Background Suboptimal adherence to data collection procedures or a study intervention is often the cause of a failed clinical trial. Data from connected sensors, including wearables, referred to here as biometric monitoring technologies (BioMeTs), are capable of capturing adherence to both digital therapeutics and digital data collection procedures, thereby providing the opportunity to identify the determinants of adherence and thereafter, methods to maximize adherence. Objective We aim to describe the methods and definitions by which adherence has been captured and reported using BioMeTs in recent years. Identifying key gaps allowed us to make recommendations regarding minimum reporting requirements and consistency of definitions for BioMeT-based adherence data. Methods We conducted a systematic review of studies published between 2014 and 2019, which deployed a BioMeT outside the clinical or laboratory setting for which a quantitative, nonsurrogate, sensor-based measurement of adherence was reported. After systematically screening the manuscripts for eligibility, we extracted details regarding study design, participants, the BioMeT or BioMeTs used, and the definition and units of adherence. The primary definitions of adherence were categorized as a continuous variable based on duration (highest resolution), a continuous variable based on the number of measurements completed, or a categorical variable (lowest resolution). Results Our PubMed search terms identified 940 manuscripts; 100 (10.6%) met our eligibility criteria and contained descriptions of 110 BioMeTs. During literature screening, we found that 30% (53/177) of the studies that used a BioMeT outside of the clinical or laboratory setting failed to report a sensor-based, nonsurrogate, quantitative measurement of adherence. We identified 37 unique definitions of adherence reported for the 110 BioMeTs and observed that uniformity of adherence definitions was associated with the resolution of the data reported. When adherence was reported as a continuous time-based variable, the same definition of adherence was adopted for 92% (46/50) of the tools. However, when adherence data were simplified to a categorical variable, we observed 25 unique definitions of adherence reported for 37 tools. Conclusions We recommend that quantitative, nonsurrogate, sensor-based adherence data be reported for all BioMeTs when feasible; a clear description of the sensor or sensors used to capture adherence data, the algorithm or algorithms that convert sample-level measurements to a metric of adherence, and the analytic validation data demonstrating that BioMeT-generated adherence is an accurate and reliable measurement of actual use be provided when available; and primary adherence data be reported as a continuous variable followed by categorical definitions if needed, and that the categories adopted are supported by clinical validation data and/or consistent with previous reports.

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A diagnosis of acute myeloid leukemia involving the central nervous system (CNS) can be confirmed through cerebrospinal fluid (CSF) and serum flow cytometry. These two detection methods should demonstrate the same immunophenotype due to hematogenous dissemination. Here, we reported a 65-year-old male diagnosed with CNS leukemia with differing immunophenotypes between CSF and peripheral blood. This immunophenotypic shift may suggest leukemic migration within the blood-brain barrier. In addition, the case highlights the concept of leukemic heterogeneity and the importance of considering cancer heterogeneity when analyzing a tumor’s genetic profile and selecting therapy for patients.

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BACKGROUND Sub-optimal adherence to data collection procedures and/or a study intervention is often the cause of a failed clinical trial. Data from biometric monitoring technologies (BioMeTs) can measure adherence to both digital therapeutics and digital data collection procedures, thereby providing the opportunity to identify the determinants of adherence and thereafter, methods to maximize adherence. OBJECTIVE Our goal was to describe the methods and definitions by which adherence has been captured and reported using BioMeTs in recent years. Identifying key gaps allowed us to make recommendations regarding minimum reporting requirements and consistency of definitions for BioMeT-based adherence data. METHODS We conducted a systematic review of studies published between 2014 and 2019 that deployed a BioMeT outside of the clinical/lab setting for which a quantitative, non-surrogate, sensor-based measurement of adherence was reported. After systematically screening manuscripts for eligibility, we extracted details regarding study design, participants, the BioMeT/s used, and the definition and units of adherence. Primary definitions of adherence were categorized as a continuous variable based on duration (highest resolution), a continuous variable based on the number of measurements completed, or a categorical variable (lowest resolution). RESULTS Our PubMed search terms identified 940 manuscripts; 100 met our eligibility criteria, which contained descriptions of 110 BioMeTs. We identified 37 unique definitions of adherence reported for 110 BioMeTs, and observed that the uniformity of adherence definitions was associated with the resolution of the data reported. When adherence was reported as a continuous time-based variable, the same definition of adherence was adopted for 92% of the tools; however, when adherence data were simplified to a categorical variable, we observed 25 unique definitions of adherence reported for 37 tools. CONCLUSIONS We recommend that: A) quantitative, non-surrogate, sensor-based, adherence data be reported for all BioMeTs when feasible; B) a clear description of the sensor/s used to capture adherence data, the algorithm/s that convert sample-level measurements to a metric of adherence, and the analytical validation data demonstrating that BioMeT-generated adherence is an accurate and reliable measurement of actual usage, be provided when available; and C) primary adherence data be reported as a continuous variable followed by categorical definitions if needed, and that the categories adopted are supported by clinical validation data and/or consistent with previous reports.