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Background: Intensive longitudinal data (ILD) collection methods have gained popularity in social and behavioral research as a tool to better understand behavior and experiences over time with reduced recall bias. Engaging participants in these studies over multiple months and ensuring high data quality are crucial but challenging due to the potential burden of repeated measurements. It is suspected that participants may engage in inattentive responding (IR) behavior to combat burden, but the processes underlying this behavior are unclear as previous studies have focused on the barriers to compliance rather than the barriers to providing high-quality data., Objective: This study aims to broaden researchers' knowledge about IR during ILD studies using qualitative analysis and uncover the underlying IR processes to aid future hypothesis generation., Methods: We explored the process of IR by conducting semistructured qualitative exit interviews with 31 young adult participants (aged 18-29 years) who completed a 12-month ILD health behavior study with daily evening smartphone-based ecological momentary assessment (EMA) surveys and 4-day waves of hourly EMA surveys. The interviews assessed participants' motivations, the impact of time-varying contexts, changes in motivation and response patterns over time, and perceptions of attention check questions (ACQs) to understand participants' response patterns and potential factors leading to IR., Results: Thematic analysis revealed 5 overarching themes on factors that influence participant engagement: (1) friends and family also had to tolerate the frequent surveys, (2) participants tried to respond to surveys quickly, (3) the repetitive nature of surveys led to neutral responses, (4) ACQs within the surveys helped to combat overly consistent response patterns, and (5) different motivations for answering the surveys may have led to different levels of data quality., Conclusions: This study aimed to examine participants' perceptions of the quality of data provided in an ILD study to contribute to the field's understanding of engagement. These findings provide insights into the complex process of IR and participant engagement in ILD studies with EMA. The study identified 5 factors influencing IR that could guide future research to improve EMA survey design. The identified themes offer practical implications for researchers and study designers, including the importance of considering social context, the consideration of dynamic motivations, and the potential benefit of including ACQs as a technique to reduce IR and leveraging the intrinsic motivators of participants. By incorporating these insights, researchers might maximize the scientific value of their multimonth ILD studies through better data collection protocols., International Registered Report Identifier (irrid): RR2-10.2196/36666., (©Shirlene D Wang, Lori Hatzinger, Jeremy Morales, Micaela Hewus, Stephen Intille, Genevieve F Dunton. Originally published in JMIR Formative Research (https://formative.jmir.org), 02.08.2024.)

We describe a smartphone/smartwatch system to evaluate anomia in individuals with aphasia by using audio-recording-based ecological momentary assessments. The system delivers object-naming assessments to a participant's smartwatch, whereby a prompt signals the availability of images of these objects on the watch screen. Participants attempt to speak the names of the images that appear on the watch display out loud and into the watch as they go about their lives. We conducted a three-week feasibility study with six participants with mild to moderate aphasia. Participants were assigned to either a nine-item (four prompts per day with nine images) or single-item (36 prompts per day with one image each) ecological momentary assessment protocol. Compliance in recording an audio response to a prompt was approximately 80% for both protocols. Qualitative analysis of the participants' interviews suggests that the participants felt capable of completing the protocol, but opinions about using a smartwatch were mixed. We review participant feedback and highlight the importance of considering a population's specific cognitive or motor impairments when designing technology and training protocols.

