Pedometer intervention and weight loss in overweight and obese adults with Type 2 diabetes: a meta-analysis

Behavioral Lifestyle Interventions for Weight Loss in Overweight or Obese Patients with Type 2 Diabetes: A Systematic Review of the Literature

PURPOSE OF REVIEW

Around 80-90% of patients with type 2 diabetes mellitus (T2DM) are overweight or obese, presenting a greater risk for serious health complications and mortality. Thus, weight loss represents a main goal for T2DM management. Although behavioral lifestyle interventions (BLIs) could help promoting weight loss in T2DM patients with overweight or obesity, their effectiveness is still controversial. This systematic review offers an updated and comprehensive picture of BLIs according to Michie's classification in T2DM patients with overweight or obesity and identifies possible factors (related to both patients and interventions) associated with weight loss. The PRISMA guidelines were followed. The literature search till March 2023 indicated 31 studies involving 42 different BLIs.

RECENT FINDINGS

Our findings suggest that structured BLIs, characterized by frequent feedback and support, can lead to a clinically meaningful 5% weight loss, regardless of specific behavioral, diet, and physical activity components. Further research should address methodological issues and heterogeneity of interventions, also considering the effect of pharmacological therapies on weight reduction. Lastly, more attention should be paid to the long-term effectiveness of behavioral lifestyle interventions and to the relationship between weight loss and diabetes.

Pedometer- and accelerometer- based physical activity interventions in Type 2 diabetes: A systematic review and meta-analysis

AIMS

Although accelerometer- and pedometer-based physical activity (PA) interventions can increase PA, there is no solid evidence for their benefits in patients with type 2 diabetes (T2DM). The aim of this systematic review and meta-analysis of randomized controlled clinical trials (RCTs) was to determine the effects of accelerometer- and pedometer-based PA interventions on hemoglobin A1c (HbA1c), fasting glucose, weight, BMI, blood pressure, lipids, and PA in adults with T2DM.

DATA SYNTHESIS

Records from MEDLINE/PubMed, EMBASE, LILACS, and Scopus were searched from inception through March 28th, 2022. RCTs of at least two weeks of duration evaluated the effectiveness of pedometers or accelerometers as motivating tools for increasing PA in T2DM patients. This study was registered with PROSPERO and followed the PRISMA reporting guide. Of the 7131 non-duplicated articles retrieved, 24 RCTs (1969 patients) were included. The mean baseline HbA1c of the experimental group of included studies varied from 6.3 ± 0.9 % to 9.0 ± 0.01 %. The accelerometer- and pedometer-based PA interventions resulted in a greater improvement in HbA1c (-0.22 %; 95%CI, -0.4 % to -0.05 %; I2 = 77 %) and triglycerides (-13.11 mg/dL; 95%CI, -25.21 to -1.02; I2 = 22 %) versus control participants. Pedometer ambulatory use as a motivating tool significantly increased PA by 2,131 steps/day (95 % CI, 1,348 to 2,914; I2 = 74 %) in T2DM patients.

CONCLUSIONS

Pedometers and accelerometers are associated with reductions in HbA1c and triglycerides when used as motivating tools. Larger and higher-quality studies are required to determine the full effects of PA as motivated by trackers in T2DM population.

Effectiveness of utilizing step-monitoring devices to prevent and treat obesity in children and adolescents: A systematic review and meta-analysis

Background

Children and adolescents who are overweight and obese represent a growing public health issue. The use of step-monitoring devices as an intervention tool may be a simple, cost-effective, and easily replicable solution for addressing obesity in children and adolescents. No prior systematic reviews have evaluated the effectiveness of utilizing step-monitoring devices as an intervention method for obesity in children and adolescents.

Methods

Previous studies on using step-monitoring devices to prevent and treat obesity in children and adolescents were identified in the following databases: Web of Science, EMBASE, PubMed, Cochrane Library, SPORTDiscus, and SCOPUS. The search period for each database ranged from the year of their inception to 8 March 2023 (updated in June 2024). Meta-analyses were performed for mean differences (MDs) in body mass index (BMI), BMI z-score (BMI-Z), body fat, waist circumference, and body weight.

Results

From 12,907 relevant records, 23 studies were included in this meta-analysis. The included studies were mainly at low risk of bias, except for blinding. Step-monitoring device-based interventions had significant effects in reducing BMI-Z (MD -0.06; 95% CI -0.10 to -0.02), body fat (MD -0.95%; 95% CI -1.35 to -0.54), and body weight (MD -1.23 kg; 95% CI -2.36 to -0.10). However, there was no significant effect on BMI (MD -0.16 kg/m2; 95% CI -0.55 to 0.22) and waist circumference (MD -0.33 cm; 95% CI -1.23 to 0.58). Subgroup analyses indicated that participants who were overweight or obese showed greater intervention effects on BMI and BMI-Z compared to participants with normal weight. The programs with an intervention duration of ≤6 months presented a greater intervention effect on BMI-Z than those with an intervention duration of more than 6 months. The programs that established goals had a greater intervention effect on body fat than those that did not.

