bs_bs_banner
doi: 10.1111/1753-0407.12234
Journal of Diabetes 7 (2015) 150–152
C O M M E N TA R Y
Helping people with diabetes to exercise Diabetes prevalence has more than doubled over the past 30 years,1 affecting nearly 400 million people worldwide, nearly half undiagnosed, with projections for this number to increase to nearly 600 million over the next two decades.2 To a large extent the growth in diabetes has been driven by overweight and lack of physical activity. Many clinical trials and observational studies have shown that lifestyle interventions encouraging diet and physical activity delay the onset of diabetes in people who have “pre-diabetes”3 and improve control of blood glucose as well as blood pressure and lipid levels in people with diabetes. Look AHEAD showed important benefits including improved glycemia with use of less medications, reduction in sleep apnea, enhanced mobility, and decreased urinary incontinence, although over 10 years it was not possible to demonstrate a reduction in cardiovascular morbidity and mortality.4 Although exercise alone may not be sufficient to improve outcome, it clearly plays an important role in diabetes.5 Exercise interventions are associated with reductions in HbA1c and lipids.6,7 and, among type 2 diabetic patients at increased cardiovascular risk, those engaging in moderate to vigorous physical activity had 15–22% reductions in likelihood of micro- and macrovascular events.8 One of us (XYL) conducted a randomized, controlled trial to evaluate the effect of intensive (8–10 Mets/h on a treadmill running 30 min/day) and moderate (3–6 Mets/h by a fast walking 30 min/day) exercise 5 days per week in 220 obese patients with nonalcoholic fatty liver disease (NAFLD) for one year. Compared with a control group receiving general lifestyle advice, fatty liver, body weight, waist circumference and blood pressure improved over 12 months (Clinical trial number:NCT01403532). Moderate exercise is as effective as intensive exercise in improving fatty liver, and reducing body weight, waist circumference and blood pressure. The dilemma is that such physical activity training necessitates measures to promote ongoing adherence by participants. A number of studies have used internetbased self-monitoring for diabetes treatment. While in managing glycemia such systems may be ineffective in improving control, given the intricate medication adjustment decisions often required,9 it appears inherently reasonable to suppose that systems to simply increase levels of physical activity might more readily lend themselves to such interventions. A practical approach in people with diabetes and at risk of its development would 150
involve ongoing assessment of physical activity levels, yet this fundamental aspect of treatment remains difficult to measure. One approach is simply to use regular online entry of physical activity to track exercise levels, which may be effective with sufficient supervision,10 although studies of this approach have noted that benefit occurs particularly among those participants making regular use of the web-based tools.11,12 Certainly the use of such an approach can offer a great deal of individual patientlevel information to be used in understanding parameters contributing to successful diet and exercise interventions.13 Such programs are limited, though, in the requirement for ongoing effort by participants and staff in entering and reviewing information pertaining to diet and activity, and the durability of participation has been an issue in a number of studies.14–16 A web-based followup of nearly 1000 individuals following a physical health-check in a workplace setting failed to show improvement in self-perceived health, obesity, elevated blood pressure, elevated cholesterol level, maximum oxygen uptake, or either direct or indirect costs,17 although another workplace-based study involving selfrecording of weight and physical activity by 222 persons showed that individuals achieving greater weight loss were better able to adhere to an online coaching program.18 Extrapolating from extensive research on dietary adherence,19 one can anticipate that self-report may lead to unreliable information pertaining to adherence to programs of increased physical activity. There is, however, great promise in the use of technologies to improve adherence to diabetes treatment regimens.20 A number of devices are commercially available or in development for tracking detailed metrics relevant to physical health, including sleep duration and quality, weight, and steps walked and duration of physical exercise. Pedometer/accelerometer-measurement of daily step-counts can be helpful, and simply having participants email step-count logs with weekly email feedback was shown to increase the weekly time spent walking in small study.21 Such approaches are feasible in ascertaining activity patterns of diabetic patients.22 More interestingly, such information can be automatically uploaded from an individual’s home or in a community setting using cellular data transmission to give ongoing accurate information of walking speed, duration, and overall energy expenditure.23 Measures of physical activity using a triaxial accelerometer have been shown to differ sig-
© 2014 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and Wiley Publishing Asia Pty Ltd
Commentary