Introduction: Estimating physical activity, sedentary behavior, and sleep from wrist-worn accelerometer data requires reliable detection of sensor nonwear and sensor wear during both sleep and wake., Purpose: This study aimed to develop an algorithm that simultaneously identifies sensor wake-wear, sleep-wear, and nonwear in 24-h wrist accelerometer data collected with or without filtering., Methods: Using sensor data labeled with polysomnography ( n = 21) and directly observed wake-wear data ( n = 31) from healthy adults, and nonwear data from sensors left at various locations in a home ( n = 20), we developed an algorithm to detect nonwear, sleep-wear, and wake-wear for "idle sleep mode" (ISM) filtered data collected in the 2011-2014 National Health and Nutrition Examination Survey. The algorithm was then extended to process original raw data collected from devices without ISM filtering. Both algorithms were further validated using a polysomnography-based sleep and wake-wear data set ( n = 22) and diary-based wake-wear and nonwear labels from healthy adults ( n = 23). Classification performance (F1 scores) was compared with four alternative approaches., Results: The F1 score of the ISM-based algorithm on the training data set using leave-one-subject-out cross-validation was 0.95 ± 0.13. Validation on the two independent data sets yielded F1 scores of 0.84 ± 0.60 for the data set with sleep-wear and wake-wear and 0.94 ± 0.04 for the data set with wake-wear and nonwear. The F1 score when using original, raw data was 0.96 ± 0.08 for the training data sets and 0.86 ± 0.18 and 0.97 ± 0.04 for the two independent validation data sets. The algorithm performed comparably or better than the alternative approaches on the data sets., Conclusions: A novel machine-learning algorithm was designed to recognize wake-wear, sleep-wear, and nonwear in 24-h wrist-worn accelerometer data that are applicable for ISM-filtered data or original raw data., (Copyright © 2022 by the American College of Sports Medicine.)

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., (©Shirlene Wang, Stephen Intille, Aditya Ponnada, Bridgette Do, Alexander Rothman, Genevieve Dunton. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 14.07.2022.)

Background: Ecological momentary assessment (EMA) has been used with young people experiencing homelessness to gather information on contexts associated with homelessness and risk behavior in real time and has proven feasible in this population. However, the extent to which EMA may affect the attitudes or behaviors of young adults who are currently or were formerly homeless and are residing in supportive housing has not been well investigated., Objective: This study aims to describe the feedback regarding EMA study participation from young adults who are currently or were formerly homeless and examine the reactivity to EMA participation and compliance., Methods: This mixed methods study used cross-sectional data collected before and after EMA, intensive longitudinal data from a 7-day EMA prompting period, and focus groups of young adults who are currently or were formerly homeless in Los Angeles, California, between 2017 and 2019., Results: Qualitative data confirmed the quantitative findings. Differences in the experience of EMA between young adults who are currently or were formerly homeless were found to be related to stress or anxiety, interference with daily life, difficulty charging, behavior change, and honesty in responses. Anxiety and depression symptomatology decreased from before to after EMA; however, compliance was not significantly associated with this decrease., Conclusions: The results point to special considerations when administering EMA to young adults who are currently or were formerly homeless. EMA appears to be slightly more burdensome for young adults who are currently homeless than for those residing in supportive housing, which are nuances to consider in the study design. The lack of a relationship between study compliance and symptomatology suggests low levels of reactivity., (©Sara Semborski, Benjamin Henwood, Brian Redline, Eldin Dzubur, Tyler Mason, Stephen Intille. Originally published in JMIR Formative Research (https://formative.jmir.org), 25.03.2022.)

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., (©Aditya Ponnada, Shirlene Wang, Daniel Chu, Bridgette Do, Genevieve Dunton, Stephen Intille. Originally published in JMIR Formative Research (https://formative.jmir.org), 09.02.2022.)

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., (© Society of Behavioral Medicine 2020. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (©Aditya Ponnada, Binod Thapa-Chhetry, Justin Manjourides, Stephen Intille. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 10.03.2021.)

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.

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.

Studies using wearable sensors to measure posture, physical activity (PA), and sedentary behavior typically use a single sensor worn on the ankle, thigh, wrist, or hip. Although the use of single sensors may be convenient, using multiple sensors is becoming more practical as sensors miniaturize., Purpose: We evaluated the effect of single-site versus multisite motion sensing at seven body locations (both ankles, wrists, hips, and dominant thigh) on the detection of physical behavior recognition using a machine learning algorithm. We also explored the effect of using orientation versus orientation-invariant features on performance., Methods: Performance (F1 score) of PA and posture recognition was evaluated using leave-one-subject-out cross-validation on a 42-participant data set containing 22 physical activities with three postures (lying, sitting, and upright)., Results: Posture and PA recognition models using two sensors had higher F1 scores (posture, 0.89 ± 0.06; PA, 0.53 ± 0.08) than did models using a single sensor (posture, 0.78 ± 0.11; PA, 0.43 ± 0.03). Models using two nonwrist sensors for posture recognition (F1 score, 0.93 ± 0.03) outperformed two-sensor models including one or two wrist sensors (F1 score, 0.85 ± 0.06). However, two-sensor models for PA recognition with at least one wrist sensor (F1 score, 0.60 ± 0.05) outperformed other two-sensor models (F1 score, 0.47 ± 0.02). Both posture and PA recognition F1 scores improved with more sensors (up to seven; 0.99 for posture and 0.70 for PA), but with diminishing performance returns. Models performed best when including orientation-based features., Conclusions: Researchers measuring posture should consider multisite sensing using at least two nonwrist sensors, and researchers measuring PA should consider multisite sensing using at least one wrist sensor and one nonwrist sensor. Including orientation-based features improved both posture and PA recognition.

