Step Monitoring to Improve ARTERial Health (SMARTER)

This study is currently recruiting participants.
Verified March 2014 by McGill University Health Center
Sponsor:
Collaborator:
Canadian Institutes of Health Research (CIHR)
Information provided by (Responsible Party):
Kaberi Dasgupta, MD, MSc, FRCP (C), McGill University Health Center
ClinicalTrials.gov Identifier:
NCT01475201
First received: November 16, 2011
Last updated: March 3, 2014
Last verified: March 2014
  Purpose

Many people with diabetes and/or high blood pressure (hypertension) are not very active. When people are more active, they can reduce the chances of having a heart attack or stroke. Walking more is a cheap and effective way to be more active and to lower the risk of heart attacks and strokes. The problem is that many people do not walk enough! The investigators will study if people with diabetes and/or hypertension walk more when the doctor gives them a prescription with the number of steps they should be walking every day. The investigators will compare this group, called 'active', to another group, called 'control', in which doctors and their patients do what they usually do, over a period of one year.

The investigators will measure the number of steps the investigators walk everyday with a step counter or pedometer. In the step count prescription group, the doctors will give to the 'active' group a pedometer, a step count record book, and step count prescriptions. The overall goal is to gradually increase daily steps. The speed of the increase in step count will be slower for less active people. At each visit the doctor will look at the step count record book. The doctor will then give a new step count prescription to the patients. Patients of the two groups will see their doctor about four times during the year, which is how often they usually see their doctor. At the end of one year, the investigators will see the difference in the hardness of the arteries between the 'active' and the 'control' groups, using simple and safe measurements, similar to ultrasound in pregnant ladies. People with hard arteries are more likely to have a heart attack or stroke.

The investigators suspect that patients who get the step count prescriptions will walk more and their arteries will be less hard than the control group. Our study will help find out if this is true. In that case, doctors should take the time to prescribe steps for all their patients with diabetes and/or hypertension.


Condition Intervention Phase
Type 2 Diabetes
Hypertension
Behavioral: Step count prescription
Behavioral: Usual care
Phase 3

Study Type: Interventional
Study Design: Allocation: Randomized
Endpoint Classification: Efficacy Study
Intervention Model: Parallel Assignment
Masking: Single Blind (Outcomes Assessor)
Primary Purpose: Treatment
Official Title: Step Monitoring to Improve ARTERial Health

Resource links provided by NLM:


Further study details as provided by McGill University Health Center:

Primary Outcome Measures:
  • change in arterial stiffness [ Time Frame: one year ] [ Designated as safety issue: No ]
    Arterial stiffness will be assessed at baseline and one year by measurement of carotid femoral pulse wave velocity through applanation tonometry.


Secondary Outcome Measures:
  • change in daily step count [ Time Frame: one year ] [ Designated as safety issue: No ]
    Step counts will be computed at baseline and final assessments using a Yamax SW-200, based on one week of recording.

  • change in physical activity [ Time Frame: one year ] [ Designated as safety issue: No ]
    Change in overall activity will be computed from one week of accelerometry data collected at baseline and at one year

  • change in physical fitness [ Time Frame: one year ] [ Designated as safety issue: No ]
    Change in physical fitness will be computed using data collected through maximal exercise stress testing at baseline and one year

  • weight change from baseline [ Time Frame: one year ] [ Designated as safety issue: No ]
    Participants will be weighed at baseline and one year in light clothes, without shoes. Change in weight and percentage change in weight from baseline will be computed.

  • body mass index change from baseline [ Time Frame: one year ] [ Designated as safety issue: No ]
    Height will be measured using a mounted stadiometer. Body mass index (BMI) will be computed as weight in kg divided by height in meters squared. Change in BMI will be calculated using measurements at baseline and at one year.

  • change in waist circumference [ Time Frame: one year ] [ Designated as safety issue: No ]
    Waist circumference will be measured midway between the iliac crest and the lower rib margin. Change in waist circumference will be computed based on data from baseline and one year.

  • change in waist- to- hip ratio [ Time Frame: one year ] [ Designated as safety issue: No ]
    Hip circumference will be measured at the point of greatest posterior extension of the buttocks. Waist (in cm) to hip (in cm) ratio (WHR) will be computed and change in WHR will be determined using data from baseline and one year.

