Ecological Momentary Assessment Data Research Articles

Supplemental Material for Affect Relative to Day-Level Drinking Initiation: Analyzing Ecological Momentary Assessment Data With Multilevel Spline Modeling

Supplemental Material for Affect Relative to Day-Level Drinking Initiation: Analyzing Ecological Momentary Assessment Data With Multilevel Spline Modeling

Extending the mixed-effects model to consider within-subject variance for Ecological Momentary Assessment data.

Ecological Momentary Assessment data present some new modeling opportunities. Typically, there are sufficient data to explicitly model the within-subject (WS) variance, and in many applications, it is of interest to allow the WS variance to depend on covariates as well as random subject effects. We describe a model that allows multiple random effects per subject in the mean model (eg, random location intercept and slopes), as well as random scale in the error variance model. We present an example of the use of this model on a real dataset and a simulation study that shows the benefit of this model, relative to simpler approaches.

Modelling suicide ideation from beep to beep: Application of network analysis to ecological momentary assessment data

•EMA data were analyzed using network analysis (e.g., temporal networks).•Suicidal ideation at t was predicted by itself and perceived burdensomeness at t-1.•Suicidal ideation at t-1 predicted perceived burdensomeness, depression etc. at t.•At the same beep, suicidal ideation was related to all variables in the network.•Patients with higher average suicidal ideation had higher average hopelessness.

Naturalistic, multimethod exploratory study of sleep duration and quality as predictors of dysregulated eating in youth with overweight and obesity

Although poor sleep has been found to adversely impact eating and weight regulation in youth, past research is limited by retrospective reporting and/or non-naturalistic designs. We investigated the feasibility of combining three momentary, ecologically valid approaches to assessing sleep and eating behavior, and associations between these constructs, among youth (aged 8-14y) with overweight/obesity (n = 40). Participants completed 14 overlapping days of actigraphy assessment and smartphone-based ecological momentary assessment (EMA) of eating behavior, of which 3 days also included computerized, self-guided 24-h dietary recall. Feasibility of completing measures concurrently was evaluated by generating frequencies of compliance. Associations between sleep indices and next-day eating behavior were examined via generalized estimating equations. Of 29 participants who provided EMA and 24-h recall data that aligned with previous night actigraphy data, both EMA and sleep data were available on an average of 8.6 out of 14 possible days, and both 24-h recall and sleep data on an average of 2.7 out of 3 possible days. Each additional hour of sleep was associated with consuming fewer calories from solid fats, alcohol, and added sugars (b = 0.70; p = .04). Combining naturalistic, momentary assessments of sleep and eating behavior appears to be acceptable in youth. Larger experimental studies are needed to further understand associations between sleep parameters and eating behavior.

The relationship between mindfulness meditation and well-being during 8 weeks of ecological momentary assessment

ObjectivesThe main effects of 8-week mindfulness-based programs (MBP) on anxiety and depression are now supported by reasonably robust evidence. However, few to no studies have looked at whether and how these main effects come to be over the course of the MBP. The goal of the present study was to look at how meditation practice predicted changes in well-being, and vice versa, at a weekly level, within an 8-week online MBP.MethodsThe participants were 457 Finnish upper secondary education students who underwent an 8-week online MBP. App-based ecological momentary assessment data were collected on how many minutes the participants meditated (daily) and their anxiety, happiness, and sleep problems (weekly). These data were analyzed using a longitudinal (nine time point) path model.ResultsParticipants’ weekly minutes of mindfulness meditation were a consistent, albeit weak, predictor of decreases in anxiety and increases in happiness. During the course of the study, answer rates declined from 75.7% (Time 0) to 27.4% (Time 8) for anxiety, happiness, and sleep and from 80.5% to 37.0% for meditation minutes.ConclusionsResults suggest well-being improvement from mindfulness meditation is an ongoing process and that ecological momentary assessment is a promising methodology for studying it.

The fragility of perceived social rank following exercise in anorexia nervosa: an ecological momentary assessment study of shame and pride.

