Journal of Traditional Chinese Medicine ›› 2022, Vol. 42 ›› Issue (2): 256-263.DOI: 10.19852/j.cnki.jtcm.2022.02.006
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Efficacy of short duration versus conventional electroacupuncture in the treatment of obesity: a randomized crossover study
Lakkana Rerksuppaphol1(
), Sanguansak Rerksuppaphol2
- 1 Department of Preventive Medicine, Faculty of Medicine, Srinakharinwirot University, Nakorn Nayok 26120, Thailand
2 Department of Pediatrics, Faculty of Medicine, Srinakharinwirot University, Nakorn Nayok 26120, Thailand
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Received:2020-12-22Accepted:2021-03-23Online:2022-04-01Published:2022-04-01 -
Contact:Lakkana Rerksuppaphol -
About author:Lakkana Rerksuppaphol, Department of Preventive Medicine, Faculty of Medicine, Srinakharinwirot Uni-versity, Nakorn Nayok 26120, Thailand. lakkana_r@hotmail.com, Telephone: +66818483183
-
Supported by:Srinakharinwirot University, Thailand (Acupuncture for Obesity Treatment)(SWU 019/2561)
Cite this article
Lakkana Rerksuppaphol, Sanguansak Rerksuppaphol. Efficacy of short duration versus conventional electroacupuncture in the treatment of obesity: a randomized crossover study[J]. Journal of Traditional Chinese Medicine, 2022, 42(2): 256-263.
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Figure 1 Study flow chart and enrolment
Figure 1 Study flow chart and enrolment
Table 1 Baseline characteristics of participants ($\bar{x}±s$)
| Characteristic | Participants (n = 76) | |
|---|---|---|
| Age (years) | 38±9 | |
| Body weight (kg) | 79±11 | |
| Body mass index (kg/m2) | 29±4 | |
| Waist circumference (cm) | 93±8 | |
| Hip circumference (cm) | 106±9 | |
| Skinfold thickness (mm) | Triceps | 34±4 |
| Biceps | 34±4 | |
| Subscapular | 34±4 | |
| Supra-Iliac | 34±3 | |
| Mid-upper arm circumference (cm) | 32±3 | |
| Body fat percentage (%) | 38±6 | |
| Fasting blood glucose (mg/dL) | 107±21 | |
| Triglyceride (mg/dL)) | 143±62 | |
| Total cholesterol (mg/dL) | 216±33 | |
| LDL-cholesterol (mg/dL) | 142±30 | |
| HDL-cholesterol (mg/dL) | 52±6 |
Table 1 Baseline characteristics of participants ($\bar{x}±s$)
| Characteristic | Participants (n = 76) | |
|---|---|---|
| Age (years) | 38±9 | |
| Body weight (kg) | 79±11 | |
| Body mass index (kg/m2) | 29±4 | |
| Waist circumference (cm) | 93±8 | |
| Hip circumference (cm) | 106±9 | |