  • change in systolic blood pressure [ Time Frame: one year ] [ Designated as safety issue: No ]
    The participant will sit at rest for at least five minutes and then undergo blood pressure assessment using the BpTRU Blood Pressure Monitor (seated position, arm supported). The blood pressure assessments will be at two- minute intervals with the patient supine and the measurement in the right arm. The first value will be discarded and the last five measurements averaged.

  • change in insulin resistance [ Time Frame: one year ] [ Designated as safety issue: No ]
    Using fasting glucose and fasting insulin measurements, insulin resistance will be computed using the Homeostatic Model Assessment- Insulin Resistance (HOMA- IR) equation [Fasting glucose (mmol/L) X Fasting insulin (mU/L) ÷ 22.5].

  • change in hemoglobin A1C in diabetes patients [ Time Frame: one year ] [ Designated as safety issue: No ]
    Change in hemoglobin A1C will be ascertained for diabetes patients between baseline and one year. A1C will be measured with an HPLC analyzer.

  • change in total cholesterol [ Time Frame: one year ] [ Designated as safety issue: No ]
    Total cholesterol will be measured using spectrophotometer at baseline and one year

  • change in high density lipoprotein cholesterol [ Time Frame: One year ] [ Designated as safety issue: No ]
    High density lipoprotein cholesterol will be measured using spectrophotometer at baseline and one year

  • Change in triglyceride levels [ Time Frame: One year ] [ Designated as safety issue: No ]
    Triglyceride levels will be measured using spectrophotometer at baseline and one year

  • change in low density lipoprotein cholesterol [ Time Frame: one year ] [ Designated as safety issue: No ]
    The low density lipoprotein cholesterol will be calculated using the Friedewald equation at baseline and one year, based on total cholesterol and high density lipoprotein cholesterol values measured using spectrophotometer.

  • change in apolipoprotein A1 [ Time Frame: one year ] [ Designated as safety issue: No ]
    Apolipoprotein A1 will be measured using the turbimetric method at baseline and one year.

  • change in Apolipoprotein B [ Time Frame: One year ] [ Designated as safety issue: No ]
    Apolipoprotein B will be measured using the turbimetric method at baseline and one year.

  • change in Apolipoprotein A1 to B ratio [ Time Frame: one year ] [ Designated as safety issue: No ]
    Apolipoproteins A1 and B will be measured using the turbimetric method at baseline and one year and the A1 to B ratio computed at these time points

  • change in total cholesterol to high density lipoprotein cholesterol ratio [ Time Frame: one year ] [ Designated as safety issue: No ]
    Total cholesterol and high density lipoprotein cholesterol will be measured using spectrophotometer at baseline and at one year; the total cholesterol to high density lipoprotein cholesterol ratio will be computed at both of these time points

  • change in high sensitivity C-reactive protein [ Time Frame: one year ] [ Designated as safety issue: No ]
    High sensitivity C-reactive protein will be assayed through an immunonephelometric method at baseline and one year

  • change in antihypertensive medication use [ Time Frame: one year ] [ Designated as safety issue: No ]
    Investigators will assess type and dose of antihypertensive medications at baseline and one year and assess whether there has been a net increase, decrease, or no change in medication use.

  • change in antihyperglycemic medication [ Time Frame: one year ] [ Designated as safety issue: No ]
    Investigators will assess type and dose of antihyperglycemic medications at baseline and one year and assess whether there has been a net increase, decrease, or no change in medication use.

  • change in lipid- lowering medications [ Time Frame: one year ] [ Designated as safety issue: No ]
    Investigators will assess type and dose of lipid- lowering medications at baseline and one year and assess whether there has been a net increase, decrease, or no change in medication use.

  • change in diastolic blood pressure [ Time Frame: one year ] [ Designated as safety issue: No ]
    The participant will sit at rest for at least five minutes and then undergo blood pressure assessment using the BpTRU Blood Pressure Monitor (seated position, arm supported). The blood pressure assessments will be at two- minute intervals with the patient supine and the measurement in the right arm. The first value will be discarded and the last five measurements averaged.