Pride and shame are self-conscious emotions closely associated with perceived social rank. Goss and Gilbert (In: Gilbert and Miles (eds) Body shame: conceptualization, research & treatment. Brunner-Routledge, Hove, 2002) proposed that weight-control behaviours in anorexia nervosa (AN) provide a temporary sense of increased rank that dissipates afterwards, thereby perpetuating further symptoms. This pilot study tested their theory by examining whether shame increases and pride decreases as time passes post-exercise in individuals with AN. Pre-exercise patterns were also explored. Over 2 weeks, 23 females with AN provided ecological momentary assessment data via six semi-random daily signals and after each exercise episode. The State Shame and Guilt Scale assessed pride and shame. Items modified from the Experience of Shame Scale and the Other as Shame Scale assessed general shame and body/eating shame. Multilevel models analyzed patterns of shame and pride prior to and following exercise episodes. In the hours after exercise, pride decreased, body/eating shame increased, and general shame increased on one measure. Pride was, therefore, higher and shame lower immediately after exercise as compared to later in the day. In the hours before exercise, pride increased, but shame levels did not change significantly. The anticipation and immediate aftermath of exercise may be associated with higher perceived social rank in people with AN, but their feelings about self may worsen as time passes after exercise. Fluctuations in shame and pride may contribute to the maintenance of exercise in people with AN. IV, multiple time series analysis.

The time-varying effect of alcohol use on cigarette smoking relapse risk

BackgroundAlcohol consumption promotes lapses to smoking among smokers trying to quit, perhaps particularly among smokers with lower dependence. We assessed how the role of alcohol in lapses varies over time. MethodsThis is a secondary analysis of ecological momentary assessment data collected from 159 daily smokers (mean age = 43.90 (SD = 10.41), 56.60% female) who drink alcohol. During the 4 weeks following initial cessation (quit >24 h), a logistic time-varying effect model (TVEM) modeled momentary assessments of lapses and temptations to smoke compared to randomly-selected moments as a function of concurrently-assessed recent alcohol use (past 15 min). Time was examined continuously. ResultsRecent alcohol use was associated with smoking lapses, particularly for less nicotine dependent individuals, and the association varied across time. For individuals who did not smoke within 5 min of waking, alcohol use became a significant predictor of lapse on Day 1 post-quit, increased in strength until Day 7, then decreased such that alcohol use was no longer associated with lapse by Day 25. For this subgroup, the associations between alcohol use and temptations were relatively stable and significant from Day 1 to 22 post-quit. Results were similar when dependence was assessed by the Nicotine Dependence Symptom Scale. ConclusionsThe association between drinking and smoking lapse and temptations varies over time, peaking early in smoking abstinence and declining thereafter. This could reflect progressive relapse of most vulnerable individuals or habituation to alcohol as a smoking cue. Interventions to prevent alcohol-related lapses are essential early in the quit period.

Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol

BackgroundThe screening of digital footprint for clinical purposes relies on the capacity of wearable technologies to collect data and extract relevant information’s for patient management. Artificial intelligence (AI) techniques allow processing of real-time observational information and continuously learning from data to build understanding. We designed a system able to get clinical sense from digital footprints based on the smartphone’s native sensors and advanced machine learning and signal processing techniques in order to identify suicide risk.Method/designThe Smartcrisis study is a cross-national comparative study. The study goal is to determine the relationship between suicide risk and changes in sleep quality and disturbed appetite. Outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) and the University Hospital of Nimes (France) will be proposed to participate to the study. Two smartphone applications and a wearable armband will be used to capture the data. In the intervention group, a smartphone application (MEmind) will allow for the ecological momentary assessment (EMA) data capture related with sleep, appetite and suicide ideations.DiscussionSome concerns regarding data security might be raised. Our system complies with the highest level of security regarding patients’ data. Several important ethical considerations related to EMA method must also be considered. EMA methods entails a non-negligible time commitment on behalf of the participants. EMA rely on daily, or sometimes more frequent, Smartphone notifications. Furthermore, recording participants’ daily experiences in a continuous manner is an integral part of EMA. This approach may be significantly more than asking a participant to complete a retrospective questionnaire but also more accurate in terms of symptoms monitoring. Overall, we believe that Smartcrises could participate to a paradigm shift from the traditional identification of risks factors to personalized prevention strategies tailored to characteristics for each patient.Trial registration numberNCT03720730. Retrospectively registered.

