Melanie I. Stuckey

Public Health Guidelines for Physical Activity: Is There an App for That? A Review of Android and Apple App Stores

Background Physical activity participation is an important behavior for modifying lifestyle-related disease risk. Mobile health apps for chronic disease management and prevention are being developed at a rapid rate. However, it is unclear whether these apps are evidence-based. Current public health recommendations for physical activity participation for adults highlight the importance of engaging in 150 minutes weekly of purposeful exercise, and muscle strengthening activities on at least 2 days of the week. Objective The aims of the present review were to (1) identify available evidence-based physical activity apps, and (2) identify technological features that could be leveraged to improve health outcomes. Methods iTunes and Google Play mobile app stores were searched using keyword and category searching during a single day (February 18, 2014) for physical activity apps available in English. The description pages of eligible apps were reviewed by 4 independent reviewers for evidence-based content, technological, and descriptive features. An a priori subset of apps was downloaded for further review (n=6 affiliated with a non-commercial agency; n=10 top rated; n=10 random selection), and developers were contacted for information regarding evidence-informed content. Results The initial search yielded 2400 apps, of which 379 apps (n=206 iTunes; n=173 Google Play) were eligible. Primary results demonstrated no apps (n=0) adhering to evidence-based guidelines for aerobic physical activity, and 7 out of 379 implementing evidence-based guidelines for resistance training physical activity. Technological features of apps included social networking (n=207), pairing with a peripheral health device (n=61), and measuring additional health parameters (n=139). Secondary results revealed 1 app that referenced physical activity guidelines (150 minutes/weekly of exercise), and demonstrated that apps were based on various physical activity reports (n=4) or personal expertise (n=2). Conclusions The present study demonstrated a shortage of evidence-based physical activity apps. This gap underscores the need for development of evidence-informed mobile apps. Results highlight the opportunity to develop evidence-informed mobile apps that can be used clinically to enhance health outcomes.

Heart rate variability and the metabolic syndrome: a systematic review of the literature

A number of cross‐sectional studies have examined associations between heart rate variability and metabolic syndrome, but differences in study populations, data collection and analysis methodologies make synthesis difficult. The purpose of this study was to systematically review published primary research examining associations between heart rate variability and metabolic syndrome or its individual risk factors.

Remote Monitoring Technologies for the Prevention of Metabolic Syndrome: The Diabetes and Technology for Increased Activity (DaTA) Study

Objectives: Remote monitoring technologies are ideally suited for rural communities with limited access to health care. In an 8-week pilot study, we examined the feasibility of implementing and conducting a technology-intensive intervention in an underserviced rural setting. Our goal was to test the utility of self-monitoring technologies, physical activity, and education as tools to manage health indicators for the development of the cardiovascular complications (CVCs) of type 2 diabetes. Research Design and Methods: The Diabetes and Technology for Increased Activity study was an open single-center study conducted in a community-based research setting. All 24 participants were provided with a Blackberry™ Smartphone, blood pressure monitor, glucometer, and pedometer. Smartphones transmitted measurements and survey results to the database, interfaced participants with the clinical team, and allowed for self-monitoring. Results: Outcomes were improved body composition, improved markers of CVC risk factors, increased daily exercise, and interest in or awareness of lifestyle changes that impact health outcomes. Participants had excellent compliance for measurements, as self-monitoring provided a sense of security that improved from week 4 to week 8. Conclusions: Our team gained substantial insight into the operational requirements of technology-facilitated health care, including redefined hours of service; data reporting, management, and access protocols; and the utility of real-time clinical measures by remote monitoring. We developed an understanding of knowledge translation strategies as well as successful motivational and educational tools. Importantly, remote monitoring technology was found to be feasible and accepted in a rural setting.

A lifestyle intervention supported by mobile health technologies to improve the cardiometabolic risk profile of individuals at risk for cardiovascular disease and type 2 diabetes: study rationale and protocol

BackgroundMetabolic syndrome is a cluster of cardiovascular risk factors that greatly increase the risk of developing cardiovascular disease and type 2 diabetes. Regular exercise improves the risk profile, but most people do not successfully change their exercise habits to beneficially reduce risk. Tailored exercise prescribed by a family physician has shown promise as a means to increase fitness and reduce cardiometabolic risk, but optimal implementation practices remain unknown. Mobile health technologies have proved to be a beneficial tool to achieve blood pressure and blood glucose control in patients with diabetes. These technologies may address the limited access to health interventions in rural and remote regions. However, the potential as a tool to support exercise-based prevention activities is not well understood. This study was undertaken to investigate the effects of a tailored exercise prescription alone or supported by mobile health technologies to improve metabolic syndrome and related cardiometabolic risk factors in rural community-dwelling adults at risk for cardiovascular disease and type 2 diabetes.Methods/DesignAdults (n = 149) with at least two metabolic syndrome risk factors were recruited from rural communities and randomized to either: 1) an intervention group receiving an exercise prescription and devices for monitoring of risk factors with a smartphone data portal equipped with a mobile health application; or 2) an active control group receiving only an exercise prescription. All participants reported to the research centre at baseline, and at 12-, 24- and 52-week follow-up visits for measurement of anthropometrics and blood pressure and for a blood draw to test blood-borne markers of cardiometabolic health. Vascular and autonomic function were examined. Fitness was assessed and exercise prescribed according to the Step Test and Exercise Prescription protocol.DiscussionThis study tested the effects of a prescriptive exercise intervention alone, versus one supported by mobile health technology on cardiometabolic risk factors. The intervention was designed to be translated into clinical or community-based programming. Results will contribute to the current literature by investigating the utility of mobile health technology support for exercise prescription interventions to improve cardiometabolic risk status and maintain improvements over time; particularly in rural communities.Trial registrationClinical trials registration: NCT01944124

