Cancer treatments such as chemotherapy and surgery cause a variety of symptoms and side effects that can impair subjective quality of life and functioning. Across a variety of cancer types, fatigue, pain, nausea, and other physical symptoms are highly prevalent and often severe [1,2], and many patients experience multiple symptoms simultaneously [3]. Patients who report more significant symptoms tend to exhibit worse performance status and functional ability [4,5]. Unfortunately, symptoms remain undetected by clinicians up to half of the time [6,7], limiting opportunities for timely and effective clinical management and resulting in undue patient suffering and functional impairment.

Remotely monitoring symptoms between hospital or clinic visits may improve our ability to capture severe or bothersome symptoms when they begin to emerge [8]. Smartphones, now owned by 81% of adults and increasing proportions of older adults, those living in rural areas, and all racial groups, offer new opportunities for remote symptom monitoring [9]. Systems leveraging smartphones for real-time patient-reported outcome (PRO) assessment during outpatient chemotherapy have been demonstrated to be feasible [10,11] and to reduce chemotherapy-related morbidity [12]. Although daily PRO symptom data are valuable, long-term assessment of PROs (eg, over months or years of chemotherapy) is burdensome. Indeed, previous work suggests that patients become significantly less compliant at recording symptoms over time [13], with patient compliance dropping to below 50% after 1 month in one longitudinal study [14]. Developing a remote symptom monitoring system that is less reliant on patient compliance may enable longitudinal symptom tracking and management throughout cancer treatment and even after treatment is completed, when symptoms persist for many survivors.

Smartphones are equipped with a rich array of sensors capable of measuring many behavioral and contextual variables, including mobility, location, ambient light and noise, and social interactions [15]. Most users keep their smartphones within arm’s reach at all times and spend over 4 hours per day interacting with the device [16]. Thus, smartphones can gather digital traces as individuals go about their daily routines. From these raw digital data, meaningful behavioral features such as number of unique locations visited, number of outgoing calls placed, and average level of ambient noise detected during the night can be calculated to provide information about behavior patterns in real-world contexts [17].

Smartwatches and other wearable commercial activity monitors are also becoming more widely used, with about 1 in 5 adults using a wearable device [9]. Wearable devices contain sensors such as accelerometers and photoplethysmography which can provide continuous information about activity, sleep, and physiology (eg, heart rate). Together, these mobile sensing technologies enable objective assessment of behavioral patterns that may reflect worsening health status, including severe or increasing symptoms. Moreover, this high-density, multimodal, and objective data collection can be completed with minimal burden to patients; this feature makes this approach highly scalable and appropriate for remotely monitoring patients, even older patients and those who are acutely ill and even over long periods. Given evidence that physical activity and sleep behaviors as well as heart rate have prognostic value in oncology, technology that enables passive quantification of these metrics holds considerable promise for clinical cancer research [18-20].

Applying machine learning classification to smartphone sensor data has been shown to accurately discriminate depressed from nondepressed individuals [21], to recognize depressive and manic episodes in patients with bipolar disorder [22-24], to predict mental health indicators in schizophrenia [25], and to detect binge drinking and other substance use [26]. These methods can also shed light on which behavioral features are most useful for detecting or predicting mental health states or risky behaviors. Work applying this approach to passively detect physical health status in patients with cancer is more limited, but results from 14 recent small studies suggest that wearable and smartphone sensor data are related to symptom burden, quality of life, and other clinical oncology outcomes [27].

The perioperative context is an especially critical time for remote patient monitoring, as complications after cancer surgery are common and can escalate into re-admissions that may be preventable if detected and managed earlier. Results from similar studies of patients undergoing surgical oncology procedures found that accelerometer data were useful for quantifying differences in postoperative recovery [28] and for predicting re-admission risk [29]. In this study, we aimed to examine whether smartphone and wearable sensors can be useful in detecting overall patient-reported symptom burden as well as 3 specific physical symptoms (fatigue, pain, and diarrhea) among patients undergoing pancreatic cancer surgery, a complex but potentially curative procedure with postoperative morbidity rates as high as 40% [30].

Models using LightGBM performed best for the population model. We used 0 as the random seed, 200 as the number of boosted trees, and 128 as the maximum tree leaves. The learning rate was chosen from {0.008, 0.01, 0.012} and the subsample ratio of columns when constructing each tree was chosen from {0.68, 0.7, 0.72}. Using this approach, models using smartphone and wearable feature data were able to correctly predict whether the next day would be a high symptom day with 73.5% accuracy (0.611 recall for the high symptom class and 0.772 AUC). This model surpassed the accuracy and performance of all 3 baseline models (Table 1).

The most important features included the most recent symptom burden score, days since surgery, average symptom burden score, duration of active and exertional activity bouts, minimum heart rate, number of unique activities, time spent at the most frequent location, maximum ambient lux, total duration of time awake and asleep, and total duration of the heart rate in cardio zone (70%-84% of the participant’s maximum heart rate) and peak zone (85%-100% of the participant’s maximum heart rate; Figure 1). In this plot, features with many instances in red with SHAP (SHapley Additive exPlanations) [38] value greater than 0 had a positive relationship with symptom burden (eg, longer median duration of nonexertional episodes related to high symptom burden), whereas those in blue had an inverse association (eg, shorter total duration of active bouts related to high symptom burden).

