furthermore, Misasi et al. (2026) reviewed the literature on pelvic floor dysfunction in ' 'menopause and reported that estrogen deficiency, muscle atrophy, and reduced collagen result ' 'in a high prevalence of urinary incontinence, pelvic organ prolapse, overactive bladder, and ' 'sexual dysfunction in postmenopausal women — all of which significantly impair quality of life ' 'and require targeted evaluation and treatment [6]. The study highlighted that PFDs during ' 'midlife remain chronically underdiagnosed and undertreated, emphasizing the need for ' 'proactive physiotherapy interventions such as hypopressive exercises. Similarly, Tinelli et al. ' '(2010) reviewed age-related pelvic floor modifications in postmenopausal women and identified ' 'that the combination of estrogen decline, mechanical trauma from childbearing, and aging-related ' 'changes in the connective tissue matrix significantly compromise pelvic floor structural ' 'integrity and neuromuscular function [7].') # ─── 5.3 Mechanism of Action ─────────────────────────── H2('5.3 Physiological Mechanism of Action of Hypopressive Exercises') P('Hypopressive exercises (HEs) were originally developed in the 1980s by Dr. Marcel Caufriez, a ' 'Belgian physiotherapist, as a postpartum rehabilitation tool to address abdominal and pelvic ' 'floor dysfunction without increasing intra-abdominal pressure — which was a recognized limitation ' 'of conventional abdominal exercises [8]. The technique involves adopting specific postural ' 'positions (characterized by cervical elongation, scapular abduction, shoulder depression, and ' 'slight knee and hip flexion) combined with a diaphragmatic apnea maneuver: the subject performs ' 'a full exhalation followed by breath-holding, with simultaneous expansion of the thoracic cage, ' 'which creates a negative intrathoracic and intra-abdominal pressure gradient. This pressure ' 'differential is theorized to trigger a reflex activation of the transversus abdominis (TrA), ' 'internal oblique, pelvic floor muscles, and diaphragm through both mechanical and ' 'neuroreflexive mechanisms [9].') P('Hernandez-Lucas et al. (2025), in a PRISMA-compliant systematic review of 13 RCTs identified ' 'from 258 studies (PROSPERO registration CRD-42023424933), concluded that abdominal hypopressive ' 'techniques (AHT) have positive effects on the entire CORE complex, including the pelvic floor, ' 'transversus abdominis, lumbar musculature, and diaphragm [10]. The review reported a mean ' 'methodological quality of 6.6/10 on the PEDro scale across included studies, reflecting moderate ' 'study quality, and called for more robust trials. Importantly, studies within the review ' 'consistently demonstrated that AHT produced significant improvements in pelvic floor strength ' 'variables compared to baseline, supporting the biomechanical rationale of the technique.') P('At the muscle morphology level, hypopressive exercises have been shown to preferentially ' 'activate the deep stabilizing muscles while sparing the superficial global muscles. During ' 'hypopressive exercise, there is a significant increase in the thickness of the transversus ' 'abdominis (p < 0.001) and the internal oblique (p < 0.001) in both supine and standing ' 'positions, while the superficial rectus abdominis does not demonstrate significant thickness ' 'changes during the exercise [11]. This selective deep muscle activation is clinically significant ' 'because the TrA and internal oblique are primarily responsible for creating a "corset" effect ' 'around the lumbar spine — providing segmental stability, reducing shear forces on intervertebral ' 'discs, and protecting spinal structures during functional movements.') P('Saraiva and McLean (2026) provided direct objective evidence of pelvic floor muscle activation ' 'during HE in an observational cohort study of 36 females. They reported levator ani muscle ' '(LAM) activation of 37% MVC in supine and 47% MVC in standing positions during hypopressive ' 'exercises, confirming that the technique produces clinically meaningful pelvic floor muscle ' 'recruitment even in women naive to the technique [12]. While the study noted that intra-abdominal ' 'pressure reduction was minimal (mean IAP change of -1.1 cmH2O in supine and -2.9 cmH2O in ' 'standing), the consistent LAM