Assessing the ability of the Fitbit Charge 2 to accurately predict VO2max.

BACKGROUND: The aim of this study was to assess the ability of the Fitbit Charge 2 (FBC2) to accurately estimate VO2max in comparison to both the gold standard VO2max test and a non-exercise VO2max prediction equation. METHODS: Thirty healthy subjects (17 men, 13 women) between the ages of 18 and 35 (age =21.7±3.1 years) were given a FBC2 to wear for seven days and followed instructions on how to obtain a cardio fitness score (CFS). VO2max was measured with an incremental test on the treadmill followed by a verification phase. VO2max was predicted via a non-exercise prediction model (N-Ex) using self-reported physical activity level. RESULTS: Measured VO2max was significantly lower than FBC2 predicted CFS (VO2max =49.91±6.83; CFS =52.53±8.43, P=0.03). N-Ex prediction was significantly lower than CFS but not significantly lower than measured VO2max (N-Ex =48.79±6.32; CFS vs. N-Ex: P=0.01; VO2max vs. N-Ex: P=0.54). Relationships between both VO2max vs. CFS and VO2max vs. N-Ex were good (ICC: VO2max vs. CFS=0.87, VO2max vs. N-Ex =0.87); Bland-Altman analysis indicated consistency of CFS measurement and lack of bias. The coefficient of variation (CV) and mean absolute percent error (MAPE) were greater with CFS than N-Ex (CV: CFS =6.5%±4.1%, N-Ex =5.6%±3.6%; MAPE: CFS =10.2%±6.7%, N-Ex =7.8%±5.0%). Heart rate (HR) estimated by the FBC2 was lower than estimated (Est) HR for pace based on HR extrapolation (FBC2 =155±18 bpm, Est =183±15 bpm, P<0.001). The difference in CFS and VO2max was inversely correlated with the difference in FBC2 HR and Estimated HR (r =-0.45, P<0.001). CONCLUSIONS: The FBC2 shows consistent, unbiased measurement of CFS while overestimating VO2max in healthy men and women. The non-exercise VO2max prediction equation provides a similar, slightly more accurate, VO2max prediction than the CFS without the need for an exercise test or purchase of a Fitbit.

Do as I tweet, not as I do: comparing physical activity data between fitness tweets and Healthy People 2020.

BACKGROUND: The goal of this research was to compare the self-reported estimates of daily physical-activity data provided to the Healthy People 2020 research team via a telephone survey to the mobile fitness app real-time reporting of physical activity using Twitter. METHODS: The fitness tweet classification data set was collected from mobile fitness app users who shared their physical activity over Twitter. Over 184 days, 2,856,534 tweets were collected in 23 different languages. However, for the purposes of this study, only the English-language tweets were analysed, resulting in a total of 1,982,653 tweets by 165,768 unique users. The information and data gleaned from this data set, which reflected 184 days of continuous data collection, were compared to the results from the Healthy People survey, which were compiled using telephone interviews of self-reported physical activity from the previous week. RESULTS: The data collected from fitness tweets using the five mobile fitness apps suggest lower percentages of people achieving both the 150 to 300 and 300+ min levels than is reflected in the Healthy People survey results. While employing Twitter and other social media as data-collection tools could help researchers obtain information that users might not remember or be willing to disclose face-to-face or over the telephone, further research is needed to determine the cause of the lower percentages found in this study. CONCLUSIONS: Though some challenges remain in using social media like Twitter to glean physical-activity data from the public, this approach holds promise for yielding valuable information and improving outcomes.