Background: Physical behavior researchers using motion sensors often use acceleration summaries to visualize, clean, and interpret data. Such output is dependent on device specifications (e.g., dynamic range, sampling rate) and/or are proprietary, which invalidate cross-study comparison of findings when using different devices. This limits flexibility in selecting devices to measure physical activity, sedentary behavior, and sleep., Purpose: Develop an open-source, universal acceleration summary metric that accounts for discrepancies in raw data among research and consumer devices., Methods: We used signal processing techniques to generate a Monitor-Independent Movement Summary unit (MIMS-unit) optimized to capture normal human motion. Methodological steps included raw signal harmonization to eliminate inter-device variability (e.g., dynamic g-range, sampling rate), bandpass filtering (0.2-5.0 Hz) to eliminate non-human movement, and signal aggregation to reduce data to simplify visualization and summarization. We examined the consistency of MIMS-units using orbital shaker testing on eight accelerometers with varying dynamic range (±2 to ±8 g) and sampling rates (20-100 Hz), and human data (N = 60) from an ActiGraph GT9X., Results: During shaker testing, MIMS-units yielded lower between-device coefficient of variations than proprietary ActiGraph and ENMO acceleration summaries. Unlike the widely used ActiGraph activity counts, MIMS-units were sensitive in detecting subtle wrist movements during sedentary behaviors., Conclusions: Open-source MIMS-units may provide a means to summarize high-resolution raw data in a device-independent manner, thereby increasing standardization of data cleaning and analytical procedures to estimate selected attributes of physical behavior across studies.

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.

Background: Understanding how engagement in mobile health (mHealth) weight loss interventions relates to weight change may help develop effective intervention strategies., Objective: This study aims to examine the (1) patterns of participant engagement overall and with key intervention components within each intervention arm in the Cell Phone Intervention For You (CITY) trial; (2) associations of engagement with weight change; and (3) participant characteristics related to engagement., Methods: The CITY trial tested two 24-month weight loss interventions. One was delivered with a smartphone app (cell phone) containing 24 components (weight tracking, etc) and included prompting by the app in predetermined frequency and forms. The other was delivered by a coach via monthly calls (personal coaching) supplemented with limited app components (18 overall) and without any prompting by the app. Engagement was assessed by calculating the percentage of days each app component was used and the frequency of use. Engagement was also examined across 4 weight change categories: gained (≥2%), stable (±2%), mild loss (≥2% to <5%), and greater loss (≥5%)., Results: Data from 122 cell phone and 120 personal coaching participants were analyzed. Use of the app was the highest during month 1 for both arms; thereafter, use dropped substantially and continuously until the study end. During the first 6 months, the mean percentage of days that any app component was used was higher for the cell phone arm (74.2%, SD 20.1) than for the personal coaching arm (48.9%, SD 22.4). The cell phone arm used the apps an average of 5.3 times/day (SD 3.1), whereas the personal coaching participants used them 1.7 times/day (SD 1.2). Similarly, the former self-weighed more than the latter (57.1% days, SD 23.7 vs 32.9% days, SD 23.3). Furthermore, the percentage of days any app component was used, number of app uses per day, and percentage of days self-weighed all showed significant differences across the 4 weight categories for both arms. Pearson correlation showed a negative association between weight change and the percentage of days any app component was used (cell phone: r=-.213; personal coaching: r=-.319), number of apps use per day (cell phone: r=-.264; personal coaching: r=-.308), and percentage of days self-weighed (cell phone: r=-.297; personal coaching: r=-.354). None of the characteristics examined, including age, gender, race, education, income, energy expenditure, diet quality, and hypertension status, appeared to be related to engagement., Conclusions: Engagement in CITY intervention was associated with weight loss during the first 6 months. Nevertheless, engagement dropped substantially early on for most intervention components. Prompting may be helpful initially. More flexible and less intrusive prompting strategies may be needed during different stages of an intervention to increase or sustain engagement. Future studies should explore the motivations for engagement and nonengagement to determine meaningful levels of engagement required for effective intervention., Trial Registration: ClinicalTrials.gov NCT01092364; https://clinicaltrials.gov/ct2/show/NCT01092364 (Archived by WebCite at http://www.webcitation.org/72V8A4e5X)., (©Pao-Hwa Lin, Steven Grambow, Stephen Intille, John A Gallis, Tony Lazenka, Hayden Bosworth, Corrine L. Voils, Gary G Bennett, Bryan Batch, Jenifer Allen, Leonor Corsino, Crystal Tyson, Laura Svetkey. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 18.10.2018.)