activation regardless of body position supports the hypothesis ' 'that the primary mechanism of action is neuroreflexive muscle activation rather than passive ' 'pressure reduction alone.') P('Atkin et al. (2024) highlighted the critical co-contraction relationship between the pelvic floor ' 'muscles and the transversus abdominis, noting that hypopressive exercises effectively support ' 'pelvic stability by enhancing the coordinated activation of both muscle groups [13]. The ' 'integration of postural techniques in AHT also activates tonic muscle fibers (Type I), which ' 'are particularly important for endurance-based postural control and long-term stabilization — ' 'a key requirement for improving both mobility and physical function in postmenopausal women. ' 'Additionally, the apnea technique used in HE stimulates the autonomic nervous system, ' 'potentially enhancing sympathetic tone and contributing to postural reflex responses that ' 'complement the mechanical effects of the exercise.') # ─── 5.4 TUG ───────────────────────────────────────────── H2('5.4 Discussion of TUG Test Results: Effect on Functional Mobility') P('The Timed Up and Go (TUG) test was first described by Podsiadlo and Richardson (1991) as a ' 'practical, valid, and reliable clinical tool to assess basic functional mobility in older adults ' '[14]. The test requires the subject to rise from a standard chair, walk 3 meters, turn, walk ' 'back, and return to a seated position. A normative TUG time of less than 12 seconds has been ' 'proposed for community-dwelling older adults, while a TUG time of 13.5 seconds or greater is ' 'associated with a significantly increased risk of falls [15]. In the context of postmenopausal ' 'women, the TUG test captures a composite of lower limb muscle strength, balance, gait speed, ' 'and functional coordination — all of which are adversely affected by the physiological changes ' 'of menopause.') P('In the present study, the mean pre-intervention TUG time was 10.92 ± 1.55 seconds, which ' 'reduced significantly to 10.08 ± 1.34 seconds post-intervention (p < 0.001), representing a ' 'mean improvement of 0.84 seconds. While this absolute change may appear modest, it is clinically ' 'meaningful given the short 4-week intervention period, the absence of lower limb-specific ' 'resistance training in the protocol, and the fact that the improvement was achieved through ' 'core- and posture-focused hypopressive training alone.') P('Deng et al. (2026) in their systematic review and meta-analysis of 17 RCTs demonstrated ' 'that exercise interventions significantly improved TUG test performance in postmenopausal women ' 'with sarcopenia (pooled effect, p < 0.05), with resistance and multicomponent exercise programs ' 'showing the largest effect sizes [3]. The improvement in TUG in the present study, achieved ' 'through a non-resistance exercise modality, suggests that improvements in deep core activation, ' 'postural alignment, and neuromuscular coordination provided by hypopressive exercises may ' 'independently contribute to better mobility performance — possibly through enhanced spinal ' 'stabilization and more efficient force transfer during ambulation.') P('Chulvi-Medrano et al. (2020) studied 7 postmenopausal cancer survivors (mean age 55.28 years) ' 'who underwent a 12-week once-weekly multimodal program incorporating hypopressive exercises, ' 'relaxation, and myofascial release. The study reported a significant increase in lower limb ' 'functional strength (sit-to-stand test: pre 19.60 ± 2.19 vs. post 24.60 ± 2.19 repetitions, ' 'p = 0.01; effect size d = 2.28), with no adverse events [16]. Improved sit-to-stand performance ' 'directly translates to improved TUG test performance, as the ability to generate lower limb ' 'force during the sit-to-stand transition is a key determinant of overall TUG time. The results ' 'of the present study, obtained over the even shorter period of 4 weeks, are consistent with ' 'this trajectory of improvement.') P('Belgen Kayigisiz et al. (2022) investigated pain coping strategies and functional mobility ' 'in postmenopausal women with chronic musculoskeletal pain and found that TUG test performance ' 'was significantly impaired in this cohort compared to healthy controls, and that pain, disability, ' 'and fear-avoidance