Ask Less, Learn More: Adapting Ecological Momentary Assessment Survey Length by Modeling Question-Answer Information Gain.

Ecological momentary assessment (EMA) is an approach to collect self-reported data repeatedly on mobile devices in natural settings. EMAs allow for temporally dense, ecologically valid data collection, but frequent interruptions with lengthy surveys on mobile devices can burden users, impacting compliance and data quality. We propose a method that reduces the length of each EMA question set measuring interrelated constructs, with only modest information loss.

mHealth-Based Just-in-Time Adaptive Intervention to Improve the Physical Activity Levels of Individuals With Spinal Cord Injury: Protocol for a Randomized Controlled Trial.

Background: The lack of regular physical activity (PA) in individuals with spinal cord injury (SCI) in the United States is an ongoing health crisis. Regular PA and exercise-based interventions have been linked with improved outcomes and healthier lifestyles among those with SCI. Providing people with an accurate estimate of their everyday PA level can promote PA.

Potential of social engagement for overcoming barriers to physical activity in individuals with spinal cord injury.

Introduction: Many barriers to physical activity (PA) exist for individuals with spinal cord injury (SCI). Social engagement may improve motivation to perform PA, which in turn may increase PA levels. This pilot study investigates how social engagement facilitated by mobile technology may reduce lack of motivation as a barrier to PA in individuals with SCI and demonstrates design implications for future technologies.

The ILHBN: challenges, opportunities, and solutions from harmonizing data under heterogeneous study designs, target populations, and measurement protocols.

The ILHBN is funded by the National Institutes of Health to collaboratively study the interactive dynamics of behavior, health, and the environment using Intensive Longitudinal Data (ILD) to (a) understand and intervene on behavior and health and (b) develop new analytic methods to innovate behavioral theories and interventions. The heterogenous study designs, populations, and measurement protocols adopted by the seven studies within the ILHBN created practical challenges, but also unprecedented opportunities to capitalize on data harmonization to provide comparable views of data from different studies, enhance the quality and utility of expensive and hard-won ILD, and amplify scientific yield. The purpose of this article is to provide a brief report of the challenges, opportunities, and solutions from some of the ILHBN's cross-study data harmonization efforts.

Examining Whether Physical Activity Location Choices Were Associated With Weekly Physical Activity Maintenance Across 13 Months of the COVID-19 Pandemic in Emerging Adults.

Background: Recent studies have shown potentially detrimental effects of the COVID-19 pandemic on physical activity (PA) in emerging adults (ages 18-29 y). However, studies that examined the effects of COVID-19 on PA location choices and maintenance for this age group remain limited. The current study investigated changes in PA location choices across 13 months during the pandemic and their associations with PA maintenance in this population.

How acute affect dynamics impact longitudinal changes in physical activity among children.

Research examined how acute affect dynamics, including stability and context-dependency, contribute to changes in children's physical activity levels as they transition from late-childhood to early-adolescence. Children (N = 151) (ages 8-12 years at baseline) participated in an ecological momentary assessment and accelerometry study with six semi-annual bursts (7 days each) across three years. A two-stage mixed-effects multiple location-scale model tested random intercept, variance, and slope estimates for positive affect as predictors of moderate-to-vigorous physical activity (MVPA).

Real-Time Data Collection to Examine Relations Between Physical Activity and Affect in Adults With Mental Illness.

Adults with serious mental illness engage in limited physical activity, which contributes to significant health disparities. This study explored the use of both ecological momentary assessments (EMAs) and activity trackers in adults with serious mental illness to examine the bidirectional relationship between activity and affect with multilevel modeling. Affective states were assessed up to seven times per day using EMA across 4 days.

Relationship between pain, fatigue, and physical activity levels during a technology-based physical activity intervention.

The majority of individuals with spinal cord injury (SCI) experience chronic pain. Chronic pain can be difficult to manage because of variability in the underlying pain mechanisms. More insight regarding the relationship between pain and physical activity (PA) is necessary to understand pain responses during PA.

How intensive longitudinal data can stimulate advances in health behavior maintenance theories and interventions.

Interventions that promote long-term maintenance of behaviors such as exercise, healthy eating, and avoidance of tobacco and excessive alcohol are critical to reduce noncommunicable disease burden. Theories of health behavior maintenance tend to address reactive (i.e.

Correction: Mobile health-based physical activity intervention for individuals with spinal cord injury in the community: A pilot study.

[This corrects the article DOI: 10.1371/journal.pone.

Mobile health-based physical activity intervention for individuals with spinal cord injury in the community: A pilot study.

Low levels of physical activity (PA) and high levels of sedentary behavior in individuals with spinal cord injury (SCI) have been associated with secondary conditions such as pain, fatigue, weight gain, and deconditioning. One strategy for promoting regular PA is to provide people with an accurate estimate of everyday PA level. The objective of this research was to use a mobile health-based PA measurement system to track PA levels of individuals with SCI in the community and provide them with a behavior-sensitive, just-in-time-adaptive intervention (JITAI) to improve their PA levels.

