Convergent validity of ActiGraph and Actical accelerometers for estimating physical activity in adults

<div><p>Purpose</p><p>The aim of the present study was to examine the convergent validity of two commonly-used accelerometers for estimating time spent in various physical activity intensities in adults.</p><p>Methods</p><p>The sample comprised 37 adults (26 males) with a mean (SD) age of 37.6 (12.2) years from San Diego, USA. Participants wore ActiGraph GT3X+ and Actical accelerometers for three consecutive days. Percent agreement was used to compare time spent within four physical activity intensity categories under three counts per minute (CPM) threshold protocols: (1) using thresholds developed specifically for each accelerometer, (2) applying ActiGraph thresholds to regression-rectified Actical CPM data, and (3) developing new ‘optimal’ Actical thresholds.</p><p>Results</p><p>Using Protocol 1, the Actical estimated significantly less time spent in light (-16.3%), moderate (-2.8%), and vigorous (-0.4%) activity than the ActiGraph, but greater time spent sedentary (+20.5%). Differences were slightly more pronounced when the low frequency extension filter on the ActiGraph was enabled. The two adjustment methods (Protocols 2 and 3) improved agreement in this sample.</p><p>Conclusions</p><p>Our findings show that ActiGraph and Actical accelerometers provide significantly different estimates of time spent in various physical activity intensities. Regression and threshold adjustment were able to reduce these differences, although some level of non-agreement persisted. Researchers should be aware of the inherent limitations of count-based physical activity assessment when reporting and interpreting study findings.</p></div

Characteristics of the sample.

<p>Characteristics of the sample.</p

Scatterplot depicting the relationship between ActiGraph (normal filter) and Actical counts per minute (CPM) (r = 0.815).

<p>Scatterplot depicting the relationship between ActiGraph (normal filter) and Actical counts per minute (CPM) (r = 0.815).</p

Minute-by-minute agreement between ActiGraph (normal filter; Freedson et al cut points [15]) and Actical using three different protocols.

<p>The highest kappa statistic(s) for each intensity level is bolded.</p

Selected sociodemographic, medical, cardio-metabolic, and sitting-related characteristics of the final analytic sample, (AusDiab 2011–12; n = 678).

<p>Selected sociodemographic, medical, cardio-metabolic, and sitting-related characteristics of the final analytic sample, (AusDiab 2011–12; n = 678).</p

Associations of sitting and prolonged sitting time, and sitting accumulation with measures of blood pressure and glucose control in Australian adults aged 36 to 80 (AusDiab 2011–12; n = 678^a).

<p>Associations of sitting and prolonged sitting time, and sitting accumulation with measures of blood pressure and glucose control in Australian adults aged 36 to 80 (AusDiab 2011–12; n = 678<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0180119#t003fn001" target="_blank">^a</a>).</p

Changes in daily sitting, standing, sit-to-stand transitions and stepping over time by condition.

<p>Changes in daily sitting, standing, sit-to-stand transitions and stepping over time by condition.</p

Additional file 2: of Cluster randomized controlled trial of a multilevel physical activity intervention for older adults

Figure S1. Gender differences in physical activity between intervention and control conditions over time, adjusting for baseline demographic differences, nesting of days within people and people within sites. (DOCX 17 kb

Additional file 3: of Cluster randomized controlled trial of a multilevel physical activity intervention for older adults

Table S2. Adverse events by condition at 12 months. (DOCX 14 kb

The association between time of day outdoors and sleep.

<p>The association between time of day outdoors and sleep.</p