nificantly between individuals who have been successful in maintaining weight loss and overweight controls.24 Such technology offers the additional potential to obtain information about much more than just walking, allowing fuller assessment of overall activity.25 Heart rate monitoring may add further useful information.26,27 Ideal systems for diabetes could use smartphones for uploading data streams, including heart rate from electrocardiographic input, pedometer/accelerometer measures of physical activity, and self-monitored capillary glucose as well as data from continuous glucose monitoring devices, and the feasibility of such approaches has been demonstrated in small, short-term studies.28 Of course, simply making physical activity monitors available does not always lead to their effective use,29 and a comprehensive intervention is required to ensure that participants in the study make use of the measures of physical activity and other behaviors, as these will otherwise lead to little benefit.30 At the same time, in studying such an approach it is necessary to control for any increase in interaction with providers occurring in an effort to assure regular use of the technology. Although at present the web- or mobile phone “app”-based information given to users is fairly minimal and offers little customization, the unique aspect of this as an intervention for the treatment of diabetes is the ease by which information can be acquired that can then be incorporated into a structured program which takes the “passively” acquired data and then returns it to the patient with objective assessment of their activity, and the use of such apps is likely to be a major part of programs to improve adherence.31 Interestingly, mobile phone App-based interventions appear to have greater HbA1c-lowering efficacy in diabetes than do interventions requiring computer use,32 suggesting that the combination of an “always-on” device to acquire behavioral information with an “always-available” mobile phone App may have great promise. References 1. Danaei G, Finucane MM, Lu Y et al. Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group (Blood Glucose). National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: Systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2·7 million participants. Lancet. 2011; 378: 31–40. 2. International Diabetes Federation Atlas. 2013. Available from: http://www.idf.org/sites/default/files/EN_6E_Atlas _Full_0.pdf (accessed 4 July 2014). 3. Knowler WC, Barrett-Connor E, Fowler SE et al. Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002; 346: 393–403.
4. Look AHEAD Research Group, Wing RR, Bolin P, Brancati FL et al. Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. N Engl J Med. 2013; 369: 145–54. 5. Thent ZC, Das S, Henry LJ. Role of exercise in the management of diabetes mellitus: The global scenario. PLoS ONE. 2013; 8: e80436. 6. Boulé NG, Haddad E, Kenny GP, Wells GA, Sigal RJ. Effects of exercise on glycemic control and body mass in type 2 diabetes mellitus: A meta-analysis of controlled clinical trials. JAMA. 2001; 286: 1218–27. 7. Balducci S, Zanuso S, Cardelli P et al. Italian Diabetes Exercise Study (IDES) Investigators. Effect of highversus low-intensity supervised aerobic and resistance training on modifiable cardiovascular risk factors in type 2 diabetes; the Italian Diabetes and Exercise Study (IDES). PLoS ONE. 2012; 7: e49297. 8. Blomster JI, Chow CK, Zoungas S et al. The influence of physical activity on vascular complications and mortality in patients with type 2 diabetes mellitus. Diabetes Obes Metab. 2013; 15: 1008–12. 9. Farmer AJ, Gibson OJ, Dudley C et al. A randomized controlled trial of the effect of real-time telemedicine support on glycemic control in young adults with type 1 diabetes (ISRCTN 46889446). Diabetes Care. 2005; 28: 2697–702. 10. Cranen K, Veld RH, Ijzerman M, Vollenbroek-Hutten M. Change of patients’ perceptions of telemedicine after brief use. Telemed J E Health. 2011; 17: 530–5. 11. Cadmus-Bertram L, Wang JB, Patterson RE, Newman VA, Parker BA, Pierce JP. Web-based self-monitoring for weight loss among overweight/obese women at increased risk for breast cancer: The HELP pilot study. Psychooncology. 2013; 22: 1821–8. 12. Webber KH, Tate DF, Ward DS, Bowling JM. Motivation and its relationship to adherence to selfmonitoring and weight loss in a 16-week Internet behavioral weight loss intervention. J Nutr Educ Behav. 2010; 42: 161–7. 13. Anton SD, LeBlanc E, Allen HR et al. Use of a computerized tracking system to monitor and provide feedback on dietary goals for calorie-restricted diets: The POUNDS LOST study. J Diabetes Sci Technol. 2012; 6: 1216–25. 14. Glasgow RE, Christiansen SM, Kurz D et al. Engagement in a diabetes self-management website: Usage patterns and generalizability of program use. J Med Internet Res. 2011; 13: e9. 15. Carr LJ, Dunsiger SI, Lewis B et al. Randomized controlled trial testing an internet physical activity intervention for sedentary adults. Health Psychol. 2013; 32: 328–36. 16. Carr LJ, Bartee RT, Dorozynski CM, Broomfield JF, Smith ML, Smith DT. Eight-month follow-up of physical activity and central adiposity: Results from an Internetdelivered randomized control trial intervention. J Phys Act Health. 2009; 6: 444–55. 17. Robroek SJ, Polinder S, Bredt FJ, Burdorf A. Costeffectiveness of a long-term Internet-delivered worksite health promotion programme on physical activity and
© 2014 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and Wiley Publishing Asia Pty Ltd
151
Commentary
18.