Technological advancements in the past decades have improved dietary intake and physical activity measurements. This report reviews current developments in dietary intake and physical activity assessment in youth. Dietary intake assessment has relied predominantly on self-report or image-based methods to measure key aspects of dietary intake (e.g., food types, portion size, eating occasion), which are prone to notable methodologic (e.g., recall bias) and logistic (e.g., participant and researcher burden) challenges. Although there have been improvements in automatic eating detection, artificial intelligence, and sensor-based technologies, participant input is often needed to verify food categories and portions. Current physical activity assessment methods, including self-report, direct observation, and wearable devices, provide researchers with reliable estimations for energy expenditure and bodily movement. Recent developments in algorithms that incorporate signals from multiple sensors and technology-augmented self-reporting methods have shown preliminary efficacy in measuring specific types of activity patterns and relevant contextual information. However, challenges in detecting resistance (e.g., in resistance training, weight lifting), prolonged physical activity monitoring, and algorithm (non)equivalence remain to be addressed. In summary, although dietary intake assessment methods have yet to achieve the same validity and reliability as physical activity measurement, recent developments in wearable technologies in both arenas have the potential to improve current assessment methods., Theme Information: This article is part of a theme issue entitled Innovative Tools for Assessing Diet and Physical Activity for Health Promotion, which is sponsored by the North American branch of the International Life Sciences Institute., (Copyright © 2018 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.)

This commentary describes the development, vision, and initial progress of the newly-founded Boston Physical Activity Resource Collaborative (BPARC). Our aims are to move the field of physical activity forward, with broader dissemination and translation, by creating a local Boston and Massachusetts hub for researchers, practitioners, advocates, and policymakers. Participants come from multiple academic and medical centers, local advocacy groups, and government agencies, all of whom are working on components of physical activity promotion. We have had initial success in collaborating on study design, methodology, and grant applications. Future endeavors aim to produce streamlined methods and products with maximal impact for the field of physical activity research, policy, and practice.

Research attempting to elucidate physical activity (PA) intention-behavior relations has focused on differences in long-term behavior forecasting between people. However, regular PA requires a repeated performance on a daily or within-daily basis. An empirical case study application is presented using intensive longitudinal data from a study of PA in adults to (a) describe the extent to which short-term intention-behavior coupling occurs and (b) explore time-varying predictors of intention formation and short-term intention-behavior coupling. Adults (n = 116) participated in three 4-day waves of ecological momentary assessment (EMA). Each day, participants received EMA questionnaires assessing short-term PA intentions and wore accelerometers to assess whether they engaged in ≥10 min of moderate-to-vigorous physical activity (MVPA) in the 3-hour period after each EMA prompt. Concurrent affective states and contexts were also assessed through EMA. Participants reported having short-term intentions to engage in PA in 41% of EMA prompts. However, participants only engaged in ≥10 min of MVPA following 16% of the prompts that short-term PA intentions were reported indicating an intention-behavior gap of 84%. Odds of intentions followed by PA were greater on occasions when individuals reported higher levels of positive affect than was typical for them. This study is the first to take an EMA approach to describe short-term intention-behavior coupling in adults. Results suggest that adults have difficulty translating intentions into behavior at the momentary level, more so than over longer timescales, and that positive affect may be a key to successfully translating intentions into behavior.

