Estimation of Symptom Severity During Chemotherapy From Passively Sensed Data: Exploratory Study.

BACKGROUND: Physical and psychological symptoms are common during chemotherapy in cancer patients, and real-time monitoring of these symptoms can improve patient outcomes. Sensors embedded in mobile phones and wearable activity trackers could be potentially useful in monitoring symptoms passively, with minimal patient burden. OBJECTIVE: The aim of this study was to explore whether passively sensed mobile phone and Fitbit data could be used to estimate daily symptom burden during chemotherapy. METHODS: A total of 14 patients undergoing chemotherapy for gastrointestinal cancer participated in the 4-week study. Participants carried an Android phone and wore a Fitbit device for the duration of the study and also completed daily severity ratings of 12 common symptoms. Symptom severity ratings were summed to create a total symptom burden score for each day, and ratings were centered on individual patient means and categorized into low, average, and high symptom burden days. Day-level features were extracted from raw mobile phone sensor and Fitbit data and included features reflecting mobility and activity, sleep, phone usage (eg, duration of interaction with phone and apps), and communication (eg, number of incoming and outgoing calls and messages). We used a rotation random forests classifier with cross-validation and resampling with replacement to evaluate population and individual model performance and correlation-based feature subset selection to select nonredundant features with the best predictive ability. RESULTS: Across 295 days of data with both symptom and sensor data, a number of mobile phone and Fitbit features were correlated with patient-reported symptom burden scores. We achieved an accuracy of 88.1% for our population model. The subset of features with the best accuracy included sedentary behavior as the most frequent activity, fewer minutes in light physical activity, less variable and average acceleration of the phone, and longer screen-on time and interactions with apps on the phone. Mobile phone features had better predictive ability than Fitbit features. Accuracy of individual models ranged from 78.1% to 100% (mean 88.4%), and subsets of relevant features varied across participants. CONCLUSIONS: Passive sensor data, including mobile phone accelerometer and usage and Fitbit-assessed activity and sleep, were related to daily symptom burden during chemotherapy. These findings highlight opportunities for long-term monitoring of cancer patients during chemotherapy with minimal patient burden as well as real-time adaptive interventions aimed at early management of worsening or severe symptoms.

Quantification of the physiological loading of one week of "pre-season" and one week of "in-season" training in professional soccer players.

The aim of this study was to quantify the physiological loads of programmed "pre-season" and "in-season" training in professional soccer players. Data for players during each period were included for analysis (pre-season, n = 12; in-season, n = 10). We monitored physiological loading of training by measuring heart rate and rating of perceived exertion (RPE). Training loads were calculated by multiplying RPE score by the duration of training sessions. Each session was sub-categorized as physical, technical/tactical, physical and technical/tactical training. Average physiological loads in pre-season (heart rate 124 ± 7 beats · min(-1); training load 4343 ± 329 Borg scale · min) were higher compared with in-season (heart rate 112 ± 7 beats · min(-1); training load 1703 ± 173 Borg scale · min) (P < 0.05) and there was a greater proportion of time spent in 80-100% maximum heart rate zones (18 ± 2 vs. 5 ± 2%; P < 0.05). Such differences appear attributable to the higher intensities in technical/tactical sessions during pre-season (pre-season: heart rate 137 ± 8 beats · min(-1); training load 321 ± 23 Borg scale · min; in-season: heart rate 114 ± 9 beats · min(-1); training load 174 ± 27 Borg scale · min; P < 0.05). These findings demonstrate that pre-season training is more intense than in-season training. Such data indicate that these adjustments in load are a direct attempt to deliver training to promote specific training adaptations.

Evaluation of Calf Muscular Function During the Recovery Phase After the Repair of an Achilles Tendon Rupture.

Background: During the recovery phase after the repair of an Achilles tendon rupture, measuring calf muscular function is important for predicting prognosis. Tensiomyography (TMG) is a selective and non-invasive diagnostic method for skeletal muscular contractile properties based on the displacement of the muscle belly. Case Presentation: Tensiomyography gives information about maximal displacement of the muscle belly (Dm), delay time, contraction time (Tc), sustain time, and relaxation time. Using Tensiomyography we evaluated a patient that had Achilles tendon rupture surgery. The contralateral normal side measurements were also performed for evaluation and comparison of the site of injury. Findings: In this study, the maximal displacement of the muscle belly changed significantly compared to other parameters. The maximal displacement of the muscle belly decreased after cast removal day and increased gradually during the early recovery phase and then slightly decreased again during the late recovery phase. Conclusions: These responses of the maximal displacement of muscle belly show a correlation with the recovery of muscular function.

Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions.

