Investigating Contextual Cues as Indicators for EMA Delivery

In this work, we attempt to determine whether the contextual information of a participant can be used to predict whether the participant will respond to a particular Ecological Momentary Assessment (EMA) prompt. We use a publicly available dataset for our work, and find that by using basic contextual features about the participant\u27s activity, conversation status, audio, and location, we can predict whether an EMA prompt triggered at a particular time will be answered with a precision of 0.647, which is significantly higher than a baseline precision of 0.410. Using this knowledge, the researchers conducting field studies can efficiently schedule EMA prompts and achieve higher response rates

Thinking About Episodic Future Events as a Way to Reduce Smoking Behavior: An Ecological Momentary Assessment Study

With nearly 35 million Americans currently estimated to smoke and an approximate seven out of ten adult smokers wanting to quit, it is clear that there is a need for enhanced smoking cessation techniques. Encouraging people to think about a future smoke-free self may help to encourage and motivate changes in smoking behavior. The present study investigated the role of an episodic future thinking manipulation on the motivation to quit smoking using ecological momentary assessment (EMA). Participants (N = 103) were randomly assigned to either an episodic future thinking (EFT) condition or an episodic recent thinking (ERT) condition, and were asked to write a short paragraph about an EFT or an ERT event from their personal life. Immediately following the writing prompt, participants answered daily questions about mental imagery, mind-wandering, craving, stress, and the motivation to quit smoking. Participants also completed individual differences questionnaires surveying their propensity for holistic thinking, mental imagery, and mind-wandering. It was hypothesized that individuals randomly assigned to the EFT (vs. ERT) condition would report greater motivation to quit smoking. Additionally, participants in the EFT (vs. ERT) condition who reported more holistic thinking were expected to report the strongest motivation to quit smoking. Finally, participants in the EFT group who reported more mental imagery and more frequent mind-wandering (for both the individual differences and daily EMA variables) were expected to report the strongest motivation to quit smoking. None of the hypotheses were supported. However, greater motivation to quit smoking was significantly correlated with greater levels of daily mental imagery and more frequent deliberate daily mind wandering (regardless of the condition). Additionally, daily average deliberate mind-wandering significantly predicted the motivation to quit smoking. Limitations and future directions are discussed

Thinking About Episodic Future Events as a Way to Reduce Smoking Behavior: An Ecological Momentary Assessment Study

With nearly 35 million Americans currently estimated to smoke and an approximate seven out of ten adult smokers wanting to quit, it is clear that there is a need for enhanced smoking cessation techniques. Encouraging people to think about a future smoke-free self may help to encourage and motivate changes in smoking behavior. The present study investigated the role of an episodic future thinking manipulation on the motivation to quit smoking using ecological momentary assessment (EMA). Participants (N = 103) were randomly assigned to either an episodic future thinking (EFT) condition or an episodic recent thinking (ERT) condition, and were asked to write a short paragraph about an EFT or an ERT event from their personal life. Immediately following the writing prompt, participants answered daily questions about mental imagery, mind-wandering, craving, stress, and the motivation to quit smoking. Participants also completed individual differences questionnaires surveying their propensity for holistic thinking, mental imagery, and mind-wandering. It was hypothesized that individuals randomly assigned to the EFT (vs. ERT) condition would report greater motivation to quit smoking. Additionally, participants in the EFT (vs. ERT) condition who reported more holistic thinking were expected to report the strongest motivation to quit smoking. Finally, participants in the EFT group who reported more mental imagery and more frequent mind-wandering (for both the individual differences and daily EMA variables) were expected to report the strongest motivation to quit smoking. None of the hypotheses were supported. However, greater motivation to quit smoking was significantly correlated with greater levels of daily mental imagery and more frequent deliberate daily mind wandering (regardless of the condition). Additionally, daily average deliberate mind-wandering significantly predicted the motivation to quit smoking. Limitations and future directions are discussed

Exploring the State-of-Receptivity for mHealth Interventions

Recent advancements in sensing techniques for mHealth applications have led to successful development and deployments of several mHealth intervention designs, including Just-In-Time Adaptive Interventions (JITAI). JITAIs show great potential because they aim to provide the right type and amount of support, at the right time. Timing the delivery of a JITAI such as the user is receptive and available to engage with the intervention is crucial for a JITAI to succeed. Although previous research has extensively explored the role of context in users’ responsiveness towards generic phone notiications, it has not been thoroughly explored for actual mHealth interventions. In this work, we explore the factors afecting users’ receptivity towards JITAIs. To this end, we conducted a study with 189 participants, over a period of 6 weeks, where participants received interventions to improve their physical activity levels. The interventions were delivered by a chatbot-based digital coach ś Ally ś which was available on Android and iOS platforms. We deine several metrics to gauge receptivity towards the interventions, and found that (1) several participant-speciic characteristics (age, personality, and device type) show signiicant associations with the overall participant receptivity over the course of the study, and that (2) several contextual factors (day/time, phone battery, phone interaction, physical activity, and location), show signiicant associations with the participant receptivity, in-the-moment. Further, we explore the relationship between the efectiveness of the intervention and receptivity towards those interventions; based on our analyses, we speculate that being receptive to interventions helped participants achieve physical activity goals, which in turn motivated participants to be more receptive to future interventions. Finally, we build machine-learning models to detect receptivity, with up to a 77% increase in F1 score over a biased random classiier