19.
20.
21.
22.
23.
24.
25.
26.
152
nutrition: A cluster randomized controlled trial. Health Educ Res. 2012; 27: 399–410. Ware LJ, Hurling R, Bataveljic O et al. Rates and determinants of uptake and use of an internet physical activity and weight management program in office and manufacturing work sites in England: Cohort study. J Med Internet Res. 2008; 10: e56. Burke LE, Wang J, Sevick MA. Self-monitoring in weight loss: A systematic review of the literature. J Am Diet Assoc. 2011; 111: 92–102. Driscoll KA, Young-Hyman D. Use of technology when assessing adherence to diabetes self-management behaviors. Curr Diabetes Rep. 2014; 14: 521–9. Dinger MK, Heesch KC, Cipriani G, Qualls M. Comparison of two email-delivered, pedometer-based interventions to promote walking among insufficiently active women. J Sci Med Sport. 2007; 10: 297–302. Dasgupta K, Joseph L, Pilote L, Strachan I, Sigal RJ, Chan C. Daily steps are low year-round and dip lower in fall/winter: findings from a longitudinal diabetes cohort. Cardiovasc Diabetol. 2010; 9: 81. Dobkin BH, Dorsch A. The promise of mHealth: Daily activity monitoring and outcome assessments by wearable sensors. Neurorehabil Neural Repair. 2011; 25: 788–98. Catenacci VA, Grunwald GK, Ingebrigtsen JP et al. Physical activity patterns using accelerometry in the National Weight Control Registry. Obesity (Silver Spring). 2011; 19: 1163–70. Bijan Najafi, David G. Armstrong, Jane Mohler. Novel Wearable Technology for Assessing Spontaneous Daily Physical Activity and Risk of Falling in Older Adults with Diabetes. J Diabetes Sci Technol. 2013; 7: 1147– 60. Segerståhl K, Oinas-Kukkonen H. Designing personal exercise monitoring employing multiple modes of
27.
28.
29.
30.
31. 32.
delivery: Implications from a qualitative study on heart rate monitoring. Int J Med Inform. 2011; 80: e203–13. Moy ML, Weston NA, Wilson EJ, Hess ML, Richardson CR. A pilot study of an Internet walking program and pedometer in COPD. Respir Med. 2012; 106: 1342–50. Doherty ST, Oh P. A multi-sensor monitoring system of human physiology and daily activities. Telemed J E Health. 2012; 18: 185–92. Slootmaker SM, Chinapaw MJ, Seidell JC, van Mechelen W, Schuit AJ. Accelerometers and Internet for physical activity promotion in youth? Feasibility and effectiveness of a minimal intervention [ISRCTN93896459]. Prev Med. 2010; 51: 31–6. Slootmaker SM, Chinapaw MJ, Schuit AJ, Seidell JC, Van Mechelen W. Feasibility and effectiveness of online physical activity advice based on a personal activity monitor: Randomized controlled trial. J Med Internet Res. 2009; 11: e27. Bloomgarden Z, Dagogo-Jack S. Five Ms of adherence. J Diabetes. 2011; 3: 169–71. Pal K, Eastwood SV, Michie S et al. Computer-based diabetes self-management interventions for adults with type 2 diabetes mellitus. Cochrane Database Syst Rev. 2013; (3): CD008776. Doi: 10.1002/14651858.CD008776 .pub2.