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.

Interventions aiming to help ethnically diverse emerging adults engage in healthy eating have had limited success. The aim of this study was to assess the feasibility of and adherence to an intervention capitalizing on mobile technology to improve healthy eating. Participants created an online photo food journal and received motivational text messages three times a day. Satisfaction with the intervention was assessed, as were control variables including depression and body dissatisfaction. In addition, weight and height were measured. Levels of adherence to the photo food journal were high with approximately two photos posted a day at baseline. However, adherence rates decreased over the course of the study. Body dissatisfaction positively predicted adherence, while body mass index negatively predicted study satisfaction. Mobile technology provides innovative avenues for healthy eating interventions. Such interventions appear acceptable and feasible for a short period; however, more work is required to evaluate their viability regarding long-term engagement., (© The Author(s) 2015.)

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.

This study used time-varying effect modeling to examine time-of-day differences in how behavioral cognitions predict subsequent physical activity (PA). Adults (N = 116) participated in three 4-day "bursts" of ecological momentary assessment (EMA). Participants were prompted with eight EMA questionnaires per day assessing behavioral cognitions (i.e., intentions, self-efficacy, outcome expectations) and wore an accelerometer during waking hours. Subsequent PA was operationalized as accelerometer-derived minutes of moderate- or vigorousintensity PA in the 2 hr following the EMA prompt. On weekdays, intentions positively predicted subsequent PA in the morning (9:25 a.m.-11:45 a.m.) and in the evening (8:15 p.m.-10:00 p.m.). Self-efficacy positively predicted subsequent PA on weekday evenings (7:35 p.m.-10:00 p.m.). Outcome expectations were unrelated to subsequent PA on weekdays. On weekend days, behavior cognitions and subsequent PA were unrelated regardless of time of day. This study identifies windows of opportunity and vulnerability for motivation-based PA interventions aiming to deliver intervention content within the context of adults' daily lives.

Background: Objective physical activity monitors (eg, accelerometers) have high rates of nonwear and do not provide contextual information about behavior., Objective: This study tested performance and value of a mobile phone app that combined objective and real-time self-report methods to measure physical activity using sensor-informed context-sensitive ecological momentary assessment (CS-EMA)., Methods: The app was programmed to prompt CS-EMA surveys immediately after 3 types of events detected by the mobile phone's built-in motion sensor: (1) Activity (ie, mobile phone movement), (2) No-Activity (ie, mobile phone nonmovement), and (3) No-Data (ie, mobile phone or app powered off). In addition, the app triggered random (ie, signal-contingent) ecological momentary assessment (R-EMA) prompts (up to 7 per day). A sample of 39 ethnically diverse high school students in the United States (aged 14-18, 54% female) tested the app over 14 continuous days during nonschool time. Both CS-EMA and R-EMA prompts assessed activity type (eg, reading or doing homework, eating or drinking, sports or exercising) and contextual characteristics of the activity (eg, location, social company, purpose). Activity was also measured with a waist-worn Actigraph accelerometer., Results: The average CS-EMA + R-EMA prompt compliance and survey completion rates were 80.5% and 98.5%, respectively. More moderate-to-vigorous intensity physical activity was recorded by the waist-worn accelerometer in the 30 minutes before CS-EMA activity prompts (M=5.84 minutes) than CS-EMA No-Activity (M=1.11 minutes) and CS-EMA No-Data (M=0.76 minute) prompts (P's<.001). Participants were almost 5 times as likely to report going somewhere (ie, active or motorized transit) in the 30 minutes before CS-EMA Activity than R-EMA prompts (odds ratio=4.91, 95% confidence interval=2.16-11.12)., Conclusions: Mobile phone apps using motion sensor-informed CS-EMA are acceptable among high school students and may be used to augment objective physical activity data collected from traditional waist-worn accelerometers.

