The Wild Wild West: A Framework to Integrate mHealth Software Applications and Wearables to Support Physical Activity Assessment, Counseling and Interventions for Cardiovascular Disease Risk Reduction

Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes

Background

Few studies assessing the correlation between patient-reported outcomes and patient-generated health data from wearable devices exist.

Objective

The aim of this study was to determine the direction and magnitude of associations between patient-generated health data (from the Fitbit Charge HR) and patient-reported outcomes for sleep patterns and physical activity in patients with type 2 diabetes mellitus (T2DM).

Methods

This was a pilot study conducted with adults diagnosed with T2DM (n=86). All participants wore a Fitbit Charge HR for 14 consecutive days and completed internet-based surveys at 3 time points: day 1, day 7, and day 14. Patient-generated health data included minutes asleep and number of steps taken. Questionnaires assessed the number of days of exercise and nights of sleep problems per week. Means and SDs were calculated for all data, and Pearson correlations were used to examine associations between patient-reported outcomes and patient-generated health data. All respondents provided informed consent before participating.

Results

The participants were predominantly middle-aged (mean 54.3, SD 13.3 years), white (80/86, 93%), and female (50/86, 58%). Use of oral T2DM medication correlated with the number of mean steps taken (r=.35, P=.001), whereas being unaware of the glycated hemoglobin level correlated with the number of minutes asleep (r=−.24, P=.04). On the basis of the Fitbit data, participants walked an average of 4955 steps and slept 6.7 hours per day. They self-reported an average of 2.0 days of exercise and 2.3 nights of sleep problems per week. The association between the number of days exercised and steps walked was strong (r=.60, P<.001), whereas the association between the number of troubled sleep nights and minutes asleep was weaker (r=.28, P=.02).

Conclusions

Fitbit and patient-reported data were positively associated for physical activity as well as sleep, with the former more strongly correlated than the latter. As extensive patient monitoring can guide clinical decisions regarding T2DM therapy, passive, objective data collection through wearables could potentially enhance patient care, resulting in better patient-reported outcomes.

Routine Assessment and Promotion of Physical Activity in Healthcare Settings: A Scientific Statement From the American Heart Association

Physical inactivity is one of the most prevalent major health risk factors, with 8 in 10 US adults not meeting aerobic and muscle-strengthening guidelines, and is associated with a high burden of cardiovascular disease. Improving and maintaining recommended levels of physical activity leads to reductions in metabolic, hemodynamic, functional, body composition, and epigenetic risk factors for noncommunicable chronic diseases. Physical activity also has a significant role, in many cases comparable or superior to drug interventions, in the prevention and management of >40 conditions such as diabetes mellitus, cancer, cardiovascular disease, obesity, depression, Alzheimer disease, and arthritis. Whereas most of the modifiable cardiovascular disease risk factors included in the American Heart Association’s My Life Check - Life’s Simple 7 are evaluated routinely in clinical practice (glucose and lipid profiles, blood pressure, obesity, and smoking), physical activity is typically not assessed. The purpose of this statement is to provide a comprehensive review of the evidence on the feasibility, validity, and effectiveness of assessing and promoting physical activity in healthcare settings for adult patients. It also adds concrete recommendations for healthcare systems, clinical and community care providers, fitness professionals, the technology industry, and other stakeholders in order to catalyze increased adoption of physical activity assessment and promotion in healthcare settings and to contribute to meeting the American Heart Association’s 2020 Impact Goals.

Comparison of In-Person and Online Motivational Interviewing–Based Health Coaching

Health coaching is a common approach for promoting lifestyle changes, but little is known about the effectiveness of different delivery methods. The purpose of this study was to evaluate the effectiveness of in-person versus online health coaching when used as part of a facilitated behavior change program. To increase translatability, the study used a naturalistic design that enabled participants to self-select the coaching delivery method (Group) as well as the target behavior (diet, physical activity, or weight management). Regardless of group, participants were provided with a behavior-based monitoring device and guided to use it by the health coach. A sample of 92 adults participated and 86 completed pre–post evaluations to assess behavior change strategies and posttest outcome measurements for their specific goal. Two-way (Group × Time) analyses of variance were used to evaluate changes in behavioral strategies. Intent-to-treat regression analyses were used to compare postintervention outcomes for groups. The in-person group had significantly higher Healthy Eating Index scores than the online group ( p < .05), but nonsignificant group differences were observed for those targeting physical activity or weight change ( p > .05). The results support the use of health coaching for promoting behavior change and suggest that online coaching may be equally effective as in-person methods.

