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BACKGROUND It is well established that lack of physical activity is detrimental to the overall health of an individual. Modern-day activity trackers enable individuals to monitor their daily activities to meet and maintain targets. This is expected to promote activity encouraging behavior, but the benefits of activity trackers attenuate over time due to waning adherence. One of the key approaches to improving adherence to goals is to motivate individuals to improve on their historic performance metrics. OBJECTIVE The aim of this work was to build a machine learning model to predict an achievable weekly activity target by considering (1) patterns in the user’s activity tracker data in the previous week and (2) behavior and environment characteristics. By setting realistic goals, ones that are neither too easy nor too difficult to achieve, activity tracker users can be encouraged to continue to meet these goals, and at the same time, to find utility in their activity tracker. METHODS We built a neural network model that prescribes a weekly activity target for an individual that can be realistically achieved. The inputs to the model were user-specific personal, social, and environmental factors, daily step count from the previous 7 days, and an entropy measure that characterized the pattern of daily step count. Data for training and evaluating the machine learning model were collected over a duration of 9 weeks. RESULTS Of 30 individuals who were enrolled, data from 20 participants were used. The model predicted target daily count with a mean absolute error of 1545 (95% CI 1383-1706) steps for an 8-week period. CONCLUSIONS Artificial intelligence applied to physical activity data combined with behavioral data can be used to set personalized goals in accordance with the individual’s level of activity and thereby improve adherence to a fitness tracker; this could be used to increase engagement with activity trackers. A follow-up prospective study is ongoing to determine the performance of the engagement algorithm.

BACKGROUND It is well reported that tracking physical activity can lead to sustained exercise routines, which can decrease disease risk. However, most stop using trackers within a couple months of initial use. The reasons people stop using activity trackers can be varied and personal. Understanding the reasons for discontinued use could lead to greater acceptance of tracking and more regular exercise engagement. OBJECTIVE The aim of this study was to determine the individualistic reasons for nonengagement with activity trackers. METHODS Overweight and obese participants (n=30) were enrolled and allowed to choose an activity tracker of their choice to use for 9 weeks. Questionnaires were administered at the beginning and end of the study to collect data on their technology use, as well as social, physiological, and psychological attributes that may influence tracker use. Closeout interviews were also conducted to further identify individual influencers and attributes. In addition, daily steps were collected from the activity tracker. RESULTS The results of the study indicate that participants typically valued the knowledge of their activity level the activity tracker provided, but it was not a sufficient motivator to overcome personal barriers to maintain or increase exercise engagement. Participants identified as extrinsically motivated were more influenced by wearing an activity tracker than those who were intrinsically motivated. During the study, participants who reported either owning multiple technology devices or knowing someone who used multiple devices were more likely to remain engaged with their activity tracker. CONCLUSIONS This study lays the foundation for developing a smart app that could promote individual engagement with activity trackers.

BACKGROUND Fever is an important vital sign and often the first one to be assessed in a sick child. In acutely ill children, caregivers are expected to monitor a child’s body temperature at home after an initial medical consult. Fever literacy of many caregivers is known to be poor, leading to fever phobia. In children with a serious illness, the responsibility of periodically monitoring temperature can add substantially to the already stressful experience of caring for a sick child. OBJECTIVE The objective of this pilot study was to assess the feasibility of using the iThermonitor, an automated temperature measurement device, for continuous temperature monitoring in postoperative and postchemotherapy pediatric patients. METHODS We recruited 25 patient-caregiver dyads from the Pediatric Surgery Department at the Massachusetts General Hospital (MGH) and the Pediatric Cancer Centers at the MGH and the Dana Farber Cancer Institute. Enrolled dyads were asked to use the iThermonitor device for continuous temperature monitoring over a 2-week period. Surveys were administered to caregivers at enrollment and at study closeout. Caregivers were also asked to complete a daily event-monitoring log. The Generalized Anxiety Disorder-7 item questionnaire was also used to assess caregiver anxiety at enrollment and closeout. RESULTS Overall, 19 participant dyads completed the study. All 19 caregivers reported to have viewed temperature data on the study-provided iPad tablet at least once per day, and more than a third caregivers did so six or more times per day. Of all participants, 74% (14/19) reported experiencing an out-of-range temperature alert at least once during the study. Majority of caregivers reported that it was easy to learn how to use the device and that they felt confident about monitoring their child’s temperature with it. Only 21% (4/9) of caregivers reported concurrently using a device other than the iThermonitor to monitor their child’s temperature during the study. Continuous temperature monitoring was not associated with an increase in caregiver anxiety. CONCLUSIONS The study results reveal that the iThermonitor is a highly feasible and easy-to-use device for continuous temperature monitoring in pediatric oncology and surgery patients. CLINICALTRIAL ClinicalTrials.gov NCT02410252; https://clinicaltrials.gov/ct2/show/NCT02410252 (Archived by WebCite at http://www.webcitation.org/73LnO7hel)

76 Background: Cancer pain affects 70-90% of advanced malignancy patients, resulting in impaired quality of life and increased healthcare utilization. Novel care delivery models are needed to optimize care for patients dealing with cancer-related pain in between clinic visits. ePAL is a smartphone application(app) that regularly monitors pain and uses artificial intelligence(AI) to differentiate urgent from non-urgent issues to intercede in real time. The purpose of this randomized controlled trial was to determine ePAL's impact on pain severity, attitudes toward cancer treatment, and healthcare utilization in patients with cancer pain. Methods: MGH Palliative Care Clinic Patients with pain from metastatic, solid-organ cancer (n=112) were recruited and randomized to either a control group (n=56) that received usual care or an intervention group (n=56) that used the ePAL app in addition to usual care for 8 weeks. The app assessed pain 3 times/week and questionnaires about pain (BPI), attitudes towards cancer treatment(BQ-II), and general anxiety(GAD-7) were given at 0, 4, and 8 weeks. A repeated measures mixed model approach assessed how outcome measures changed over time. Models controlled for baseline differences at enrollment and random slopes in addition to baseline depression score, age and sex(alpha=0.05). Results: Pain severity (BPI) and negative attitudes toward cancer pain treatment (BQ-II) decreased significantly for those using the app compared to controls(coeff. -0.09, 95% CI: -0.17, -0.007, p=0.034 and coeff. -0.037, 95% CI: -0.072, -0.001, p=0.042 respectively). Anxiety scores increased for those using ePAL compared to controls (coeff. 0.21, 95% CI: 0.039, 0.37, p=0.015). Over 8-weeks, ePAL users had 40% fewer inpatient hospital admissions compared to controls (n=15 vs. n=25, p=0.048). Conclusions: To our knowledge, this is the first mobile app to utilize AI and clinical algorithms to significantly decrease pain and reduce overall inpatient hospitalizations in patients with cancer-related pain. Clinical trial information: NCT02069743.