Background: Decisions to perform moderate-to-vigorous physical activity (MVPA) involve behavioral cognitive processes that may differ within individuals depending on the situation., Methods: Ecological momentary assessment (EMA) was used to examine the relationships of momentary behavioral cognitions (ie, self-efficacy, outcome expectancy, intentions) with MVPA (measured by accelerometer). A sample of 116 adults (mean age, 40.3 years; 72.4% female) provided real-time EMA responses via mobile phones across 4 days. Multilevel models were used to test whether momentary behavioral cognitions differed across contexts and were associated with subsequent MVPA. Mixed-effects location scale models were used to examine whether subject-level means and within-subjects variances in behavioral cognitions were associated with average daily MVPA., Results: Momentary behavioral cognitions differed across contexts for self-efficacy (P = .007) but not for outcome expectancy (P = .53) or intentions (P = .16). Momentary self-efficacy, intentions, and their interaction predicted MVPA within the subsequent 2 hours (Ps < .01). Average daily MVPA was positively associated with within-subjects variance in momentary self-efficacy and intentions for physical activity (Ps < .05)., Conclusions: Although momentary behavioral cognitions are related to subsequent MVPA, adults with higher average MVPA have more variation in physical activity self-efficacy and intentions. Performing MVPA may depend more on how much behavioral cognitions vary across the day than whether they are generally high or low.

Objective: To evaluate the capacity of a mobile technology-based intervention to support healthy eating among ethnic minority female students., Participants: Forty-three African American and Hispanic female students participated in a 3-week intervention between January and May 2013., Methods: Participants photographed their meals using their smart phone camera and received motivational text messages 3 times a day. At baseline, postintervention, and 10 weeks after the intervention, participants reported on fruit, vegetable, and sugar-sweetened beverage consumption. Participants were also weighed at baseline., Results: Among participants with body mass index (BMI) ≥25, fruit and vegetable consumption increased with time (p < .01). Among participants with BMI <21, consumption of fruit decreased (p < .05), whereas the consumption of vegetables remained stable. No effects were found for sugar-sweetened beverage consumption., Conclusion: Mobile technology-based interventions could facilitate healthy eating among female ethnic minority college students, particularly those with higher BMI.

The current study used a novel real-time data capture strategy, ecological momentary assessment (EMA), to examine whether within-day variability in stress and context leads to exacerbations in asthma symptomatology in the everyday lives of ethnic minority adolescents. Low-income Hispanic adolescents (N = 20; 7th-12th grade; 54% male) with chronic asthma completed 7 days of EMA on smartphones, with an average of five assessments per day during non-school time. EMA surveys queried about where (e.g., home, outdoors) and with whom (e.g., alone, with friends) participants were at the time of the prompt. EMA surveys also assessed over the past few hours whether participants had experienced specific stressors (e.g., being teased, arguing with anyone), asthma symptoms (e.g., wheezing, coughing), or used an asthma inhaler. Multilevel models tested the independent relations of specific stressors and context to subsequent asthma symptoms adjusting for age, gender, and chronological day in the study. Being outdoors, experiencing disagreements with parents, teasing, and arguing were associated with more severe self-reported asthma symptoms in the next few hours (ps < .05). Being alone and having too much to do were unrelated to the experience of subsequent self-reported asthma symptoms. Using a novel real-time data capture strategy, results provide preliminary evidence that being outdoors and experiencing social stressors may induce asthma symptoms in low-income Hispanic children and adolescents with chronic asthma. The results of this preliminary study can serve as a basis for larger epidemiological and intervention studies., (© The Author(s) 2015.)

Objective: Higher positive and lower negative affective response during physical activity may reinforce motivation to engage in future activity. However, affective response during physical activity is typically examined under controlled laboratory conditions. This research used ecological momentary assessment (EMA) to examine social and physical contextual influences on momentary affective response during physical activity in naturalistic settings., Method: Participants included 116 adults (mean age = 40.3 years, 73% female) who completed 8 randomly prompted EMA surveys per day for 4 days across 3 semiannual waves. EMA surveys measured current activity level, social context, and physical context. Participants also rated their current positive and negative affect. Multilevel models assessed whether momentary physical activity level moderated differences in affective response across contexts controlling for day of the week, time of day, and activity intensity (measured by accelerometer)., Results: The Activity Level × Alone interaction was significant for predicting positive affect (β = -0.302, SE = 0.133, p = .024). Greater positive affect during physical activity was reported when with other people (vs. alone). The Activity Level × Outdoors interaction was significant for predicting negative affect (β = -0.206, SE = 0.097, p = .034). Lower negative affect during physical activity was reported outdoors (vs. indoors)., Conclusions: Being with other people may enhance positive affective response during physical activity, and being outdoors may dampen negative affective response during physical activity., ((c) 2015 APA, all rights reserved).)

