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Diabetes predicts decreased quality of life among community-dwelling seniors undertaking progressive resistance exercise: an observational study.


Due to the increased number of elderly individuals with musculoskeletal diseases in developed countries including Japan (WHO Scientific Group 2003), a variety of group-based exercise regimens for the elderly have been proposed (Ades et al 1996, Ettinger et al 1997, Suzuki et al 2004). Among these, progressive resistance exercise has been shown to improve muscle strength and gait speed (Latham et al 2004). However, adverse effects are a major concern for elderly people undertaking exercise. A reduction in quality of life measured by the SF-36 after exercise was observed in 26% (bodily pain) and 32% (mental health) of 286 Japanese seniors (Ministry of Health Labour and Welfare 2005). However, adverse effects associated with progressive resistance exercise have been poorly reported, and it is therefore difficult to assess the risk related to progressive resistance exercise (Latham et al 2004). Identification of baseline characteristics associated with a reduction in quality of life would provide insight helpful to the design and implementation of exercise programs for older people. No previous studies have been conducted to investigate the relationship between the characteristics of participants undertaking progressive resistance exercise and quality of life.

Therefore, the aim of the current study was to investigate predictors of outcome in terms of quality of life to class-based progressive resistance exercise among community-dwelling Japanese people over 65 years. The specific research questions were:

1. What baseline characteristics predict good quality of life among community-dwelling Japanese seniors undertaking a three-month progressive resistance exercise program?

2. What baseline characteristics predict poor quality of life?



A prospective observational study was conducted with elderly participants of progressive resistance exercise programs. Between January 2007 and March 2008, volunteers were recruited from several three-month group-based progressive resistance exercise programs held in Tokyo, Japan. The progressive resistance exercise program used in the class was standardised according to the guidelines of the Tokyo Metropolitan Institute of Gerontology (see Appendix 1 on the eAddenda for program details). The program was performed for 1.5 hours per session, with 2 sessions per week for 3 months, using exercise machines for knee extension, hip abduction, recumbent squat, seated row, and abdominal flexion. Characteristics of the participants and quality of life were measured at baseline (Month 0) and at the end of the program three months later (Month 3).


Participants of the progressive resistance exercise programs were included if they were over 65 years, were community dwelling, had low level of disability (determined using a simple, self-administered disability assessment questionnaire, see Appendix 2 on the eAddenda) and had no contraindication to undertaking exercise (assessed by family doctor or a medical check-up). They were excluded if they had a terminal illness or dementia (assessed from their medical history from on-site interviews by staff nurses and medical reports by family doctor), or if they had participated in other exercise classes in the previous six months.

Outcome measures

Predictors included baseline demographic characteristics (such as age, sex, body mass index), presence of comorbidities (such as osteoarthritis of the hip or knee, spinal condition, osteoporosis, hyperlipidaemia, diabetes mellitus, neurogenic disorders, pulmonary disease, cardiac disease, hypertension), frequency of habitual exercise in the past three months, number of falls in the past year, and activity limitations such as Timed Up and Go (Podsiadlo and Richardson 1991) in s, maximum walking speed in m/s, maximum grip strength in kg, and Timed One-Legged Stand with eyes open in s (Table 1).

The outcome of interest was quality of life measured using the Japanese version of the SF-36, which has been validated by Fukuhara et al (1998). The subscales: physical functioning, physical role, bodily pain, general health, vitality, social functioning, emotional role, and mental health of the Short Form-36 (SF-36) were measured on a 0 to 100 scale.

Data analysis

Definition of good outcome in terms of quality of life: The subscales of the SF-36 that were improved after the three-month program were identified using paired t-tests with level of significance set at p < 0.01 to avoid Type I errors related to multiple comparisons. Four subscales: bodily pain, vitality, social functioning and mental health were identified (see Table 2). A good outcome was defined as achieving > 10% increase in two or more of the four subscales and < 10% decrease in the rest. The cut-off of 10% was determined based on the minimal clinically-important change of 10% previously determined by Angst et al (2001) and 11% by Quintana et al (2005) for the SF-36.

Definition of poor outcome in terms of quality of life:

The subscales of the SF-36 where > 20% of participants showed > 10% decrease after the three-month program were identified. Two subscales, physical role and vitality, were identified. A poor outcome was defined as achieving > 12% decrease in at least one of the two subscales and < 10% increase in the rest.

Identification of predictors of 'good' and 'poor' outcome: Independent t-tests or Chi-square tests were used to first identify univariate predictors of good and poor outcome depending on whether the target variable was continuous. Participants who attended less than 70% of the exercise sessions were excluded from the analysis. The level of significance was set at p < 0.25 to ensure that potential predictors were not excluded at this stage. Significant predictors were then entered into the logistic regression analysis.