Health promotion through primary care: enhancing self-management with activity prescription and mHealth.

Abstract Background: It is well established in the literature that regular participation in physical activity is effective for chronic disease management and prevention. Remote monitoring technologies (ie, mHealth) hold promise for engaging patients in self-management of many chronic diseases. The purpose of this study was to test the effectiveness of an mHealth study with tailored physical activity prescription targeting changes in various intensities of physical activity (eg, exercise, sedentary behavior, or both) for improving physiological and behavioral markers of lifestyle-related disease risk. Methods: Forty-five older adults (aged 55–75 years; mean age 63 ± 5 years) were randomly assigned to receive a personal activity program targeting changes to either daily exercise, sedentary behavior, or both. All participants received an mHealth technology kit including smartphone, blood pressure monitor, glucometer, and pedometer. Participants engaged in physical activity programming at home during the 12-week intervention period and submitted physical activity (steps/day), blood pressure (mm Hg), body weight (kg), and blood glucose (mmol/L) measures remotely using study-provided devices. Results: There were no differences between groups at baseline (P > 0.05). The intervention had a significant effect (F(10 488) = 2.947, P = 0.001, ηP2 = 0.057), with similar changes across all groups for physical activity, body weight, and blood pressure (P > 0.05). Changes in blood glucose were significantly different between groups, with groups prescribed high-intensity activity (ie, exercise) demonstrating greater reductions in blood glucose than the group prescribed changes to sedentary behavior alone (P < 0.05). Conclusions: Findings demonstrate the utility of pairing mHealth technologies with activity prescription for prevention of lifestyle-related chronic diseases among an at-risk group of older men and women. Results support the novel approach of prescribing changes to sedentary behaviors (alone, and in conjunction with exercise) to reduce risk of developing lifestyle-related chronic conditions.

Diabetes and Technology for Increased Activity Study: The Effects of Exercise and Technology on Heart Rate Variability and Metabolic Syndrome Risk Factors

This study tested the hypothesis that an 8-week exercise intervention supported by mobile health (mHealth) technology would improve metabolic syndrome (MetS) risk factors and heart rate variability (HRV) in a population with MetS risk factors. Participants (n = 12; three male; aged 56.9 ± 7.0 years) reported to the laboratory for assessment of MetS risk factors and fitness (VO2max) at baseline (V 0) and after 8-weeks (V 2) of intervention. Participants received an individualized exercise prescription and a mHealth technology kit for remote monitoring of blood pressure (BP), blood glucose, physical activity, and body weight via smartphone. Participants underwent 24-h ambulatory monitoring of R–R intervals following V 0 and V 2. Low and high frequency powers of HRV were assessed from the recording and the ratio of low-to-high frequency powers and low and high frequency powers in normalized units were calculated. One-way repeated measures analysis of variance showed that waist circumference (V 0: 113.1 ± 11.0 cm, V 2: 108.1 ± 14.7 cm; p = 0.004) and diastolic BP (V 0: 81 ± 6 mmHg, V 2: 76 ± 11 mmHg; p = 0.04) were reduced and VO2max increased (V 0: 31.3 ml/kg/min, V 2: 34.8 ml/kg/min; p = 0.02) with no changes in other MetS risk factors. Low and high frequency powers in normalized units were reduced (V 0: 75.5 ± 12.0, V 2: 72.0 ± 12.1; p = 0.03) and increased (V 0: 24.5 ± 12.0, V 2: 28.0 ± 12.1; p = 0.03), respectively, with no other changes in HRV. Over the intervention period, changes in systolic BP were correlated negatively with the changes in R–R interval (r = −0.600; p = 0.04) and positively with the changes in heart rate (r = 0.611; p = 0.03), with no other associations between MetS risk factors and HRV parameters. Thus, this 8-week mHealth supported exercise intervention improved MetS risk factors and HRV parameters, but only changes in systolic BP were associated with improved autonomic function.

