Multi-profile hidden Markov model for mood, dietary intake, and physical activity in an intervention study of childhood obesity

Ensemble Approach to Combining Episode Prediction Models Using Sequential Circadian Rhythm Sensor Data from Mental Health Patients

Managing mood disorders poses challenges in counseling and drug treatment, owing to limitations. Counseling is the most effective during hospital visits, and the side effects of drugs can be burdensome. Patient empowerment is crucial for understanding and managing these triggers. The daily monitoring of mental health and the utilization of episode prediction tools can enable self-management and provide doctors with insights into worsening lifestyle patterns. In this study, we test and validate whether the prediction of future depressive episodes in individuals with depression can be achieved by using lifelog sequence data collected from digital device sensors. Diverse models such as random forest, hidden Markov model, and recurrent neural network were used to analyze the time-series data and make predictions about the occurrence of depressive episodes in the near future. The models were then combined into a hybrid model. The prediction accuracy of the hybrid model was 0.78; especially in the prediction of rare episode events, the F1-score performance was approximately 1.88 times higher than that of the dummy model. We explored factors such as data sequence size, train-to-test data ratio, and class-labeling time slots that can affect the model performance to determine the combinations of parameters that optimize the model performance. Our findings are especially valuable because they are experimental results derived from large-scale participant data analyzed over a long period of time.

A hidden Markov modeling approach combining objective measure of activity and subjective measure of self-reported sleep to estimate the sleep-wake cycle

Characterizing the sleep-wake cycle in adolescents is an important prerequisite to better understand the association of abnormal sleep patterns with subsequent clinical and behavioral outcomes. The aim of this research was to develop hidden Markov models (HMM) that incorporate both objective (actigraphy) and subjective (sleep log) measures to estimate the sleep-wake cycle using data from the NEXT longitudinal study, a large population-based cohort study. The model was estimated with a negative binomial distribution for the activity counts (1-minute epochs) to account for overdispersion relative to a Poisson process. Furthermore, self-reported measures were dichotomized (for each one-minute interval) and subject to misclassification. We assumed that the unobserved sleep-wake cycle follows a two-state Markov chain with transitional probabilities varying according to a circadian rhythm. Maximum-likelihood estimation using a backward-forward algorithm was applied to fit the longitudinal data on a subject by subject basis. The algorithm was used to reconstruct the sleep-wake cycle from sequences of self-reported sleep and activity data. Furthermore, we conduct simulations to examine the properties of this approach under different observational patterns including both complete and partially observed measurements on each individual.

Septic Shock: Phenotypes and Outcomes

INTRODUCTION

Sepsis is a heterogeneous syndrome that results in life-threatening organ dysfunction. Our goal was to determine the relevant variables and patient phenotypes to use in predicting sepsis outcomes.

METHODS

We performed an ancillary study concerning 119 patients with septic shock at intensive care unit (ICU) admittance (T0). We defined clinical worsening as having an increased sequential organ failure assessment (SOFA) score of ≥ 1, 48 h after admission (ΔSOFA ≥ 1). We performed univariate and multivariate analyses based on the 28-day mortality rate and ΔSOFA ≥ 1 and determined three patient phenotypes: safe, intermediate and unsafe. The persistence of the intermediate and unsafe phenotypes after T0 was defined as a poor outcome.

RESULTS

At T0, the multivariate analysis showed two variables associated with 28-day mortality rate: norepinephrine dose and serum lactate concentration. Regarding ΔSOFA ≥ 1, we identified three variables at T0: norepinephrine dose, lactate concentration and venous-to-arterial carbon dioxide difference (P(v-a)CO₂). At T0, the three phenotypes (safe, intermediate and unsafe) were found in 28 (24%), 70 (59%) and 21 (18%) patients, respectively. We thus suggested using an algorithm featuring norepinephrine dose, lactate concentration and P(v-a)CO₂ to predict patient outcomes and obtained an area under the curve (AUC) of 74% (63-85%).

CONCLUSION

Our findings highlight the fact that identifying relevant variables and phenotypes may help physicians predict patient outcomes.

Tolerability of bevacizumab and chemotherapy in a phase 3 clinical trial with human epidermal growth factor receptor 2-negative breast cancer: A trajectory analysis of adverse events

BACKGROUND

E5103 was a study designed to evaluate the efficacy and safety of bevacizumab. It was a negative trial for the end points of invasive disease-free survival and overall survival. The current work examines the tolerability of bevacizumab and other medication exposures with respect to clinical outcomes and patient-reported outcomes (PROs).

