Accelerometer-derived physical activity and sedentary behaviors in individuals with newly diagnosed type 2 diabetes: A cross-sectional study from the Danish nationwide DD2 cohort

The Danish Centre for Strategic Research in Type 2 Diabetes (DD2) Project Cohort and Biobank from 2010 Through 2023-A Cohort Profile Update

Purpose

This paper provides an overview of the Danish Centre for Strategic Research in Type 2 Diabetes (DD2) cohort and biobank, including baseline characteristics of participants enrolled up to 2023, and post-enrollment rates of cardiovascular disease outcomes and mortality.

Methods

Since 2010, the DD2 project has enrolled individuals with type 2 diabetes mellitus (T2DM) recently diagnosed by general practitioners and by hospital-based clinicians across Denmark. Data from questionnaires, clinical examinations, and biological samples are collected at enrollment. Additional baseline and longitudinal follow-up data are accessed via linkage to health registries.

Results

Between 2010 and 2023, the DD2 project enrolled 11,369 participants (41.3% women, median age 61.4 years). Median T2DM duration at enrollment was 1.3 years, and median BMI was 31.6 kg/m2 for women and 30.5 kg/m2 for men. 18.3% were smokers, 5.7% consumed more than 14/21 units of alcohol weekly (women/men), and 17.9% reported leisure-time physical inactivity. Original midwife records dating back >80 years revealed that 20.2% of cohort participants had birth weights <3000 g. Based on complete hospital contact history 10 years before enrollment, 20.7% of cohort participants had macrovascular complications, 17.0% had microvascular complications, and 21.7% had kidney disease based on eGFR or urine albumin-creatinine measurements. At enrollment, statins were used by 68.2%, antihypertensive drugs by 69.9%, and glucose-lowering drugs by 86.5% of individuals. Median HbA1c was 48 mmol/mol and median LDL cholesterol 2.2 mmol/L. Genome-wide genotyping and biomarker data have been analyzed for over 9000 individuals. During the current follow-up time from the enrollment date (median 7.9 years), incident cardiovascular disease rate has been 13.8 per 1000 person-years and the mortality rate has been 17.6 per 1000 person-years.

Conclusion

The DD2 cohort, with its detailed information and long-term follow up, can improve our understanding of the progression and prevention of complications among individuals with newly diagnosed T2DM.

Data Analytics in Physical Activity Studies With Accelerometers: Scoping Review

BACKGROUND

Monitoring free-living physical activity (PA) through wearable devices enables the real-time assessment of activity features associated with health outcomes and provision of treatment recommendations and adjustments. The conclusions of studies on PA and health depend crucially on reliable statistical analyses of digital data. Data analytics, however, are challenging due to the various metrics adopted for measuring PA, different aims of studies, and complex temporal variations within variables. The application, interpretation, and appropriateness of these analytical tools have yet to be summarized.

OBJECTIVE

This research aimed to review studies that used analytical methods for analyzing PA monitored by accelerometers. Specifically, this review addressed three questions: (1) What metrics are used to describe an individual's free-living daily PA? (2) What are the current analytical tools for analyzing PA data, particularly under the aims of classification, association with health outcomes, and prediction of health events? and (3) What challenges exist in the analyses, and what recommendations for future research are suggested regarding the use of statistical methods in various research tasks?

METHODS

This scoping review was conducted following an existing framework to map research studies by exploring the information about PA. Three databases, PubMed, IEEE Xplore, and the ACM Digital Library, were searched in February 2024 to identify related publications. Eligible articles were classification, association, or prediction studies involving human PA monitored through wearable accelerometers.

RESULTS

After screening 1312 articles, 428 (32.62%) eligible studies were identified and categorized into at least 1 of the following 3 thematic categories: classification (75/428, 17.5%), association (342/428, 79.9%), and prediction (32/428, 7.5%). Most articles (414/428, 96.7%) derived PA variables from 3D acceleration, rather than 1D acceleration. All eligible articles (428/428, 100%) considered PA metrics represented in the time domain, while a small fraction (16/428, 3.7%) also considered PA metrics in the frequency domain. The number of studies evaluating the influence of PA on health conditions has increased greatly. Among the studies in our review, regression-type models were the most prevalent (373/428, 87.1%). The machine learning approach for classification research is also gaining popularity (32/75, 43%). In addition to summary statistics of PA, several recent studies used tools to incorporate PA trajectories and account for temporal patterns, including longitudinal data analysis with repeated PA measurements and functional data analysis with PA as a continuum for time-varying association (68/428, 15.9%).

CONCLUSIONS

Summary metrics can quickly provide descriptions of the strength, frequency, and duration of individuals' overall PA. When the distribution and profile of PA need to be evaluated or detected, considering PA metrics as longitudinal or functional data can provide detailed information and improve the understanding of the role PA plays in health. Depending on the research goal, appropriate analytical tools can ensure the reliability of the scientific findings.

Relationships between physical activity and other health-related measures using state-based prevalence estimates

Background

Both physical activity and muscle-strengthening activity have known relationships with other health-related variables such as alcohol and tobacco use, diet, and health-related quality of life (HRQOL). The purpose of this study was to explore and quantify the associations between physical activity measures and health-related variables at the higher state level.