BACKGROUND: Real-time detection of drinking could improve timely delivery of interventions aimed at reducing alcohol consumption and alcohol-related injury, but existing detection methods are burdensome or impractical. OBJECTIVE: To evaluate whether phone sensor data and machine learning models are useful to detect alcohol use events, and to discuss implications of these results for just-in-time mobile interventions. METHODS: 38 non-treatment seeking young adult heavy drinkers downloaded AWARE app (which continuously collected mobile phone sensor data), and reported alcohol consumption (number of drinks, start/end time of prior day's drinking) for 28days. We tested various machine learning models using the 20 most informative sensor features to classify time periods as non-drinking, low-risk (1 to 3/4 drinks per occasion for women/men), and high-risk drinking (>4/5 drinks per occasion for women/men). RESULTS: Among 30 participants in the analyses, 207 non-drinking, 41 low-risk, and 45 high-risk drinking episodes were reported. A Random Forest model using 30-min windows with 1day of historical data performed best for detecting high-risk drinking, correctly classifying high-risk drinking windows 90.9% of the time. The most informative sensor features were related to time (i.e., day of week, time of day), movement (e.g., change in activities), device usage (e.g., screen duration), and communication (e.g., call duration, typing speed). CONCLUSIONS: Preliminary evidence suggests that sensor data captured from mobile phones of young adults is useful in building accurate models to detect periods of high-risk drinking. Interventions using mobile phone sensor features could trigger delivery of a range of interventions to potentially improve effectiveness.

Detecting Drinking Episodes in Young Adults Using Smartphone-based Sensors.

Alcohol use in young adults is common, with high rates of morbidity and mortality largely due to periodic, heavy drinking episodes (HDEs). Behavioral interventions delivered through electronic communication modalities (e.g., text messaging) can reduce the frequency of HDEs in young adults, but effects are small. One way to amplify these effects is to deliver support materials proximal to drinking occasions, but this requires knowledge of when they will occur. Mobile phones have built-in sensors that can potentially be useful in monitoring behavioral patterns associated with the initiation of drinking occasions. The objective of our work is to explore the detection of daily-life behavioral markers using mobile phone sensors and their utility in identifying drinking occasions. We utilized data from 30 young adults aged 21-28 with past hazardous drinking and collected mobile phone sensor data and daily Experience Sampling Method (ESM) of drinking for 28 consecutive days. We built a machine learning-based model that is 96.6% accurate at identifying non-drinking, drinking and heavy drinking episodes. We highlight the most important features for detecting drinking episodes and identify the amount of historical data needed for accurate detection. Our results suggest that mobile phone sensors can be used for automated, continuous monitoring of at-risk populations to detect drinking episodes and support the delivery of timely interventions.

3D surface profiling and high resolution imaging for refining the florin rings and epicuticular wax crystals of Pinus koraiensis needles.

3D surface profiling and high resolution imaging were performed to refine the Florin rings and epicuticular wax crystals of Pinus koraiensis needles. Needles were collected from four-year-old seedlings and air-dried for surface observations. Field emission scanning electron microscopy revealed that stomata were found on the abaxial (lower) surface of needles. Measured as ca. 40 µm long, they were largely elliptical or oval-shaped. Epicuticular wax crystals were present in the epistomatal chambers as well as on the surrounding epidermis. Rodlets were prevalently found on the stomatal bands and furrows as well as within the epistomatal chambers. The presence of wax tubules was ascertained by the distinct terminal openings at their ends. The occurrence of wax ridges was evident on the epidermis near the saw-tooth margins (nonstomatal areas). No distinct wax ridges were detected on the dewaxed needles. Raised Florin rings were distinct on the stomata. White light scanning interferometry showed that the diameter and width of stomata were ca. 44.02 ± 3.33 µm and 32.10 ± 3.30 µm, respectively. Measured from the neighboring epidermis to the stomatal aperture, the mean height of the stoma reached ca. 6.23 ± 1.28 µm. Focus variation metrology allowed measuring the mean elevation angle of the stoma, reaching ca. 41.41 ± 11.25°. This is the first report on a novel approach to the establishment of quantitative criteria of Florin ring classification by nontactile 3D surface profiling beyond the previous qualitative descriptions of Florin rings of coniferous species.

Estimation of carbon storage based on individual tree detection in Pinus densiflora stands using a fusion of aerial photography and LiDAR data.

The objective of this study was to estimate the carbon storage capacity of Pinus densiflora stands using remotely sensed data by combining digital aerial photography with light detection and ranging (LiDAR) data. A digital canopy model (DCM), generated from the LiDAR data, was combined with aerial photography for segmenting crowns of individual trees. To eliminate errors in over and under-segmentation, the combined image was smoothed using a Gaussian filtering method. The processed image was then segmented into individual trees using a marker-controlled watershed segmentation method. After measuring the crown area from the segmented individual trees, the individual tree diameter at breast height (DBH) was estimated using a regression function developed from the relationship observed between the field-measured DBH and crown area. The above ground biomass of individual trees could be calculated by an image-derived DBH using a regression function developed by the Korea Forest Research Institute. The carbon storage, based on individual trees, was estimated by simple multiplication using the carbon conversion index (0.5), as suggested in guidelines from the Intergovernmental Panel on Climate Change. The mean carbon storage per individual tree was estimated and then compared with the field-measured value. This study suggested that the biomass and carbon storage in a large forest area can be effectively estimated using aerial photographs and LiDAR data.