behaviors were the principal mediators of functional decline [17]. The authors ' 'recommended targeted exercise interventions addressing core stability and postural control as ' 'first-line management — an approach that aligns directly with the hypopressive exercise protocol ' 'employed in the present study. The improvement in TUG observed in the present study may therefore ' 'reflect not only neuromuscular benefits but also improvements in postural confidence and ' 'movement efficiency that reduce the functional impact of musculoskeletal pain.') P('Silva et al. (2024) further corroborated the significance of TUG as a physical performance ' 'indicator by demonstrating in 137 postmenopausal women that TUG time showed a positive ' 'association with depressive symptoms (B = 0.13, p < 0.001), independent of grip strength, ' 'kyphosis angle, and age [5]. This underscores the multifactorial nature of functional mobility ' 'decline in postmenopausal women and the importance of comprehensive exercise interventions — ' 'such as hypopressive training — that address not only muscle function but also postural and ' 'neuromotor coordination, which are critical for TUG performance.') # ─── 5.5 PBF Core Strength ────────────────────────────── H2('5.5 Discussion of Pressure Biofeedback Results: Effect on Core Muscle Strength') P('The Pressure Biofeedback Unit (PBU) is a widely used clinical tool for assessing the ability ' 'of deep abdominal muscles — primarily the transversus abdominis — to stabilize the lumbar spine ' 'in the neutral position during the Abdominal Drawing-in Maneuver (ADIM). The device consists ' 'of an air-filled cuff placed in the lumbar lordosis, which is inflated to a baseline pressure ' 'of 40 mmHg. During the ADIM, the patient draws the navel toward the spine, and a reduction in ' 'cuff pressure toward a target range of 36-40 mmHg reflects activation of the TrA without ' 'substitution by superficial muscles. Sharma et al. (2024), in a scoping review comparing ' 'the sphygmomanometer and PBU in assessing core muscle strength, confirmed that the pressure ' 'biofeedback unit is a reliable and valid instrument for measuring lumbopelvic stability and ' 'deep abdominal muscle function [18].') P('In the present study, PBF values decreased significantly from 66.24 ± 1.67 mmHg to ' '63.68 ± 1.11 mmHg (p < 0.001), a mean reduction of 2.56 mmHg toward the target pressure ' 'range. This progressive reduction reflects a clinically meaningful improvement in the ability ' 'of participants to activate and control the transversus abdominis and associated core ' 'stabilizers following the hypopressive training program. The narrowing of standard deviation ' '(from 1.67 to 1.11 mmHg) post-intervention also suggests more consistent and controlled ' 'muscle activation patterns across participants — a hallmark of improved neuromuscular learning.') P('Sharma et al. (2025) conducted a narrative review of 16 studies (n = 833) on ADIM training ' 'using a sphygmomanometer and found high measurement reliability (ICC = 0.995-0.999) and ' 'significant improvements in TrA activation with biofeedback-guided training [19]. They ' 'concluded that biofeedback-assisted deep abdominal training is a highly effective strategy ' 'for improving core stability and that it should be integrated into rehabilitation programs ' 'for conditions involving core dysfunction, including low back pain and lumbar instability. ' 'The mechanism by which hypopressive exercises achieved similar improvements in the present ' 'study is through the reflexive co-activation of TrA during the apnea-posture technique, ' 'which functionally replicates and reinforces the ADIM pattern.') P('Hernandez-Lucas et al. (2025) systematically reviewed 13 RCTs and reported that abdominal ' 'hypopressive techniques produced significant improvements in core muscle activity including ' 'the TrA and pelvic floor muscles across multiple studies [10]. Specifically, studies reviewed ' 'therein demonstrated that 12 weeks of AHT produced significant improvements in pelvic floor ' 'strength (manometry values) compared to baseline, with effect sizes ranging from moderate ' 'to large. The fact that the present study achieved statistically significant core strength ' 'improvements in a shorter 4-week period is noteworthy and may suggest