Associations of device-measured physical activity and sedentary time with quality of life and fatigue in newly diagnosed breast cancer patients: Baseline results from the AMBER cohort study.

BACKGROUND: This study examined associations of device-measured physical activity and sedentary time with quality of life (QOL) and fatigue in newly diagnosed breast cancer patients in the Alberta Moving Beyond Breast Cancer (AMBER) cohort study. METHODS: After diagnosis, 1409 participants completed the SF-36 version 2 and the Fatigue Scale, wore an ActiGraph device on their right hip to measure physical activity, and an activPAL device on their thigh to measure sedentary time (sitting/lying) and steps. ActiGraph data was analyzed using a hybrid machine learning method (R Sojourn package, Soj3x) and activPAL data were analyzed using activPAL algorithms (PAL Software version 8). Quantile regression was used to examine cross-sectional associations of QOL and fatigue with steps, physical activity, and sedentary hours at the 25th, 50th, and 75th percentiles of the QOL and fatigue distributions. RESULTS: Total daily moderate and vigorous physical activity (MVPA) hours was positively associated with better physical QOL at the 25th (ß = 2.14, p = <.001), 50th (ß = 1.98, p = <.001), and 75th percentiles (ß = 1.25, p = .003); better mental QOL at the 25th (ß = 1.73, p = .05) and 50th percentiles (ß = 1.07, p = .03); and less fatigue at the 25th (ß = 4.44, p < .001), 50th (ß = 3.08, p = <.001), and 75th percentiles (ß = 1.51, p = <.001). Similar patterns of associations were observed for daily steps. Total sedentary hours was associated with worse fatigue at the 25th (ß = -0.58, p = .05), 50th (ß = -0.39, p = .06), and 75th percentiles (ß = -0.24, p = .02). Sedentary hours were not associated with physical or mental QOL. CONCLUSIONS: MVPA and steps were associated with better physical and mental QOL and less fatigue in newly diagnosed breast cancer patients. Higher sedentary time was associated with greater fatigue symptoms.

Piloting Smart Home Sensors to Detect Overnight Respiratory and Withdrawal Symptoms in Adults Prescribed Opioids.

BACKGROUND: Novel strategies are needed to curb the opioid overdose epidemic. Smart home sensors have been successfully deployed as digital biomarkers to monitor health conditions, yet they have not been used to assess symptoms important to opioid use and overdose risks. AIM: This study piloted smart home sensors and investigated their ability to accurately detect clinically pertinent symptoms indicative of opioid withdrawal or respiratory depression in adults prescribed methadone. METHODS: Participants (n = 4; 3 completed) were adults with opioid use disorder exhibiting moderate levels of pain intensity, withdrawal symptoms, and sleep disturbance. Participants were invited to two 8-hour nighttime sleep opportunities to be recorded in a sleep research laboratory, using observed polysomnography and ambient smart home sensors attached to lab bedroom walls. Measures of feasibility included completeness of data captured. Accuracy was determined by comparing polysomnographic data of sleep/wake and respiratory status assessments with time and event sensor data. RESULTS: Smart home sensors captured overnight data on 48 out of 64 hours (75% completeness). Sensors detected sleep/wake patterns in alignment with observed sleep episodes captured by polysomnography 89.4% of the time. Apnea events (n = 118) were only detected with smart home sensors in two episodes where oxygen desaturations were less severe (>80%). CONCLUSIONS: Smart home technology could serve as a less invasive substitute for biologic monitoring for adults with pain, sleep disturbances, and opioid withdrawal symptoms. Supplemental sensors should be added to detect apnea events. Such innovations could provide a step forward in assessing overnight symptoms important to populations taking opioids.

The Alberta moving beyond breast cancer (AMBER) cohort study: baseline description of the full cohort.