Using mHealth Technology in a Self-Management Intervention to Promote Physical Activity Among Adults With Chronic Disabling Conditions: Randomized Controlled Trial

BACKGROUND

Physical activity is considered a comprehensive approach for managing limitations in physical function among adults with chronic disabling conditions. However, adults with chronic disabling conditions often face many barriers to engaging in physical activity. A strategy to promote physical activity among adults with chronic disabling conditions is to encourage the use of mobile health (mHealth) apps.

OBJECTIVE

The objective of this pilot study was to examine the potential benefits of using commercially available mHealth apps in a self-management intervention among 46 adults with musculoskeletal or neurological conditions.

METHODS

Participants were randomized to one of 3 intervention groups: (1) mHealth-based self-management intervention, (2) paper-based self-management intervention, and (3) contact-control intervention. Participants in all 3 groups met in person once and received 3 follow-up phone calls with a trained graduate assistant. Participants in the mHealth-based and paper-based groups received a computer tablet or a paper diary, respectively, to facilitate goal setting, self-monitoring, and action planning. Participants in the contact-control group received information on healthy behaviors without being taught skills to change behaviors. The following outcomes were measured at baseline and at the 7th week: physical activity (Physical Activity and Disability Survey-revised), psychosocial factors (self-efficacy, self-regulation, and social support), and physical function (Patient Report Outcomes Measurement Information System, 6-min walk test, 1-min chair stands, and 1-min arm curls).

RESULTS

Repeated-measures multivariate analysis of variance (MANOVA) indicated significant differences between groups in physical activity levels (Wilks λ=0.71, F_6,76=2.34, P=.04). Both the mHealth-based and paper-based groups had large effect size increases in planned exercise and leisure-time physical activity compared with the contact-control group (Cohen d=1.20 and d=0.82, respectively). Repeated-measures MANOVA indicated nonsignificant differences between groups in psychosocial factors (Wilks λ=0.85, F_6,76=1.10, P=.37). However, both the mHealth-based and paper-based groups had moderate effect size improvements in self-efficacy (d=0.48 and d=0.75, respectively) and self-regulation (d=0.59 and d=0.43, respectively) compared with the contact-control group. Repeated-measures MANOVA indicated nonsignificant differences between groups in physical function (Wilks λ=0.94, F_8,66=0.27, P=.97). There were small and nonsignificant changes between the mHealth-based and paper-based groups with regard to most outcomes. However, the mHealth-based group had moderate effect size increases (d=0.47) in planned exercise and leisure-time physical activity compared with the paper-based group.

CONCLUSIONS

We found that using commercially available mHealth apps in a self-management intervention shows promise in promoting physical activity among adults with musculoskeletal and neurological conditions. Further research is needed to identify the best ways of using commercially available mobile apps in self-management interventions.

TRIAL REGISTRATION

Clinicaltrials.gov NCT02833311; https://clinicaltrials.gov/ct2/show/NCT02833311 (Archived by WebCite at http://www.webcitation.org/6vDVSAw1w).

Challenges for learning health systems in the NHS. Case study: electronic health records in cardiology

Electronic health records (EHRs) are at the centre of advances in health informatics, but also many other innovations in healthcare. However, there are still obstacles to implementation and realisation of the full potential of EHRs as there are with learning health systems (LHS). Cardiovascular disease, in the UK and globally, carries greater morbidity and mortality than any other disease. Therefore, planning and delivery of health services represent major costs to individuals and populations. Both the scale of disease burden and the growing role of technology in cardiology practice make analysis of experiences with EHRs in cardiology a useful lens through which to view achievements and gaps to date. In this article regarding LHS, EHRs in cardiology are used as a case study of LHS in the NHS.

Promoting active travel to school: a systematic review (2010-2016)

Background

Interventions aiming to promote active school travel (AST) are being implemented globally to reverse AST decline. This systematic literature provides an update of AST interventions assessing study quality and theory use to examine progress in the field.

Methods

A systematic review was conducted to identify and analyse AST interventions published between 2010 and 2016. Seven databases were searched and exclusion criteria were applied to identify 18 AST interventions. Interventions were assessed using the Active Living by Design (ALBD) Community Action (5P) Model and the Evaluation of Public Health Practice Projects (EPHPP). Methods used to evaluate the effectiveness of each intervention and their outcomes and extent of theory use were examined.

