Table 3.

Recommendations for capturing and reporting adherence measured by biometric monitoring technologies (BioMeTs).

Identified gaps and recommendations			Case study [27]
Gap 1: Quantitative, nonsurrogate, sensor-based adherence data were not reported in 29.9% of screened manuscripts that captured BioMeT data outside the clinical or laboratory setting.
	Recommendation 1: Investigators are encouraged to develop and/or use BioMeT sensors to capture sensor-based adherence data in addition to their primary purpose.	This study aimed to evaluate adherence to a physical activity among students recruited from 20 schools. Quantitative adherence data were derived from wrist-worn accelerometers, considered a direct reflection of wear-time.
	Recommendation 2: Where feasible, we encourage investigators to collect and report adherence data that are a direct reflection of actual use, rather than a surrogate.	N/Aa
Gap 2: BioMeT manufacturer or model and software information was missing for 10% and 68% of included tools, respectively.
	Recommendation 3: In addition to reporting the BioMeT manufacturer or model and software used for generating adherence data (where applicable), we recommend that investigators provide a clear description of the sensor or sensors capturing adherence data.	BioMeT model: GENEActiv wrist-worn device (ActivInsights Ltd). Sensor description: 3-axis accelerometer. Software: GENEActiv PC software (version 2.9), with subsequent signal processing performed in R-package (GGIR; version 1.2-2).
	Recommendation 4: We recommend that investigators describe the algorithm or algorithms that convert sample-level measurements into a measurement of adherence. If a description is not available from the manufacturer, this should be stated.	The paper included the data sampling frequency (100 Hz); a description of the signal processing steps including calibration; the epoch length (5 seconds) over which the sample-level data were averaged; and the units (milligravitational units; m g). A description of the nonwear detection algorithm was summarized as, “Non-wear is estimated on the basis of the SD and value range of each axis, calculated for 60-min windows with 15-min sliding window. The window is classified as non-wear if, for at least two of the three axes, the SD is less than 13 mg or the value range is less than 50 mg.”
	Recommendation 5: We recommend that investigators describe the analytic validation data supporting the adherence algorithm; that is, the data indicating that adherence per the BioMeT is an accurate estimate of actual use. If analytic validation data is not available, this should be stated.	A reference to previous verification and analytic validation work was included.
Gap 3: Heterogeneity of adherence definitions increased alongside decreasing resolution of adherence data reported.
	Recommendation 6: We recommend that investigators using BioMeTs that are either passive (designed to capture data passively over long periods) or session-based (designed for user engagement at certain time points, for which the duration of use is meaningful) report primary adherence as a continuous variable of time; that is, total minutes or hours or days, or average hours per day, days per week, and so on. Example of a passive BioMeT: smart clothing. Example of a session-based BioMeT: connected exercise equipment.	The BioMeT was categorized as passive, as the wrist-worn accelerometer was designed to capture data continuously over 3 separate periods of 7 days. Adherence was reported as the total hours of wear-time, and hours per day of wear-time.
	Recommendation 7: We recommend that investigators using BioMeTs that are task based (designed for user engagement at certain time points, for which the duration of use is not meaningful) report primary adherence as a continuous variable; that is, the number of tasks or days completed. Example of a task-based BioMeT: connected scale.	N/A, as the BioMeT was categorized as passive rather than task based.
	Recommendation 8: We recommend that categorical adherence data are reported only in addition to continuous adherence data; for example, the percentage of participants with use >x hours per day or percentage of participants completing >y tasks.	Categorical adherence data included the number of participants with ≥16 hours of wear-time per day.
	Recommendation 9: We recommend that categorical definitions of adherence be based on clinical validation data indicating the level of adherence associated with a clinically meaningful change in the outcome of interest, when available. If clinical validation data are not available, this should be stated.	The investigators include a reference to previous work that adopted the threshold of ≥16 hours of wear-time per day and describe another study that compared thresholds of 8 hours, 16 hours, and 24 hours of wear-time.

^aN/A: not applicable.