Conclusions

Step-monitoring devices may be an effective and generalizable intervention tool for the prevention and treatment of obesity in children and adolescents. Future studies should further explore how to set step goals and the duration of interventions to achieve better intervention effects.

Association Between Mobile Health App Engagement and Weight Loss and Glycemic Control in Adults With Type 2 Diabetes and Prediabetes (D'LITE Study): Prospective Cohort Study

Background

Mobile health apps are increasingly used as early intervention to support behavior change for diabetes prevention and control, with the overarching goal of lowering the overall disease burden.

Objective

This prospective cohort study conducted in Singapore aimed to investigate app engagement features and their association with weight loss and improved glycemic control among adults with diabetes and prediabetes from the intervention arm of the Diabetes Lifestyle Intervention using Technology Empowerment randomized controlled trial.

Methods

Diabetes and prediabetes participants (N=171) with a median age of 52 years, BMI of 29.3 kg/m², and glycated hemoglobin (HbA_1c) level of 6.5% and who were being assigned the Nutritionist Buddy Diabetes app were included. Body weight and HbA_1c were measured at baseline, 3 months, and 6 months. A total of 476,300 data points on daily app engagement were tracked via the backend dashboard and developer’s report. The app engagement data were analyzed by quartiles and weekly means expressed in days per week. Linear mixed model analysis was used to determine the associations between the app engagements with percentage weight and HbA_1c change.

Results

The median overall app engagement rate was maintained above 90% at 6 months. Participants who were actively engaged in ≥5 app features were associated with the greatest overall weight reduction of 10.6% from baseline (mean difference −6, 95% CI −8.9 to −3.2; P<.001) at 6 months. Adhering to the carbohydrate limit of >5.9 days per week and choosing healthier food options for >4.3 days per week had the most impact, eliciting weight loss of 9.1% (mean difference −5.2, 95% CI −8.2 to −2.2; P=.001) and 8.8% (mean difference −4.2, 95% CI −7.1 to −1.3; P=.005), respectively. Among the participants with diabetes, those who had a complete meal log for >5.1 days per week or kept within their carbohydrate limit for >5.9 days per week each achieved greater HbA_1c reductions of 1.2% (SD 1.3%; SD 1.5%), as compared with 0.2% (SD 1%; SD 0.6%). in the reference groups who used the features <1.1 or ≤2.5 days per week, respectively.

Conclusions

Higher app engagement led to greater weight loss and HbA_1c reduction among adults with overweight or obesity with type 2 diabetes or prediabetes.

Trial Registration

Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12617001112358; https://anzctr.org.au/Trial/Registration/TrialReview.aspx?ACTRN=12617001112358

Effectiveness of wearable activity trackers to increase physical activity and improve health: a systematic review of systematic reviews and meta-analyses

Wearable activity trackers offer an appealing, low-cost tool to address physical inactivity. This systematic review of systematic reviews and meta-analyses (umbrella review) aimed to examine the effectiveness of activity trackers for improving physical activity and related physiological and psychosocial outcomes in clinical and non-clinical populations. Seven databases (Embase, MEDLINE, Ovid Emcare, Scopus, SPORTDiscus, the Cochrane Library, and Web of Science) were searched from database inception to April 8, 2021. Systematic reviews of primary studies using activity trackers as interventions and reporting physical activity, physiological, or psychosocial outcomes were eligible for inclusion. In total, 39 systematic reviews and meta-analyses were identified, reporting results from 163 992 participants spanning all age groups, from both healthy and clinical populations. Taken together, the meta-analyses suggested activity trackers improved physical activity (standardised mean difference [SMD] 0·3-0·6), body composition (SMD 0·7-2·0), and fitness (SMD 0·3), equating to approximately 1800 extra steps per day, 40 min per day more walking, and reductions of approximately 1 kg in bodyweight. Effects for other physiological (blood pressure, cholesterol, and glycosylated haemoglobin) and psychosocial (quality of life and pain) outcomes were typically small and often non-significant. Activity trackers appear to be effective at increasing physical activity in a variety of age groups and clinical and non-clinical populations. The benefit is clinically important and is sustained over time. Based on the studies evaluated, there is sufficient evidence to recommend the use of activity trackers.