We also generated population models for diarrhea, fatigue, and pain, respectively. All steps are the same as above except for the target values. Instead of calculating the labels based on the summation of all 10 symptom ratings, diarrhea score or fatigue score or pain score is applied directly.

Like the overall symptom burden results, LightGBM models outperformed all 3 baseline models and predicted next-day diarrhea with 79.0% accuracy (AUC 83.41%), next-day fatigue with 75.8% accuracy (AUC 80.29%), and next-day pain with 79.6% accuracy (AUC 83.48%; Table 2). Location features are very important for diarrhea prediction, while step features and sleep features are very important for fatigue prediction and pain prediction, respectively. The most recent symptom burden score, days since surgery, and average symptom burden score are the most important features for all symptoms.

The purpose of this prospective longitudinal study was to evaluate passive smartphone and wearable sensor features as predictors of symptom burden in perioperative patients undergoing pancreatic surgery. Results suggest that machine learning models developed using mobile sensor data were more accurate than non–sensor-based baseline models in predicting whether the next-day patient-reported overall symptom burden would be higher than average for that patient. The most important features for symptom prediction included features related to physical activity, heart rate, and location. Models also accurately predicted next-day diarrhea, fatigue, and pain, although the most important features in each model differed across specific symptoms.

This work contributes to a small but growing literature investigating associations between consumer mobile sensors and clinical outcomes in oncology [27]. Similar to studies of patients undergoing chemotherapy [39] and hematopoietic cell transplant [40], features related to physical activity were most strongly related to fluctuations in physical symptom severity. Feature importance revealed that these were not simple features such as daily step counts but rather features reflecting patterns of activity and included measurements from both wearable Fitbit devices (eg, number, total duration, and maximum duration of active bouts) and smartphones (eg, duration of nonexertional episodes from phone accelerometer, number of unique activities recognized). Heart rate and sleep features were also important, suggesting that future work in this area should consider using wearable devices that enable collection of 24-hour behavioral and physiological data and examination of circadian rest-activity rhythms previously linked to outcomes in patients with cancer [41].

Because wearable and smartphone sensor data can be collected continuously as patients go about their daily lives, requiring minimal effort or attention from patients or their caregivers, mobile sensing offers an opportunity for long-term remote patient monitoring over months or years of cancer treatment and survivorship. This study supports the feasibility of collecting mobile sensor data, even from patients who are seriously ill during times of acute sickness and recovery. Despite undergoing invasive surgery and (for most patients) grappling with one of the deadliest cancer diagnoses, over 80% of participants had sufficient sensor data for analyses. This is also noteworthy given that the average age of patients was over 65 and that, as these data were collected in 2017, participants varied considerably in their comfort and familiarity with mobile technology.

Although models trained on past mobile sensor data outperformed baseline models, model performance still may not be adequate for clinical implementation. For example, recall of the high overall symptom burden class (when timely clinical action would be needed) was only 61%, meaning nearly 40% of high symptom days would be missed by our model. This may be due in part to the relatively small sample and data set, the use of study-provided (rather than personal) smartphones, or the powerful effect of major abdominal surgery and prolonged hospitalization on patient symptom profiles as well as behavior. Future studies with larger samples that collect data using their own personal devices over a period with less dramatic shifts in symptoms and behavior may yield better model performance. In future studies with larger data sets more robust to class imbalance, setting a higher threshold for severe symptoms requiring care provider attention or intervention may also result in more clinically useful models. Regardless, mobile sensor data may be a useful complement to patient-reported symptom data, allowing for a more personalized and adaptive delivery of symptom ratings when behavioral fluctuations are detected, reducing patient burden and improving early capture of worsening side effects and symptoms. Predictive models based on sensor and patient-reported data could also be used to deliver symptom self-management instructions to patients, an approach demonstrated to benefit patients undergoing pancreatic cancer surgery [42].

Given the small data set, we focused on building population models that used data from all other participants, which also may have constrained model performance. Because each participant had on average only 30 rows of data, individual models were unstable, but with more training data could be useful in learning patterns based on each participant’s behavior and its relationship to symptoms and developing more accurate predictions. Developing models based on similar subgroups of participants (based on demographic, clinical, or behavioral factors) could be a useful approach for future work and could yield superior results to a single population model.

Strengths of the study include longitudinal sensor data collection over a wide perioperative window, from presurgery to 60 days after discharge following pancreatic surgery. We considered a wide range of features from both wearable and smartphone sensors and examined prediction of next-day overall symptom burden as well as next-day pain, fatigue, and diarrhea specifically. Our models were also trained on past data only so that we could evaluate how well models could perform if implemented in real-world clinical settings.

This study suggests that digital biomarkers may be useful in predicting patient-reported symptom burden during cancer treatment. In an ongoing study, we are following up on this work by collecting 3 months of smartphone and wearable sensor data as well as daily symptom reports from a large sample of patients undergoing outpatient chemotherapy. With a larger outpatient sample using their own smartphones, we hope to improve upon the models developed here and to use real-time next-day symptom predictions to deliver more timely and personalized symptom management support.