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Edward H. Ip, Santiago Saldana, Grisel Trejo, Sarah A. Marshall, Cynthia K. Suerken, Wei Lang, Thomas A. Arcury and Sara A. Quandt


Obesity disproportionately affects children of Latino farmworkers. Further research is needed to identify patterns of physical activity (PA) in this group and understand how PA affects Body Mass Index (BMI) percentile.


Two hundred and forty-four participants ages 2.5 to 3.5 in the Niños Sanos longitudinal study wore accelerometers that measured daily PA. Several PA-related parameters formed a profile for conducting hidden Markov modeling (HMM), which identified different states of PA.


Latino farmworker children were generally sedentary. Two different states were selected using HMM—less active and more active. In the more active state; members spent more minutes in moderate-vigorous physical activity (MVPA). Most children were in the less active state at any given time; however, switching between states occurred commonly. One variable—mother’s concern regarding lack of PA—was a marginally significant predictor of membership in the more active state. State did not predict BMI or weight percentile after adjusting for caloric intake.


Most children demonstrated high amounts of sedentary behavior, and rates of MVPA fell far below recommended levels for both states. The lack of statistically significant results for risk factors and PA state on weight-related outcomes is likely due to the homogeneous behaviors of the children.

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Joshua Twaites, Richard Everson, Joss Langford and Melvyn Hillsdon

– 126 . doi:10.1016/j.hlpt.2012.07.003 10.1016/j.hlpt.2012.07.003 Anderson , M.M. ( 2013 ). Physical activity recognition of free-living data using change-point detection algorithms and hidden Markov models (Master thesisOregon State).Retrieved from https

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Jeremy A. Steeves, Scott A. Conger, Joe R. Mitrzyk, Trevor A. Perry, Elise Flanagan, Alecia K. Fox, Trystan Weisinger and Alexander H.K. Montoye

its ability to classify exercise type, and approached that achieved by the two-sensor system (95%) used by Chang et al. that collected raw accelerometer data, and counted repetitions using Hidden Markov models and a peak counting algorithm ( Chang et al., 2007 ). The excellent repetition

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Anantha Narayanan, Farzanah Desai, Tom Stewart, Scott Duncan and Lisa Mackay

participant compared with lab-based studies, and they facilitate the development of sequence-based models (eg, Hidden Markov model). 65 Although the findings from this scoping review display some preliminary patterns, researchers must be aware that other crucial study factors such as the complexity of the

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Mohammad Reza Pourahmadi, Ismail Ebrahimi Takamjani, Shapour Jaberzadeh, Javad Sarrafzadeh, Mohammad Ali Sanjari, Rasool Bagheri and Morteza Taghipour

. Modeling movement primitives with hidden Markov models for robotic and biomedical applications . Methods Mol Biol . 2017 ; 1552 : 199 – 213 . 10.1007/978-1-4939-6753-7_15 28224501 56. Sung PS . A kinematic analysis for shoulder and pelvis coordination during axial trunk rotation in subjects with and