The Effectiveness of Physical Activity-Promoting Web- and Mobile-Based Distance Weight Loss Interventions on Body Composition in Rehabilitation Settings: Systematic Review, Meta-analysis, and Meta-Regression Analysis

BACKGROUND

Overweight and obesity are major problems worldwide, and they lead to an increased risk for several diseases. The use of technology in the treatment of obesity is promising, but in the existing literature, there is considerable uncertainty regarding its efficacy. In this review, we included web- and mobile-based weight loss interventions that were implemented remotely in rehabilitation settings.

OBJECTIVE

The aim of this systematic review is to study the effectiveness of physical activity-promoting web- and mobile-based distance weight loss interventions in rehabilitation settings on body composition in comparison with control groups that did not use technology.

METHODS

Studies were searched from 9 databases. The inclusion criteria were as follows: population: age 18-65 years; intervention: physical activity-promoting web- and mobile-based distance weight loss interventions; comparison: control groups without the use of technology; outcome: changes in BMI, waist circumference, or body fat percentage; study design: randomized controlled trial. The quality of the studies was assessed by 2 researchers. Meta-analysis was performed, and we also conducted a meta-regression analysis to evaluate the factors associated with the changes in body composition outcomes if statistical heterogeneity was observed.

RESULTS

The meta-analysis included 30 studies. The mean quality of the studies was 7 of 13 (SD 1.9; range 3-10). A statistically significant difference was observed in BMI (mean difference [MD] 0.83, 95% CI 0.51-1.15 kg/m²; P<.001), waist circumference (MD 2.45, 95% CI 1.83-3.07 cm; P<.001), and body fat percentage (MD 1.07%, 95% CI 0.74%-1.41%; P<.001) in favor of the weight loss groups using web- or mobile-based interventions. Meta-regression analyses found an association between personal feedback and BMI (P=.04), but other factors did not play a role in explaining statistical heterogeneity.

CONCLUSIONS

Web- and mobile-based distance weight loss interventions significantly reduced BMI, waist circumference, and body fat percentage. Future studies should focus on the comparability of the intervention content. Future studies are needed to better understand weight loss and identify which components are essential in achieving it.

TRIAL REGISTRATION

PROSPERO CRD42016035831; https://tinyurl.com/7c93tvd4.

Effects of a three-armed randomised controlled trial using self-monitoring of daily steps with and without counselling in prediabetes and type 2 diabetes-the Sophia Step Study

BACKGROUND

This aimed to evaluate the effects of self-monitoring of daily steps with or without counselling support on HbA1c, other cardiometabolic risk factors and objectively measured physical activity (PA) during a 2-year intervention in a population with prediabetes or type 2 diabetes.

METHODS

The Sophia Step Study was a three-armed parallel randomised controlled trial. Participants with prediabetes or type 2 diabetes were recruited in a primary care setting. Allocation (1:1:1) was made to a multi-component intervention (self-monitoring of steps with counselling support), a single-component intervention (self-monitoring of steps without counselling support) or standard care. Data were collected for primary outcome HbA1c at baseline and month 6, 12, 18 and 24. Physical activity was assessed as an intermediate outcome by accelerometer (ActiGraph GT1M) for 1 week at baseline and the 6-, 12-, 18- and 24-month follow-up visits. The intervention effects were evaluated by a robust linear mixed model.

RESULTS

In total, 188 subjects (64, 59, 65 in each group) were included. The mean (SD) age was 64 (7.7) years, BMI was 30.0 (4.4) kg/m2 and HbA1c was 50 (11) mmol/mol, 21% had prediabetes and 40% were female. The dropout rate was 11% at 24 months. Effect size (CI) for the primary outcome (HbA1c) ranged from -1.3 (-4.8 to 2.2) to 1.1 (-2.4 to 4.6) mmol/mol for the multi-component vs control group and from 0.3 (-3.3 to 3.9) to 3.1 (-0.5 to 6.7) mmol/mol for the single-component vs control group. Effect size (CI) for moderate-to-vigorous physical activity ranged from 8.0 (0.4 to 15.7) to 11.1 (3.3 to 19.0) min/day for the multi-component vs control group and from 7.6 (-0.4 to 15.6) to 9.4 (1.4 to 17.4) min/day for the single-component group vs control group.

CONCLUSION

This 2-year intervention, including self-monitoring of steps with or without counselling, prevented a decrease in PA but did not provide evidence for improved metabolic control and cardiometabolic risk factors in a population with prediabetes or type 2 diabetes.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT02374788 . Registered 2 March 2015-Retrospectively registered.

Using Step Counts to Prescribe Physical Activity: What Is the Optimal Dose?