Logistic regression was used to identify multivariate predictors of 'good' and 'poor' outcome with the level of significance set at p < 0.05. A clinical prediction rule was derived for each outcome, and the predictive accuracy of each prediction rule was calculated using positive and negative likelihood ratios (LR+, LR-). Additionally, odds ratios adjusted for age, sex, and body mass index were computed to indicate the strength of association between the predictors and good or poor outcome.


Flow of participants through the study A total of 79 people were invited to participate in the study. At baseline, one person chose not to answer the questions and another could not complete the physical tests due to fatigue. During the three-month progressive resistance exercise program, 9 (11%) participants dropped out due variously to health-related problems (n = 1), contraindications (n = 2), family conflicts (n = 1), increased pain (n = 1), falls at or around the home (n =2), and admission to hospital (n = 2). At Month 3, two (3%) participants were absent and one had incomplete data. Participants who had attendance rates below 70% were excluded from the analysis (n = 2). Therefore, data from 63 (76%) participants were included in the final analyses.

The baseline characteristics of the participants are summarised in Table 1. The participants had a body mass index within the normal range, and the majority were hypertensive, had few co-morbidities, had exercised at least three times in the past three months, and had no falls in the past 12 months.

Quality of life and the change after the three-month progressive resistance exercise program are presented in Table 2. At Month 3, significant (p < 0.01) improvements were observed in the subscales of bodily pain (p < 0.001), vitality (p = 0.002), social functioning (p < 0.001), and mental health (p < 0.001). At Month 3, more than 20% of the participants showed a decrease of more than 10% in the subscales of physical role and vitality.

Clinical prediction rules

The univariate analysis revealed that a lower body mass index, a longer timed one-legged stand, a low number of co-morbidities, and an absence of diabetes, neurogenic disorders, and hypertension at baseline predicted a good outcome. A shorter timed one-legged stand, a high number of co-morbidities, and the presence of diabetes and hypertension predicted a poor outcome.

In the multivariate analysis, only the presence of diabetes negatively predicted a good outcome with an adjusted OR of 0.20 (95% CI 0.05 to 0.88) and positively predicted a poor outcome with an adjusted OR of 4.40 (95% CI 1.21 to 18.86) (Boxes 1 and 2). For example, a female with diabetes aged 76 years with body mass index of 23.1 at baseline would have a probability of 26% of improving by > 10% in the bodily pain, vitality, social functioning and mental health subscales of SF-36 after a three-month progressive resistance exercise program. In contrast, a male with diabetes aged 80 years with body mass index of 28.0 would have a probability of 60% of decreasing > 10 % in the physical role and vitality subscales of SF-36 after a three-month progressive resistance exercise program.
Box 1. Mean (95% CI) regression coefficients of
predictors, clinical prediction rule, and accuracy of
prediction of a good outcome

Regression coefficients of predictors

 Sex = -0.50 (-1.06 to 0.05)
 Age = 0.00 (-0.05 to 0.05)
 BMI = -0.11 (-0.22 to -0.00)
 Diabetes = -1.60 (-2.35 to -0.85)
 Constant = 3.11 (-1.67 to 7.88)

Clinical prediction rule

 Odds of a good outcome = [e.sup.3.11]
 x [e.sup.-0.50 male (1)]
 x [e.sup.0.00 age (yr)]
 x [e.sup.-0.11 BMI (kg/m2)]
 x [e.sup.-1.60 diabetes (1)]
 Probability of a
 good outcome = Odds (good outcome)/Odds (good outcome) + 1

Accuracy of clinical prediction rule

 LR+ = 1.25 (0.97 to 1.57)
 LR- = 0.44 (0.16 to 1.15)

Box 2. Mean (95% CI) regression coefficients of
predictors, clinical prediction rule, and accuracy of
prediction of a poor outcome

Regression coefficients of predictors
 Sex = 0.80 (0.13 to 1.46)
 Age = 0.03 (-0.02 to 0.09)
 BMI = 0.08 (-0.05 to 0.21)
 Diabetes = 1.48 (0.74 to 2.22)
 Constant = -6.53 (-12.32 to -0.73)

Clinical prediction rule

 Odds of a poor outcome = [e.sup.-6.53]
 x [e.sup.0.80 male (1)]
 x [e.sup.0.03 age (yr)]
 x [e.sup.0.08 BMI (kg/m2)]
 x [e.sup.1.48 diabetes (1)]
 Probability of a
 poor outcome = Odds (poor outcome)/Odds (poor outcome) + 1