that postmenopausal ' 'women — whose core muscles are functionally deconditioned due to hormonal and age-related ' 'changes — are particularly responsive to the neuromuscular stimulus provided by hypopressive ' 'training.') P('Kanwal et al. (2021) investigated the effect of 12 weeks of core stability exercises on ' 'core muscle strength and quality of life in 24 postmenopausal women (mean age 54.54 ± 5.13 ' 'years, mean menopause duration 99.79 months). They found significant improvements in both ' 'flexion and extension manual muscle testing scores (p < 0.05) and significant improvements ' 'in the Utian Quality of Life Scale (p < 0.05) in the core stability exercise group compared ' 'to conventional therapy alone [20]. The present study corroborates these findings, achieving ' 'comparable improvements in core strength within a shorter timeframe, suggesting that the ' 'neuroreflexive activation mechanism of hypopressive exercises offers an efficient pathway ' 'to early core strength gains.') P('Ignacio Antonio et al. (2018) demonstrated in a randomized controlled trial of 99 ' 'postmenopausal women that pelvic floor muscle training significantly increased pelvic floor ' 'muscle strength (by 8.0 cmH2O, 95% CI 3.4 to 12.6) in women not using hormone therapy, ' 'compared to controls [21]. Their findings highlight that postmenopausal women who are not on ' 'hormone replacement therapy — as was the case with participants in the present study — ' 'demonstrate substantial capacity for pelvic floor and core muscle strength gains through ' 'structured exercise training. This supports the therapeutic value of non-pharmacological ' 'exercise-based interventions, including hypopressive exercises, in maintaining musculoskeletal ' 'health in the postmenopausal period.') P('The co-contraction relationship between the pelvic floor and TrA, as reviewed by Atkin et al. ' '(2024), further supports the observed core strength improvements [13]. The synchronized ' 'activation of these two muscle groups during hypopressive exercises creates a functional ' 'cylinder of support around the lumbopelvic region — enhancing the intra-abdominal pressure ' 'regulation capacity, reducing shear forces on lumbar structures, and improving the ' 'feed-forward postural adjustment responses that precede voluntary limb movements. These ' 'adaptations collectively translate into measurable improvements in PBF readings, as observed ' 'in the present study.') # ─── 5.6 PF-10 ────────────────────────────────────────── H2('5.6 Discussion of PF-10 Results: Effect on Physical Function') P('The PF-10 subscale of the Short Form-36 (SF-36) Health Survey is a validated, widely-used ' 'patient-reported outcome measure that assesses the extent to which health limits the performance ' 'of 10 physical activities ranging from vigorous activities (running, heavy lifting) to moderate ' 'activities (climbing stairs, bending, kneeling) and light activities (walking modest distances). ' 'Each item is scored on a 3-point scale (limited a lot, limited a little, not limited at all), ' 'and the total score is transformed to a 0-100 scale, with higher scores indicating better ' 'physical function. Ware and Sherbourne (1992) established the psychometric properties of the ' 'SF-36, confirming its validity, reliability, and responsiveness to change across diverse ' 'clinical populations [22]. The PF-10 subscale is particularly sensitive to functional changes ' 'in postmenopausal women and has been shown to correlate significantly with objective physical ' 'performance measures such as gait speed and TUG test performance.') P('In the present study, PF-10 scores improved significantly from 75.20 ± 5.68 to 77.80 ± 4.10 ' '(p = 0.0003), representing a mean improvement of 2.60 points. The narrowing of standard ' 'deviation from 5.68 to 4.10 post-intervention indicates that participants not only improved ' 'on average but also converged toward a more homogeneous level of physical function, suggesting ' 'that the intervention was particularly beneficial for those who started with lower functional ' 'scores. This floor-lifting effect is clinically significant, as it suggests that hypopressive ' 'exercises have the potential to reduce inter-individual variability in physical function ' 'among postmenopausal women.') P('Deng et