ABSTRACT

An activity goal of 10,000 steps per day is widely known but was not initially developed based on scientific evidence. The last several years have seen an increase in both the availability of consumer-based step counting devices and research examining the link between daily step counts and various health outcomes. Daily step counts are an intuitive metric of physical activity and are useful for goal-setting and motivating behavioral change. This article reviews the current evidence for daily step counts as related to common health conditions to determine activity goals to prescribe to patients, and also discusses factors to consider when counseling patients on physical activity.

Defining Valid Activity Monitor Data: A Multimethod Analysis of Weight-Loss Intervention Participants' Barriers to Wear and First 100 Days of Physical Activity

Despite the popularity of commercially available wearable activity monitors (WAMs), there is a paucity of consistent methodology for analyzing large amounts of accelerometer data from these devices. This multimethod study aimed to inform appropriate Fitbit wear thresholds for physical activity (PA) outcomes assessment in a sample of 616 low-income, majority Latina patients with obesity enrolled in a behavioral weight-loss intervention. Secondly, this study aimed to understand intervention participants' barriers to Fitbit use. We applied a heart rate (HR) criterion (≥10 h/day) and a step count (SC) criterion (≥1000 steps/day) to 100 days of continuous activity monitor data. We examined the prevalence of valid wear and PA outcomes between analytic subgroups of participants who met the HR criterion, SC criterion, or both. We undertook qualitative analysis of research staff notes and participant interviews to explore barriers to valid Fitbit data collection. Overall, one in three participants did not meet the SC criterion for valid wear in Weeks 1 and 13; however, we found the SC criterion to be more inclusive of participants who did not use a smartphone than the HR criterion. Older age, higher body mass index (BMI), barriers to smartphone use, device storage issues, and negative emotional responses to WAM-based self-monitoring may predict higher proportions of invalid WAM data in weight-loss intervention research.

Self-weighing frequency and the incidence of type 2 diabetes: post hoc analysis of a cluster-randomized controlled trial

Objectives

Frequent self-weighing is associated with weight loss and maintenance, but the relationship between frequent self-weighing and the incidence of type 2 diabetes (T2D) remains unclear. The study aim was to examine the association between self-weighing frequency and the incidence of T2D in people with impaired fasting glucose (IFG).

Results

We tested the hypothesis that self-weighing frequency and the incidence of T2D are associated in 2607 people with IFG (1240 in the intervention arm; 1367 in the self-directed control arm). Both arms received a weighing scale with storage function. Healthcare providers offered a one-year goal-focused lifestyle intervention via phone. Participants were divided into 4 categories based on self-weighing frequency (No data sent [reference group], low: < 2 times/week, middle: 3–4 times/week, and high: 5–7 times/week). The adjusted hazard ratio (AHR) and 95% confidence interval (CI) were calculated. In the intervention arm, middle- and high-frequency self-weighing were associated with a decreased incidence of T2D relative to the reference group (AHR = 0.56, 95% CI [0.32, 0.98] and AHR = 0.43, 95% CI [0.25, 0.74], respectively). In the control arm, high-frequency self-weighing was also associated with a decreased incidence of T2D relative to the reference group (AHR = 0.54, 95% CI [0.35, 0.83]).

Trial registration This trial has been registered with the University Hospital Medical Information Network (UMIN000000662).

Effectiveness comparisons of catgut implantation at acupoint for obese type 2 diabetes: A protocol for systematic review and meta analysis

BACKGROUND

With the change of people's life style, many more people are suffering from obese type 2 diabetes mellitus (T2DM). Acupoint catgut embedding is one of the acupuncture treatment principles in traditional Chinese medicine, which is widely used in the treatment of obese T2DM. However, there is no systematic review of the therapeutic effect of acupoint catgut embedding on obesity T2DM. Therefore, this article aims at the meta-analysis of acupoint catgut embedding in the treatment of obese T2DM, to clarify its curative effect.

METHODS

A structured and systemic literature search was conducted in the following databases up to December 1, 2019: PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, EMBASE, CNKI, Wanfang Database. We will use the Review Manager 5.3 software provided by Cochrane collaborative network for statistical analysis. Then we assessed the quality and risk of the included studies and observed the outcome measures.

RESULTS

This meta-analysis will further determine the beneficial efficacy of acupoint catgut embedding on obesity T2DM.

CONCLUSION

The purpose of this meta-analysis is to explore the effect of acupoint catgut embedding intervention on obese T2DM patients, and provide more options for clinicians and patients to treat obese T2DM.

ETHICS AND DISSEMINATION

This systemic review will evaluate the efficacy and safety of acupoint catgut embedding in the treatment of obesity T2DM. Since all the data included are published, the systematic review does not need ethical approval.

REGISTRATION NUMBER

CRD42020160801.