Zachary Bloomgarden Editor-in-ChiefMount Sinai School of Medicine New York, NY, USA Xiao-Ying Li Associate EditorShanghai Institute of Endocrine and Metabolic Diseases Shanghai, China
© 2014 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and Wiley Publishing Asia Pty Ltd
doi: 10.1111/1753-0407.12234
Journal of Diabetes 7 (2015) 150–152
C O M M E N TA R Y
Helping people with diabetes to exercise Diabetes prevalence has more than doubled over the past 30 years,1 affecting nearly 400 million people worldwide, nearly half undiagnosed, with projections for this number to increase to nearly 600 million over the next two decades.2 To a large extent the growth in diabetes has been driven by overweight and lack of physical activity. Many clinical trials and observational studies have shown that lifestyle interventions encouraging diet and physical activity delay the onset of diabetes in people who have “pre-diabetes”3 and improve control of blood glucose as well as blood pressure and lipid levels in people with diabetes. Look AHEAD showed important benefits including improved glycemia with use of less medications, reduction in sleep apnea, enhanced mobility, and decreased urinary incontinence, although over 10 years it was not possible to demonstrate a reduction in cardiovascular morbidity and mortality.4 Although exercise alone may not be sufficient to improve outcome, it clearly plays an important role in diabetes.5 Exercise interventions are associated with reductions in HbA1c and lipids.6,7 and, among type 2 diabetic patients at increased cardiovascular risk, those engaging in moderate to vigorous physical activity had 15–22% reductions in likelihood of micro- and macrovascular events.8 One of us (XYL) conducted a randomized, controlled trial to evaluate the effect of intensive (8–10 Mets/h on a treadmill running 30 min/day) and moderate (3–6 Mets/h by a fast walking 30 min/day) exercise 5 days per week in 220 obese patients with nonalcoholic fatty liver disease (NAFLD) for one year. Compared with a control group receiving general lifestyle advice, fatty liver, body weight, waist circumference and blood pressure improved over 12 months (Clinical trial number:NCT01403532). Moderate exercise is as effective as intensive exercise in improving fatty liver, and reducing body weight, waist circumference and blood pressure. The dilemma is that such physical activity training necessitates measures to promote ongoing adherence by participants. A number of studies have used internetbased self-monitoring for diabetes treatment. While in managing glycemia such systems may be ineffective in improving control, given the intricate medication adjustment decisions often required,9 it appears inherently reasonable to suppose that systems to simply increase levels of physical activity might more readily lend themselves to such interventions. A practical approach in people with diabetes and at risk of its development would 150
involve ongoing assessment of physical activity levels, yet this fundamental aspect of treatment remains difficult to measure. One approach is simply to use regular online entry of physical activity to track exercise levels, which may be effective with sufficient supervision,10 although studies of this approach have noted that benefit occurs particularly among those participants making regular use of the web-based tools.11,12 Certainly the use of such an approach can offer a great deal of individual patientlevel information to be used in understanding parameters contributing to successful diet and exercise interventions.13 Such programs are limited, though, in the requirement for ongoing effort by participants and staff in entering and reviewing information pertaining to diet and activity, and the durability of participation has been an issue in a number of studies.14–16 A web-based followup of nearly 1000 individuals following a physical health-check in a workplace setting failed to show improvement in self-perceived health, obesity, elevated blood pressure, elevated cholesterol level, maximum oxygen uptake, or either direct or indirect costs,17 although another workplace-based study involving selfrecording of weight and physical activity by 222 persons showed that individuals achieving greater weight loss were better able to adhere to an online coaching program.18 Extrapolating from extensive research on dietary adherence,19 one can anticipate that self-report may lead to unreliable information pertaining to adherence to programs of increased physical activity. There is, however, great promise in the use of technologies to improve adherence to diabetes treatment regimens.20 A number of devices are commercially available or in development for tracking detailed metrics relevant to physical health, including sleep duration and quality, weight, and steps walked and duration of physical exercise. Pedometer/accelerometer-measurement of daily step-counts can be helpful, and simply having participants email step-count logs with weekly email feedback was shown to increase the weekly time spent walking in small study.21 Such approaches are feasible in ascertaining activity patterns of diabetic patients.22 More interestingly, such information can be automatically uploaded from an individual’s home or in a community setting using cellular data transmission to give ongoing accurate information of walking speed, duration, and overall energy expenditure.23 Measures of physical activity using a triaxial accelerometer have been shown to differ sig-
© 2014 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and Wiley Publishing Asia Pty Ltd
