The Estimation of Caloric Expenditure Using Three Triaxial Accelerometers

Accelerometer-based activity monitors are commonly used to measure physical activity energy expenditure (PAEE). Newly designed wrist and hip-worn triaxial accelerometers claim to accurately predict PAEE across a range of activities. Purpose: To determine if the Nike FuelBand (NFB), Fitbit (FB) and ActiGraph GT3X+ (AG) estimate PAEE in various activities. Methods: 21 healthy, college-aged adults wore a NFB on the right wrist, a FB on the left hip, and AG on the right hip, while performing 17 activities. AG data were analyzed using Freedson’s kcal regression equation. PAEE was measured using the Cosmed K4b2 (K4). Repeated measures ANOVAs were used to compare mean differences in PAEE (kcal/min). Paired sample t-tests with Bonferroni adjustments were used to locate significant differences. Results: For each device, the mean difference in PAEE was significantly different from the K4 (NFB, -0.45 + 2.8, FB, 0.48 + 2.27, AG, 0.64 + 2.59 kcal/min, p = 0.01). The NFB significantly overestimated most walking activities (e.g., regular walking; K4, 3.1 + 0.2 vs. NFB, 4.6 + 0.2 kcal/min) and activities with arm movements (e.g., sweeping; K4, 3.0 + 0.8 vs. NFB, 4.7 + 0.4 kcal/min, p \u3c 0.05). The NFB trended towards overestimating sport activities (basketball; K4, 10.8 + 0.8 vs. NFB, 12.2 + 0.5 kcal/min) (racquetball; K4, 9.6 + 0.8 vs. NFB 11.1 + 0.5 kcal/min). The FB and the AG significantly overestimated walking (K4, 3.1 + 0.2; FB, 5.4 + 0.3, AG, 5.8 + 0.4 kcal/min, p = 0.01) and underestimated PAEE of most activities with arm movements (e.g., Air Dyne, K4 5.6 + 0.2; Fitbit, 0.3 + 0.2; AG, 0.2 + 0.1 kcal/min, p \u3c 0.05) (racquetball, K4, 9.6 + 0.8 kcal/minute vs. FB, 7.4 + 0.6 kcal/minute, vs. AG, 6.5 + 0.4 kcal/minute, p \u3c 0.05). Conclusion: The NFB overestimated PAEE during most activities with arm movements and tended to overestimate sport activities, while the AG and FB overestimated walking and underestimated activities with arm movements. Overall, the wrist-worn NFB had similar accuracy to the waist-worn triaxial accelerometers; however, none of the devices were able to estimate PAEE across a range of activities

Differential Gene Expression in High- and Low-Active Inbred Mice

Numerous candidate genes have been suggested in the recent literature with proposed roles in regulation of voluntary physical activity, with little evidence of these genes' functional roles. This study compared the haplotype structure and expression profile in skeletal muscle and brain of inherently high- (C57L/J) and low- (C3H/HeJ) active mice. Expression of nine candidate genes [Actn2, Actn3, Casq1, Drd2, Lepr, Mc4r, Mstn, Papss2, and Glut4 (a.k.a. Slc2a4)] was evaluated via RT-qPCR. SNPs were observed in regions of Actn2, Casq1, Drd2, Lepr, and Papss2; however, no SNPs were located in coding sequences or associated with any known regulatory sequences. In mice exposed to a running wheel, Casq1 (P = 0.0003) and Mstn (P = 0.002) transcript levels in the soleus were higher in the low-active mice. However, when these genes were evaluated in naïve animals, differential expression was not observed, demonstrating a training effect. Among naïve mice, no genes in either tissue exhibited differential expression between strains. Considering that no obvious SNP mechanisms were determined or differential expression was observed, our results indicate that genomic structural variation or gene expression data alone is not adequate to establish any of these genes' candidacy or causality in relation to regulation of physical activity

Differential Gene Expression in High- and Low-Active Inbred Mice

Numerous candidate genes have been suggested in the recent literature with proposed roles in regulation of voluntary physical activity, with little evidence of these genes’ functional roles. This study compared the haplotype structure and expression profile in skeletal muscle and brain of inherently high- (C57L/J) and low- (C3H/HeJ) active mice. Expression of nine candidate genes [Actn2, Actn3, Casq1, Drd2, Lepr, Mc4r, Mstn, Papss2, and Glut4 (a.k.a. Slc2a4)] was evaluated via RT-qPCR. SNPs were observed in regions of Actn2, Casq1, Drd2, Lepr, and Papss2; however, no SNPs were located in coding sequences or associated with any known regulatory sequences. In mice exposed to a running wheel, Casq1 (P=0.0003) and Mstn (P=0.002) transcript levels in the soleus were higher in the low-active mice. However, when these genes were evaluated in naïve animals, differential expression was not observed, demonstrating a training effect. Among naïve mice, no genes in either tissue exhibited differential expression between strains. Considering that no obvious SNP mechanisms were determined or differential expression was observed, our results indicate that genomic structural variation or gene expression data alone is not adequate to establish any of these genes’ candidacy or causality in relation to regulation of physical activity