Implementation of a multilevel, multicomponent intervention for obesity control in Native American communities (OPREVENT2): challenges and lessons learned

OPREVENT2 was a multilevel, multicomponent (MLMC) adult obesity prevention that sought to improve access and demand for healthier food and physical activity opportunities in six Native American communities in the Southwest and Midwest. OPREVENT2 worked with worksites, food stores, schools (grades 2-6), through social media and mailings, and with a local community action committee (CAC), in each of the three intervention communities, and was implemented in six phases. We conducted a process evaluation to assess implementation of each intervention component in terms of reach, dose delivered and fidelity. Implementation of each component was classified as high, medium or low according to set standards, and reported back at the end of each phase, allowing for improvements. The school and worksite components were implemented with high reach, dose delivered and fidelity, with improvement over time. The school program had only moderate reach and dose delivered, as did the social media component. The CAC achieved high reach and dose delivered. Overall, study reach and dose delivered reached a high implementation level, whereas fidelity was medium. Great challenges exist in the consistent implementation of MLMC interventions. The detailed process evaluation of the OPREVENT2 trial allowed us to carefully assess the relative strengths and limitations of each intervention component.

Mobile Application Interventions and Weight Loss in Type 2 Diabetes: A Meta-Analysis

OBJECTIVE

This meta-analysis aimed to assess the effect of mobile application (app) interventions on weight loss in patients with type 2 diabetes.

METHODS

Electronic databases were searched for randomized controlled trials examining the use of mobile app interventions with outcomes on weight loss evaluated by body weight or other measures such as BMI or waist circumference. A random-effects model was applied to obtain weight mean differences and 95% CIs.

RESULTS

Fourteen studies enrolling 2,129 patients with type 2 diabetes were included. Mobile app interventions could significantly reduce body weight (weight mean difference, -0.84 kg; 95% CI: -1.51 to -0.17 kg) and lower waist circumference (-1.35 cm; 95% CI: -2.16 to -0.55 cm) but may not decrease BMI (-0.08 kg/m² ; 95% CI: -0.41 to 0.25 kg/m² ). The reductions appeared to be more pronounced in patients with obesity or among studies using mobile app interventions combined with other behavior components. However, weight loss was not moderated by the functionalities of the mobile apps (all P_interaction  > 0.05) or by the intervention duration (all P > 0.87).

CONCLUSIONS

Mobile app interventions lead to weight loss in patients with type 2 diabetes and are worth recommending for weight loss promotion.

Reprint of: Blood Sugar Regulation for Cardiovascular Health Promotion and Disease Prevention: JACC Health Promotion Series

The primary objective of this study was to analyze the most up-to-date evidence regarding whether and how blood sugar regulation influences cardiovascular health promotion and disease prevention by carrying out an umbrella review. Three separate, systematic literature searches identified 2,343 papers in total. Overall, 44 studies were included for data extraction and analysis. The included systematic reviews and meta-analyses published between January 1, 2016, and December 31, 2017, were of good to very good quality (median Overview Quality Assessment Questionnaire score = 17). Identified evidence suggests that cardiovascular disease (CVD) prevention services should consider regulation of blood glucose as a key target for intervention. Furthermore, the recommendations for effective intervention and service development/training described here for prevention of CVD should be adopted into evidence-based practice guidelines. Multidisciplinary teams should be formed to deliver multicomponent interventions in community-based settings. There may be substantial opportunities for integrating CVD and diabetes prevention services.

Blood Sugar Regulation for Cardiovascular Health Promotion and Disease Prevention: JACC Health Promotion Series

The primary objective of this study was to analyze the most up-to-date evidence regarding whether and how blood sugar regulation influences cardiovascular health promotion and disease prevention by carrying out an umbrella review. Three separate, systematic literature searches identified 2,343 papers in total. Overall, 44 studies were included for data extraction and analysis. The included systematic reviews and meta-analyses published between January 1, 2016, and December 31, 2017, were of good to very good quality (median Overview Quality Assessment Questionnaire score = 17). Identified evidence suggests that cardiovascular disease (CVD) prevention services should consider regulation of blood glucose as a key target for intervention. Furthermore, the recommendations for effective intervention and service development/training described here for prevention of CVD should be adopted into evidence-based practice guidelines. Multidisciplinary teams should be formed to deliver multicomponent interventions in community-based settings. There may be substantial opportunities for integrating CVD and diabetes prevention services.

SMART goals setting and biometric changes in obese adults with multimorbidity: Secondary analysis of a randomized controlled trial

Objectives:

Clinicians recommend diet and exercise for overweight/obese patients. We conducted a secondary analysis of a randomized controlled clinical trial evaluating goal setting and pedometer use versus usual care on weight, waist circumference, and blood pressure of patients with multiple chronic conditions.

