Sedentary behavior compensation to 1‐year exercise RCT in patients with type 2 diabetes

The Physical Activity Assessment of Adults With Type 2 Diabetes Using Accelerometer-Based Cut Points: Scoping Review

Background

Incorporating physical activity into lifestyle routines is recommended for individuals with type 2 diabetes. Accelerometers offer a promising method for objectively measuring physical activity and for assessing interventions. However, the existing literature for accelerometer-measured physical activity among middle-aged and older adults with type 2 diabetes is lacking.

Objective

This study aims to identify research studies in which accelerometer-based cut points were used to classify the physical activity intensity of middle-aged to older adults with type 2 diabetes as sedentary, light, moderate, vigorous, and very vigorous, and to determine if validated accelerometer cut points specifically for this population exist.

Methods

We followed the Joanna Briggs Institute methodology for scoping reviews. Between June 23 and July 12, 2020, two reviewers independently screened records from four databases (PubMed, Web of Science, Embase, Engineering Village) and the ActiGraph Corp web site for eligible studies that included patients with type 2 diabetes with a sample mean age ≥50 years, used research-grade accelerometers, applied cut points to categorize objectively measured physical activity, and were available in English. We excluded studies reporting exclusively steps or step counts measured by accelerometers or pedometers and conference abstracts or other sources that did not have a full text available. Data extraction was completed using Microsoft Excel. Data for the following variables were tabulated based on frequency distributions: study design, accelerometer type, device placement, epoch length, total wear time, and cut points used. Study aims and participant demographic data were summarized.

Results

A total of 748 records were screened at the abstract level, and 88 full-text articles were assessed for eligibility. Ultimately, 46 articles were retained and analyzed. Participants’ mean ages ranged from 50 to 79.9 years. The ActiGraph accelerometer and the Freedson et al and Troiano et al counts-per-minute cut points were the most frequently used across the literature. Freedson et al and Troiano et al counts-per-minute cut points for light, moderate, and vigorous activity correspond to <1952, 1952-5724, and ≥5725, and 100-2019, 2020-5998, and ≥5999, respectively. The Lopes et al cut points were developed by calibrating the ActiGraph in middle-aged and older adults with overweight/obesity and type 2 diabetes. These counts-per-minute thresholds are ≥200 (light), ≥1240 (moderate), and ≥2400 (vigorous), and were applied in 1 interventional study.

Conclusions

An assortment of accelerometer cut points have been used by researchers to categorize physical activity intensity for middle-aged and older adults with diabetes. Only one set of cut points was validated and calibrated in our population of interest. Additional research is warranted to address the need for diabetes-specific cut points to inform public health recommendations. This includes confirmation that the Lopes et al cut points reflect clinically meaningful changes in physical activity for adults with diabetes who have comorbidities other than overweight/obesity and the development of relative intensity cut points that may be more suitable for those with suboptimal physical functioning.

Development and Validation of Open-Source Activity Intensity Count and Activity Intensity Classification Algorithms from Raw Acceleration Signals of Wearable Sensors

BACKGROUND

A popular outcome in rehabilitation studies is the activity intensity count, which is typically measured from commercially available accelerometers. However, the algorithms are not openly available, which impairs long-term follow-ups and restricts the potential to adapt the algorithms for pathological populations. The objectives of this research are to design and validate open-source algorithms for activity intensity quantification and classification.

METHODS

Two versions of a quantification algorithm are proposed (fixed [FB] and modifiable bandwidth [MB]) along with two versions of a classification algorithm (discrete [DM] vs. continuous methods [CM]). The results of these algorithms were compared to those of a commercial activity intensity count solution (ActiLife) with datasets from four activities (n = 24 participants).

RESULTS

The FB and MB algorithms gave similar results as ActiLife (r > 0.96). The DM algorithm is similar to a ActiLife (r ≥ 0.99). The CM algorithm differs (r ≥ 0.89) but is more precise.

CONCLUSION

The combination of the FB algorithm with the DM results is a solution close to that of ActiLife. However, the MB version remains valid while being more adaptable, and the CM is more precise. This paper proposes an open-source alternative for rehabilitation that is compatible with several wearable devices and not dependent on manufacturer commercial decisions.