Predictive Modeling of Postpartum Blood Pressure Spikes.

BACKGROUND: Hypertensive disorders of pregnancy (HDP) are one of the leading causes of maternal morbidity and mortality worldwide. Management of these conditions can pose many clinical dilemmas, and can be particularly challenging during the immediate postpartum period. Models for predicting and managing postpartum hypertension are necessary to help address this clinical challenge. OBJECTIVE: To evaluate predictive models of blood pressure (BP) spikes postpartum (PP) and investigate clinical management strategies to optimize care. METHODS: A retrospective cohort study of PP women participating in remote BP monitoring. Postpartum BP spike was defined as BP ≥ 140/ 90 if on an antihypertensive and BP ≥ 150/100 if not on an antihypertensive. We identified 3 risk level patient clusters (low, medium, and high) in predicting patient risk of BP spike PP days 3-7. The variables used in defining these clusters were peak systolic blood pressure (SBP) before discharge, BMI, patient SBP per-trimester, heart rate, gestational age, maternal age, chronic hypertension and gestational hypertension. For each risk cluster, we focused on two treatments: 1) PP length of stay < or >/= 3 days and 2) discharge with or without BP medications. We evaluated the effectiveness of the treatments on different subgroups of patients by estimating the conditional average treatment effect (CATE) values in each cluster respectively using a causal forest. Moreover, for all patients, we considered discharge with medication policies depending on different discharge BP thresholds. We used a doubly robust policy evaluation method to compare the effectiveness of the policies. RESULTS: 413 patients were included, of which 267 (64.6%) had a PP BP spike. The treatments for patients at medium and high risk were considered beneficial, the 95% confidence intervals of constant marginal average treatment effect for antihypertensive use at discharge were (-3.482, 4.840) and (-5.539, 4.315); and for a longer stay were (-5.544, 3.866) and (-7.200, 4.302). For patients at low risk, the treatments were not critical in preventing a BP spike with 95% confidence intervals of constant marginal average treatment effect (1.074, 15.784) and (-2.913, 9.021) respectively. We considered the option to discharge with antihypertensive use at different BP thresholds a) ≥ 130 mmHg and/or ≥ 80, b) ≥ 140 mmHg and/or ≥ 90, c) ≥ 150 mmHg and/or ≥ 100, or d) ≥ 160 mmHg and/or ≥ 110, we found that policy (b) was the best option at P<0.05. CONCLUSIONS: We identified 3 possible strategies to prevent outpatient BP spikes PP: 1) medium and high-risk patients should be considered for a longer PP stay or participate in daily home monitoring, 2) medium and high-risk patients should be prescribed antihypertensives at discharge, and 3) antihypertensive treatment should be prescribed if patients are discharged with BP ≥ 140/90.

Behavioral Analytics for Myopic Agents.

Many multi-agent systems have a single coordinator providing incentives to a large number of agents. Two challenges faced by the coordinator are a finite budget from which to allocate incentives, and an initial lack of knowledge about the utility function of the agents. Here, we present a behavioral analytics approach for solving the coordinator's problem when the agents make decisions by maximizing utility functions that depend on prior system states, inputs, and other parameters that are initially unknown. Our behavioral analytics framework involves three steps: first, we develop a model that describes the decision-making process of an agent; second, we use data to estimate the model parameters for each agent and predict their future decisions; and third, we use these predictions to optimize a set of incentives that will be provided to each agent. The framework and approaches we propose in this paper can then adapt incentives as new information is collected. Furthermore, we prove that the incentives computed by this approach are asymptotically optimal with respect to a loss function that describes the coordinator's objective. We optimize incentives with a decomposition scheme, where each sub-problem solves the coordinator's problem for a single agent, and the master problem is a pure integer program. We conclude with a simulation study to evaluate the effectiveness of our approach for designing a personalized weight loss program. The results show that our approach maintains efficacy of the program while reducing its costs by up to 60%, while adaptive heuristics provide substantially less savings.

Applying Natural Language Processing to Understand Motivational Profiles for Maintaining Physical Activity After a Mobile App and Accelerometer-Based Intervention: The mPED Randomized Controlled Trial.