| Skinfold thickness (mm) | Triceps | 34±4 |
| Biceps | 34±4 | |
| Subscapular | 34±4 | |
| Supra-Iliac | 34±3 | |
| Mid-upper arm circumference (cm) | 32±3 | |
| Body fat percentage (%) | 38±6 | |
| Fasting blood glucose (mg/dL) | 107±21 | |
| Triglyceride (mg/dL)) | 143±62 | |
| Total cholesterol (mg/dL) | 216±33 | |
| LDL-cholesterol (mg/dL) | 142±30 | |
| HDL-cholesterol (mg/dL) | 52±6 |
Table 2 Anthropometric data and biochemistry parameters at each evaluation of participants allocated to group order AB ($\bar{x}±s$)
| Parameter | Baseline (n = 38) | 30 min EA effect (n = 38) | Start of 5 min EA (n = 38) | 5 min EA effect (n = 38) |
|---|---|---|---|---|
| Weight (kg) | 71.9±9.3 | –1.5 (–1.8, –1.2) | 71.7±9.4 | –1.5 (–1.8, –1.3) |
| BMI (kg/m2) | 28.7±9.1 | –0.61 (–0.75, –0.46) | 28.6±4.1 | –0.60 (–0.69, –0.51) |
| Waist (cm) | 92.1±7.6 | –4.5 (–5.4, –3.6) | 91.3±8.3 | –3.7 (–4.4, –3.0) |
| Hip (cm) | 105.5±7.5 | –4.5 (–5.3, –3.8) | 104.1±8.5 | –3.7 (–4.4, –3.1) |
| TCF (mm) | 34.8±4.0 | –3.1 (–3.7, –2.5) | 33.1±2.4 | –2.8 (–3.1, –2.4) |
| BCF (mm) | 34.4±2.9 | –2.9 (–3.4, –2.4) | 32.8±2.4 | –2.6 (–2.9, –2.2) |
| SSSF (mm) | 34.5±4.0 | –2.3 (–3.0, –1.6) | 31.9±2.4 | –2.2 (–2.7, –1.7) |
| SISF (mm) | 34.3±3.6 | –2.8 (–3.3, –2.4) | 32.6±2.4 | –2.4 (–2.7, –2.1) |
| MUAC (cm) | 33.2±3.3 | –2.6 (–2.9, –2.2) | 32.7±2.8 | –2.2 (–2.5, –1.8) |
| BF% (%) | 37.8±6.0 | –0.7 (–0.9, –0.5) | 37.7±5.9 | –0.7 (–0.8, –0.6) |
| FBG (mg/dL) | 111.3±11.7 | –9.2 (–13.3, –5.2) | 110.4±22.3 | –13.1 (–17.7, –8.4) |
| TG (mg/dL) | 140.3±47.6 | –32.7 (–47.4, –18.0) | 151.3±70.1 | –27.5 (–46.0, –9.1) |
| TC (mg/dL) | 218.0±31.6 | –45.9 (–57.3, –34.6) | 203.8±27.0 | –38.8 (–48.1, –29.6) |
| LDL–C (mg/dL) | 146.1±29.5 | –43.8 (–58.3, –29.3) | 115.3±26.8 | –26.8 (–34.2, –19.4) |
| HDL–C (mg/dL) | 52.4±6.5 | 1.9 (–1.9, 5.8) | 50.9±6.2 | 2.7 (0.2, 5.3) |
Table 2 Anthropometric data and biochemistry parameters at each evaluation of participants allocated to group order AB ($\bar{x}±s$)
| Parameter | Baseline (n = 38) | 30 min EA effect (n = 38) | Start of 5 min EA (n = 38) | 5 min EA effect (n = 38) |
|---|---|---|---|---|
| Weight (kg) | 71.9±9.3 | –1.5 (–1.8, –1.2) | 71.7±9.4 | –1.5 (–1.8, –1.3) |
| BMI (kg/m2) | 28.7±9.1 | –0.61 (–0.75, –0.46) | 28.6±4.1 | –0.60 (–0.69, –0.51) |
| Waist (cm) | 92.1±7.6 | –4.5 (–5.4, –3.6) | 91.3±8.3 | –3.7 (–4.4, –3.0) |
| Hip (cm) | 105.5±7.5 | –4.5 (–5.3, –3.8) | 104.1±8.5 | –3.7 (–4.4, –3.1) |