Detecting Receptivity for mHealth Interventions in the Natural Environment

JITAI is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user's receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions. We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach -- Walkie -- that provided physical-activity interventions and motivated participants to achieve their step goals. The Walkie app included two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a static model that was built before the study started and remained constant for all participants and an adaptive model that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a control model that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptiveComment: This paper is currently under submission. Please contact the authors for more detai

Detecting Receptivity for mHealth Interventions in the Natural Environment

Just-In-Time Adaptive Intervention (JITAI) is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user's receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions. We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach - Ally - that provided physical-activity interventions and motivated participants to achieve their step goals. We extended the original Ally app to include two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a static model that was built before the study started and remained constant for all participants and an adaptive model that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a control model that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptive model increased over the course of the study

Detecting Receptivity for mHealth Interventions in the Natural Environment

Just-In-Time Adaptive Intervention (JITAI) is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user\u27s receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions. We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach - Ally - that provided physical-activity interventions and motivated participants to achieve their step goals. We extended the original Ally app to include two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a static model that was built before the study started and remained constant for all participants and an adaptive model that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a control model that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptive model increased over the course of the study

Continuous Estimation of Smoking Lapse Risk from Noisy Wrist Sensor Data Using Sparse and Positive-Only Labels

Estimating the imminent risk of adverse health behaviors provides opportunities for developing effective behavioral intervention mechanisms to prevent the occurrence of the target behavior. One of the key goals is to find opportune moments for intervention by passively detecting the rising risk of an imminent adverse behavior. Significant progress in mobile health research and the ability to continuously sense internal and external states of individual health and behavior has paved the way for detecting diverse risk factors from mobile sensor data. The next frontier in this research is to account for the combined effects of these risk factors to produce a composite risk score of adverse behaviors using wearable sensors convenient for daily use. Developing a machine learning-based model for assessing the risk of smoking lapse in the natural environment faces significant outstanding challenges requiring the development of novel and unique methodologies for each of them. The first challenge is coming up with an accurate representation of noisy and incomplete sensor data to encode the present and historical influence of behavioral cues, mental states, and the interactions of individuals with their ever-changing environment. The next noteworthy challenge is the absence of confirmed negative labels of low-risk states and adequate precise annotations of high-risk states. Finally, the model should work on convenient wearable devices to facilitate widespread adoption in research and practice. In this dissertation, we develop methods that account for the multi-faceted nature of smoking lapse behavior to train and evaluate a machine learning model capable of estimating composite risk scores in the natural environment. We first develop mRisk, which combines the effects of various mHealth biomarkers such as stress, physical activity, and location history in producing the risk of smoking lapse using sequential deep neural networks. We propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of smoking lapse. To circumvent the lack of confirmed negative labels (i.e., annotated low-risk moments) and only a few positive labels (i.e., sensor-based detection of smoking lapse corroborated by self-reports), we propose a new loss function to accurately optimize the models. We build the mRisk models using biomarker (stress, physical activity) streams derived from chest-worn sensors. Adapting the models to work with less invasive and more convenient wrist-based sensors requires adapting the biomarker detection models to work with wrist-worn sensor data. To that end, we develop robust stress and activity inference methodologies from noisy wrist-sensor data. We first propose CQP, which quantifies wrist-sensor collected PPG data quality. Next, we show that integrating CQP within the inference pipeline improves accuracy-yield trade-offs associated with stress detection from wrist-worn PPG sensors in the natural environment. mRisk also requires sensor-based precise detection of smoking events and confirmation through self-reports to extract positive labels. Hence, we develop rSmoke, an orientation-invariant smoking detection model that is robust to the variations in sensor data resulting from orientation switches in the field. We train the proposed mRisk risk estimation models using the wrist-based inferences of lapse risk factors. To evaluate the utility of the risk models, we simulate the delivery of intelligent smoking interventions to at-risk participants as informed by the composite risk scores. Our results demonstrate the envisaged impact of machine learning-based models operating on wrist-worn wearable sensor data to output continuous smoking lapse risk scores. The novel methodologies we propose throughout this dissertation help instigate a new frontier in smoking research that can potentially improve the smoking abstinence rate in participants willing to quit

The Promise of Just-in-Time Adaptive Interventions for Organizational Scholarship and Practice: Conceptual Development and Research Agenda

Organizational researchers are now making widespread use of ecological momentary assessments but have not yet taken the logical next step to ecological momentary interventions, also called Just-in-Time Adaptive Interventions (JITAIs). JITAIs have the potential to test within-person causal theories and maximize practical benefits to participants through two developmental phases: The microrandomized trial and the randomized controlled trial, respectively. In the microrandomized trial design, within-person randomization and experimental manipulation maximize internal validity at the within-person level. In the randomized controlled trial design, interventions are delivered in a timely and ecological manner while avoiding unnecessary and ill-timed interventions that potentially increase participant fatigue and noncompliance. Despite these potential advantages, the development and implementation of JITAIs require consideration of many conceptual and methodological factors. Given the benefits of JITAIs, but also the various considerations involved in using them, this review introduces organizational behavior and human resources researchers to JITAIs, provides guidelines for JITAI design, development, and evaluation, and describes the extensive potential of JITAIs in organizational behavior and human resources research