PURPOSE: The Alberta Moving Beyond Breast Cancer (AMBER) Study is an ongoing prospective cohort study investigating how direct measures of physical activity (PA), sedentary behavior (SB), and health-related fitness (HRF) are associated with survival after breast cancer. METHODS: Women in Alberta with newly diagnosed stage I (≥ T1c) to IIIc breast cancer were recruited between 2012 and 2019. Baseline assessments were completed within 90 days of surgery. Measurements included accelerometers to measure PA and SB; a graded treadmill test with gas exchange analysis to measure cardiorespiratory fitness (VO2peak); upper and lower body muscular strength and endurance; dual-X-ray absorptiometry to measure body composition; and questionnaires to measure self-reported PA and SB. RESULTS: At baseline, the 1528 participants' mean age was 56 ± 11 years, 59% were post-menopausal, 62% had overweight/obesity, and 55% were diagnosed with stage II or III disease. Based on device measurements, study participants spent 8.9 ± 1.7 h/day sedentary, 4.4 ± 1.2 h/day in light-intensity activity, 0.9 ± 0.5 h/day in moderate-intensity activity, and 0.2 ± 0.2 h/day in vigorous-intensity activity. For those participants who reached VO2peak, the average aerobic fitness level was 26.6 ± 6 ml/kg/min. Average body fat was 43 ± 7.1%. CONCLUSION: We have established a unique cohort of breast cancer survivors with a wealth of data on PA, SB, and HRF obtained through both direct and self-reported measurements. Study participants are being followed for at least ten years to assess all outcomes after breast cancer. These data will inform clinical and public health guidelines on PA, SB, and HRF for improving breast cancer outcomes.

Automated Smart Home Assessment to Support Pain Management: Multiple Methods Analysis.

BACKGROUND: Poorly managed pain can lead to substance use disorders, depression, suicide, worsening health, and increased use of health services. Most pain assessments occur in clinical settings away from patients' natural environments. Advances in smart home technology may allow observation of pain in the home setting. Smart homes recognizing human behaviors may be useful for quantifying functional pain interference, thereby creating new ways of assessing pain and supporting people living with pain. OBJECTIVE: This study aimed to determine if a smart home can detect pain-related behaviors to perform automated assessment and support intervention for persons with chronic pain. METHODS: A multiple methods, secondary data analysis was conducted using historic ambient sensor data and weekly nursing assessment data from 11 independent older adults reporting pain across 1-2 years of smart home monitoring. A qualitative approach was used to interpret sensor-based data of 27 unique pain events to support clinician-guided training of a machine learning model. A periodogram was used to calculate circadian rhythm strength, and a random forest containing 100 trees was employed to train a machine learning model to recognize pain-related behaviors. The model extracted 550 behavioral markers for each sensor-based data segment. These were treated as both a binary classification problem (event, control) and a regression problem. RESULTS: We found 13 clinically relevant behaviors, revealing 6 pain-related behavioral qualitative themes. Quantitative results were classified using a clinician-guided random forest technique that yielded a classification accuracy of 0.70, sensitivity of 0.72, specificity of 0.69, area under the receiver operating characteristic curve of 0.756, and area under the precision-recall curve of 0.777 in comparison to using standard anomaly detection techniques without clinician guidance (0.16 accuracy achieved; P<.001). The regression formulation achieved moderate correlation, with r=0.42. CONCLUSIONS: Findings of this secondary data analysis reveal that a pain-assessing smart home may recognize pain-related behaviors. Utilizing clinicians' real-world knowledge when developing pain-assessing machine learning models improves the model's performance. A larger study focusing on pain-related behaviors is warranted to improve and test model performance.

Analyzing Sensor-Based Individual and Population Behavior Patterns via Inverse Reinforcement Learning.

Digital markers of behavior can be continuously created, in everyday settings, using time series data collected by ambient sensors. The goal of this work was to perform individual- and population-level behavior analysis from such time series sensor data. In this paper, we introduce a novel algorithm-Resident Relative Entropy-Inverse Reinforcement Learning (RRE-IRL)-to perform an analysis of a single smart home resident or a group of residents, using inverse reinforcement learning. By employing this method, we learnt an individual's behavioral routine preferences. We then analyzed daily routines for an individual and for eight smart home residents grouped by health diagnoses. We observed that the behavioral routine preferences changed over time. Specifically, the probability that the observed behavior was the same at the beginning of data collection as it was at the end (months later) was lower for residents experiencing cognitive decline than for cognitively healthy residents. When comparing aggregated behavior between groups of residents from the two diagnosis groups, the behavioral difference was even greater. Furthermore, the behavior preferences were used by a random forest classifier to predict a resident's cognitive health diagnosis, with an accuracy of 0.84.

Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection.