| TCF (mm) | 34.8±4.0 | –3.1 (–3.7, –2.5) | 33.1±2.4 | –2.8 (–3.1, –2.4) |
| BCF (mm) | 34.4±2.9 | –2.9 (–3.4, –2.4) | 32.8±2.4 | –2.6 (–2.9, –2.2) |
| SSSF (mm) | 34.5±4.0 | –2.3 (–3.0, –1.6) | 31.9±2.4 | –2.2 (–2.7, –1.7) |
| SISF (mm) | 34.3±3.6 | –2.8 (–3.3, –2.4) | 32.6±2.4 | –2.4 (–2.7, –2.1) |
| MUAC (cm) | 33.2±3.3 | –2.6 (–2.9, –2.2) | 32.7±2.8 | –2.2 (–2.5, –1.8) |
| BF% (%) | 37.8±6.0 | –0.7 (–0.9, –0.5) | 37.7±5.9 | –0.7 (–0.8, –0.6) |
| FBG (mg/dL) | 111.3±11.7 | –9.2 (–13.3, –5.2) | 110.4±22.3 | –13.1 (–17.7, –8.4) |
| TG (mg/dL) | 140.3±47.6 | –32.7 (–47.4, –18.0) | 151.3±70.1 | –27.5 (–46.0, –9.1) |
| TC (mg/dL) | 218.0±31.6 | –45.9 (–57.3, –34.6) | 203.8±27.0 | –38.8 (–48.1, –29.6) |
| LDL–C (mg/dL) | 146.1±29.5 | –43.8 (–58.3, –29.3) | 115.3±26.8 | –26.8 (–34.2, –19.4) |
| HDL–C (mg/dL) | 52.4±6.5 | 1.9 (–1.9, 5.8) | 50.9±6.2 | 2.7 (0.2, 5.3) |
Table 3 Anthropometric data and biochemistry parameters at each evaluation of participants allocated to group BA ($\bar{x}±s$)
| Parameter | Baseline (n = 38) | 5 min EA effect (n = 38) | Start of 30 min EA (n = 38) | 30 min EA effect (n = 38) |
|---|---|---|---|---|
| Weight (kg) | 73.4±13.3 | –1.4 (–1.7, –1.0) | 73.4±13.7 | –1.8 (–2.1, –1.5) |
| BMI (kg/m2) | 28.6±4.5 | –0.56 (–0.69, –0.42) | 28.5±4.5 | –0.70 (–0.81, –0.59) |
| Waist (cm) | 93.2±9.0 | –4.2 (–5.2, –3.3) | 91.8±8.1 | –3.0 (–4.3, –1.7) |
| Hip (cm) | 105.7±10.7 | –3.7 (–5.3, –2.1) | 105.4±10.7 | –4.6 (–5.6, –3.5) |
| TCF (mm) | 33.1±3.8 | –2.2 (–2.6, –1.9) | 32.6±2.6 | –2.1 (–2.4, –1.7) |
| BCF (mm) | 33.3±4.3 | –1.8 (–2.5, –1.3) | 33.0±2.4 | –2.6 (–3.0, –2.2) |
| SSSF (mm) | 34.2±4.8 | –2.4 (–3.1, –1.7) | 32.8±2.1 | –2.7 (–3.0, –2.3) |
| SISF (mm) | 33.7±4.2 | –2.7 (–3.1, –2.2) | 32.7±2.4 | –2.6 (–2.9, –2.2) |
| MUAC (cm) | 31.9±2.8 | –2.1 (–2.7, –1.5) | 31.9±2.6 | –2.1 (–2.4, –1.8) |
| BF% (%) | 37.5±6.0 | –0.7 (–0.8, –0.5) | 37.5±5.9 | –0.8 (–1.0, –0.7) |
| FBG (mg/dL) | 102.9±23.7 | –4.8 (–11.6, 2.0) | 100.2±16.8 | –2.7 (–8.9, 3.5) |
| TG (mg/dL) | 146.5±74.8 | –39.9 (–65.3, –14.5) | 142.9±66.4 | –26.2 (–38.4, –13.9) |
| TC (mg/dL) | 214.3±34.1 | –42.2 (–56.9, –27.4) | 210.9±41.2 | –48.8 (–62.1, –35.5) |
| LDL-C (mg/dL) | 138.3±30.5 | –35.3 (–49.8, –20.9) | 130.9±38.0 | –38.9 (–51.1, –26.0) |
| HDL-C (mg/dL) | 51.8±6.6 | 4.9 (3.2, 6.7) | 52.7±6.0 | 1.1 (–2.6, 4.8) |