Estimated Enrollment: 364
Study Start Date: February 2012
Estimated Study Completion Date: October 2016
Estimated Primary Completion Date: October 2015 (Final data collection date for primary outcome measure)
Arms Assigned Interventions
Experimental: Step Count Prescription Arm
The active trial arm intervention consists of usual care plus step count prescription delivered by the treating doctor, over a one-year period.
Behavioral: Step count prescription
Treating physicians will provide a pedometer, pedometer log, and step count prescription. The aim is a net increase of at least 3,000 steps/day over one year. The time frame for this increase will be 10 months for sedentary participants (<5,000 steps/day), 7 months for low active participants (5,000-7,499 steps/day), and 5 months for somewhat active participants (7,500-9,999 steps/day). If goals are not met, the doctor and participant will review barriers and facilitators, and a more individualized prescription will be formulated (e.g. lower incremental step count targets or slower rate of dose escalation). For participants who meet goals, the doctor and participant will together decide whether to aim for a further increase.
Active Comparator: Usual care arm
The control trial arm will receive usual care alone, over a one-year period (i.e. no step count prescription but, in accordance with guidelines, including advice to engage in 30-60 minutes of activity on most days of the week). Consistent with clinical practice guidelines, our collaborating doctors have indicated that the usual care of the target population requires clinic visits at roughly three-month intervals to ensure vascular risk factor monitoring and management.
Behavioral: Usual care
The control trial arm will receive usual care alone, over a one-year period (i.e. no step count prescription but, in accordance with guidelines, including advice to engage in 30-60 minutes of activity on most days of the week).

  Hide Detailed Description

Detailed Description:

BACKGROUND: High walking levels reduce myocardial infarction, stroke, and mortality rates in overweight/obese patients with diabetes and/or hypertension, but our own work, led by Nominated Principal Applicant K. Dasgupta, indicates low daily step counts in these patients, at approximately 5,000 steps/day on average with a further 15% reduction during fall and winter. A meta-analysis of physical activity programs indicates that pedometer-based monitoring programs can lead to higher daily step counts, but evidence for impact on arterial health is limited, as is evidence for the effectiveness of a pedometer-based strategy specifically implemented into the usual clinical care of patients with diabetes and/or hypertension. The SMARTER trial will address these knowledge gaps.

PRIMARY RESEARCH QUESTION: Among sedentary overweight/obese adults with diabetes and/or hypertension do physician-delivered step count prescriptions integrated into usual care reduce arterial stiffness more than usual care alone, over a one-year period? Arterial stiffness (primary outcome), a summative indicator of arterial health, is more precise and reliable than individual risk factors. An analysis of the Framingham Heart Study demonstrated that, even after adjustment for traditional risk factors, increased arterial stiffness was independently associated with a 48% increase in vascular disease risk. Co-Principal Applicant S. Daskalopoulou is an expert in the noninvasive assessment of arterial stiffness and has a well-equipped Vascular Lab funded through a CFI grant.

STUDY DESIGN: Randomized, allocation concealed, single-blind (outcome assessors), intervention allocation ratio 1:1, multisite clinical trial. This design will allow for the level A evidence necessary to justify widespread change in clinical practice.

TRIAL SYNOPSIS: Given that the majority of diabetes and hypertension patients are managed in primary care settings, the SMARTER trial interventions will be delivered through the large network of primary care clinics accessible to Co-Principal Applicant E. Rosenberg as well as diabetes and internal medicine clinics throughout Montreal where patients may receive their primary diabetes and hypertension follow-up. Twenty-four collaborating physicians have been identified. Physicians/clinic staff will obtain assent from candidates within their practice for contact by the SMARTER coordinator. The number of collaborating physicians continues to be increased, including physicians at primary care, diabetes, hypertension, internal medicine, and endocrinology clinics. Eligibility: Candidates will be adults with 25≤BMI<40 kg/m2 followed for diabetes and/or hypertension and sedentary to somewhat active. Evaluations: Formal trial evaluations, conducted at baseline and 12 months, will include assessments of arterial stiffness (carotid femoral pulse wave velocity measured noninvasively with applanation tonometry); step counts (pedometer with concealed window) and physical activity (accelerometer) worn for one week; fitness (exercise stress test; ˙VO2max); anthropometric parameters; and individual vascular risk factors. Intervention arm: The physician gives the active trial participants a pedometer, log book, and a step count prescription based on the baseline daily step count. The time frame for a > 3,000 steps/day net increase is 10 months for sedentary participants (<5,000 steps/day), 7 months for low active participants (5,000-7,499 steps/day), and 5 months for somewhat active participants (7,500-9,999 steps/day). There will be four clinic visits over one year. Control arm: Same visit frequency with advice to engage in 30-60 minutes of activity on most days of the week. Sample Size: Allowing for a loss to follow-up of up to 17% based on our previous studies, investigators will require a sample size of 364 individuals (i.e. 182 per arm) to detect a 10% difference in change in arterial stiffness between our active and control arms to an accuracy of +/- 5% over a one-year period. Analysis: Intention-to-treat. Between-arm differences in 'after minus before changes' with 95% CIs for main analysis.