Methods

This cross-sectional study used data from the 2017 and 2019 Behavioral Risk Factor Surveillance System surveys. State-based prevalence (%) estimates were computed for meeting physical activity guidelines (PA), meeting muscle-strengthening activity guidelines (MS), both PA and MS (MB), drinking alcohol (D1), heavy alcohol drinking (HD), fruit consumption (F1), vegetable consumption (V1), good self-rated health (GH), overweight (OW), obesity (OB), current smoking (SN), and smokeless tobacco use (SL). Descriptive statistics, correlation coefficients, and data visualization methods were employed.

Results

Strongest associations were seen between PA and F1 (2017: r=0.717 & 2019: r=0.695), MS and OB (2017: r=-0.781 & 2019: r=-0.599), PA and GH (2017: r=0.631 & 2019: r=0.649), PA and OB (2017: r=-0.645 & 2019: r=-0.763), and MB and SN (2017: r=-0.713 & 2019: r=-0.645). V1 was associated only with PA (2017: r=0.335 & 2019: r=0.357) whereas OW was not associated only with PA. Canonical correlation analysis showed the physical activity variables were directly related (r c=0.884, P<0.001) to the health variables.

Conclusion

This study used high-level data to support the many known relationships between PA measures and health-related variables.

Detailed descriptions of physical activity patterns among individuals with diabetes and prediabetes: the Lolland-Falster Health Study

INTRODUCTION

This study aimed to describe objectively measured physical activity patterns, including daily activity according to day type (weekdays and weekend days) and the four seasons, frequency, distribution, and timing of engagement in activity during the day in individuals with diabetes and prediabetes and compared with individuals with no diabetes.

RESEARCH DESIGN AND METHODS

This cross-sectional study included data from the Danish household-based, mixed rural-provincial population study, The Lolland-Falster Health Study from 2016 to 2020. Participants were categorized into diabetes, prediabetes, and no diabetes based on their glycated hemoglobin level and self-reported use of diabetes medication. Outcome was physical activity in terms of intensity (time spent in sedentary, light, moderate, vigorous, and moderate to vigorous physical activity (MVPA) intensities), adherence to recommendations, frequency and distribution of highly inactive days (<5 min MVPA/day), and timing of engagement in activity assessed with a lower-back worn accelerometer.

RESULTS

Among 3157 participants, 181 (5.7 %) had diabetes and 568 (18.0 %) had prediabetes. Of participants with diabetes, 63.2% did not adhere to the WHO recommendations of weekly MVPA, while numbers of participants with prediabetes and participants with no diabetes were 59.5% and 49.6%, respectively. Around a third of participants with diabetes were highly inactive daily (<5 min MVPA/day) and had >2 consecutive days of inactivity during a 7-days period. Mean time spent physically active at any intensity (light, moderate, and vigorous) during a day was lower among participants with diabetes compared with participants with no diabetes and particularly from 12:00 to 15:00 (mean difference of -6.3 min MVPA (95% CI -10.2 to -2.4)). Following adjustments, significant differences in physical activity persisted between diabetes versus no diabetes, but between participants with prediabetes versus no diabetes, results were non-significant after adjusting for body mass index.

CONCLUSIONS

Inactivity was highly prevalent among individuals with diabetes and prediabetes, and distinct daily activity patterns surfaced when comparing these groups with those having no diabetes. This highlights a need to optimize current diabetes treatment and prevention to accommodate the large differences in activity engagement.

Differences in Associated Factors of Sedentary Behavior by Diabetes Mellitus Status: A Nationwide Cross-Sectional Study

This study aimed to identify the lifestyle and comorbidity factors associated with sedentary behavior by diabetes mellitus (DM) status. A total of 17,832 participants aged ≥50 years from the Korea National Health and Nutrition Examination Survey were included. Factors associated with long sedentary time (LST, ≥420 min/day) in individuals with and without DM (non-DM) were assessed. Among individuals with DM, LST was independently associated with excessive alcohol drinking (OR, 1.34; 95% CI, 1.02-1.74) and cardiovascular disease (OR, 1.47; 95% CI, 1.16-1.85). In individuals without DM, cancer (OR, 1.24; 95% CI, 1.06-1.44) and past smoking (OR, 1.16; 95% CI, 1.01-1.35) were independently associated with LST. Obesity (DM: OR, 1.28; 95% CI, 1.05-1.54; non-DM: OR, 1.24; 95% CI, 1.11-1.37), insufficient aerobic exercise (DM: OR, 1.55; 95% CI, 1.30-1.84; non-DM: OR, 1.50; 95% CI, 1.37-1.63), current smoking (DM: OR, 1.51; 95% CI, 1.11-2.05; non-DM: OR, 1.23; 95% CI, 1.05-1.45), and arthritis (DM: OR, 1.28; 95% CI, 1.04-1.56; non-DM: OR, 1.15; 95% CI, 1.04-1.27) were consistently associated with LST regardless of DM status. To reduce sedentary behavior time, it is important to consider an individual's diabetes status and adopt a personalized approach.