Table 3 Anthropometric data and biochemistry parameters at each evaluation of participants allocated to group BA ($\bar{x}±s$)
| Parameter | Baseline (n = 38) | 5 min EA effect (n = 38) | Start of 30 min EA (n = 38) | 30 min EA effect (n = 38) |
|---|---|---|---|---|
| Weight (kg) | 73.4±13.3 | –1.4 (–1.7, –1.0) | 73.4±13.7 | –1.8 (–2.1, –1.5) |
| BMI (kg/m2) | 28.6±4.5 | –0.56 (–0.69, –0.42) | 28.5±4.5 | –0.70 (–0.81, –0.59) |
| Waist (cm) | 93.2±9.0 | –4.2 (–5.2, –3.3) | 91.8±8.1 | –3.0 (–4.3, –1.7) |
| Hip (cm) | 105.7±10.7 | –3.7 (–5.3, –2.1) | 105.4±10.7 | –4.6 (–5.6, –3.5) |
| TCF (mm) | 33.1±3.8 | –2.2 (–2.6, –1.9) | 32.6±2.6 | –2.1 (–2.4, –1.7) |
| BCF (mm) | 33.3±4.3 | –1.8 (–2.5, –1.3) | 33.0±2.4 | –2.6 (–3.0, –2.2) |
| SSSF (mm) | 34.2±4.8 | –2.4 (–3.1, –1.7) | 32.8±2.1 | –2.7 (–3.0, –2.3) |
| SISF (mm) | 33.7±4.2 | –2.7 (–3.1, –2.2) | 32.7±2.4 | –2.6 (–2.9, –2.2) |
| MUAC (cm) | 31.9±2.8 | –2.1 (–2.7, –1.5) | 31.9±2.6 | –2.1 (–2.4, –1.8) |
| BF% (%) | 37.5±6.0 | –0.7 (–0.8, –0.5) | 37.5±5.9 | –0.8 (–1.0, –0.7) |
| FBG (mg/dL) | 102.9±23.7 | –4.8 (–11.6, 2.0) | 100.2±16.8 | –2.7 (–8.9, 3.5) |
| TG (mg/dL) | 146.5±74.8 | –39.9 (–65.3, –14.5) | 142.9±66.4 | –26.2 (–38.4, –13.9) |
| TC (mg/dL) | 214.3±34.1 | –42.2 (–56.9, –27.4) | 210.9±41.2 | –48.8 (–62.1, –35.5) |
| LDL-C (mg/dL) | 138.3±30.5 | –35.3 (–49.8, –20.9) | 130.9±38.0 | –38.9 (–51.1, –26.0) |
| HDL-C (mg/dL) | 51.8±6.6 | 4.9 (3.2, 6.7) | 52.7±6.0 | 1.1 (–2.6, 4.8) |
Table 4 Carry-over effect, period effect and treatment effect
| Parameter | Carry-over effect Mean [95% CI] of the difference between baseline and end of washout by group of order | Period effect Mean difference between treatments’ change by group order | Treatment effect Mean change between post- and pre-treatment in the whole sample (n = 76) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group AB | Group BA | Group AB | Group BA | P-valuea | EA30b | EA5b | Mean difference between EA5-EA30 (95% CI) | P-valuec | ||||||
| Weight (kg) | –0.2 (–0.6, 0.3) | 0.01 (–0.3, 0.3) | 0.02 | –0.40 | 0.161 | –1.6 (–1.9, –1.4) | –1.5 (–1.7, –1.2) | –0.2 (–0.5, 0.1) | 0.200 | |||||
| BMI (kg/m2) | –0.08 (–0.26, 0.10) | –0.01 (–0.1, 0.1) | –0.01 | –0.14 | 0.232 | –0.65 (–0.74, –0.56) | –0.58 (–0.66, –0.50) | –0.08 (–0.19, 0.04) | 0.190 | |||||
| Waist (cm) | –0.71 (–1.44, 0.04) | –1.42 (–3.1, 0.2) | –0.84 | 0.21 | 0.233 | –4.3 (–5.0, –3.5) | –3.9 (–4.5, –3.4) | –0.3 (–1.2, 0.6) | 0.474 | |||||