Results

Seven out of 18 studies reported theory use. The analysis of the interventions using the ALBD Community Action Model showed that Preparation and Promotion were used much more frequently than Policy and Physical projects. The methodological quality 14 out of 18 included interventions were assessed as weak according to the EPHPP framework.

Conclusion

Noted improvements were an increase in use of objective measures. Lack of theory, weak methodological design and a lack of reliable and valid measurement were observed. Given that change is evident when theory is used and when policy changes are included extended use of the ALBD model and socio-ecological frameworks are recommended in future.

Electronic supplementary material

The online version of this article (doi:10.1186/s12889-017-4648-2) contains supplementary material, which is available to authorized users.

Improving physical health of patients with severe mental disorders: a critical review of lifestyle psychosocial interventions

INTRODUCTION

People with severe mental disorders have a mortality rate that is more than two times higher than the general population, with at least a decade of potential years of life lost. People with mental disorders have a significantly higher risk of obesity, hyperglycemia and metabolic syndrome, which are related to modifiable risk factors, such as heavy smoking, poor physical activities, and inappropriate unhealthy diet, which can be improved through lifestyle changes. Areas covered: Lifestyle behaviours are amenable to change through the adoption of specific psychosocial interventions, and several approaches have been promoted. In the present review, the authors aim to: 1) critically analyze studies involving multimodal lifestyle interventions; 2) discuss the way forward to integrate these interventions in clinical routine care. Expert commentary: The psychoeducational approaches developed for the improvement of healthy lifestyle behaviours differ for several aspects: 1) the format (individual vs. group); 2) the setting (outpatient vs. inpatient vs. home-based); 3) the professional characteristics of the staff running the intervention (psychiatrists or nurses or dietitians or psychologists); 4) the active ingredients of the intervention (education only or inclusion of motivational interview or of problem solving); 5) the duration of treatment (ranging from 3 months to 2 years).

Lifestyle and precision diabetes medicine: will genomics help optimise the prediction, prevention and treatment of type 2 diabetes through lifestyle therapy?

Precision diabetes medicine, the optimisation of therapy using patient-level biomarker data, has stimulated enormous interest throughout society as it provides hope of more effective, less costly and safer ways of preventing, treating, and perhaps even curing the disease. While precision diabetes medicine is often framed in the context of pharmacotherapy, using biomarkers to personalise lifestyle recommendations, intended to lower type 2 diabetes risk or to slow progression, is also conceivable. There are at least four ways in which this might work: (1) by helping to predict a person's susceptibility to adverse lifestyle exposures; (2) by facilitating the stratification of type 2 diabetes into subclasses, some of which may be prevented or treated optimally with specific lifestyle interventions; (3) by aiding the discovery of prognostic biomarkers that help guide timing and intensity of lifestyle interventions; (4) by predicting treatment response. In this review we overview the rationale for precision diabetes medicine, specifically as it relates to lifestyle; we also scrutinise existing evidence, discuss the barriers germane to research in this field and consider how this work is likely to proceed.

Research Opportunities and Challenges in the Era of Healthy Living Medicine: Unlocking the Potential

Non-communicable diseases, such as cardiovascular disease (CVD), obesity, cancer, pulmonary disease, and diabetes are a very high global health concern. The health costs of risk factors for CVD, such as hypertension (HTN), is mounting and are unrelenting. As an example, it is estimated that direct and indirect costs due to HTN amounted to $46.4 billion in 2011 and projections of six-fold increases by 2030; the importance of low-cost nonpharmacological interventions involving collaborative teams of health care professionals is at a critical junction. Certainly, the data supported by research including some clinical trials for healthy living interventions support deploying health education, nutrition, smoking cessation, and physical activity(PA) in preventing CVD risk, such as HTN. Exercise training (ET) for blood pressure (BP) control has been shown to be an effective and integral component of BP management. However, less is known about what optimization of PA/ET modalities with nutrition and lifestyle tracking with modern era technologies will bring to this equation. New research methods may need to consider how to collaborate to collect data in using teams of researchers while interacting with community centers, school systems, and in traditional health care practices. This review will discuss and present what is known about the research that support modern era healthy living medicine and how this data may be integrated in venues that support health lifestyle in the community (i.e. schools and the work place).