A Latent Gaussian process model for analysing intensive longitudinal data.

Intensive longitudinal studies are becoming progressively more prevalent across many social science areas, and especially in psychology. New technologies such as smart-phones, fitness trackers, and the Internet of Things make it much easier than in the past to collect data for intensive longitudinal studies, providing an opportunity to look deep into the underlying characteristics of individuals under a high temporal resolution. In this paper we introduce a new modelling framework for latent curve analysis that is more suitable for the analysis of intensive longitudinal data than existing latent curve models. Specifically, through the modelling of an individual-specific continuous-time latent process, some unique features of intensive longitudinal data are better captured, including intensive measurements in time and unequally spaced time points of observations. Technically, the continuous-time latent process is modelled by a Gaussian process model. This model can be regarded as a semi-parametric extension of the classical latent curve models and falls under the framework of structural equation modelling. Procedures for parameter estimation and statistical inference are provided under an empirical Bayes framework and evaluated by simulation studies. We illustrate the use of the proposed model though the analysis of an ecological momentary assessment data set.

Mindfulness-Oriented Recovery Enhancement reduces opioid craving among individuals with opioid use disorder and chronic pain in medication assisted treatment: Ecological momentary assessments from a stage 1 randomized controlled trial

BackgroundMethadone maintenance therapy (MMT) is an efficacious form of medication assisted treatment for opioid use disorder (OUD), yet many individuals on MMT relapse. Chronic pain and deficits in positive affective response to natural rewards may result in dysphoria that fuels opioid craving and promotes relapse. As such, behavioral therapies that ameliorate chronic pain and enhance positive affect may serve as useful adjuncts to MMT. This analysis of ecological momentary assessment (EMA) data from a Stage 1 randomized clinical trial examined effects of Mindfulness-Oriented Recovery Enhancement (MORE) on opioid craving, pain, and positive affective state. MethodsParticipants with OUD and chronic pain (N = 30) were randomized to 8 weeks of MORE or treatment as usual (TAU). Across 8 weeks of treatment, participants completed up to 112 random EMA measures of craving, pain, and affect, as well as event-contingent craving ratings. Multilevel models examined the effects of MORE on craving, pain, and affect, as well as the association between positive affect and craving. ResultsEMA showed significantly greater improvements in craving, pain unpleasantness, stress, and positive affect for participants in MORE than for participants in TAU. Participants in MORE reported having nearly 1.3 times greater self-control over craving than those in TAU. Further, positive affect was associated with reduced craving, an association that was significantly stronger among participants in MORE than TAU. ConclusionMORE may be a useful non-pharmacological adjunct among individuals with OUD and chronic pain in MMT.

Mining spatial associations between daily activities and health using EMA–GPS data

AbstractThe geographical explicit ecological momentary assessment (GEMA) data collection platform provides extremely rich geospatial datasets and is very promising to gain behavior insights linking mobility, activities, and health. However, the task of analyzing these large datasets effectively is not straightforward, because they often involve a large multivariable dimension and rich qualitative data formats. Responding to the call for innovative analytic approaches in GIScience, this article advocates the use of spatial association rule mining (SARM) to extract frequent associations among daily activities, daily mobility, and health, including both physical health (e.g. pain) and mental health (e.g. happiness). This inductive mining approach works robustly with large datasets and is suitable for both qualitative and quantitative studies. A novel visualization technique to analyze the mined rules is also developed and presented.

Deep Sequential Models for Suicidal Ideation From Multiple Source Data.

