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). Multi-year declines in MVPA were greater for children who had greater subject-level variance in positive affect. Children who experienced more positive affect when alone did not experience steeper declines in physical activity. Interventions aiming for long-term modifications in children's physical activity may focus on buffering the effects of within-day fluctuations in affect or tailoring programs to fit the needs of "acute dynamic process phenotypes."

MixWILD: A program for examining the effects of variance and slope of time-varying variables in intensive longitudinal data.

The use of intensive sampling methods, such as ecological momentary assessment (EMA), is increasingly prominent in medical research. However, inferences from such data are often limited to the subject-specific mean of the outcome and between-subject variance (i.e., random intercept), despite the capability to examine within-subject variance (i.e., random scale) and associations between covariates and subject-specific mean (i.e., random slope). MixWILD (Mixed model analysis With Intensive Longitudinal Data) is statistical software that tests the effects of subject-level parameters (variance and slope) of time-varying variables, specifically in the context of studies using intensive sampling methods, such as ecological momentary assessment. MixWILD combines estimation of a stage 1 mixed-effects location-scale (MELS) model, including estimation of the subject-specific random effects, with a subsequent stage 2 linear or binary/ordinal logistic regression in which values sampled from each subject's random effect distributions can be used as regressors (and then the results are aggregated across replications). Computations within MixWILD were written in FORTRAN and use maximum likelihood estimation, utilizing both the expectation-maximization (EM) algorithm and a Newton-Raphson solution. The mean and variance of each individual's random effects used in the sampling are estimated using empirical Bayes equations. This manuscript details the underlying procedures and provides examples illustrating standalone usage and features of MixWILD and its GUI. MixWILD is generalizable to a variety of data collection strategies (i.e., EMA, sensors) as a robust and reproducible method to test predictors of variability in level 1 outcomes and the associations between subject-level parameters (variances and slopes) and level 2 outcomes.

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. METHODS: Emerging adults (N = 197) living in the United States completed weekly survey on personal smartphones (May 2020-June 2021) regarding PA location choices and maintenance. Mixed-effects models examined the main effects of PA location choice and its interaction with weeks into the pandemic on participants' PA maintenance. RESULTS: On a given week, participants performing PA on roads/sidewalks or at parks/open spaces were 1½ and 2 times as likely to maintain PA levels, respectively. Moreover, after September 2021, weeks when individuals performed PA on roads/sidewalks had a protective effect on PA maintenance. CONCLUSIONS: Performing PA on roads/sidewalks and at parks/open spaces was associated with PA maintenance during the COVID-19 pandemic. PA promotion and intervention efforts for emerging adults during large-scale disruptions to daily life should focus on providing programmed activities in open spaces to maintain their PA levels.

Intensive Longitudinal Data Collection Using Microinteraction Ecological Momentary Assessment: Pilot and Preliminary Results.