Accuracy of clinical prediction rule

 LR+ = 7.15 (0.95 to 52.64)
 LR- = 0.87 (0.80 to 1.00)


While undertaking a three-month progressive resistance exercise program, the quality of life of Japanese seniors improved, most notably in terms of bodily pain, vitality, social functioning, and mental health subscales of the SF36. These findings are consistent with two previous studies conducted in Japan (Chiba et al 2006, Inaba et al 2008). However, quality of life in terms of physical function, physical role, and general health did not improve, in line with previous investigations (Damush and Damush 1999, Latham et al 2004, Nelson et al 2004). This may be due to adverse events occurring during exercise in some participants (Haykowsky et al 1996, Latham et al 2004, Pollock et al 1991).

Older people with diabetes mellitus were 80% less likely to improve in bodily pain, vitality, social functioning, and mental health subscales of the SF-36 after a three-month progressive resistance exercise program. Furthermore, the odds of a 'poor' outcome in physical role and vitality were more than four times greater in participants with diabetes mellitus. It is biologically plausible that, during and after intensive exercise, elderly people with diabetes who have long-term complications develop exercise-induced hypo--and hyperglycaemia and soft tissue injuries (Horton 2006). This is supported by a randomised trial showing that 38% of participants with diabetes mellitus in a 22-week exercise class experienced these adverse events compared with 14% of controls with the condition (p = 0.001) (Sigal et al 2007). Consequently, although exercise is one of the most effective interventions in improving glycaemic control and increasing muscle mass for people with diabetes mellitus (Constans and Lecomte 2007, Sigal et al 2007), it can also lead to adverse events.

The current study indicates that the predictive accuracy of the clinical prediction rule for both good and poor outcomes is limited. However, the positive likelihood ratio of 7.15 suggests that the clinical prediction rule for a poor outcome may be useful in identifying participants who are at increased risk for a decrease in quality of life after undertaking an exercise program (Cleland 2005). Due to their limited accuracy, the clinical prediction rules derived in the current study should be validated in other populations.

This study has several limitations. The results were obtained from a convenience sampling of Japanese older people and may thus not extrapolate to other populations. Only a short-term progressive resistance exercise program was undertaken, so quality of life after a long-term program is unknown. Medical history of the participants was obtained partly by self-report, which may have lead to diagnostic inaccuracy. However, self-reports of medical history have been shown to be accurate when compared with medical records (Bush et al 1989, Walker et al 1998). Finally, the dropout rate in the current study was 24%, which may have caused a selection bias.

In conclusion, the quality of life improved in terms of bodily pain, vitality, social functioning and mental health but not physical function, physical role, emotional role, and general health in a community-dwelling Japanese elderly population at risk of disability who undertook a three-month group-based progressive resistance exercise program. However, people with diabetes mellitus were at greater risk of decreasing their quality of life. Therefore, health care providers need to monitor carefully participants with this disease who are undertaking progressive resistance exercise.

eAddenda: Appendix 1 and 2 available at

Acknowledgements: I thank Professor Yoshio Nakamura, Waseda University, for his input into this study, and Keisuke Utsugi and Katsuhiro Morita, Waseda Elderly Health Association, as liaisons with municipal governments in Tokyo.

Ethics: This study was approved by the Human Research Ethics Committee, Waseda University, Tokyo. Written informed consent was gained from all participants before data collection began.

Competing interests: None declared.


Ades PA, Ballor DL, Ashikaga T, Utton JL, Nair KS (1996) Weight training improves walking endurance in healthy elderly persons. Annals of Internal Medicine 124: 568-572.

Angst F, Aeschlimann A, Stucki G (2001) Smallest detectable and minimal clinically important differences of rehabilitation intervention with their implications for required sample sizes using WOMAC and SF-36 quality of life measurement instruments in patients with osteoarthritis of the lower extremities. Arthritis and Rheumatism 45: 384-391.

Bush TL, Miller SR, Golden AL, Hale WE (1989) Self-report and medical record report agreement of selected medical conditions in the elderly. American Journal of Public Health 79: 1554-1556.

Chiba A, Miura M, Ohyama H, Takemori K, Yamamoto H (2006) [Positive effects of resistance strength training on QOL in the frail elderly trans]. Nippon Koshu Eisei Zasshi 53: 851-858.

Cleland J (2005) Orthopaedic clinical examination: an evidence based approach for physical therapists. Elsevier Health Science.

Constans T, Lecomte P (2007) Non pharmacological treatments in elderly diabetics. Diabetes and Metabolism 33 Suppl 1: S79-86.