| Hip (cm) | –1.4 (–2.6, –0.3) | 0.3 (–2.5, 1.9) | –0.78 | –0.87 | 0.936 | –4.5 (–5.3, –3.8) | –3.7 (–5.3, –2.1) | –0.8 (–2.6, 0.9)d | 0.344 | |||||
| TCF (mm) | –1.7 (–2.6, –0.7) | –0.5 (–1.6, 0.6) | –0.32 | 0.16 | 0.191 | –3.1 (–3.7, –2.5) e | –2.2 (–2.6, –1.9) | –0.9 (–1.6, –0.2)d | 0.015 | |||||
| BCF (mm) | –1.6 (–2.3, –0.8) | –0.3 (–1.3, 0.7) | –0.37 | 0.68 | 0.481 | –2.9 (–3.4, –2.4) | –1.9 (–2.5, –1.3) | –1.0 (–1.8, –0.3)d | 0.008 | |||||
| SSSF (mm) | –2.6 (–3.7, –1.4) | –1.4 (–2.7, –0.1) | –0.13 | –0.29 | 0.781 | –2.3 (–3.0, –1.7) | –2.4 (–3.1, –1.7) | –0.1 (–0.9, 1.0)d | 0.911 | |||||
| SISF (mm) | –1.7 (–2.7, –0.7) | –1.1 (–2.0, –0.1) | –0.42 | 0.11 | 0.096 | –2.8 (–3.3, –2.4) | –2.7 (–3.1, –2.2) | –0.2 (–0.8, 0.5)d | 0.624 | |||||
| MUAC (cm) | –0.5 (–1.1, 0.1) | 0 (–0.5, 0.5) | –0.42 | 0.03 | 0.201 | –2.3 (–2.6, –2.1) | –2.1 (–2.5, –1.8) | –0.2 (–0.5, 0.1) | 0.260 | |||||
| BF% (%) | –0.1 (–0.3, 0.1) | –0.01 (–0.2, 0.1) | –0.01 | –0.17 | 0.232 | –0.8 (–0.9, –0.7) | –0.7 (–0.9, –0.6) | –0.1 (–0.2, 0.1) | 0.190 | |||||
| FBG (mg/dL) | –0.9 (–7.0, 5.1) | –2.7 (–11.3, 6.0) | 3.8 | 2.1 | 0.735 | –6.0 (–25.8, –6.5) | –8.9 (–13.1, –4.8) | 2.9 (–1.9, 7.8) | 0.231 | |||||
| TG (mg/dL) | 11.0 (–5.6, 27.6) | –3.7 (–22.1, 14.8) | –5.2 | 13.7 | 0.209 | –29.4 (–38.8, –20.1) | –33.7 (–49.1, –18.3) | 4.3 (–10.6, 19.1) | 0.570 | |||||
| TC (mg/dL) | –14.2 (–26.4, –1.9) | –3.3 (–13.0, 6.3) | –7.1 | –6.6 | 0.963 | –45.9 (–57.3, –34.6) | –42.2 (–56.9, –27.5) | –3.8 (–22.1, 14.5)d | 0.683 | |||||
| LDL-C (mg/dL) | –30.8 (–43.3, –18.3) | –7.4 (–18.2, 3.5) | –17.0 | –3.5 | 0.248 | –43.8 (–58.7, –29.3) | –35.3 (–49.8, –20.1) | –8.4 (–28.5, 11.7)d | 0.407 | |||||
| HDL-C (mg/dL) | –1.5 (–3.9, 0.9) | 0.8 (–1.3, 2.9) | –0.8 | –3.8 | 0.277 | 1.5 (–1.1, 4.2) | 3.8 (2.3, 5.4) | –2.3 (–5.1, 0.5) | 0.100 | |||||
Table 4 Carry-over effect, period effect and treatment effect
| Parameter | Carry-over effect Mean [95% CI] of the difference between baseline and end of washout by group of order | Period effect Mean difference between treatments’ change by group order | Treatment effect Mean change between post- and pre-treatment in the whole sample (n = 76) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Group AB | Group BA | Group AB | Group BA | P-valuea | EA30b | EA5b | Mean difference between EA5-EA30 (95% CI) | P-valuec | ||||||
