argus smartphone app  (Azumio Inc)

Journal: NPJ Digital Medicine

Article Title: Best practices for analyzing large-scale health data from wearables and smartphone apps

doi: 10.1038/s41746-019-0121-1

Figure Lengend Snippet: Overview of best practices for analyzing large-scale physical activity and health behavior datasets from commercial smartphone apps and wearable devices. The process is highly iterative, as indicated by the arrows flowing in both directions. By sharing results, along with data and software tools, your work can help inspire new research, completing the circle

Article Snippet: The Argus smartphone app (Azumio, Inc.), includes a social network that users can opt to join.

Techniques: Activity Assay, Software

Journal: NPJ Digital Medicine

Article Title: Best practices for analyzing large-scale health data from wearables and smartphone apps

doi: 10.1038/s41746-019-0121-1

Figure Lengend Snippet: Comparison of demographics of U.S. users of a smartphone and data from traditional surveillance studies. a Body Mass Index (BMI) distribution of users of the Argus app (blue) vs. the U.S. population as measured in the National Health and Nutrition Examination Survey (NHANES; red). b Age distribution of users of the Argus app vs. NHANES. The counts for the NHANES sample are weighted according to the NHANES-provided sample weights, thus the distributions approximate the general U.S. population and the total of the weighted counts in the histogram matches the number of individuals in the 2011–2012 NHANES study year. While there are differences between the distributions, the app dataset, due to its massive size, has large coverage of users between the ages of 15 and 70 and BMIs from 20 to 40. For example, the dataset includes 32,000 individuals in the U.S. over age 60 and 113,000 individuals in the U.S. whose BMI classifies them as obese

Article Snippet: The Argus smartphone app (Azumio, Inc.), includes a social network that users can opt to join.

Techniques: Comparison

Journal: NPJ Digital Medicine

Article Title: Best practices for analyzing large-scale health data from wearables and smartphone apps

doi: 10.1038/s41746-019-0121-1

Figure Lengend Snippet: Verifying that a smartphone app dataset reproduces previously reported relationships between physical activity, geographic location, age, and gender. In our study of activity inequality, we conducted extensive analyses comparing the app dataset to previously published datasets. a WHO physical activity measure versus smartphone activity measure (LOESS fit). The WHO measure corresponds to the percentage of the population meeting the WHO guidelines for moderate to vigorous physical activity based on self-report. The smartphone activity measure is based on accelerometer-defined average daily steps. We found a correlation of r = 0.3194 between the two measures ( P < 0.05). Note that this comparison is limited because there is no direct correspondence between the two measures—values of self-reported and accelerometer-defined activity can differ, and the WHO confidence intervals are very large for many countries. b WHO obesity estimates based on self-reports to survey conductors, versus obesity estimates in our dataset, based on height and weight reported to the activity-tracking app (LOESS fit). We found a significant correlation of r = 0.691 between the two estimates ( P < 10 −6 ). c Gender gap in activity estimated from smartphones is strongly correlated with previously reported estimates based on self-report (LOESS fit). We found that the difference in average steps per day between females and males is strongly correlated to the difference in the fraction of each gender who report being sufficiently active according to the WHO ( r = 0.52, P < 10 −3 ). d Daily step counts are shown across age for all users. Error bars correspond to bootstrapped 95% confidence intervals. Observed trends in the dataset are consistent with previous findings; that is, activity decreases with increasing BMI – and is lower in females than in males. , – This figure is adapted from our previous work and reproduced with permission

Article Snippet: The Argus smartphone app (Azumio, Inc.), includes a social network that users can opt to join.

Techniques: Activity Assay, Comparison

Journal: NPJ Digital Medicine

Article Title: Best practices for analyzing large-scale health data from wearables and smartphone apps

doi: 10.1038/s41746-019-0121-1

Figure Lengend Snippet: Example of a natural experiment using observational data from a smartphone app for tracking activity. The Argus smartphone app (Azumio, Inc.), includes a social network that users can opt to join. Althoff and colleagues sought to uncover if and how forming social connections affects social activity. Since users who join and are active in the social network may be more intrinsically motivated to increase their activity, they used a natural experiment to isolate the effects of social influence from other factors that could influence activity. In particular, they compared the change in activity between a individuals who sent out a friend request (question mark) that was immediately accepted (check mark) and b individuals whose friend request was not accepted for more than 7 days. Note the curves in a and b are for illustrative purposes and do not represent actual subjects. Once a friendship is accepted, the user receives notifications of their connections’ activities (e.g., going for a run), and can comment on their connections’ activity posts (denoted by the heart, text box, and notification bell in a and b ). Since the two groups were similar in all aspects except whether their friend request was accepted within 7 days, the additional increase in activity of the direct acceptance group can be attributed to social influence. c This social influence resulted in users taking 400 more steps per day on average. Error bars indicate bootstrapped 95% confidence intervals

Article Snippet: The Argus smartphone app (Azumio, Inc.), includes a social network that users can opt to join.

Techniques: Activity Assay

Journal: NPJ Digital Medicine

Article Title: Best practices for analyzing large-scale health data from wearables and smartphone apps

doi: 10.1038/s41746-019-0121-1

Figure Lengend Snippet: Example of propensity scoring to isolate the effects of a treatment that comes in different doses (physical activity) from other confounding factors. The Argus smartphone app (Azumio, Inc.) collects minute by minute step counts. a For each user, we can construct a plot of activity bout length (X) vs. the average number of minutes per day spent in activity of at least X minutes. We call the area under this curve an individual’s activity persistence. In the figure we include users with at least 10 days of step tracking data. b We next want to understand how activity persistence influences quantities like BMI. Since individuals with higher or lower activity persistence may be different in other ways that influence BMI (such as age and gender), we used inverse probability of treatment weighting (IPTW) to isolate the effects of activity. The grey curve shows the BMI of individuals in each decile of activity persistence (where higher deciles indicate more bouts of longer duration), without any weighting. The green curve shows the relationship after we have used IPTW to minimize the influence of other factors like age and gender on the estimated BMI for each decile of activity persistence. Error bars correspond to bootstrapped 95% confidence intervals

Article Snippet: The Argus smartphone app (Azumio, Inc.), includes a social network that users can opt to join.

Techniques: Activity Assay, Construct