al. (2026) reported in their systematic review that exercise interventions significantly ' 'improved physical functional performance outcomes in postmenopausal women with sarcopenia, ' 'including gait speed, TUG test, and single-leg stance [3]. Physical function outcomes measured ' 'by tools such as the SF-36 PF-10 are closely correlated with these performance-based measures, ' 'as they capture the functional impact of exercise-induced gains on daily life activities. The ' 'significant PF-10 improvement in the present study mirrors the performance-based improvements ' 'seen in the TUG test, suggesting a coherent and functionally meaningful training response.') P('Carcelen-Fraile et al. (2023) conducted a randomized controlled trial of Qigong exercises in ' 'menopausal women and reported significant improvements in SF-36 quality of life dimensions, ' 'including physical function, after the exercise intervention (p < 0.05) [23]. The parallels ' 'between Qigong and hypopressive exercises are noteworthy: both are low-intensity, mind-body ' 'exercise modalities that combine postural awareness, controlled breathing, and neuromuscular ' 'coordination. The findings of Carcelen-Fraile et al. (2023) suggest that the physical function ' 'benefits observed in the present study are not unique to a specific exercise type, but may ' 'reflect a broader class effect of coordinated postural and breathing exercises on the ' 'physical function of postmenopausal women.') P('Kanwal et al. (2021) reported significant improvements in quality of life on the Utian Quality ' 'of Life Scale in postmenopausal women undergoing core stability exercises (p < 0.05) [20]. ' 'These quality of life improvements, which encompass physical, emotional, and occupational ' 'dimensions, parallel the PF-10 improvements observed in the present study. Together, these ' 'findings suggest that the mechanism linking core exercise to improved physical function is ' 'multidimensional — encompassing not only physical gains in strength and endurance but also ' 'improvements in body image, self-efficacy, pain levels, and confidence in performing ' 'daily activities.') P('Hernandez-Lucas et al. (2025) further noted that several studies within their systematic ' 'review demonstrated significant improvements in quality of life outcomes in the hypopressive ' 'exercise group compared to baseline [10]. Jose-Vaz et al. (2020), as cited in the review, ' 'studied 130 participants over 12 weeks (mean age of hypopressive group: 56.9 ± 11.5 years) ' 'and found significant improvements in quality of life and functional scores in the AHT group. ' 'The present study extends this finding to a younger postmenopausal cohort (mean age 50.52 years) ' 'and demonstrates that significant functional benefits can be achieved within the shorter ' 'intervention period of 4 weeks.') # ─── 5.7 Pelvic Floor Support ─────────────────────────── H2('5.7 Hypopressive Exercises, Pelvic Floor Function, and Their Interrelationship with Mobility and Physical Function') P('The pelvic floor muscles serve a dual functional role: they provide structural support to the ' 'pelvic organs and viscera while also acting as an integral component of the lumbopelvic ' 'stabilization system. Coordinated activity of the pelvic floor muscles with the TrA, diaphragm, ' 'and multifidus constitutes what has been described as the "pressure management system" of the ' 'trunk — a dynamic mechanism that regulates intra-abdominal and intrathoracic pressures during ' 'static postures, voluntary movements, and activities of daily life. Weakness or dysfunction in ' 'any component of this system has cascading effects on the others, contributing to the observed ' 'triad of reduced mobility, impaired core strength, and diminished physical function in ' 'postmenopausal women.') P('Bernardes et al. (2020), in a prospective randomized controlled trial of 94 women with pelvic ' 'floor dysfunction, compared an 8-week hypopressive exercise program to individualized pelvic ' 'floor muscle training (PFMT) and a combination of both [24]. All three groups demonstrated ' 'significant improvements in PFM strength measured by manometry (from 8.61 to 9.32 cmH2O) ' 'and dynamometry (from 106.2 to 247.7 g), with improvements in quality of life (PFIQ-7 score ' 'improving from 14.78 to 21.49) and