BACKGROUND: Ecological momentary assessment (EMA) uses mobile technology to enable in situ self-report data collection on behaviors and states. In a typical EMA study, participants are prompted several times a day to answer sets of multiple-choice questions. Although the repeated nature of EMA reduces recall bias, it may induce participation burden. There is a need to explore complementary approaches to collecting in situ self-report data that are less burdensome yet provide comprehensive information on an individual's behaviors and states. A new approach, microinteraction EMA (µEMA), restricts EMA items to single, cognitively simple questions answered on a smartwatch with single-tap assessments using a quick, glanceable microinteraction. However, the viability of using µEMA to capture behaviors and states in a large-scale longitudinal study has not yet been demonstrated. OBJECTIVE: This paper describes the µEMA protocol currently used in the Temporal Influences on Movement & Exercise (TIME) Study conducted with young adults, the interface of the µEMA app used to gather self-report responses on a smartwatch, qualitative feedback from participants after a pilot study of the µEMA app, changes made to the main TIME Study µEMA protocol and app based on the pilot feedback, and preliminary µEMA results from a subset of active participants in the TIME Study. METHODS: The TIME Study involves data collection on behaviors and states from 246 individuals; measurements include passive sensing from a smartwatch and smartphone and intensive smartphone-based hourly EMA, with 4-day EMA bursts every 2 weeks. Every day, participants also answer a nightly EMA survey. On non-EMA burst days, participants answer µEMA questions on the smartwatch, assessing momentary states such as physical activity, sedentary behavior, and affect. At the end of the study, participants describe their experience with EMA and µEMA in a semistructured interview. A pilot study was used to test and refine the µEMA protocol before the main study. RESULTS: Changes made to the µEMA study protocol based on pilot feedback included adjusting the single-question selection method and smartwatch vibrotactile prompting. We also added sensor-triggered questions for physical activity and sedentary behavior. As of June 2021, a total of 81 participants had completed at least 6 months of data collection in the main study. For 662,397 µEMA questions delivered, the compliance rate was 67.6% (SD 24.4%) and the completion rate was 79% (SD 22.2%). CONCLUSIONS: The TIME Study provides opportunities to explore a novel approach for collecting temporally dense intensive longitudinal self-report data in a sustainable manner. Data suggest that µEMA may be valuable for understanding behaviors and states at the individual level, thus possibly supporting future longitudinal interventions that require within-day, temporally dense self-report data as people go about their lives.

Investigating Microtemporal Processes Underlying Health Behavior Adoption and Maintenance: Protocol for an Intensive Longitudinal Observational Study.

BACKGROUND: Young adulthood (ages 18-29 years) is marked by substantial weight gain, leading to increased lifetime risks of chronic diseases. Engaging in sufficient levels of physical activity and sleep, and limiting sedentary time are important contributors to the prevention of weight gain. Dual-process models of decision-making and behavior that delineate reflective (ie, deliberative, slow) and reactive (ie, automatic, fast) processes shed light on different mechanisms underlying the adoption versus maintenance of these energy-balance behaviors. However, reflective and reactive processes may unfold at different time scales and vary across people. OBJECTIVE: This paper describes the study design, recruitment, and data collection procedures for the Temporal Influences on Movement and Exercise (TIME) study, a 12-month intensive longitudinal data collection study to examine real-time microtemporal influences underlying the adoption and maintenance of physical activity, sedentary behavior, and sleep. METHODS: Intermittent ecological momentary assessment (eg, intentions, self-control) and continuous, sensor-based passive monitoring (eg, location, phone/app use, activity levels) occur using smartwatches and smartphones. Data analyses will combine idiographic (person-specific, data-driven) and nomothetic (generalizable, theory-driven) approaches to build models that may predict within-subject variation in the likelihood of behavior "episodes" (eg, ≥10 minutes of physical activity, ≥120 minutes of sedentary time, ≥7 hours sleep) and "lapses" (ie, not attaining recommended levels for ≥7 days) as a function of reflective and reactive factors. RESULTS: The study recruited young adults across the United States (N=246). Rolling recruitment began in March 2020 and ended August 2021. Data collection will continue until August 2022. CONCLUSIONS: Results from the TIME study will be used to build more predictive health behavior theories, and inform personalized behavior interventions to reduce obesity and improve public health. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36666.

Measuring Criterion Validity of Microinteraction Ecological Momentary Assessment (Micro-EMA): Exploratory Pilot Study With Physical Activity Measurement.