IMPORTANCE: With increasing numbers of diabetes and hypertension patients, there is a pressing need for effective and efficient clinical practice strategies to help physicians support their patients to achieve the arterial health benefits of higher physical activity levels. The SMARTER trial seeks to provide such a tool. If effectiveness is demonstrated, all efforts will be made for the inclusion of our approach in Clinical Practice Guidelines for diabetes and hypertension, and investigators will develop training tools (manuals, websites, CD-ROMs) to allow maximal uptake of our proposed strategy.

AN OBSERVATIONAL SUBSTUDY: Novel Real-Time Measurement of Physical Activity Patterns in Type 2 Diabetes and Hypertension Through GPS Monitoring and Accelerometry

In addition to the main clinical trial, we are conducting additional measurements among consenting type 2 diabetes patients in order to examine the effects of the walkability of their home neighbourhood on their baseline step count and time at different physical activity intensities (accelerometer measurement already being performed through SMARTER). The additional measurements include wearing a Geographical Positioning Systems (GPS) device for the 7-day period that they wear the pedometer with concealed viewing window and accelerometer. The GPS device collects time-stamped location information such that X,Y coordinates are collected. These are used to determine the times that they are within or outside neighbourhood buffer zones.

For the assessment of neighbourhood walkability,the parameters assessed include population density, pedestrian-friendly design and diversity of destinations - commonly referred to in the urban planning literature as the 3D's. The variables that best capture density, design, and diversity include residential density, street connectivity and land use mix. Residential density is defined as the number of residences per square kilometre of residential land area. Street connectivity is defined as the number of ≥3-way intersections per square kilometre in neighbourhood, where a greater number of intersections facilitates movement between origins (e.g., residences) and destinations (e.g., shops and parks).Land-use mix is a measure of the number of different land uses located within a neighbourhood.Land use mix is assessed via an entropy score - a value between zero and one that captures the degree of heterogeneity of land uses in a neighbourhood. A subcomponent of land use mix that may be a particularly important for encouraging individuals to walk within their neighbourhood and that is easily incorporated into the design of new neighbourhoods is greenspace/recreational land area.

We are using Geographical Information System (GIS) mapping (computer-based assessment of neighbourhood characteristics derived from existing data sources that have some spatially referenced identification, such as a home address) to measure these facets of neighbourhood walkability.In brief, each of the variables will be derived by geocoding participants six-digit home postal codes, constructing 1-kilometre polygonal buffers zones around each participants home address (i.e., a geographical zone around the centroid of the postal code area) and calculating the measures of interest for each neighbourhood using tools within a GIS software package (ArcGIS) and publically available shape files.

Means and standard deviations will be used to describe the number of steps per day occurring specifically in home neighbourhoods (i.e., as determined through GPS) and overall. Multiple linear regression analyses will be used to assess the relationship between 1) home neighbourhood environments and the number of steps taken per day in the home neighbourhood and 2) home neighbourhood environments and the number of steps taken per day taken in any location. These analyses will be repeated with time at moderate to vigorous activity in lieu of steps as the outcome variable. Several variables measured through SMARTER will be considered for exclusion in models (e.g., age, sex, educational level, BMI).

This observational substudy is partly funded by an operating grant from the Heart and Stroke Foundation (Quebec) awarded to K. Dasgupta (Principal Investigator) and Nancy Ross (Co-Principal Investigator on substudy) and is being led by Samantha Hajna, their doctoral candidate student.