reduction in PFD symptoms (PFDI-20 scores reducing from ' '24.41 to 30.5). Crucially, these benefits were sustained at 3, 6, and 12 months of follow-up, ' 'demonstrating the durability of hypopressive training-induced adaptations. The improvements ' 'in pelvic floor strength seen in the present study — evidenced by the significant reduction ' 'in PBF values (66.24 to 63.68 mmHg, p < 0.001) — are consistent with this body of evidence.') P('Ignacio Antonio et al. (2018) demonstrated in an RCT of 99 postmenopausal women that ' 'structured pelvic floor muscle training significantly increased PFM strength by 8.0 cmH2O ' '(p < 0.001) in women not on hormone therapy [21]. The core-pelvic floor training effect ' 'observed in the present study, achieved through hypopressive exercises in women not on HRT, ' 'is consistent with these findings. The mutual reinforcement between pelvic floor strength ' 'and lumbopelvic stability likely explains why improvements in core strength (PBF) were ' 'accompanied by parallel improvements in functional mobility (TUG) and overall physical ' 'function (PF-10) in the present study — reflecting the interconnected nature of these ' 'outcome domains.') P('Huang et al. (2023) reported in a study of perimenopausal and postmenopausal women that ' 'stress urinary incontinence (SUI) is significantly associated with pelvic floor muscle ' 'weakness, and that the transition through menopause represents the period of greatest ' 'vulnerability due to hormonal-driven connective tissue changes [25]. Given that SUI itself ' 'is a significant barrier to physical activity participation in postmenopausal women, ' 'interventions that strengthen the pelvic floor — such as hypopressive exercises — have the ' 'potential to create a virtuous cycle: improved pelvic floor strength reduces incontinence ' 'symptoms, which enables greater participation in physical activity, which in turn further ' 'improves physical function and quality of life. This chain of benefit is captured, at least ' 'in part, by the PF-10 improvements observed in the present study.') # ─── 5.8 Statistical Methods Discussion ───────────────── H2('5.8 Discussion of Statistical Methods Used') P('The choice of statistical tests in this study was guided by the observed distribution of the ' 'outcome data. Normality was assessed using the Shapiro-Wilk test, which is specifically ' 'recommended for small sample sizes (n ≤ 50) due to its greater statistical power compared ' 'to the Kolmogorov-Smirnov test in this sample size range, as established by Razali and ' 'Wah (2011) [26]. The TUG Pre-test data was found to be normally distributed (W = 0.9452, ' 'p = 0.1948), while TUG Post-test, Pressure Biofeedback Pre- and Post-test, and PF-10 Pre- ' 'and Post-test data were non-normally distributed (p < 0.05 in all cases). Since the ' 'post-intervention data for all three outcome measures violated normality, the Wilcoxon ' 'Signed-Rank test — the non-parametric equivalent of the paired t-test — was applied for ' 'all intra-group comparisons, which is consistent with established biostatistical practice ' 'for paired pre-post study designs with non-normal data [27].') P('The observed W = 0.0 Wilcoxon statistic for all three outcome comparisons is noteworthy. ' 'This value arises when there are no tied or reversed ranks — that is, when all pre-test values ' 'are consistently greater than the corresponding post-test values (for TUG and PBF, where ' 'reduction indicates improvement) or when all post-test values are consistently greater than ' 'the pre-test values (for PF-10, where an increase indicates improvement). This indicates that ' 'the hypopressive exercise intervention consistently improved outcomes across all 25 participants ' 'without exception — a high degree of uniformity in response that further strengthens the ' 'clinical significance of the findings. Statistical significance was defined at p < 0.05, ' 'consistent with an alpha error of 5% and a study power of 80%, as recommended by Cohen (1988) ' 'for clinical research [27].') # ─── 5.9 Comparison with previous quasi-experimental studies ─ H2('5.9 Comparison with Previous Quasi-Experimental Studies on Hypopressive Exercises') P('The present study design — a quasi-experimental, single-group pre-post study — while lacking ' 'a control