BACKGROUND: Ecological momentary assessment (EMA) is an in situ method of gathering self-report on behaviors using mobile devices. In typical phone-based EMAs, participants are prompted repeatedly with multiple-choice questions, often causing participation burden. Alternatively, microinteraction EMA (micro-EMA or µEMA) is a type of EMA where all the self-report prompts are single-question surveys that can be answered using a 1-tap glanceable microinteraction conveniently on a smartwatch. Prior work suggests that µEMA may permit a substantially higher prompting rate than EMA, yielding higher response rates and lower participation burden. This is achieved by ensuring µEMA prompt questions are quick and cognitively simple to answer. However, the validity of participant responses from µEMA self-report has not yet been formally assessed. OBJECTIVE: In this pilot study, we explored the criterion validity of µEMA self-report on a smartwatch, using physical activity (PA) assessment as an example behavior of interest. METHODS: A total of 17 participants answered 72 µEMA prompts each day for 1 week using a custom-built µEMA smartwatch app. At each prompt, they self-reported whether they were doing sedentary, light/standing, moderate/walking, or vigorous activities by tapping on the smartwatch screen. Responses were compared with a research-grade activity monitor worn on the dominant ankle simultaneously (and continuously) measuring PA. RESULTS: Participants had an 87.01% (5226/6006) µEMA completion rate and a 74.00% (5226/7062) compliance rate taking an average of only 5.4 (SD 1.5) seconds to answer a prompt. When comparing µEMA responses with the activity monitor, we observed significantly higher (P<.001) momentary PA levels on the activity monitor when participants self-reported engaging in moderate+vigorous activities compared with sedentary or light/standing activities. The same comparison did not yield any significant differences in momentary PA levels as recorded by the activity monitor when the µEMA responses were randomly generated (ie, simulating careless taps on the smartwatch). CONCLUSIONS: For PA measurement, high-frequency µEMA self-report could be used to capture information that appears consistent with that of a research-grade continuous sensor for sedentary, light, and moderate+vigorous activity, suggesting criterion validity. The preliminary results show that participants were not carelessly answering µEMA prompts by randomly tapping on the smartwatch but were reporting their true behavior at that moment. However, more research is needed to examine the criterion validity of µEMA when measuring vigorous activities.

Signaligner Pro: A Tool to Explore and Annotate Multi-day Raw Accelerometer Data.

Human activity recognition using wearable accelerometers can enable in-situ detection of physical activities to support novel human-computer interfaces. Many of the machine-learning-based activity recognition algorithms require multi-person, multi-day, carefully annotated training data with precisely marked start and end times of the activities of interest. To date, there is a dearth of usable tools that enable researchers to conveniently visualize and annotate multiple days of high-sampling-rate raw accelerometer data. Thus, we developed Signaligner Pro, an interactive tool to enable researchers to conveniently explore and annotate multi-day high-sampling rate raw accelerometer data. The tool visualizes high-sampling-rate raw data and time-stamped annotations generated by existing activity recognition algorithms and human annotators; the annotations can then be directly modified by the researchers to create their own, improved, annotated datasets. In this paper, we describe the tool's features and implementation that facilitate convenient exploration and annotation of multi-day data and demonstrate its use in generating activity annotations.

An empirical example of analysis using a two-stage modeling approach: within-subject association of outdoor context and physical activity predicts future daily physical activity levels.

People differ from each other to the extent to which momentary factors, such as context, mood, and cognitions, influence momentary health behaviors. However, statistical models to date are limited in their ability to test whether the association between two momentary variables (i.e., subject-level slopes) predicts a subject-level outcome. This study demonstrates a novel two-stage statistical modeling strategy that is capable of testing whether subject-level slopes between two momentary variables predict subject-level outcomes. An empirical case study application is presented to examine whether there are differences in momentary moderate-to-vigorous physical activity (MVPA) levels between the outdoor and indoor context in adults and whether these momentary differences predict mean daily MVPA levels 6 months later. One hundred and eight adults from a multiwave longitudinal study provided 4 days of ecological momentary assessment (during baseline) and accelerometry data (both at baseline and 6 month follow-up). Multilevel data were analyzed using an open-source program (MixWILD) to test whether momentary strength between outdoor context and MVPA during baseline was associated with average daily MVPA levels measured 6 months later. During baseline, momentary MVPA levels were higher in outdoor contexts as compared to indoor contexts (b = 0.07, p < .001). Participants who had more momentary MVPA when outdoors (vs. indoors) during baseline (i.e., a greater subject-level slope) had higher daily MVPA at the 6 month follow-up (b = 0.09, p < .05). This empirical example shows that the subject-level momentary association between specific context (i.e., outdoors) and health behavior (i.e., physical activity) may contribute to overall engagement in that behavior in the future. The demonstrated two-stage modeling approach has extensive applications in behavioral medicine to analyze intensive longitudinal data collected from wearable sensors and mobile devices.