METHODS

Adverse events (AEs) collected from the Common Terminology Criteria for Adverse Events were summarized to form an AE profile at each treatment cycle. All-grade and high-grade events were separately analyzed. The change in the AE profile over the treatment cycle was delineated as distinct AE trajectory clusters. AE-related and any-reason early treatment discontinuations were treated as clinical outcome measures. PROs were measured with the Functional Assessment of Cancer Therapy-Breast + Lymphedema. The relationships between the AE trajectory and early treatment discontinuation as well as PROs were analyzed.

RESULTS

More than half of all AEs (57.5%) were low-grade. A cluster of patients with broad and mixed AE (all-grade) trajectory grades was significantly associated with any-reason early treatment discontinuation (odds ratio [OR], 2.87; P = .01) as well as AE-related discontinuation (OR, 4.14; P = .001). This cluster had the highest count of all-grade AEs per cycle in comparison with other clusters. Another cluster of patients with primary neuropathic AEs in their trajectories had poorer physical well-being in comparison with a trajectory of no or few AEs (P < .01). A high-grade AE trajectory did not predict discontinuations.

CONCLUSIONS

A sustained and cumulative burden of across-the-board toxicities, which were not necessarily all recognized as high-grade AEs, contributed to early treatment discontinuation. Patients with neuropathic all-grade AEs may require additional attention for preventing deterioration in their physical well-being.

Identifying Social Network Conditions that Facilitate Sedentary Behavior Change: The Benefit of Being a "Bridge" in a Group-based Intervention

Using data from one of the first trials to try to leverage social networks as a mechanism for obesity intervention, we examined which social network conditions amplified behavior change. Data were collected as part of a community-based healthy lifestyle intervention in Nashville, USA, between June 2014 and July 2017. Adults randomized to the intervention arm were assigned to a small group of 10 participants that met in person for 12 weekly sessions. Intervention small group social networks were measured three times; sedentary behavior was measured by accelerometry at baseline and 12 months. Multivariate hidden Markov models classified people into distinct social network trajectories over time, based on the structure of the emergent network and where the individual was embedded. A multilevel regression analysis assessed the relationship between network trajectory and sedentary behavior (N = 261). Being a person that connected clusters of intervention participants at any point during the intervention predicted an average reduction of 31.3 min/day of sedentary behavior at 12 months, versus being isolated [95% CI: (−61.4, −1.07), p = 0.04]. Certain social network conditions may make it easier to reduce adult sedentary behavior in group-based interventions. While further research will be necessary to establish causality, the implications for intervention design are discussed.

Estimating recurrence and incidence of preterm birth subject to measurement error in gestational age: A hidden Markov modeling approach

Prediction of preterm birth as well as characterizing the etiological factors affecting both the recurrence and incidence of preterm birth (defined as gestational age at birth ≤ 37 wk) are important problems in obstetrics. The National Institute of Child Health and Human Development (NICHD) consecutive pregnancy study recently examined this question by collecting data on a cohort of women with at least 2 pregnancies over a fixed time interval. Unfortunately, measurement error due to the dating of conception may induce sizable error in computing gestational age at birth. This article proposes a flexible approach that accounts for measurement error in gestational age when making inference. The proposed approach is a hidden Markov model that accounts for measurement error in gestational age by exploiting the relationship between gestational age at birth and birth weight. We initially model the measurement error as being normally distributed, followed by a mixture of normals that has been proposed on the basis of biological considerations. We examine the asymptotic bias of the proposed approach when measurement error is ignored and also compare the efficiency of this approach to a simpler hidden Markov model formulation where only gestational age and not birth weight is incorporated. The proposed model is compared with alternative models for estimating important covariate effects on the risk of subsequent preterm birth using a unique set of data from the NICHD consecutive pregnancy study.

Child Feeding Style and Dietary Outcomes in a Cohort of Latino Farmworker Families

BACKGROUND

The high level of obesity in Latino children, especially in farmworker families, may be partly attributed to feeding styles of parents. Feeding styles used in Latino farmworker families have not been well characterized.

OBJECTIVE

This study sought to identify and describe feeding styles used by mothers in farmworker families with 2.5- to 3.5-year-old children, describe how styles change over time, and characterize the relationship of feeding styles to dietary outcomes and measures of overweight and obesity.

DESIGN

This was a longitudinal cohort study, with families participating for a 2-year period; surveys were administered to mothers with varying frequency depending on the instrument, and dietary measurements were collected at baseline and 12 and 24 months.