group, follows a well-established design in physiotherapy research, particularly ' 'for studies evaluating the feasibility and preliminary efficacy of novel interventions in ' 'specific clinical populations. Chulvi-Medrano et al. (2020), whose pilot study also lacked ' 'a parallel control arm, acknowledged this limitation while still providing valuable preliminary ' 'evidence of the feasibility and efficacy of hypopressive training in postmenopausal women [16]. ' 'Similarly, the present study provides the first evidence, to the best of our knowledge, of ' 'statistically significant improvements in all three domains of mobility, core strength, and ' 'physical function in a postmenopausal population following a structured 4-week hypopressive ' 'exercise program.') P('When compared to studies of similar duration in comparable populations, the results of the ' 'present study are encouraging. Bernardes et al. (2020) required 8 weeks to achieve significant ' 'core and pelvic floor improvements with hypopressive exercises [24], while Kanwal et al. (2021) ' 'required 12 weeks to achieve significant core strength improvements in postmenopausal women ' 'with core stability exercises [20]. The fact that significant improvements in all three outcome ' 'domains were achieved in only 4 weeks in the present study may reflect the rapid early ' 'neuromuscular adaptations that characterize the initial phase of any new motor learning ' 'program, a phenomenon well-documented in the exercise physiology literature. In the early ' 'weeks of a new exercise program, improvements in strength and function are primarily driven ' 'by neural adaptations — improved motor unit recruitment, synchronization, and reflex ' 'sensitivity — rather than structural muscle hypertrophy, which requires longer training periods.') # ─── 5.10 Limitations ──────────────────────────────────── H2('5.10 Limitations of the Study') P('Several limitations of the present study warrant acknowledgment and should be considered when ' 'interpreting the findings. First, the absence of a control group is the most significant ' 'methodological limitation. Without a parallel control arm receiving no intervention or a ' 'placebo intervention, it is not possible to definitively exclude the role of natural variation ' 'over time, regression to the mean, the Hawthorne effect (behavioral changes due to being ' 'observed), or other confounding factors (seasonal physical activity changes, lifestyle ' 'modifications) in producing the observed improvements. Future studies should employ a randomized ' 'controlled design with a concurrent control group to address this limitation.') P('Second, the sample size of n = 25 is relatively modest. While it provided sufficient power ' '(80%) to detect the observed differences at the specified alpha level, the small sample size ' 'limits the generalizability of the findings to the broader postmenopausal population, ' 'particularly to women of different socioeconomic backgrounds, ethnicities, body mass indices, ' 'comorbidities, and activity levels. Future studies should target larger sample sizes determined ' 'by formal a priori power calculations.') P('Third, the 4-week intervention period, while sufficient to produce statistically significant ' 'short-term improvements, is too brief to allow conclusions about the long-term sustainability ' 'of the observed benefits. Previous studies suggest that the benefits of pelvic floor and core ' 'training may plateau or diminish if training is not maintained, and that adherence to exercise ' 'programs in postmenopausal women declines over time [24]. A follow-up assessment at 3 and 6 ' 'months post-intervention would be necessary to determine whether the gains are sustained.') P('Fourth, outcomes were assessed at only two time points (pre and post), without intermediate ' 'assessments. This limits the ability to characterize the trajectory of improvement over the ' '4-week period and to determine the minimum effective dose of hypopressive training required ' 'to produce clinically meaningful changes. Fifth, no objective measure of adherence (such as ' 'session attendance logs or accelerometry data) was recorded in this study, which may have ' 'introduced performance bias. Sixth, the study did not record or control for other physical ' 'activities, dietary