Methods:

In this trial, we recruited and randomized patients over 18 years with multiple chronic conditions. There were two groups with an immediate intervention group who received behavioral coaching and a pedometer versus a delayed control who received the intervention after 2 months. We evaluated body weight, waist circumference, and blood pressure as outcomes. We used analysis of covariance to evaluate differences between the intervention and the control groups.

Results:

Of 130 patients, mean age was 63.4 years (SD, 17.3). At 2 months, intervention participants lost 0.2 kg versus a 0.1-kg gain in the control participants (P = .44). The immediate intervention group had significantly smaller waist circumference change at 2-month follow-up compared to control at −1.6 cm (95% confidence interval = −3.1 to −0.1), which was driven by an increase in waist circumference in the delayed control group. No difference in systolic blood pressure was observed.

Discussion:

We observed no difference in weight or blood pressure between the groups with obesity and multiple chronic conditions.

Behavior Modification in Prediabetes and Diabetes: Potential Use of Real-Time Continuous Glucose Monitoring

Real-time continuous glucose monitoring (RT-CGM) provides real time glucose readings to participants wearing the device. The ability to see changes in glucose has the potential to provide immediate feedback to users on food choices and physical activity. The National Diabetes Prevention Program is currently the only reimbursable intervention for diabetes prevention and weight loss. The purpose of this article is to review the CGM literature on measurements other than Hemoglobin A1c (HbA1c) changes and hypoglycemia and discuss RT-CGM potential use as a behavior modification tool for lifestyle changes and weight reduction in people with prediabetes and type 2 diabetes (T2D).

Increasing physical activity for veterans in the Mental Health Intensive Case Management Program: A community-based intervention

AIMS AND OBJECTIVES

Individuals with severe mental illness (SMI), experience increased mortality-20 years greater disparity for men and 15 years greater disparity for women-compared to the general population (Thornicroft G. Physical health disparities and mental illness: The scandal of premature mortality. Br J Psychiatr. 2011;199:441-442). Numerous factors contribute to premature mortality in persons with SMI, including suicide and accidental death (Richardson RC, Faulkner G, McDevitt J, Skrinar GS, Hutchinson D, Piette JD. Integrating physical activity into mental health services for persons with serious mental illness. Psychiatr Serv. 2005;56(3):324-331; Thornicroft G. Physical health disparities and mental illness: The scandal of premature mortality. Br J Psychiatr. 2011;199:441-442), but research has shown that adverse health behaviors-including smoking, low rate of physical activity, poor diet, and high alcohol consumption-also significantly contribute to premature deaths (Jones J. Life expectancy in mental illness. Psychiatry Services. 2010. Retrieved from http://psychcentral.com/news/2010/07/13/life-expectancy-in-mental-illness). This quality improvement (QI) project sought to improve health and wellness for veterans in the Mental Health Intensive Case Management Program (MHICM), which is a community-based intensive program for veterans with SMI at risk for decompensation and frequent hospitalizations. At the time of this QI project, the program had 69 veterans who were assessed and treated weekly in their homes. The project introduced a pedometer steps intervention adapted from the VA MOVE! Program-a physical activity and weight management program-with the addition of personalized assistance from trained mental health professionals in the veteran's home environment. Because a large percentage of the veterans in the MHICM program had high blood pressure and increased weight, these outcomes were the focus of this project. Through mental health case management involvement and the comfort of their familiar living environment, veterans were assisted to meet their physical and mental health goals with a program that could easily be integrated into their daily lives.

BACKGROUND

Healthy People 2020 developed goals to improve levels of physical activity and has ranked physical activity as a leading health indicator (US DHHS. Office of Disease Prevention and Health Promotion. Physical activity topic overview. In Healthy People 2020. 2016. Retrieved from https://www.healthypeople.gov/2020/topics-objectives/topic/physical-activity). Individuals with SMI are significantly less active than the general population (Shor and Shalev, 2014). It is sometimes difficult for the average individual to obtain the recommended 10,000 steps and even more difficult for those with SMI. Lifestyle modifications, in particular diet and exercise, are recommended for improvement of chronic disease outcomes (US Preventive Services Counseling Task Force, 2016). The health benefits of physical activity for people with SMI are mixed (Pearsall R, Smith D, Pelosi A, Geddes J. Exercise therapy in adults with serious mental illness: A systematic review and meta-analysis. BMC Psychiatr. 2014;14:117). Some studies found significant physical health benefits, while others did not. However, according to a review by Soundy et al., physical exercise is shown to not only have physical benefits but also psychosocial benefits. One of the barriers that hinder participation in physical activities is accessibility (Shor and Shalev, 2014). Integrating a more personalized supported, and in-home pedometer program into mental healthcare should ensure better access to interventions that could possibly reverse the causes of premature death.

