The Case for the Patient-Centric Development of Novel Digital Sleep Assessment Tools in Major Depressive Disorder.

Background: Depression imposes a major burden on public health as the leading cause of disability worldwide. Sleep disturbance is a core symptom of depression that affects the vast majority of patients. Nonetheless, it is frequently not resolved by depression treatment and may even be worsened through some pharmaceutical interventions. Disturbed sleep negatively impact patients' quality of life, and persistent sleep disturbance increases the risk of recurrence, relapse, and even suicide. However, the development of novel treatments that might improve sleep problems is hindered by the lack of reliable low-burden objective measures that can adequately assess disturbed sleep in this population. Summary: Developing improved digital measurement tools that are fit for use in clinical trials for major depressive disorder could promote the inclusion of sleep as a focus for treatment, clinical drug development, and research. This perspective piece explores the path toward the development of novel digital measures, reviews the existing evidence on the meaningfulness of sleep in depression, and summarizes existing methods of sleep assessments, including the use of digital health technologies. Key Messages: Our objective was to make a clear call to action and path forward for the qualification of new digital outcome measures which would enable assessment of sleep disturbance as an aspect of health that truly matters to patients, promoting sleep as an important outcome for clinical development, and ultimately ensure that disturbed sleep will not remain the forgotten symptom of depression.

A Framework to Evaluate Devices That Assess Physical Behavior.

Body-worn devices that estimate physical behavior have tremendous potential to address key research gaps. However, there is no consensus on how devices and processing methods should be developed and evaluated, resulting in large differences in summary estimates and confusion for end users. We propose a phase-based framework for developing and evaluating devices that emphasizes robust validation studies in naturalistic conditions.

Physical Activity Energy Expenditure and Total Daily Energy Expenditure in Successful Weight Loss Maintainers.

OBJECTIVE: The objective of this study was to compare physical activity energy expenditure (PAEE) and total daily energy expenditure (TDEE) in successful weight loss maintainers (WLM) with normal weight controls (NC) and controls with overweight/obesity (OC). METHODS: Participants were recruited in three groups: WLM (n = 25, BMI 24.1 ± 2.3 kg/m2 ; maintaining ≥ 13.6-kg weight loss for ≥ 1 year), NC (n = 27, BMI 23.0 ± 2.0 kg/m2 ; similar to current BMI of WLM), and OC (n = 28, BMI 34.3 ± 4.8 kg/m2 ; similar to pre-weight loss BMI of WLM). TDEE was measured using the doubly labeled water method. Resting energy expenditure (REE) was measured using indirect calorimetry. PAEE was calculated as (TDEE - [0.1 × TDEE] - REE). RESULTS: PAEE in WLM (812 ± 268 kcal/d, mean ± SD) was significantly higher compared with that in both NC (621 ± 285 kcal/d, P < 0.01) and OC (637 ± 271 kcal/d, P = 0.02). As a result, TDEE in WLM (2,495 ± 366 kcal/d) was higher compared with that in NC (2,195 ± 521 kcal/d, P = 0.01) but was not significantly different from that in OC (2,573 ± 391 kcal/d). CONCLUSIONS: The high levels of PAEE and TDEE observed in individuals maintaining a substantial weight loss (-26.2 ± 9.8 kg maintained for 9.0 ± 10.2 years) suggest that this group relies on high levels of energy expended in physical activity to remain in energy balance (and avoid weight regain) at a reduced body weight.

Energy Expenditure for 70 Activities in Children and Adolescents.

BACKGROUND: Thirty-five percent of the activities assigned MET values in the Compendium of Energy Expenditures for Youth were obtained from direct measurement of energy expenditure (EE). The aim of this study was to provide directly measured EE for several different activities in youth. METHODS: Resting metabolic rate (RMR) of 178 youths (80 females, 98 males) was first measured. Participants then performed structured activity bouts while wearing a portable metabolic system to directly measure EE. Steady-state oxygen consumption data were used to compute activity METstandard (activity VO2/3.5) and METmeasured (activity VO2/measured RMR) for the different activities. RESULTS: Rates of EE were measured for 70 different activities and ranged from 1.9 to 12.0 METstandard and 1.5 to 10.0 METmeasured. CONCLUSION: This study provides directly measured energy cost values for 70 activities in children and adolescents. It contributes empirical data to support the expansion of the Compendium of Energy Expenditures for Youth.

Estimating energy expenditure using heat flux measured at a single body site.