Investigating Health Risk Environments in Housing Programs for Young Adults: Protocol for a Geographically Explicit Ecological Momentary Assessment Study.

Background: Young adults who experience homelessness are exposed to environments that contribute to risk behavior. However, few studies have examined how access to housing may affect the health risk behaviors of young adults experiencing homelessness.

Objective: This paper describes the Log My Life study that uses an innovative, mixed-methods approach based on geographically explicit ecological momentary assessment (EMA) through cell phone technology to understand the risk environment of young adults who have either enrolled in housing programs or are currently homeless.

Affective response during physical activity: Within-subject differences across phases of behavior change.

Objective: Affective response during physical activity may be a key factor reinforcing future behavior. However, little is known about how affective responses during physical activity may differ across phases of behavior change. This study used real-time Ecological Momentary Assessment (EMA) to examine within-subject differences in affective response during physical activity in daily life as individuals transitioned across phases of behavior change.

Classifier Personalization for Activity Recognition Using Wrist Accelerometers.

Intersubject variability in accelerometer-based activity recognition may significantly affect classification accuracy, limiting a reliable extension of methods to new users. In this paper, we propose an approach for personalizing classification rules to a single person. We demonstrate that the method improves activity detection from wrist-worn accelerometer data on a four-class recognition problem of interest to the exercise science community, where classes are ambulation, cycling, sedentary, and other.

Response patterns and intra-dyadic factors related to compliance with ecological momentary assessment among mothers and children.

Ecological momentary assessment (EMA) is a real-time sampling strategy that may address limitations in health research, such as the inability to examine how processes unfold on a daily basis. However, EMA studies are prone to limited data availability due to difficulties in implementing sophisticated protocols and systematic non-compliance with prompts, resulting in biased estimates and limited statistical power. The objectives of this study were to describe the availability of data, to examine response patterns, and to analyze factors related to EMA prompt compliance in a dyadic EMA study with mothers and children.

REAL-TIME SUBJECTIVE ASSESSMENT OF PSYCHOLOGICAL STRESS: ASSOCIATIONS WITH OBJECTIVELY-MEASURED PHYSICAL ACTIVITY LEVELS.

Psychosocial stress may be a factor in the link between physical activity and obesity. This study examines how the daily experience of psychosocial stress influences physical activity levels and weight status in adults. This study reports temporally ordered relationships between sedentary, light, and moderate-to-vigorous physical activity levels and real-time reports of subjective psychosocial stress levels.

Household chaos and sleep-disturbing behavior of family members: results of a pilot study of African American early adolescents.

Background: Although disorganized, chaotic households have been linked to poorer sleep outcomes, how household chaos actually manifests itself in the behaviors of others around the bedtime of a child or adolescent is not well understood.

Objective: To determine whether household chaos was associated with specific, nightly sleep-disturbing activities of adolescents' family members.

Design: Longitudinal study.

Activity Recognition in Youth Using Single Accelerometer Placed at Wrist or Ankle.

Purpose: State-of-the-art methods for recognizing human activity using raw data from body-worn accelerometers have primarily been validated with data collected from adults. This study applies a previously available method for activity classification using wrist or ankle accelerometer to data sets collected from both adults and youth.

Methods: An algorithm for detecting activity from wrist-worn accelerometers, originally developed using data from 33 adults, is tested on a data set of 20 youth (age, 13 ± 1.

Estimation of Energy Expenditure for Wheelchair Users Using a Physical Activity Monitoring System.

Objective: To develop and evaluate energy expenditure (EE) estimation models for a physical activity monitoring system (PAMS) in manual wheelchair users with spinal cord injury (SCI).

Design: Cross-sectional study.

Setting: University-based laboratory environment, a semistructured environment at the National Veterans Wheelchair Games, and the participants' home environments.

Cell phone intervention for you (CITY): A randomized, controlled trial of behavioral weight loss intervention for young adults using mobile technology.

Objective: To determine the effect on weight of two mobile technology-based (mHealth) behavioral weight loss interventions in young adults.

Methods: Randomized, controlled comparative effectiveness trial in 18- to 35-year-olds with BMI ≥ 25 kg/m(2) (overweight/obese), with participants randomized to 24 months of mHealth intervention delivered by interactive smartphone application on a cell phone (CP); personal coaching enhanced by smartphone self-monitoring (PC); or Control.

Results: The 365 randomized participants had mean baseline BMI of 35 kg/m(2) .

Accelerometry-based Recognition of the Placement Sites of a Wearable Sensor.

This work describes an automatic method to recognize the position of an accelerometer worn on five different parts of the body: ankle, thigh, hip, arm and wrist from raw accelerometer data. Automatic detection of body position of a wearable sensor would enable systems that allow users to wear sensors flexibly on different body parts or permit systems that need to automatically verify sensor placement. The two-stage location detection algorithm works by first detecting time periods during which candidates are walking (regardless of where the sensor is positioned).