Damush TM, Damush JG, Jr. (1999) The effects of strength training on strength and health-related quality of life in older adult women. The Gerontologist 39: 705-710.

Ettinger WH, Jr., Burns R, Messier SP, Applegate W, Rejeski WJ, Morgan T et al (1997) A randomized trial comparing aerobic exercise and resistance exercise with a health education program in older adults with knee osteoarthritis. The Fitness Arthritis and Seniors Trial (FAST). JAMA 277: 25-31.

Fukuhara S, Bito S, Green J, Hsiao A, Kurokawa K (1998) Translation, adaptation, and validation of the SF-36 Health Survey for use in Japan. Journal of Clinical Epidemiology 51: 1037-1044.

Haykowsky MJ, Findlay JM, Ignaszewski AP (1996) Aneurysmal subarachnoid hemorrhage associated with weight training: three case reports. Clinical Journal of Sport Medicine 6: 52-55.

Horton ES (2006) Diabetes Mellitus. In Frontera WR, Slovik DM, Dawson DM (Eds) Exercise in Rehabilitation Medicine (2nd edn). Illinois: Human Kinetics, pp 144-156.

Inaba Y, Obuchi S, Arai T, Satake K, Takahira N (2008) The long-term effects of progressive resistance training on health-related quality in older adults. Journal of Physiological Anthropology 27: 57-61.

Latham NK, Bennett DA, Stretton CM, Anderson CS (2004) Systematic review of progressive resistance strength training in older adults. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences 59: 48-61.

Ministry of Health Labour and Welfare (2005). An interim report of municipal experimental projects for disability prevention (Author's translation). Tokyo: Department of Health and Welfare Bureau for the Elderly, Ministry of Health, Labour and Welfare (Japan).

Nelson ME, Layne JE, Bernstein MJ, Nuernberger A, Castaneda C, Kaliton D et al (2004) The effects of multidimensional home-based exercise on functional performance in elderly people. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences 59: 154-160.

Podsiadlo D, Richardson S (1991) The timed 'Up & Go': a test of basic functional mobility for frail elderly persons. Journal of the American Geriatrics Society 39: 142-148.

Pollock ML, Carroll JF, Graves JE, Leggett SH, Braith RW, Limacher M et al (1991) Injuries and adherence to walk/jog and resistance training programs in the elderly. Medicine and Science in Sports and Exercise 23: 1194-2000.

Quintana JM, Escobar A, Bilbao A, Arostegui I, Lafuente I, Vidaurreta I (2005) Responsiveness and clinically important differences for the WOMAC and SF-36 after hip joint replacement. Osteoarthritis and Cartilage 13: 1076-1083.

Sigal RJ, Kenny GP, Boule NG, Wells GA, Prud'homme D, Fortier M et al (2007) Effects of aerobic training, resistance training, or both on glycemic control in type 2 diabetes: a randomized trial. Annals of Internal Medicine 147: 357-369.

Suzuki T, Kim H, Yoshida H, Ishizaki T (2004) Randomized controlled trial of exercise intervention for the prevention of falls in community-dwelling elderly Japanese women. Journal of Bone and Mineral Metabolism 22: 602-611.

Walker MK, Whincup PH, Shaper AG, Lennon LT, Thomson AG (1998) Validation of patient recall of doctor-diagnosed heart attack and stroke: a postal questionnaire and record review comparison. American Journal of Epidemiology 148: 355-361.

WHO Scientific Group (2003) The burden of musculoskeletal conditions at the start of the new millennium (No. 919). Geneva: World Health Organization.

Kotaro Tamari

Waseda University, Japan

Correspondence: Dr Kotaro Tamari, School of Health Science, Kibi International University, Japan. Email:
Table 1. Baseline characteristics of participants.

Characteristic (n = 63)

Age (yr), mean (SD) 76 (6)
Sex, n female (%) 34 (54)
BMI (kg/[m.sup.2]), mean (SD) 23.1 (2.7)
Osteoarthritis, n (%) 16 (25)
Spinal disease and condition, n (%) 19 (30)
Osteoporosis, n (%) 6 (10)
Hyperlipidaemia, n (%) 21 (33)
Diabetes mellitus, n (%) 11 (18)
Neurogenic disorder, n (%) 7 (11)
Pulmonary disease, n (%) 12 (19)
Cardiac disease, n (%) 15 (24)
Hypertension, n (%) 35 (56)
Low co-morbidities, n with < 3 (%) 36 (57)
Amount of exercise in the last 3 months, n (%)
 None 21 (33)
 1-2 times 7 (11)
 3-12 times 21 (33)
 > 3 times per week 14 (22)
Number of falls in the last 12 months, n (%)
 None 39 (62)
 1 14 (22)
 2 2 (3)
 3 or more 8 (13)
Activity limitations, mean (SD)
 Timed up and Go (s) 6.9 (2.6)
 Maximum walking speed (m/s) 1.63 (0.72)
 Maximum grip strength (kg) 23.2 (7.9)
 Timed One-Legged Stand (s) 32 (22)

Table 2. Mean (SD) scores for quality of life at baseline and
after a three-month progressive resistance exercise program and
significance of change overtime (p value) and number (%) of
participants who had a decrease in quality of life of greater
than 10%.