Continuous monitoring of complex activities is valuable for understanding human behavior and providing activity-aware services. At the same time, recognizing these activities requires both movement and location information that can quickly drain batteries on wearable devices. In this paper, we introduce Change Point-based Activity Monitoring (CPAM), an energy-efficient strategy for recognizing and monitoring a range of simple and complex activities in real time. CPAM employs unsupervised change point detection to detect likely activity transition times. By adapting the sampling rate at each change point, CPAM reduces energy consumption by 74.64% while retaining the activity recognition performance of continuous sampling. We validate our approach using smartwatch data collected and labeled by 66 subjects. Results indicate that change point detection techniques can be effective for reducing the energy footprint of sensor-based mobile applications and that automated activity labels can be used to estimate sensor values between sampling periods.

Study of Effectiveness of Prior Knowledge for Smart Home Kit Installation.

Smart-Home in a Box (SHiB) is a ubiquitous system that intends to improve older adults' life quality. SHiB requires self-installation before use. Our previous study found that it is not easy for seniors to install SHiB correctly. SHiB CBLE is a computer-based learning environment that is designed to help individuals install a SHiB kit. This article presents an experiment examining how smart home sensor installation was affected by knowledge gained from two methods, SHiB CBLE, and a written document. Results show that participants who were trained by the CBLE took significantly (p<0.05) less time in the installation session than those in the control group. The accuracy rate of SHiB kit installation is 78% for the group trained by the CBLE and 77% for the control group. Participants trained by the CBLE showed significantly (p<0.01) higher confidence in the actual installation than those in the control group. These results suggest that having a training before the actual installation will help installers avoid unnecessary work, shorten the installation time, and increase installers' confidence.

SynSys: A Synthetic Data Generation System for Healthcare Applications.

Creation of realistic synthetic behavior-based sensor data is an important aspect of testing machine learning techniques for healthcare applications. Many of the existing approaches for generating synthetic data are often limited in terms of complexity and realism. We introduce SynSys, a machine learning-based synthetic data generation method, to improve upon these limitations. We use this method to generate synthetic time series data that is composed of nested sequences using hidden Markov models and regression models which are initially trained on real datasets. We test our synthetic data generation technique on a real annotated smart home dataset. We use time series distance measures as a baseline to determine how realistic the generated data is compared to real data and demonstrate that SynSys produces more realistic data in terms of distance compared to random data generation, data from another home, and data from another time period. Finally, we apply our synthetic data generation technique to the problem of generating data when only a small amount of ground truth data is available. Using semi-supervised learning we demonstrate that SynSys is able to improve activity recognition accuracy compared to using the small amount of real data alone.

Real-Time Change Point Detection with application to Smart Home Time Series Data.

Change Point Detection (CPD) is the problem of discovering time points at which the behavior of a time series changes abruptly. In this paper, we present a novel real-time nonparametric change point detection algorithm called SEP, which uses Separation distance as a divergence measure to detect change points in high-dimensional time series. Through experiments on artificial and real-world datasets, we demonstrate the usefulness of the proposed method in comparison with existing methods.

Robot-Enabled Support of Daily Activities in Smart Home Environments.

Smart environments offer valuable technologies for activity monitoring and health assessment. Here, we describe an integration of robots into smart environments to provide more interactive support of individuals with functional limitations. RAS, our Robot Activity Support system, partners smart environment sensing, object detection and mapping, and robot interaction to detect and assist with activity errors that may occur in everyday settings. We describe the components of the RAS system and demonstrate its use in a smart home testbed. To evaluate the usability of RAS, we also collected and analyzed feedback from participants who received assistance from RAS in a smart home setting as they performed routine activities.

Using Smart City Technology to Make Healthcare Smarter.

Smart cities use information and communication technologies (ICT) to scale services include utilities and transportation to a growing population. In this article we discuss how smart city ICT can also improve healthcare effectiveness and lower healthcare cost for smart city residents. We survey current literature and introduce original research to offer an overview of how smart city infrastructure supports strategic healthcare using both mobile and ambient sensors combined with machine learning. Finally, we consider challenges that will be faced as healthcare providers make use of these opportunities.

Automatic assessment of functional health decline in older adults based on smart home data.

In the context of an aging population, tools to help elderly to live independently must be developed. The goal of this paper is to evaluate the possibility of using unobtrusively collected activity-aware smart home behavioral data to automatically detect one of the most common consequences of aging: functional health decline. After gathering the longitudinal smart home data of 29 older adults for an average of >2 years, we automatically labeled the data with corresponding activity classes and extracted time-series statistics containing 10 behavioral features. Using this data, we created regression models to predict absolute and standardized functional health scores, as well as classification models to detect reliable absolute change and positive and negative fluctuations in everyday functioning. Functional health was assessed every six months by means of the Instrumental Activities of Daily Living-Compensation (IADL-C) scale. Results show that total IADL-C score and subscores can be predicted by means of activity-aware smart home data, as well as a reliable change in these scores. Positive and negative fluctuations in everyday functioning are harder to detect using in-home behavioral data, yet changes in social skills have shown to be predictable. Future work must focus on improving the sensitivity of the presented models and performing an in-depth feature selection to improve overall accuracy.