BACKGROUND: Regular physical activity is associated with reduced risk of chronic illnesses. Despite various types of successful physical activity interventions, maintenance of activity over the long term is extremely challenging. OBJECTIVE: The aims of this original paper are to 1) describe physical activity engagement post intervention, 2) identify motivational profiles using natural language processing (NLP) and clustering techniques in a sample of women who completed the physical activity intervention, and 3) compare sociodemographic and clinical data among these identified cluster groups. METHODS: In this cross-sectional analysis of 203 women completing a 12-month study exit (telephone) interview in the mobile phone-based physical activity education study were examined. The mobile phone-based physical activity education study was a randomized, controlled trial to test the efficacy of the app and accelerometer intervention and its sustainability over a 9-month period. All subjects returned the accelerometer and stopped accessing the app at the last 9-month research office visit. Physical engagement and motivational profiles were assessed by both closed and open-ended questions, such as "Since your 9-month study visit, has your physical activity been more, less, or about the same (compared to the first 9 months of the study)?" and, "What motivates you the most to be physically active?" NLP and cluster analysis were used to classify motivational profiles. Descriptive statistics were used to compare participants' baseline characteristics among identified groups. RESULTS: Approximately half of the 2 intervention groups (Regular and Plus) reported that they were still wearing an accelerometer and engaging in brisk walking as they were directed during the intervention phases. These numbers in the 2 intervention groups were much higher than the control group (overall P=.01 and P=.003, respectively). Three clusters were identified through NLP and named as the Weight Loss group (n=19), the Illness Prevention group (n=138), and the Health Promotion group (n=46). The Weight Loss group was significantly younger than the Illness Prevention and Health Promotion groups (overall P<.001). The Illness Prevention group had a larger number of Caucasians as compared to the Weight Loss group (P=.001), which was composed mostly of those who identified as African American, Hispanic, or mixed race. Additionally, the Health Promotion group tended to have lower BMI scores compared to the Illness Prevention group (overall P=.02). However, no difference was noted in the baseline moderate-to-vigorous intensity activity level among the 3 groups (overall P>.05). CONCLUSIONS: The findings could be relevant to tailoring a physical activity maintenance intervention. Furthermore, the findings from NLP and cluster analysis are useful methods to analyze short free text to differentiate motivational profiles. As more sophisticated NL tools are developed in the future, the potential of NLP application in behavioral research will broaden. TRIAL REGISTRATION: ClinicalTrials.gov NCT01280812; https://clinicaltrials.gov/ct2/show/NCT01280812 (Archived by WebCite at http://www.webcitation.org/70IkGagAJ).

Evaluating Machine Learning-Based Automated Personalized Daily Step Goals Delivered Through a Mobile Phone App: Randomized Controlled Trial.

BACKGROUND: Growing evidence shows that fixed, nonpersonalized daily step goals can discourage individuals, resulting in unchanged or even reduced physical activity. OBJECTIVE: The aim of this randomized controlled trial (RCT) was to evaluate the efficacy of an automated mobile phone-based personalized and adaptive goal-setting intervention using machine learning as compared with an active control with steady daily step goals of 10,000. METHODS: In this 10-week RCT, 64 participants were recruited via email announcements and were required to attend an initial in-person session. The participants were randomized into either the intervention or active control group with a one-to-one ratio after a run-in period for data collection. A study-developed mobile phone app (which delivers daily step goals using push notifications and allows real-time physical activity monitoring) was installed on each participant's mobile phone, and participants were asked to keep their phone in a pocket throughout the entire day. Through the app, the intervention group received fully automated adaptively personalized daily step goals, and the control group received constant step goals of 10,000 steps per day. Daily step count was objectively measured by the study-developed mobile phone app. RESULTS: The mean (SD) age of participants was 41.1 (11.3) years, and 83% (53/64) of participants were female. The baseline demographics between the 2 groups were similar (P>.05). Participants in the intervention group (n=34) had a decrease in mean (SD) daily step count of 390 (490) steps between run-in and 10 weeks, compared with a decrease of 1350 (420) steps among control participants (n=30; P=.03). The net difference in daily steps between the groups was 960 steps (95% CI 90-1830 steps). Both groups had a decrease in daily step count between run-in and 10 weeks because interventions were also provided during run-in and no natural baseline was collected. CONCLUSIONS: The results showed the short-term efficacy of this intervention, which should be formally evaluated in a full-scale RCT with a longer follow-up period. TRIAL REGISTRATION: ClinicalTrials.gov: NCT02886871; https://clinicaltrials.gov/ct2/show/NCT02886871 (Archived by WebCite at http://www.webcitation.org/6wM1Be1Ng).

Personalizing Mobile Fitness Apps using Reinforcement Learning.

Despite the vast number of mobile fitness applications (apps) and their potential advantages in promoting physical activity, many existing apps lack behavior-change features and are not able to maintain behavior change motivation. This paper describes a novel fitness app called CalFit, which implements important behavior-change features like dynamic goal setting and self-monitoring. CalFit uses a reinforcement learning algorithm to generate personalized daily step goals that are challenging but attainable. We conducted the Mobile Student Activity Reinforcement (mSTAR) study with 13 college students to evaluate the efficacy of the CalFit app. The control group (receiving goals of 10,000 steps/day) had a decrease in daily step count of 1,520 (SD ± 740) between baseline and 10-weeks, compared to an increase of 700 (SD ± 830) in the intervention group (receiving personalized step goals). The difference in daily steps between the two groups was 2,220, with a statistically significant p = 0.039.