| Weight (kg) | –0.2 (–0.6, 0.3) | 0.01 (–0.3, 0.3) | 0.02 | –0.40 | 0.161 | –1.6 (–1.9, –1.4) | –1.5 (–1.7, –1.2) | –0.2 (–0.5, 0.1) | 0.200 | |||||
| BMI (kg/m2) | –0.08 (–0.26, 0.10) | –0.01 (–0.1, 0.1) | –0.01 | –0.14 | 0.232 | –0.65 (–0.74, –0.56) | –0.58 (–0.66, –0.50) | –0.08 (–0.19, 0.04) | 0.190 | |||||
| Waist (cm) | –0.71 (–1.44, 0.04) | –1.42 (–3.1, 0.2) | –0.84 | 0.21 | 0.233 | –4.3 (–5.0, –3.5) | –3.9 (–4.5, –3.4) | –0.3 (–1.2, 0.6) | 0.474 | |||||
| Hip (cm) | –1.4 (–2.6, –0.3) | 0.3 (–2.5, 1.9) | –0.78 | –0.87 | 0.936 | –4.5 (–5.3, –3.8) | –3.7 (–5.3, –2.1) | –0.8 (–2.6, 0.9)d | 0.344 | |||||
| TCF (mm) | –1.7 (–2.6, –0.7) | –0.5 (–1.6, 0.6) | –0.32 | 0.16 | 0.191 | –3.1 (–3.7, –2.5) e | –2.2 (–2.6, –1.9) | –0.9 (–1.6, –0.2)d | 0.015 | |||||
| BCF (mm) | –1.6 (–2.3, –0.8) | –0.3 (–1.3, 0.7) | –0.37 | 0.68 | 0.481 | –2.9 (–3.4, –2.4) | –1.9 (–2.5, –1.3) | –1.0 (–1.8, –0.3)d | 0.008 | |||||
| SSSF (mm) | –2.6 (–3.7, –1.4) | –1.4 (–2.7, –0.1) | –0.13 | –0.29 | 0.781 | –2.3 (–3.0, –1.7) | –2.4 (–3.1, –1.7) | –0.1 (–0.9, 1.0)d | 0.911 | |||||
| SISF (mm) | –1.7 (–2.7, –0.7) | –1.1 (–2.0, –0.1) | –0.42 | 0.11 | 0.096 | –2.8 (–3.3, –2.4) | –2.7 (–3.1, –2.2) | –0.2 (–0.8, 0.5)d | 0.624 | |||||
| MUAC (cm) | –0.5 (–1.1, 0.1) | 0 (–0.5, 0.5) | –0.42 | 0.03 | 0.201 | –2.3 (–2.6, –2.1) | –2.1 (–2.5, –1.8) | –0.2 (–0.5, 0.1) | 0.260 | |||||
| BF% (%) | –0.1 (–0.3, 0.1) | –0.01 (–0.2, 0.1) | –0.01 | –0.17 | 0.232 | –0.8 (–0.9, –0.7) | –0.7 (–0.9, –0.6) | –0.1 (–0.2, 0.1) | 0.190 | |||||
| FBG (mg/dL) | –0.9 (–7.0, 5.1) | –2.7 (–11.3, 6.0) | 3.8 | 2.1 | 0.735 | –6.0 (–25.8, –6.5) | –8.9 (–13.1, –4.8) | 2.9 (–1.9, 7.8) | 0.231 | |||||
| TG (mg/dL) | 11.0 (–5.6, 27.6) | –3.7 (–22.1, 14.8) | –5.2 | 13.7 | 0.209 | –29.4 (–38.8, –20.1) | –33.7 (–49.1, –18.3) | 4.3 (–10.6, 19.1) | 0.570 | |||||
| TC (mg/dL) | –14.2 (–26.4, –1.9) | –3.3 (–13.0, 6.3) | –7.1 | –6.6 | 0.963 | –45.9 (–57.3, –34.6) | –42.2 (–56.9, –27.5) | –3.8 (–22.1, 14.5)d | 0.683 | |||||
| LDL-C (mg/dL) | –30.8 (–43.3, –18.3) | –7.4 (–18.2, 3.5) | –17.0 | –3.5 | 0.248 | –43.8 (–58.7, –29.3) | –35.3 (–49.8, –20.1) | –8.4 (–28.5, 11.7)d | 0.407 | |||||
| HDL-C (mg/dL) | –1.5 (–3.9, 0.9) | 0.8 (–1.3, 2.9) | –0.8 | –3.8 | 0.277 | 1.5 (–1.1, 4.2) | 3.8 (2.3, 5.4) | –2.3 (–5.1, 0.5) | 0.100 | |||||
| 1. |
Chu DT, Minh Nguyet NT, Dinh TC et al. An update on physical health and economic consequences of overweight and obesity. Diabetes Metab Syndr 2018; 12: 1095-100.
