Comparison of raw accelerometry data from ActiGraph, Apple Watch, Garmin, and Fitbit using a mechanical shaker table

The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin's concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein.

The utility of behavioral biometrics in user authentication and demographic characteristic detection: a scoping review

BACKGROUND

Objective measures of screen time are necessary to better understand the complex relationship between screen time and health outcomes. However, current objective measures of screen time (e.g., passive sensing applications) are limited in identifying the user of the mobile device, a critical limitation in children's screen time research where devices are often shared across a family. Behavioral biometrics, a technology that uses embedded sensors on modern mobile devices to continuously authenticate users, could be used to address this limitation.

OBJECTIVE

The purpose of this scoping review was to summarize the current state of behavioral biometric authentication and synthesize these findings within the scope of applying behavioral biometric technology to screen time measurement.

METHODS

We systematically searched five databases (Web of Science Core Collection, Inspec in Engineering Village, Applied Science & Technology Source, IEEE Xplore, PubMed), with the last search in September of 2022. Eligible studies were on the authentication of the user or the detection of demographic characteristics (age, gender) using built-in sensors on mobile devices (e.g., smartphone, tablet). Studies were required to use the following methods for authentication: motion behavior, touch, keystroke dynamics, and/or behavior profiling. We extracted study characteristics (sample size, age, gender), data collection methods, data stream, model evaluation metrics, and performance of models, and additionally performed a study quality assessment. Summary characteristics were tabulated and compiled in Excel. We synthesized the extracted information using a narrative approach.

RESULTS

Of the 14,179 articles screened, 122 were included in this scoping review. Of the 122 included studies, the most highly used biometric methods were touch gestures (n = 76) and movement (n = 63), with 30 studies using keystroke dynamics and 6 studies using behavior profiling. Of the studies that reported age (47), most were performed exclusively in adult populations (n = 34). The overall study quality was low, with an average score of 5.5/14.

CONCLUSION

The field of behavioral biometrics is limited by the low overall quality of studies. Behavioral biometric technology has the potential to be used in a public health context to address the limitations of current measures of screen time; however, more rigorous research must be performed in child populations first.

SYSTEMATIC REVIEW REGISTRATION

The protocol has been pre-registered in the Open Science Framework database ( https://doi.org/10.17605/OSF.IO/92YCT ).

Contextual influences on implementation of online behavioral obesity treatment in primary care: formative evaluation guided by the consolidated framework for implementation research

Online behavioral obesity treatment is a promising first-line approach to weight management in primary care. However, little is known about contextual influences on implementation. Understand qualitative contextual factors that affect the implementation process, as experienced by key primary care stakeholders implementing the program. Online behavioral obesity treatment was implemented across a 60-clinic primary care practice network. Patients were enrolled by nurse care managers (NCMs; N = 14), each serving 2-5 practices. NCMs were randomized to one of two implementation conditions-"Basic" (standard implementation) or "Enhanced" (i.e., with added patient tracking features and more implementation strategies employed). NCMs completed qualitative interviews guided by the Consolidated Framework for Implementation Research (CFIR). Interviews were transcribed and analyzed via directed content analysis. Emergent categories were summarized by implementation condition and assigned a valence according to positive/negative influence. Individuals in the Enhanced condition viewed two aspects of the intervention as more positively influencing than Basic NCMs: Design Quality & Packaging (i.e., online program aesthetics), and Cost (i.e., no-cost program, clinician time savings). In both conditions, strongly facilitating factors included: Compatibility between intervention and clinical context; Intervention Source (from a trusted local university); and Evidence Strength & Quality supporting effectiveness. Findings highlight the importance of considering stakeholders' perspectives on the most valued types of evidence when introducing a new intervention, ensuring the program aligns with organizational priorities, and considering how training resources and feedback on patient progress can improve implementation success for online behavioral obesity treatment in primary care.