Background/aims: The obesity epidemic has spread to young adults, and obesity is a significant risk factor for cardiovascular disease. The prominence and increasing functionality of mobile phones may provide an opportunity to deliver longitudinal and scalable weight management interventions in young adults. The aim of this article is to describe the design and development of the intervention tested in the Cell Phone Intervention for You study and to highlight the importance of adaptive intervention design that made it possible. The Cell Phone Intervention for You study was a National Heart, Lung, and Blood Institute-sponsored, controlled, 24-month randomized clinical trial comparing two active interventions to a usual-care control group. Participants were 365 overweight or obese (body mass index≥25 kg/m2) young adults., Methods: Both active interventions were designed based on social cognitive theory and incorporated techniques for behavioral self-management and motivational enhancement. Initial intervention development occurred during a 1-year formative phase utilizing focus groups and iterative, participatory design. During the intervention testing, adaptive intervention design, where an intervention is updated or extended throughout a trial while assuring the delivery of exactly the same intervention to each cohort, was employed. The adaptive intervention design strategy distributed technical work and allowed introduction of novel components in phases intended to help promote and sustain participant engagement. Adaptive intervention design was made possible by exploiting the mobile phone's remote data capabilities so that adoption of particular application components could be continuously monitored and components subsequently added or updated remotely., Results: The cell phone intervention was delivered almost entirely via cell phone and was always-present, proactive, and interactive-providing passive and active reminders, frequent opportunities for knowledge dissemination, and multiple tools for self-tracking and receiving tailored feedback. The intervention changed over 2 years to promote and sustain engagement. The personal coaching intervention, alternatively, was primarily personal coaching with trained coaches based on a proven intervention, enhanced with a mobile application, but where all interactions with the technology were participant-initiated., Conclusion: The complexity and length of the technology-based randomized clinical trial created challenges in engagement and technology adaptation, which were generally discovered using novel remote monitoring technology and addressed using the adaptive intervention design. Investigators should plan to develop tools and procedures that explicitly support continuous remote monitoring of interventions to support adaptive intervention design in long-term, technology-based studies, as well as developing the interventions themselves., (© The Author(s) 2015.)

Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static "snapshots" of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing "gold standard" measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a "knowledge commons," which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.

Parental stress is an understudied factor that may compromise parenting practices related to children's dietary intake, physical activity, and obesity. However, studies examining these associations have been subject to methodological limitations, including cross-sectional designs, retrospective measures, a lack of stress biomarkers, and the tendency to overlook momentary etiologic processes occurring within each day. This paper describes the recruitment, data collection, and data analytic protocols for the MATCH (Mothers And Their Children's Health) study, a longitudinal investigation using novel real-time data capture strategies to examine within-day associations of maternal stress with children's physical activity and dietary intake, and how these effects contribute to children's obesity risk. In the MATCH study, 200 mothers and their 8 to 12 year-old children are participating in 6 semi-annual assessment waves across 3 years. At each wave, measures for mother-child dyads include: (a) real-time Ecological Momentary Assessment (EMA) of self-reported daily psychosocial stressors (e.g., work at a job, family demands), feeling stressed, perceived stress, parenting practices, dietary intake, and physical activity with time and location stamps; (b) diurnal salivary cortisol patterns, accelerometer-monitored physical activity, and 24-hour dietary recalls; (c) retrospective questionnaires of sociodemographic, cultural, family, and neighborhood covariates; and (d) height, weight, and waist circumference. Putative within-day and longitudinal effects of maternal stress on children's dietary intake, physical activity, and body composition will be tested through multilevel modeling and latent growth curve models, respectively. The results will inform interventions that help mothers reduce the negative effects of stress on weight-related parenting practices and children's obesity risk., (Copyright © 2015 Elsevier Inc. All rights reserved.)