  Eligibility

Ages Eligible for Study:   18 Years to 95 Years
Genders Eligible for Study:   Both
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  • Followed by a SMARTER collaborating doctor
  • BMI ≥ 25 kg/m2 but < 40 kg/m2 (i.e. overweight to class II obese)
  • Type 2 diabetes and/or hypertension
  • Conversant in either English or French

Exclusion Criteria:

  • ≥ 150 minutes of leisure time physical activity per week be self- report
  • Acute or chronic co-morbid conditions that may affect the ability or likelihood to adhere to trial procedures (e.g. inflammatory arthritis, active malignancy, major depression or other significant psychiatric disorders, and/or significant visual impairment)
  • Pregnancy/planning a pregnancy
  • Baseline step count averaging ≥ 10,000 steps/day at baseline assessment
  • Arrhythmia that prevents accurate assessment of carotid-femoral pulse wave velocity (e.g., atrial fibrillation)
  Contacts and Locations
Please refer to this study by its ClinicalTrials.gov identifier: NCT01475201

Contacts
Contact: Debbie Chan, BSc 514-934-1934 ext 44835 debbie.chan@clinepi.mcgill.ca

Locations
Canada, Quebec
St. Mary's Hospital Center Recruiting
Montreal, Quebec, Canada, H3T 1M5
Contact: Cindy Ibberson, BA    514-345-3511 ext 5593    cindy.ibberson@ssss.gouv.qc.ca   
Contact: Ellen Rosenberg, MD    514-345-3511 ext 5620    ellen.rosenberg@mcgill.ca   
Principal Investigator: Ellen Rosenberg, MD         
Lakeshore General Hospital Recruiting
Montreal, Quebec, Canada, H9R 2Y2
Contact: Henry Jay Biem, MD       biemh@videotron.ca   
Principal Investigator: Henry Jay Biem, MD         
Jewish General Hospital Recruiting
Montreal, Quebec, Canada, H3T 1E2
Contact: Debbie Chan, BSc    5149341934    debbie.chan@clinepi.mcgill.ca   
Principal Investigator: Roland Grad, MD, MSc         
Principal Investigator: Stavroula Christopoulos, MD         
McGill University Health Centre - Royal Victoria Hosptial Recruiting
Montreal, Quebec, Canada, H3A 1A1
Contact: Debbie Chan, BSc    514-934-1934 ext 44835    debbie.chan@clinepi.mcgill.ca   
Principal Investigator: Kaberi Dasgupta, MD, MSc         
McGill University Health Centre - Montreal General Hospital Recruiting
Montreal, Quebec, Canada, H3G 1A4
Contact: Debbie Chan, BSc    514-934-1934 ext 44835    debbie.chan@clinepi.mcgill.ca   
Principal Investigator: Stella Daskalopoulou, MD         
Sponsors and Collaborators
McGill University Health Center
Canadian Institutes of Health Research (CIHR)
Investigators
Principal Investigator: Kaberi Dasgupta, MD, MSc McGill University and McGill University Health Centre
Principal Investigator: Stella Daskalopoulou, MD, PhD McGill University and McGill University Health Centre
Principal Investigator: Ellen Rosenberg, MD McGill University and St. Mary's Hospital Center
  More Information

Publications:
Responsible Party: Kaberi Dasgupta, MD, MSc, FRCP (C), Associate Professor of Medicine and Physician Scientist, McGill University Health Center
ClinicalTrials.gov Identifier: NCT01475201     History of Changes
Other Study ID Numbers: CIHR-MOP-114996
Study First Received: November 16, 2011
Last Updated: March 3, 2014
Health Authority: Canada: Ethics Review Committee

Keywords provided by McGill University Health Center:
Type 2 diabetes
Hypertension
Primary Care
Physical Activity
Behavioural Intervention
Pedometer
Accelerometer
Arterial Stiffness
Carotid femoral pulse wave velocity
Vascular disease risk
Vascular disease prevention
Intervention

Additional relevant MeSH terms:
Diabetes Mellitus
Diabetes Mellitus, Type 2
Hypertension
Glucose Metabolism Disorders
Metabolic Diseases
Endocrine System Diseases
Vascular Diseases
Cardiovascular Diseases

ClinicalTrials.gov processed this record on April 17, 2014