DOI PMID |
| 2. |
Kopelman P. Health risks associated with overweight and obesity. Obes Rev 2007; 8 Suppl 1: 13-7.
DOI URL |
| 3. |
Adams KF, Schatzkin A, Harris TB et al. Overweight, obesity, and mortality in a large prospective cohort of persons 50 to 71 years old. N Engl J Med 2006; 355: 763-78.
DOI URL |
| 4. |
Finkelstein EA, Trogdon JG, Cohen JW, Dietz W. Annual medical spending attributable to obesity: payer-and service-specific estimates. Health Aff (Millwood) 2009; 28: w822-31.
DOI URL |
| 5. |
Dee A, Callinan A, Doherty E et al. Overweight and obesity on the island of Ireland: an estimation of costs. BMJ Open 2015; 5: e006189.
DOI URL |
| 6. |
Mohaupt MG, Karas RH, Babiychuk EB, et al. Association between statin-associated myopathy and skeletal muscle damage. CMAJ 2009; 181: E11-8.
DOI URL |
| 7. |
Wu J, Song Y, Li H, Chen J. Rhabdomyolysis associated with fibrate therapy: review of 76 published cases and a new case report. Eur J Clin Pharmacol 2009; 65: 1169-74.
DOI URL |
| 8. |
Langevin HM, Schnyer R, MacPherson H, et al. Manual and electrical needle stimulation in acupuncture research: pitfalls and challenges of heterogeneity. J Altern Complement Med 2015; 21: 113-28.
DOI URL |
| 9. |
Belivani M, Dimitroula C, Katsiki N, Apostolopoulou M, Cummings M, Hatzitolios AI. Acupuncture in the treatment of obesity: a narrative review of the literature. Acupunct Med 2013; 31: 88-97.
DOI PMID |
| 10. |
Weisell RC. Body mass index as an indicator of obesity. Asia Pac J Clin Nutr 2002; 11 Suppl 8: S681-4.
DOI URL |
| 11. |
Rerksuppaphol L, Rerksuppaphol S. Efficacy of electro-acupuncture at the main acupoints for weight reduction in Thai obese women. Asian Biomed 2010; 4: 943-7.
DOI URL |
| 12. | Rerksuppaphol L, Rerksuppaphol S. Efficacy of transcutaneous electrical acupoint stimulation compared to electroacupuncture at the main acupoints for weight reduction in obese Thai women. Int J Collaborative Res Intern Med Public Health 2011; 3: 811-20. |
| 13. |
Deurenberg P, Weststrate JA, Seidell JC. Body mass index as a measure of body fatness: age- and sex-specific prediction formulas. Br J Nutr 1991; 65: 105-14.
DOI URL |
| 14. |
Cho SH, Lee JS, Thabane L, Lee J. Acupuncture for obesity: a systematic review and Meta-analysis. Int J Obes (Lond) 2009; 33: 183-96.
DOI URL |
| 15. |
Zhang RQ, Tan J, Li FY, Ma YH, Han LX, Yang XL. Acupuncture for the treatment of obesity in adults: a systematic review and Meta-analysis. Postgrad Med J 2017; 93: 743-51.
DOI URL |
| 16. |
Fang S, Wang M, Zheng Y, Zhou S, Ji G. Acupuncture and lifestyle modification treatment for obesity: a Meta-analysis. Am J Chin Med 2017; 45: 239-54.
DOI URL |
| 17. | Zhang Y, Li J, Mo G, et al. Acupuncture and related therapies for obesity: a network Meta-analysis. Evid Based Complement Alternat Med 2018; 2018: 9569685. |
| 18. |
Kim SY, Shin IS, Park YJ. Effect of acupuncture and intervention types on weight loss: a systematic review and Meta-analysis. Obes Rev 2018; 19: 1585-96.
DOI PMID |
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