INTRODUCTION: The Personal Calorie Monitor (PCM) is a portable direct calorimeter that estimates energy expenditure (EE) from measured heat flux (i.e., the sum of conductive, convective, radiative, and evaporative heat). PURPOSE: The primary aim of this study was to compare EE estimated from measures of heat flux with those measured using indirect calorimetry in a thermoneutral environment (26°C). A secondary aim was to determine whether exposure to ambient temperature below thermoneutral condition (19°C) influences the accuracy of the PCM. METHODS: Thirty-four adults (mean ± SD: age, 28 ± 5 yr; body mass index, 22.9 ± 2.6 kg · m(-2)) were studied for 5 h in a whole-room indirect calorimeter (IC) in thermoneutral and cool conditions. Participants wore the PCM on their upper arm and completed two 20-min treadmill walking bouts (0% grade, 3 mph). The remaining time was spent sedentary (e.g., watching television, using a computer). RESULTS: In thermoneutral conditions, EE values (mean (95% confidence interval)) measured by IC and PCM were 560.0 (526.5-593.5) and 623.3 (535.5-711.1) kcal, respectively. In cool conditions, EE values measured by IC and PCM were 572.5 (540.9-604.0) and 745.5 (668.1-822.8) kcal, respectively. Under thermoneutral conditions, mean PCM minute-by-minute EE tracked closely with IC, resulting in a small nonsignificant bias (63 kcal (-5.8 to 132.4)). During cool conditions, mean PCM minute-by-minute EE did not track IC, resulting in a large bias (173.0 kcal (93.9-252.1)) (P <; 0.001). CONCLUSIONS: This study demonstrated the validity of using measured heat flux to estimate EE. However, accuracy may be impaired in cool conditions possibly because of excess heat loss from the exposed limbs.

Energy cost of common activities in children and adolescents.

BACKGROUND: The Compendium of Energy Expenditures for Youth assigns MET values to a wide range of activities. However, only 35% of activity MET values were derived from energy cost data measured in youth; the remaining activities were estimated from adult values. PURPOSE: To determine the energy cost of common activities performed by children and adolescents and compare these data to similar activities reported in the compendium. METHODS: Thirty-two children (8-11 years old) and 28 adolescents (12-16 years) completed 4 locomotion activities on a treadmill (TRD) and 5 age-specific activities of daily living (ADL). Oxygen consumption was measured using a portable metabolic analyzer. RESULTS: In children, measured METs were significantly lower than compendium METs for 3 activities [basketball, bike riding, and Wii tennis (1.1-3.5 METs lower)]. In adolescents, measured METs were significantly lower than compendium METs for 4 ADLs [basketball, bike riding, board games, and Wii tennis (0.3-2.5 METs lower)] and 3 TRDs [2.24 m · s(-1), 1.56 m · s(-1), and 1.34 m · s(-1) (0.4-0.8 METs lower)]. CONCLUSION: The Compendium of Energy Expenditures for Youth is an invaluable resource to applied researchers. Inclusion of empirically derived data would improve the validity of the Compendium of Energy Expenditures for Youth.

Accuracy of accelerometer regression models in predicting energy expenditure and METs in children and youth.

This study examined the validity of commonly used regression equations for the Actigraph and Actical accelerometers in predicting energy expenditure (EE) in children and adolescents. Sixty healthy (8-16 yrs) participants completed four treadmill (TM) and five self-paced activities of daily living (ADL). Four Actigraph (AG) and three Actical (AC) regression equations were used to estimate EE. Bias (± 95% CI) and root mean squared errors were used to assess the validity of the regression equations compared with indirect calorimetry. For children, the Freedson (AG) model accurately predicted EE for all activities combined and the Treuth (AG) model accurately predicted EE for TM activities. For adolescents, the Freedson model accurately predicted EE for TM activities and the Treuth model accurately predicted EE for all activities and for TM activities. No other equation accurately estimated EE. The percent agreement for the AG and AC equations were better for light and vigorous compared with moderate intensity activities. The Trost (AG) equation most accurately classified all activity intensity categories. Overall, equations yield inconsistent point estimates of EE.

A comprehensive evaluation of commonly used accelerometer energy expenditure and MET prediction equations.

Numerous accelerometers and prediction methods are used to estimate energy expenditure (EE). Validation studies have been limited to small sample sizes in which participants complete a narrow range of activities and typically validate only one or two prediction models for one particular accelerometer. The purpose of this study was to evaluate the validity of nine published and two proprietary EE prediction equations for three different accelerometers. Two hundred and seventy-seven participants completed an average of six treadmill (TRD) (1.34, 1.56, 2.23 ms(-1) each at 0 and 3% grade) and five self-paced activities of daily living (ADLs). EE estimates were compared with indirect calorimetry. Accelerometers were worn while EE was measured using a portable metabolic unit. To estimate EE, 4 ActiGraph prediction models were used, 5 Actical models, and 2 RT3 proprietary models. Across all activities, each equation underestimated EE (bias -0.1 to -1.4 METs and -0.5 to -1.3 kcal, respectively). For ADLs EE was underestimated by all prediction models (bias -0.2 to -2.0 and -0.2 to -2.8, respectively), while TRD activities were underestimated by seven equations, and overestimated by four equations (bias -0.8 to 0.2 METs and -0.4 to 0.5 kcal, respectively). Misclassification rates ranged from 21.7 (95% CI 20.4, 24.2%) to 34.3% (95% CI 32.3, 36.3%), with vigorous intensity activities being most often misclassified. Prediction equations did not yield accurate point estimates of EE across a broad range of activities nor were they accurate at classifying activities across a range of intensities (light <3 METs, moderate 3-5.99 METs, vigorous ≥ 6 METs). Current prediction techniques have many limitations when translating accelerometer counts to EE.