Commentary
nificantly between individuals who have been successful in maintaining weight loss and overweight controls.24 Such technology offers the additional potential to obtain information about much more than just walking, allowing fuller assessment of overall activity.25 Heart rate monitoring may add further useful information.26,27 Ideal systems for diabetes could use smartphones for uploading data streams, including heart rate from electrocardiographic input, pedometer/accelerometer measures of physical activity, and self-monitored capillary glucose as well as data from continuous glucose monitoring devices, and the feasibility of such approaches has been demonstrated in small, short-term studies.28 Of course, simply making physical activity monitors available does not always lead to their effective use,29 and a comprehensive intervention is required to ensure that participants in the study make use of the measures of physical activity and other behaviors, as these will otherwise lead to little benefit.30 At the same time, in studying such an approach it is necessary to control for any increase in interaction with providers occurring in an effort to assure regular use of the technology. Although at present the web- or mobile phone “app”-based information given to users is fairly minimal and offers little customization, the unique aspect of this as an intervention for the treatment of diabetes is the ease by which information can be acquired that can then be incorporated into a structured program which takes the “passively” acquired data and then returns it to the patient with objective assessment of their activity, and the use of such apps is likely to be a major part of programs to improve adherence.31 Interestingly, mobile phone App-based interventions appear to have greater HbA1c-lowering efficacy in diabetes than do interventions requiring computer use,32 suggesting that the combination of an “always-on” device to acquire behavioral information with an “always-available” mobile phone App may have great promise. References 1. Danaei G, Finucane MM, Lu Y et al. Global Burden of Metabolic Risk Factors of Chronic Diseases Collaborating Group (Blood Glucose). National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: Systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2·7 million participants. Lancet. 2011; 378: 31–40. 2. International Diabetes Federation Atlas. 2013. Available from: http://www.idf.org/sites/default/files/EN_6E_Atlas _Full_0.pdf (accessed 4 July 2014). 3. Knowler WC, Barrett-Connor E, Fowler SE et al. Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002; 346: 393–403.
4. Look AHEAD Research Group, Wing RR, Bolin P, Brancati FL et al. Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes. N Engl J Med. 2013; 369: 145–54. 5. Thent ZC, Das S, Henry LJ. Role of exercise in the management of diabetes mellitus: The global scenario. PLoS ONE. 2013; 8: e80436. 6. Boulé NG, Haddad E, Kenny GP, Wells GA, Sigal RJ. Effects of exercise on glycemic control and body mass in type 2 diabetes mellitus: A meta-analysis of controlled clinical trials. JAMA. 2001; 286: 1218–27. 7. Balducci S, Zanuso S, Cardelli P et al. Italian Diabetes Exercise Study (IDES) Investigators. Effect of highversus low-intensity supervised aerobic and resistance training on modifiable cardiovascular risk factors in type 2 diabetes; the Italian Diabetes and Exercise Study (IDES). PLoS ONE. 2012; 7: e49297. 8. Blomster JI, Chow CK, Zoungas S et al. The influence of physical activity on vascular complications and mortality in patients with type 2 diabetes mellitus. Diabetes Obes Metab. 2013; 15: 1008–12. 9. Farmer AJ, Gibson OJ, Dudley C et al. A randomized controlled trial of the effect of real-time telemedicine support on glycemic control in young adults with type 1 diabetes (ISRCTN 46889446). Diabetes Care. 2005; 28: 2697–702. 10. Cranen K, Veld RH, Ijzerman M, Vollenbroek-Hutten M. Change of patients’ perceptions of telemedicine after brief use. Telemed J E Health. 2011; 17: 530–5. 11. Cadmus-Bertram L, Wang JB, Patterson RE, Newman VA, Parker BA, Pierce JP. Web-based self-monitoring for weight loss among overweight/obese women at increased risk for breast cancer: The HELP pilot study. Psychooncology. 2013; 22: 1821–8. 12. Webber KH, Tate DF, Ward DS, Bowling JM. Motivation and its relationship to adherence to selfmonitoring and weight loss in a 16-week Internet behavioral weight loss intervention. J Nutr Educ Behav. 2010; 42: 161–7. 13. Anton SD, LeBlanc E, Allen HR et al. Use of a computerized tracking system to monitor and provide feedback on dietary goals for calorie-restricted diets: The POUNDS LOST study. J Diabetes Sci Technol. 2012; 6: 1216–25. 14. Glasgow RE, Christiansen SM, Kurz D et al. Engagement in a diabetes self-management website: Usage patterns and generalizability of program use. J Med Internet Res. 2011; 13: e9. 15. Carr LJ, Dunsiger SI, Lewis B et al. Randomized controlled trial testing an internet physical activity intervention for sedentary adults. Health Psychol. 2013; 32: 328–36. 16. Carr LJ, Bartee RT, Dorozynski CM, Broomfield JF, Smith ML, Smith DT. Eight-month follow-up of physical activity and central adiposity: Results from an Internetdelivered randomized control trial intervention. J Phys Act Health. 2009; 6: 444–55. 17. Robroek SJ, Polinder S, Bredt FJ, Burdorf A. Costeffectiveness of a long-term Internet-delivered worksite health promotion programme on physical activity and
© 2014 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and Wiley Publishing Asia Pty Ltd
151
Commentary
18.
