Background Fitness trackers and smart watches are frequently used to collect data in longitudinal medical studies. They allow continuous recording in real-life settings, potentially revealing previously uncaptured variabilities of biophysiological parameters and diseases. Adequate device accuracy is a prerequisite for meaningful research. Objective This study aims to assess the heart rate recording accuracy in two previously unvalidated devices: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Methods Participants performed a study protocol comprising 5 resting and sedentary, 2 low-intensity, and 3 high-intensity exercise phases, lasting an average of 19 minutes 27 seconds. Participants wore two wearables simultaneously during all activities: Fitbit Charge 4 and Samsung Galaxy Watch Active2. Reference heart rate data were recorded using a medically certified Holter electrocardiogram. The data of the reference and evaluated devices were synchronized and compared at 1-second intervals. The mean, mean absolute error, mean absolute percentage error, Lin concordance correlation coefficient, Pearson correlation coefficient, and Bland-Altman plots were analyzed. Results A total of 23 healthy adults (mean age 24.2, SD 4.6 years) participated in our study. Overall, and across all activities, the Fitbit Charge 4 slightly underestimated the heart rate, whereas the Samsung Galaxy Watch Active2 overestimated it (−1.66 beats per minute [bpm]/3.84 bpm). The Fitbit Charge 4 achieved a lower mean absolute error during resting and sedentary activities (seated rest: 7.8 vs 9.4; typing: 8.1 vs 11.6; laying down [left]: 7.2 vs 9.4; laying down [back]: 6.0 vs 8.6; and walking slowly: 6.8 vs 7.7 bpm), whereas the Samsung Galaxy Watch Active2 performed better during and after low- and high-intensity activities (standing up: 12.3 vs 9.0; walking fast: 6.1 vs 5.8; stairs: 8.8 vs 6.9; squats: 15.7 vs 6.1; resting: 9.6 vs 5.6 bpm). Conclusions Device accuracy varied with activity. Overall, both devices achieved a mean absolute percentage error of just <10%. Thus, they were considered to produce valid results based on the limits established by previous work in the field. Neither device reached sufficient accuracy during seated rest or keyboard typing. Thus, both devices may be eligible for use in respective studies; however, researchers should consider their individual study requirements.

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Objective: Clinical urine tests are a key component of prenatal care. As of now, urine test strips are evaluated through a time consuming, often error-prone and operator-dependent visual color comparison of test strips and reference cards by medical staff. Methods and procedures: This work presents an automated pipeline for urinalysis with urine test strips using smartphone camera images in home environments, combining several image processing and color combination techniques. Our approach is applicable to off-the-shelf test strips in home conditions with no additional hardware required. For development and evaluation of our pipeline we collected image data from two sources: i) A user study (26 participants, 150 images) and ii) a lab study (135 images). Results: We trained a region-based convolutional neural network that is able to detect the urine test strip location and orientation in images with a wide variety of light conditions, backgrounds and perspectives with an accuracy of 85.5%. The reference card can be robustly detected through a feature matching approach in 98.6% of the images. Color comparison by Hue channel (0.81 F1-Score), Matching factor (0.80 F1-Score) and Euclidean distance (0.70 F1-Score) were evaluated to determine the urinalysis results. Conclusion: We show that an automated smartphone-based colorimetric analysis of urine test strips in a home environment is feasible. It facilitates examinations and provides the possibility to shift care into an at-home environment. Clinical impact: The findings demonstrate that routine urine examinations can be transferred into the home environment using a smartphone. Simultaneously, human error is avoided, accuracy is increased and medical staff is relieved.

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