PARTICIPANTS/SETTING

Eligible participants were self-identified Latino women with a co-resident child aged 2.5 to 3.5 years old and at least one household member engaged in farm work during the previous year. The sample included 248 farmworker families enrolled between 2011 and 2012 in the Niños Sanos study, a longitudinal investigation of Latino mothers and their young children in rural North Carolina. Eleven families provided incomplete dietary data, so the analysis included 237 families. Fifteen families were lost to follow-up and 12 withdrew during the course of the study.

MAIN OUTCOME MEASURES

Feeding style was assessed using items from the Caregiver's Feeding Style Questionnaire, selected dietary components were assessed using the Revised Children's Diet Quality Index, and weight outcomes were determined using body mass index-for-age percentile. Performance on the Caregiver's Feeding Style Questionnaire items was used to assign mothers to one of four feeding style states.

STATISTICAL ANALYSES PERFORMED

Exploratory factor analysis was conducted on baseline data to verify the replicability of the factor structure of the instrument Caregiver's Feeding Style Questionnaire. Hidden Markov Model analysis was used to delineate different subtypes of feeding style. Multivariable mixed-effects regression models were used to assess the impact of feeding style on selected dietary components, energy intake, and body mass index-for-age percentile.

RESULTS

Four distinct states emerged from the Hidden Markov Model: low parent-centered (PC)/moderate child-centered (CC) feeding style (28% at baseline), high PC/CC without physical control (24%), high PC/CC (26%), and moderate PC/CC (22%). The low PC/moderate CC state increased in prevalence over time. Compared to high PC/CC, the low PC/moderate CC state was associated with greater intake of added sugars (P<0.01), lower intake of whole grains and vegetables (P<0.01), and lower overall diet quality (P<0.05). Children in low PC/moderate CC also had higher mean body mass index percentiles (76.2 percentile vs 66.7 percentile in high PC/CC; P<0.001).

CONCLUSIONS

High PC feeding along with high CC feeding is associated with improved diet quality and weight outcomes for children in the study.

Longitudinal Examination of Symptom Profiles Among Breast Cancer Survivors

CONTEXT

Identification of cancer patients with similar symptom profiles may facilitate targeted symptom management.

OBJECTIVES

To identify subgroups of breast cancer survivors based on differential experience of symptoms, examine change in subgroup membership over time, and identify relevant characteristics and quality of life (QOL) among subgroups.

METHODS

Secondary analyses of data from 653 breast cancer survivors recruited within eight months of diagnosis who completed questionnaires at five time points. Hidden Markov modeling was used to 1) formulate symptom profiles based on prevalence and severity of eight symptoms commonly associated with breast cancer and 2) estimate probabilities of changing subgroup membership over 18 months of follow-up. Ordinal repeated measures were used to 3) identify patient characteristics related to subgroup membership and 4) evaluate the relationship between symptom subgroup and QOL.

RESULTS

A seven-subgroup model provided the best fit: 1) low symptom burden, 2) mild fatigue, 3) mild fatigue and mild pain, 4) moderate fatigue and moderate pain, 5) moderate fatigue and moderate psychological, 6) moderate fatigue, mild pain, mild psychological, and 7) high symptom burden. Seventy percent of survivors remained in the same subgroup over time. In multivariable analyses, chemotherapy and greater illness intrusiveness were significantly related to greater symptom burden, while not being married or partnered, no difficulty paying for basics, and greater social support were protective. Higher symptom burden was associated with lower QOL. Survivors who reported psychological symptoms had significantly lower QOL than did survivors with pain symptoms.

CONCLUSION

Cancer survivors can be differentiated by their symptom profiles.

Reconciling statistical and systems science approaches to public health

Although systems science has emerged as a set of innovative approaches to study complex phenomena, many topically focused researchers including clinicians and scientists working in public health are somewhat befuddled by this methodology that at times appears to be radically different from analytic methods, such as statistical modeling, to which the researchers are accustomed. There also appears to be conflicts between complex systems approaches and traditional statistical methodologies, both in terms of their underlying strategies and the languages they use. We argue that the conflicts are resolvable, and the sooner the better for the field. In this article, we show how statistical and systems science approaches can be reconciled, and how together they can advance solutions to complex problems. We do this by comparing the methods within a theoretical framework based on the work of population biologist Richard Levins. We present different types of models as representing different tradeoffs among the four desiderata of generality, realism, fit, and precision.