Associations between heart rate variability, metabolic syndrome risk factors, and insulin resistance

The purpose of this study was to examine differences in heart rate variability (HRV) in metabolic syndrome (MetS) and to determine associations between HRV parameters, MetS risk factors, and insulin resistance (homeostasis model assessment for insulin resistance (HOMA-IR)). Participants (n = 220; aged 23-70 years) were assessed for MetS risk factors (waist circumference, blood pressure, fasting plasma glucose, triglycerides, and high-density lipoprotein cholesterol) and 5-min supine HRV (time and frequency domain and nonlinear). HRV was compared between those with 3 or more (MetS+) and those with 2 or fewer MetS risk factors (MetS-). Multiple linear regression models were built for each HRV parameter to investigate associations with MetS risk factors and HOMA-IR. Data with normal distribution are presented as means ± SD and those without as median [interquartile range]. In women, standard deviation of R-R intervals 38.0 [27.0] ms, 44.5 [29.3] ms; p = 0.020), low-frequency power (5.73 ± 1.06 ln ms(2), 6.13 ± 1.05 ln ms(2); p = 0.022), and the standard deviation of the length of the Poincaré plot (46.8 [31.6] ms, 58.4 [29.9] ms; p = 0.014) were lower and heart rate was higher (68 [13] beats/min, 64 [12] beats/min; p = 0. 018) in MetS+ compared with MetS-, with no differences in men. Waist circumference was most commonly associated with HRV, especially frequency domain parameters. HOMA-IR was associated with heart rate. In conclusion, MetS+ women had a less favourable HRV profile than MetS- women, but there were no differences in men. HOMA-IR was associated with heart rate, not HRV.

Validation of the Step Test and Exercise Prescription Tool for Adults

OBJECTIVE Canadian clinical practice guidelines for the management of diabetes highlight the value of physical activity for improving clinical markers and risk factors for diabetes. The use of tools like the Step Test and Exercise Prescription (STEP) in clinical practice facilitates chronic disease management and prevention through the promotion of healthy physical activity. The validity and reliability of STEP has been established previously for use with adults 65 to 85 years of age. The purpose of this study was to test the validity of the prediction equation for VO₂max from the STEP tool for use with adults older than 65 years. METHODS Forty participants completed both the predictive self-paced VO₂max stepping protocol from the STEP tool, and a maximal graded exercise treadmill test with breath-by-breath analysis of expired gases. Tests were completed in random order, and participants rested between tests until blood pressure returned to baseline. RESULTS The average age of the sample was 43±14 years. There was a strong relationship between predicted VO₂max from STEP and direct measures of VO₂max from the maximal treadmill test in the present study (r=.78, p<0.001). Bland-Altman analysis demonstrated statistical agreement between tests, although there was systematic overestimation of 6.4 mL/kg/min. CONCLUSIONS STEP is an evidence-based cardiorespiratory fitness assessment and exercise prescription tool appropriate for use by various health professionals in clinical practice. The prediction equation for VO₂max from the STEP tool is valid for use with adults 18 to 85 years of age. However, more research is warranted to explore age corrections to the prediction algorithm among younger adults.

Prescribing Physical Activity Through Primary Care: Does Activity Intensity Matter?

Abstract Background: Physical activity guidelines recommend engaging in moderate- and vigorous-intensity physical activity to elicit health benefits. Similarly, these higher intensity ranges for activity are typically targeted in healthy living interventions (ie, exercise prescription). Comparatively less attention has been focused on changing lower intensity physical activity (ie, sedentary activity) behaviors. The purpose of this study was to explore the effects of prescribing changes to physical activity of various intensities (ie, sedentary through exercise) through the primary care setting. Methods: Sixty older adults (aged 55–75 years; mean age 63 ± 5 years) volunteered to participate, and were randomly assigned to 4 groups: 3 receiving an activity prescription intervention targeting a specific intensity of physical activity (exercise, sedentary, or both), and 1 control group. During the 12-week intervention period participants followed personalized activity programs at home. Basic clinical measures (anthropometrics, blood pressure, aerobic fitness) and blood panel for assessing cardiometabolic risk (glucose, lipid profile) were conducted at baseline (week 0) and follow-up (week 12) in a primary care office. Results: There were no differences between groups at baseline (P > 0.05). The intervention changed clinical (F(5, 50) = 20.458, P = 0.000, ηP2 = 0.672) and blood panel measures (F(5, 50) = 4.576, P = 0.002, ηP2 = 0.314) of cardiometabolic health. Post hoc analyses indicted no differences between groups (P > 0.05). Conclusion: Physical activity prescription of various intensities through the primary care setting improved cardiometabolic health status. To our knowledge, this is the first report of sedentary behavior prescription (alone, or combined with exercise) in primary care. The findings support the ongoing practice of fitness assessment and physical activity prescription for chronic disease management and prevention.

Innovation to Reduce Cardiovascular Complications of Diabetes at the Intersection of Discovery, Prevention and Knowledge Exchange

This article describes selected primary outcomes from a series of linked, collaborative projects among multidisciplinary investigators from Canada and Finland dedicated to quantifying the benefits and detriments of prescriptive exercise in the prevention and control of the cardiovascular complications (CVCs) of diabetes along the continuum of disease risk.