METHODS

The program was offered to 69 veterans in the MHICM. Forty-nine agreed to start the program and 20 declined. Twenty-five clients actually started the program with 17 veterans completing it. Preimplementation data included collecting blood pressure and weight measures for all veterans in the MHICM program. Additionally, a focus group was held with case managers to obtain a group perspective on motivating veterans to participate in this program. Further, a teaching session was held to review pedometers use, the client video, the client booklet, methods for getting veterans started, and the progression of the walking intervention. The pedometer physical activity intervention continued for 2 months. At the end of the 2 months, aggregate de-identified data on number of steps, blood pressure, and weight were collected. At the end of the program, the data were reviewed, synthesized, and analyzed, being careful to account for potentially intervening conditions and other chronic illnesses.

RESULTS

The postimplementation data revealed that the mean weight decreased by 9 lbs. The percentage of controlled blood pressure increased from 60 to 84, while the percentage of uncontrolled blood pressure decreased from 40 to 16.

CONCLUSION

Implementation of a multiple component personalized exercise intervention program for veterans with SMI contributed to reduction in weight and blood pressure.

Aerobic Interval Training and Cardiometabolic Health in Patients with Type 2 Diabetes: A Meta-Analysis

Vigorous to maximal aerobic interval training (INT) has received remarkable interest in improving cardiometabolic outcomes for type 2 diabetes patients recently, yet with inconsistent findings. This meta-analysis was aimed to quantify its effectiveness in type 2 diabetes. Randomized controlled trials (RCTs) were identified by searches of 3 databases to October 2017, which evaluated the effects of INT with a minimal training duration of 8 weeks vs. moderate-intensity continuous training (MICT) or non-exercise training (NET) among type 2 diabetes patients on outcomes including cardiorespiratory fitness, glycemic control, body composition, blood pressure, and lipid profiles. Weighted mean differences with 95% confidence intervals (CIs) were calculated with the random-effects model. Nine datasets from 7 RCTs with 189 patients were included. Compared with MICT, INT improved maximal oxygen consumption (VO_2max) by 2.60 ml/kg/min (95% CI: 1.32 to 3.88 ml/kg/min, P <0.001) and decreased hemoglobin A1c (HbA1c) by 0.26% (95% CI: −0.46% to −0.07%, P = 0.008). These outcomes for INT were also significant vs. energy expenditure-matched MICT, with VO_2max increased by 2.18 ml/kg/min (P = 0.04) and HbA1c decreased by 0.28% (P = 0.01). Yet their magnitudes of changes were larger compared with NET, with VO_2max increased by 6.38 ml/kg/min (P <0.001) and HbA1c reduced by 0.83% (P = 0.004). Systolic blood pressure could be lowered by INT compared with energy expenditure-matched MICT or NET (both P <0.05), but other cardiometabolic markers and body composition were not significantly altered in general. In conclusion, despite a limited number of studies, INT improves cardiometabolic health especially for VO_2max and HbA1c among patients with type 2 diabetes, and might be considered an alternative to MICT. Yet the optimal training protocols still require to be established.

What Matters in Weight Loss? An In-Depth Analysis of Self-Monitoring

BACKGROUND

Using technology to self-monitor body weight, dietary intake, and physical activity is a common practice used by consumers and health companies to increase awareness of current and desired behaviors in weight loss. Understanding how to best use the information gathered by these relatively new methods needs to be further explored.

OBJECTIVE

The purpose of this study was to analyze the contribution of self-monitoring to weight loss in participants in a 6-month commercial weight-loss intervention administered by Retrofit and to specifically identify the significant contributors to weight loss that are associated with behavior and outcomes.

METHODS

A retrospective analysis was performed using 2113 participants enrolled from 2011 to 2015 in a Retrofit weight-loss program. Participants were males and females aged 18 years or older with a starting body mass index of ≥25 kg/m2, who also provided a weight measurement at the sixth month of the program. Multiple regression analysis was performed using all measures of self-monitoring behaviors involving weight measurements, dietary intake, and physical activity to predict weight loss at 6 months. Each significant predictor was analyzed in depth to reveal the impact on outcome.

RESULTS

Participants in the Retrofit Program lost a mean -5.58% (SE 0.12) of their baseline weight with 51.87% (1096/2113) of participants losing at least 5% of their baseline weight. Multiple regression model (R²=.197, P<0.001) identified the following measures as significant predictors of weight loss at 6 months: number of weigh-ins per week (P<.001), number of steps per day (P=.02), highly active minutes per week (P<.001), number of food log days per week (P<.001), and the percentage of weeks with five or more food logs (P<.001). Weighing in at least three times per week, having a minimum of 60 highly active minutes per week, food logging at least three days per week, and having 64% (16.6/26) or more weeks with at least five food logs were associated with clinically significant weight loss for both male and female participants.