Implementing Wearable Sensors for Continuous Assessment of Daytime Heart Rate Response in Inpatient Rehabilitation.

BACKGROUND: It is unclear whether wearable heart rate (HR) sensors can be worn continuously in inpatient rehabilitation to assess cardiorespiratory training response. If feasible, these sensors offer a low-cost low-maintenance method for assessing HR response in this setting. We determined feasibility of wearable sensors for assessing HR response to daytime therapy activities in inpatient rehabilitation within a cardiorespiratory training zone equal to 55-80% of maximal HR (target HR [THR]) for at least two 10-min bouts, 3-5 days per week. Secondarily, we determined episodes of excessive HR (EHR >80% of maximal HR). MATERIALS AND METHODS: Subjects 44-80 years of age with diagnoses of stroke, cardiac disorders, orthopedic disorders, medically complex conditions, or pulmonary disorders wore wrist-mounted HR sensors day and night throughout inpatient rehabilitation. The proportion of subjects meeting THR thresholds and experiencing EHR episodes was quantified. Multiple regression predicted THR and EHR outcomes from age, sex, length of stay, and motor function at admission and discharge. RESULTS: Across subjects, 97,800 min of HR data were analyzed. Sixty percent of subjects met THR thresholds for cardiorespiratory benefit. Age was the single significant predictor of percent of days meeting the THR threshold (R = 0.58, p = 0.024). Forty-seven percent of subjects experienced EHR episodes on at least 1 day. No subjects experienced sensor-related adverse events, and no protocol deviations occurred from inadvertent sensor removal. CONCLUSIONS: Most subjects experienced HR increases sufficient to obtain cardiorespiratory benefit. Likewise, most subjects had episodes of EHR. Wearable sensors were feasible for continuously assessing HR response, suggesting expanded opportunity in inpatient rehabilitation research and treatment.

Analyzing Sensor-Based Time Series Data to Track Changes in Physical Activity during Inpatient Rehabilitation.

Time series data collected from sensors can be analyzed to monitor changes in physical activity as an individual makes a substantial lifestyle change, such as recovering from an injury or illness. In an inpatient rehabilitation setting, approaches to detect and explain changes in longitudinal physical activity data collected from wearable sensors can provide value as a monitoring, research, and motivating tool. We adapt and expand our Physical Activity Change Detection (PACD) approach to analyze changes in patient activity in such a setting. We use Fitbit Charge Heart Rate devices with two separate populations to continuously record data to evaluate PACD, nine participants in a hospitalized inpatient rehabilitation group and eight in a healthy control group. We apply PACD to minute-by-minute Fitbit data to quantify changes within and between the groups. The inpatient rehabilitation group exhibited greater variability in change throughout inpatient rehabilitation for both step count and heart rate, with the greatest change occurring at the end of the inpatient hospital stay, which exceeded day-to-day changes of the control group. Our additions to PACD support effective change analysis of wearable sensor data collected in an inpatient rehabilitation setting and provide insight to patients, clinicians, and researchers.

Activity Learning as a Foundation for Security Monitoring in Smart Homes.

Smart environment technology has matured to the point where it is regularly used in everyday homes as well as research labs. With this maturation of the technology, we can consider using smart homes as a practical mechanism for improving home security. In this paper, we introduce an activity-aware approach to security monitoring and threat detection in smart homes. We describe our approach using the CASAS smart home framework and activity learning algorithms. By monitoring for activity-based anomalies we can detect possible threats and take appropriate action. We evaluate our proposed method using data collected in CASAS smart homes and demonstrate the partnership between activity-aware smart homes and biometric devices in the context of the CASAS on-campus smart apartment testbed.

Collegial Activity Learning between Heterogeneous Sensors.