Quality of Life Month 0 Month 3 Month 3 minus Month 0
SF-36 (0 to 100)
 Significance > 10% decrease

Physical function 72 (20) 77 (18) 0.02 11 (18)
Physical role 68 (24) 74 (22) 0.08 13 (21)
Bodily pain 62 (22) 72 (21) < 0.001 7 (11)
General health 58 (15) 62 (14) 0.03 12 (19)
Vitality 62 (20) 69 (18) 0.002 16 (25)
Social functioning 71 (27) 86 (19) < 0.001 8 (13)
Emotional role 66 (26) 75 (22) 0.01 8 (13)
Mental health 65 (20) 76 (16) < 0.001 5 (8)

Table 3. Mean difference or odds ratio (95% CI) and statistical
significance (p value) between a good or poor outcome for each

Predictor Good outcome

 Mean difference Significance
 or odds ratio

Age 0.43 (-2.50 to 3.38) 0.77
Female sex 1.31 (0.48 to 3.59) 0.60
BMI -0.82 (-2.18 to 0.53) 0.23
Osteoarthritis 0.87 (0.28 to 2.75) 0.82
Spinal disease and condition 0.95 (0.32 to 2.83) 0.93
Osteoporosis 1.46 (0.25 to 8.60) 0.68
Hyperlipidaemia 1.22 (0.42 to 3.56) 0.72
Diabetes mellitus 0.20 (0.05 to 0.84) 0.03
Neurogenic disorder 0.24 (0.04 to 1.35) 0.11
Pulmonary disease 0.98 (0.27 to 3.51) 0.98
Cardiac disease 0.75 (0.23 to 2.41) 0.63
Hypertension 0.28 (0.10 to 0.83) 0.02
< 3 co-morbidities 2.15 (0.77 to 6.00) 0.14
Exercise [greater than or equal
 to] 1/mth in last 3 mth 1.13 (0.41 to 3.09) 0.82
Fall [greater than or equal
 to] 1 in last 12 mth 0.97 (0.35 to 2.73) 0.96
Timed up and Go -0.31 (-1.66 to 1.05) 0.65
Maximum walking speed 0.05 (-0.66 to 0.75) 0.90
Maximum grip strength 0.94 (-3.14 to 5.02) 0.65
Timed One-Legged Stand 7.69 (-3.67 to 19.05) 0.18

Predictor Poor outcome

 Mean difference Significance
 or odds ratio

Age 0.79 (-2.70 to 4.27) 0.65
Female sex 0.56 (0.17 to 1.87) 0.35
BMI 0.51 (-1.11 to 2.13) 0.53
Osteoarthritis 1.23 (0.33 to 4.66) 0.76
Spinal disease and condition 1.39 (0.40 to 4.88) 0.61
Osteoporosis 0.68 (0.07 to 6.32) 0.73
Hyperlipidaemia 1.15 (0.33 to 3.98) 0.83
Diabetes mellitus 3.98 (0.99 to 15.94) 0.05
Neurogenic disorder 1.48 (0.25 to 8.52) 0.67
Pulmonary disease 0.65 (0.13 to 3.39) 0.61
Cardiac disease 1.46 (0.25 to 8.60) 0.68
Hypertension 3.82 (0.95 to 15.40) 0.06
< 3 co-morbidities 0.48 (0.14 to 1.58) 0.23
Exercise [greater than or equal
 to] 1/mth in last 3 mth 1.09 (0.33 to 3.60) 0.89
Fall [greater than or equal
 to] 1 in last 12 mth 0.58 (0.16 to 2.11) 0.41
Timed up and Go 0.27 (-1.34 to 1.88) 0.74
Maximum walking speed -0.06 (-0.89 to 0.77) 0.89
Maximum grip strength 0.18 (-4.66 to 5.02) 0.94
Timed One-Legged Stand -8.61 (-22.08 to 4.87) 0.21
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Title Annotation:Research
Author:Tamari, Kotaro
Publication:Australian Journal of Physiotherapy
Article Type:Clinical report
Geographic Code:8AUST
Date:Sep 1, 2009
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