CONCLUSIONS

The self-monitoring behaviors of self-weigh-in, daily steps, high-intensity activity, and persistent food logging were significant predictors of weight loss during a 6-month intervention.

Impact of accelerometer and pedometer use on physical activity and glycaemic control in people with Type 2 diabetes: a systematic review and meta-analysis

BACKGROUND

Self-directed pedometer use increases physical activity levels in the general population; however, evidence of benefit for Type 2 diabetes is unclear and has not been systematically reviewed for accelerometers.

AIM

To examine the impact of using physical activity monitoring devices (pedometers and accelerometers) on free-living physical activity and HbA_1c levels in people with Type 2 diabetes.

METHODS

We conducted a systematic literature review. Bibliographic databases included Medline, Embase, Web of Science, CINAHL, SportDiscus and the Cochrane Central Register of Controlled Trials. We included controlled trials evaluating interventions based on the use of pedometers or accelerometers to promote physical activity in people with Type 2 diabetes. Primary outcomes were physical activity (min/week or steps) and HbA_1c [mmol/mol (%)]. Secondary outcomes were weight, blood pressure and lipid profile.

RESULTS

Twelve trials (1458 participants) were identified, of which nine studied pedometers and three accelerometers. Random-effects meta-analysis showed an overall increase in physical activity (standardized mean difference 0.57, 95% CI 0.24, 0.91) in the intervention groups. Accelerometers and pedometers produced a similar effect size. No significant differences were observed in HbA_1c , BMI, blood pressure or lipid profile.

CONCLUSIONS

People with Type 2 diabetes, provided with an accelerometer or pedometer, substantially increased their free-living physical activity. There is no evidence that monitor use alone improves HbA_1c or other clinical outcomes. Further trials are needed to compare the relative effects of activity monitors within differing complex interventions.

Do activity monitors increase physical activity in adults with overweight or obesity? A systematic review and meta-analysis

OBJECTIVE

To systematically assess contemporary knowledge regarding behavioral physical activity interventions including an activity monitor (BPAI+) in adults with overweight or obesity.

METHODS

PubMed/MEDLINE, Embase, CINAHL, PsycINFO, CENTRAL, and PEDro were searched for eligible full-text articles up to 1 July 2015. Studies eligible for inclusion were (randomized) controlled trials describing physical activity outcomes in adults with overweight or obesity. Methodological quality was independently assessed employing the Cochrane Collaboration's tool for risk of bias.

RESULTS

Fourteen studies (1,157 participants) were included for systematic review and 11 for meta-analysis. A positive trend in BPAI+ effects on several measures of physical activity was ascertained compared with both wait list or usual care and behavioral physical activity interventions without an activity monitor (BPAI-). No convincing evidence of BPAI+ effectiveness on weight loss was found compared with BPAI-.

CONCLUSIONS

Behavioral physical activity interventions with an activity monitor increase physical activity in adults with overweight or obesity. Also, adding an activity monitor to behavioral physical activity interventions appears to increase the effect on physical activity, although current evidence has not yet provided conclusive evidence for its effectiveness.

Pedometer intervention and weight loss in overweight and obese adults with Type 2 diabetes: a meta-analysis

AIM

Although pedometer intervention is effective in increasing physical activity among adults with Type 2 diabetes, its impact on weight loss remains unclear. This meta-analysis was aimed to assess whether pedometer intervention promotes weight loss.

METHODS

Three different databases were searched for randomized controlled trials (RCTs) published in English up to April 2015. Studies were included if they investigated the effects of pedometer intervention on weight loss, as measured by BMI or weight. Effect sizes were aggregated using a random-effects model. Subgroup and meta-regression analyses were used to identify potential moderators. Eleven RCTs with 1258 participants were included. All enrolled participants were overweight or obese.

RESULTS

Pedometer intervention led to significantly decreased BMI [weighted mean difference (WMD) -0.15 kg/m(2) , 95% confidence interval (CI) -0.29 to -0.02 kg/m(2) ] and reduced weight (WMD -0.65 kg, 95% CI -1.12 to -0.17 kg). Dietary counselling seemed to be a key predictor of the observed changes. However, none of the following variables had a significant influence: step goal setting, baseline age, BMI, weight, sex distribution, disease duration, intervention duration, and baseline values or change scores for total or moderate-to-vigorous physical activity. After completion of the pedometer intervention, non-significant declines in BMI and weight were observed during the follow-up periods.

CONCLUSIONS

Pedometer intervention promotes modest weight loss, but its association with physical activity requires further clarification. Future studies are also required to document dietary and sedentary behaviour changes to facilitate the use of pedometers for weight loss in overweight and obese adults with Type 2 diabetes.