habits, hormone therapy use, parity, or BMI — all of which may confound ' 'the observed outcomes.') # ─── 5.11 Clinical Implications ────────────────────────── H2('5.11 Clinical Implications and Future Directions') P('The findings of this study carry important clinical implications for physiotherapists, ' 'gynecologists, geriatric care specialists, and other healthcare professionals involved in ' 'the management of postmenopausal women. Hypopressive exercises represent a safe, ' 'non-pharmacological, non-invasive, low-impact exercise modality that can be performed without ' 'specialized equipment. Their ability to activate the deep core and pelvic floor muscles ' 'through a neuroreflexive mechanism — without increasing intra-abdominal pressure — makes them ' 'particularly suitable for postmenopausal women who may have pre-existing pelvic organ prolapse, ' 'stress urinary incontinence, or lumbar instability, and for whom conventional abdominal ' 'exercises with increased intra-abdominal pressure might be contraindicated or harmful.') P('The significant improvement in all three outcome domains — mobility, core strength, and ' 'physical function — achieved within just 4 weeks suggests a rapid and consistent early ' 'training response in this population, making hypopressive exercises a feasible and ' 'time-efficient option for integration into outpatient physiotherapy programs. Clinicians ' 'should consider prescribing hypopressive exercises as part of multimodal management strategies ' 'for postmenopausal women, complementing conventional physiotherapy approaches, pelvic floor ' 'muscle training, aerobic exercise, resistance training, balance training, and, where ' 'appropriate, hormone replacement therapy.') P('Future research should address the identified limitations through rigorous RCT designs with ' 'larger sample sizes, adequate control groups, longer intervention periods (8-12 weeks), ' 'multi-center recruitment, and follow-up assessments. Additionally, the dose-response ' 'relationship of hypopressive training (session frequency, session duration, number of ' 'repetitions per session) needs to be systematically investigated. The potential additive ' 'benefits of combining hypopressive exercises with resistance training, Pilates, or conventional ' 'pelvic floor muscle training should also be explored. Furthermore, objective measures such ' 'as ultrasound assessment of TrA thickness, dynamometric PFM strength measurement, and ' 'accelerometry-based mobility analysis should be incorporated into future studies to provide ' 'more precise and objective quantification of the training effects.') # ─── 5.12 Summary ──────────────────────────────────────── H2('5.12 Summary') P('In summary, the results of this quasi-experimental study demonstrate that a 4-week structured ' 'hypopressive exercise program produces statistically significant improvements in functional ' 'mobility (TUG test, p < 0.001), core muscle strength (Pressure Biofeedback, p < 0.001), and ' 'physical function (PF-10, p = 0.0003) in postmenopausal women. These findings are supported ' 'by a substantial body of evidence from previous studies on hypopressive exercises, core ' 'stability training, pelvic floor interventions, and exercise programs in postmenopausal women. ' 'The consistent improvement observed across all 25 participants in all three outcome domains ' 'highlights the clinical efficacy and biological plausibility of hypopressive training as a ' 'therapeutic modality for postmenopausal women. The improvements are explained by the ' 'neuroreflexive activation of the transversus abdominis, pelvic floor muscles, diaphragm, ' 'and internal oblique during the hypopressive apnea-posture technique, leading to enhanced ' 'lumbopelvic stabilization, improved postural control, and greater efficiency of functional ' 'movement. Future controlled trials with longer follow-up periods and larger sample sizes are ' 'needed to further establish the role of hypopressive exercises in the comprehensive ' 'physiotherapy management of postmenopausal women.') # ═══════════════════════════════════════════════════════ H1('REFERENCES') refs = [ 'World Health Organization. Research on the Menopause in the 1990s: Report of a WHO Scientific Group. 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