Designing Videogames to Crowdsource Accelerometer Data Annotation for Activity Recognition Research.

Human activity recognition using wearable accelerometers can enable in-situ detection of physical activities to support novel human-computer interfaces and interventions. However, developing valid algorithms that use accelerometer data to detect everyday activities often requires large amounts of training datasets, precisely labeled with the start and end times of the activities of interest. Acquiring annotated data is challenging and time-consuming. Applied games, such as human computation games (HCGs) have been used to annotate images, sounds, and videos to support advances in machine learning using the collective effort of "non-expert game players." However, their potential to annotate accelerometer data has not been formally explored. In this paper, we present two proof-of-concept, web-based HCGs aimed at enabling game players to annotate accelerometer data. Using results from pilot studies with Amazon Mechanical Turk players, we discuss key challenges, opportunities, and, more generally, the potential of using applied videogames for annotating raw accelerometer data to support activity recognition research.

Microinteraction Ecological Momentary Assessment Response Rates: Effect of Microinteractions or the Smartwatch?

Mobile-based ecological-momentary-assessment (EMA) is an in-situ measurement methodology where an electronic device prompts a person to answer questions of research interest. EMA has a key limitation: interruption burden. Microinteraction-EMA(µEMA) may reduce burden without sacrificing high temporal density of measurement. In µEMA, all EMA prompts can be answered with 'at a glance' microinteractions. In a prior 4-week pilot study comparing standard EMA delivered on a phone (phone-EMA) vs. µEMA delivered on a smartwatch (watch-µEMA), watch-µEMA demonstrated higher response rates and lower perceived burden than phone-EMA, even when the watch-µEMA interruption rate was 8 times more than phone-EMA. A new 4-week dataset was gathered on smartwatch-based EMA (i.e., watch-EMA with 6 back-to-back, multiple-choice questions on a watch) to compare whether the high response rates of watch-µEMA previously observed were a result of using microinteractions, or due to the novelty and accessibility of the smartwatch. No statistically significant differences in compliance, completion, and first-prompt response rates were observed between phone-EMA and watch-EMA. However, watch-µEMA response rates were significantly higher than watch-EMA. This pilot suggests that (1) the high compliance and low burden previously observed in watch-µEMA is likely due to the microinteraction question technique, not simply the use of the watch versus the phone, and that (2) compliance with traditional EMA (with long surveys) may not improve simply by moving survey delivery from the phone to a smartwatch.

µEMA: Microinteraction-based Ecological Momentary Assessment (EMA) Using a Smartwatch.

Ecological Momentary Assessment (EMA) is a method of in situ data collection for assessment of behaviors, states, and contexts. Questions are prompted during everyday life using an individual's mobile device, thereby reducing recall bias and increasing validity over other self-report methods such as retrospective recall. We describe a microinteraction-based EMA method ("micro" EMA, or µEMA) using smartwatches, where all EMA questions can be answered with a quick glance and a tap - nearly as quickly as checking the time on a watch. A between-subjects, 4-week pilot study was conducted where µEMA on a smartwatch (n=19) was compared with EMA on a phone (n=14). Despite an ≈8 times increase in the number of interruptions, µEMA had a significantly higher compliance rate, completion rate, and first prompt response rate, and µEMA was perceived as less distracting. The temporal density of data collection possible with µEMA could prove useful in ubiquitous computing studies.