19.
20.
21.
22.
23.
24.
25.
26.
152
nutrition: A cluster randomized controlled trial. Health Educ Res. 2012; 27: 399–410. Ware LJ, Hurling R, Bataveljic O et al. Rates and determinants of uptake and use of an internet physical activity and weight management program in office and manufacturing work sites in England: Cohort study. J Med Internet Res. 2008; 10: e56. Burke LE, Wang J, Sevick MA. Self-monitoring in weight loss: A systematic review of the literature. J Am Diet Assoc. 2011; 111: 92–102. Driscoll KA, Young-Hyman D. Use of technology when assessing adherence to diabetes self-management behaviors. Curr Diabetes Rep. 2014; 14: 521–9. Dinger MK, Heesch KC, Cipriani G, Qualls M. Comparison of two email-delivered, pedometer-based interventions to promote walking among insufficiently active women. J Sci Med Sport. 2007; 10: 297–302. Dasgupta K, Joseph L, Pilote L, Strachan I, Sigal RJ, Chan C. Daily steps are low year-round and dip lower in fall/winter: findings from a longitudinal diabetes cohort. Cardiovasc Diabetol. 2010; 9: 81. Dobkin BH, Dorsch A. The promise of mHealth: Daily activity monitoring and outcome assessments by wearable sensors. Neurorehabil Neural Repair. 2011; 25: 788–98. Catenacci VA, Grunwald GK, Ingebrigtsen JP et al. Physical activity patterns using accelerometry in the National Weight Control Registry. Obesity (Silver Spring). 2011; 19: 1163–70. Bijan Najafi, David G. Armstrong, Jane Mohler. Novel Wearable Technology for Assessing Spontaneous Daily Physical Activity and Risk of Falling in Older Adults with Diabetes. J Diabetes Sci Technol. 2013; 7: 1147– 60. Segerståhl K, Oinas-Kukkonen H. Designing personal exercise monitoring employing multiple modes of
27.
28.
29.
30.
31. 32.
delivery: Implications from a qualitative study on heart rate monitoring. Int J Med Inform. 2011; 80: e203–13. Moy ML, Weston NA, Wilson EJ, Hess ML, Richardson CR. A pilot study of an Internet walking program and pedometer in COPD. Respir Med. 2012; 106: 1342–50. Doherty ST, Oh P. A multi-sensor monitoring system of human physiology and daily activities. Telemed J E Health. 2012; 18: 185–92. Slootmaker SM, Chinapaw MJ, Seidell JC, van Mechelen W, Schuit AJ. Accelerometers and Internet for physical activity promotion in youth? Feasibility and effectiveness of a minimal intervention [ISRCTN93896459]. Prev Med. 2010; 51: 31–6. Slootmaker SM, Chinapaw MJ, Schuit AJ, Seidell JC, Van Mechelen W. Feasibility and effectiveness of online physical activity advice based on a personal activity monitor: Randomized controlled trial. J Med Internet Res. 2009; 11: e27. Bloomgarden Z, Dagogo-Jack S. Five Ms of adherence. J Diabetes. 2011; 3: 169–71. Pal K, Eastwood SV, Michie S et al. Computer-based diabetes self-management interventions for adults with type 2 diabetes mellitus. Cochrane Database Syst Rev. 2013; (3): CD008776. Doi: 10.1002/14651858.CD008776 .pub2.
Zachary Bloomgarden Editor-in-ChiefMount Sinai School of Medicine New York, NY, USA Xiao-Ying Li Associate EditorShanghai Institute of Endocrine and Metabolic Diseases Shanghai, China
© 2014 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and Wiley Publishing Asia Pty Ltd
