Challenges, Opportunities, and Solutions for Implementation of Diabetes Programs in Guatemala.

<p>Challenges, Opportunities, and Solutions for Implementation of Diabetes Programs in Guatemala.</p

Summary of models on leg movement rates.

BackgroundTools to accurately assess infants’ neurodevelopmental status very early in their lives are limited. Wearable sensors may provide a novel approach for very early assessment of infant neurodevelopmental status. This may be especially relevant in rural and low-resource global settings.MethodsWe conducted a longitudinal observational study and used wearable sensors to repeatedly measure the kinematic leg movement characteristics of 41 infants in rural Guatemala three times across full days between birth and 6 months of age. In addition, we collected sociodemographic data, growth data, and caregiver estimates of swaddling behaviors. We used visual analysis and multivariable linear mixed models to evaluate the associations between two leg movement kinematic variables (awake movement rate, peak acceleration per movement) and infant age, swaddling behaviors, growth, and other covariates.ResultsMultivariable mixed models of sensor data showed age-dependent increases in leg movement rates (2.16 [95% CI 0.80,3.52] movements/awake hour/day of life) and movement acceleration (5.04e-3 m/s2 [95% CI 3.79e-3, 6.27e-3]/day of life). Swaddling time as well as growth status, poverty status and multiple other clinical and sociodemographic variables had no impact on either movement variable.ConclusionsCollecting wearable sensor data on young infants in a rural low-resource setting is feasible and can be used to monitor age-dependent changes in movement kinematics. Future work will evaluate associations between these kinematic variables from sensors and formal developmental measures, such as the Bayley Scales of Infant and Toddler Development.</div

Flow diagram of subject participation in a study of wearable sensors to assess leg movement in infants in rural Guatemala.

Flow diagram of subject participation in a study of wearable sensors to assess leg movement in infants in rural Guatemala.</p

Summary of models on peak acceleration per movement.

Summary of models on peak acceleration per movement.</p

Baseline characteristics of participants (N = 41).

Baseline characteristics of participants (N = 41).</p

Age dependent change in hours of infant swaddling.

The top row shows caregiver estimated hours infants were swaddled (A) in total, (B) laid down on the floor, or (C) against caregivers during a visit (sensors on). The bottom row displays analogous measure (D) in total, (B) when swaddled and laid down, and (C) when swaddled against caregivers. Total swaddle time is the sum of time swaddled and laid down and time swaddled against caregivers. Thick black curves are LOESS (locally estimated scatterplot smoothing) curves fitted to data to estimate age-associated change of swaddling hours. Shading around the curves indicate 95% confidence intervals.</p

Age dependent change of the movement characteristics.

(A) Leg movement rate, (B) left leg peak acceleration per movement, and (C) right leg peak acceleration per movement are plotted against age at each recording. Age is in days since birth. Connected gray dots are estimated hours of infant swaddling reported for each infant across three visits. Thick black lines are linear estimation of the age dependent trends. Shading around the lines indicate 95% confidence intervals.</p

Estimated hours of infant swaddling at each study visit.

Estimated hours of infant swaddling at each study visit.</p

Growth and movement characteristics of infants per visit.

Growth and movement characteristics of infants per visit.</p

Clinical profile of Type 2 Diabetes Cohort.

<p>Clinical profile of Type 2 Diabetes Cohort.</p