Activity recognition algorithms have matured and become more ubiquitous in recent years. However, these algorithms are typically customized for a particular sensor platform. In this paper we introduce PECO, a Personalized activity ECOsystem, that transfers learned activity information seamlessly between sensor platforms in real time so that any available sensor can continue to track activities without requiring its own extensive labeled training data. We introduce a multi-view transfer learning algorithm that facilitates this information handoff between sensor platforms and provide theoretical performance bounds for the algorithm. In addition, we empirically evaluate PECO using datasets that utilize heterogeneous sensor platforms to perform activity recognition. These results indicate that not only can activity recognition algorithms transfer important information to new sensor platforms, but any number of platforms can work together as colleagues to boost performance.

A Survey of Methods for Time Series Change Point Detection.

Change points are abrupt variations in time series data. Such abrupt changes may represent transitions that occur between states. Detection of change points is useful in modelling and prediction of time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. This survey article enumerates, categorizes, and compares many of the methods that have been proposed to detect change points in time series. The methods examined include both supervised and unsupervised algorithms that have been introduced and evaluated. We introduce several criteria to compare the algorithms. Finally, we present some grand challenges for the community to consider.

Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications.

Recent progress in Internet of Things (IoT) platforms has allowed us to collect large amounts of sensing data. However, there are significant challenges in converting this large-scale sensing data into decisions for real-world applications. Motivated by applications like health monitoring and intervention and home automation we consider a novel problem called Activity Prediction, where the goal is to predict future activity occurrence times from sensor data. In this paper, we make three main contributions. First, we formulate and solve the activity prediction problem in the framework of imitation learning and reduce it to a simple regression learning problem. This approach allows us to leverage powerful regression learners that can reason about the relational structure of the problem with negligible computational overhead. Second, we present several metrics to evaluate activity predictors in the context of real-world applications. Third, we evaluate our approach using real sensor data collected from 24 smart home testbeds. We also embed the learned predictor into a mobile-device-based activity prompter and evaluate the app for 9 participants living in smart homes. Our results indicate that our activity predictor performs better than the baseline methods, and offers a simple approach for predicting activities from sensor data.

Forecasting Occurrences of Activities.

While activity recognition has been shown to be valuable for pervasive computing applications, less work has focused on techniques for forecasting the future occurrence of activities. We present an activity forecasting method to predict the time that will elapse until a target activity occurs. This method generates an activity forecast using a regression tree classifier and offers an advantage over sequence prediction methods in that it can predict expected time until an activity occurs. We evaluate this algorithm on real-world smart home datasets and provide evidence that our proposed approach is most effective at predicting activity timings.

The Alberta Moving Beyond Breast Cancer (AMBER) Cohort Study: Recruitment, Baseline Assessment, and Description of the First 500 Participants.

BACKGROUND: To our knowledge, the Alberta Moving Beyond Breast Cancer (AMBER) Study is the first and only prospective cohort study of breast cancer survivors that includes objectively-measured physical activity (PA), sedentary behavior, health-related fitness (HRF), and biologic mechanisms focused on understanding breast cancer outcomes. The purpose of the present study was to report on the feasibility of recruitment, baseline measurement completion, and the representativeness of the first 500 participants. METHODS: AMBER is enrolling newly diagnosed stage I (≥T1c) to IIIc breast cancer survivors in Alberta, Canada. Baseline assessments are completed soon after diagnosis and include cardiorespiratory fitness, musculoskeletal fitness, body composition, objective and self-reported PA and sedentary behavior, lymphedema, and blood collection. RESULTS: Between July 2012 and November 2014, AMBER recruited its first 500 participants from a pool of 1,447 (35 %) eligible breast cancer survivors. Baseline HRF assessments were completed on ≥85 % of participants with the exception of upper body strength. Collection of ≥4 days/week of monitoring for the Actigraph GT3X® and ActivPAL® were obtained from 90 % of participants. Completion rates were also high for blood (99 %), lymphedema (98 %), and questionnaires (95 %) including patient-reported outcomes and correlates of exercise. The first 500 participants in AMBER are an average age of 56 years, 60 % are overweight or obese, and 58 % have disease stage II or III. CONCLUSION: Despite the modest recruitment rate and younger age, AMBER has demonstrated that many newly diagnosed breast cancer survivors are willing and able to complete a wide array of sophisticated and physically demanding HRF and PA assessments soon after diagnosis. AMBER is a unique breast cancer survivor cohort that may inform future randomized controlled trials on lifestyle and breast cancer outcomes as well as PA behavior change in breast cancer survivors. Moreover, AMBER may also inform guidelines on PA, sedentary behavior, and HRF for improving breast cancer outcomes and survivorship.