This paper presents a novel method for predicting suicidal ideation from electronic health records (EHR) and ecological momentary assessment (EMA) data using deep sequential models. Both EHR longitudinal data and EMA question forms are defined by asynchronous, variable length, randomly sampled data sequences. In our method, we model each of them with a recurrent neural network, and both sequences are aligned by concatenating the hidden state of each of them using temporal marks. Furthermore, we incorporate attention schemes to improve performance in long sequences and time-independent pre-trained schemes to cope with very short sequences. Using a database of 1023 patients, our experimental results show that the addition of EMA records boosts the system recall to predict the suicidal ideation diagnosis from 48.13% obtained exclusively from EHR-based state-of-the-art methods to 67.78%. Additionally, our method provides interpretability through the t-distributed stochastic neighbor embedding (t-SNE) representation of the latent space. Furthermore, the most relevant input features are identified and interpreted medically.

How Is Exposure to Tobacco Outlets Within Activity Spaces Associated With Daily Tobacco Use Among Youth? A Mediation Analysis.

We investigated associations between exposure to tobacco outlets within activity spaces and daily tobacco use, and whether exposure to adults or peers using tobacco mediate these relationships. We used Geographic Ecological Momentary Assessment data over 14 days from 85 youth aged 16-20 years in eight mid-sized California city areas. Tobacco outlet addresses and global positioning systems locations were geocoded and activity spaces were constructed by joining sequential points. We assessed daily number of tobacco outlets within 50 or 100 m of activity space polylines and number of minutes participants were within 50 or 100 m of tobacco outlets each day; daily use of tobacco; and whether participants saw (1) adults and (2) people their age (peers) using tobacco each day. Controlling for demographics, results of multilevel structural equation models showed no association between number of tobacco outlets within 50 m of polylines and tobacco use (probit regression coefficient: 0.01, p = .82). However, we found evidence of an indirect effect (p = .001) through daily exposure to peers using tobacco. Specifically, greater number of tobacco outlets within 50 m of polylines was positively associated with seeing peers use tobacco (probit regression coefficient: 0.10, p < .001). In turn, seeing peers use tobacco was positively associated with tobacco use on that day (probit regression coefficient: 2.23, p < .001). Similar results were found for number of tobacco outlets within 100 m of polylines. Exposure to tobacco outlets within activity spaces affects youth tobacco use through daily exposure to peers who use tobacco. Using real-time-ordered data, this article examines whether exposure to adults and peers using tobacco mediate associations between exposure to tobacco outlets within activity spaces and daily tobacco use among youth. Results suggest that exposure to tobacco outlets within activity spaces affects daily tobacco use through exposure to peers who use tobacco. These findings provide additional significant support for policy makers who are considering regulating the number and density of tobacco retailers and point to the importance of interventions focused on peer tobacco use and youths' daily environments to reduce tobacco use.

Methodology Used in Ecological Momentary Assessment Studies About Sedentary Behavior in Children, Adolescents, and Adults: Systematic Review Using the Checklist for Reporting Ecological Momentary Assessment Studies.

BackgroundThe use of ecological momentary assessment (EMA) to measure sedentary behavior (SB) in children, adolescents, and adults can increase the understanding of the role of the context of SB in health outcomes.ObjectiveThe aim of this study was to systematically review literature to describe EMA methodology used in studies on SB in youth and adults, verify how many studies adhere to the Methods aspect of the Checklist for Reporting EMA Studies (CREMAS), and detail measures used to assess SB and this associated context.MethodsA systematic literature review was conducted in the PubMed, Scopus, Web of Science, PsycINFO, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and SPORTDiscus databases, covering the entire period of existence of the databases until January 2018.ResultsThis review presented information about the characteristics and methodology used in 21 articles that utilized EMA to measure SB in youth and adults. There were more studies conducted among youth compared with adults, and studies of youth included more waves and more participants (n=696) than studies with adults (n=97). Most studies (85.7%) adhered to the Methods aspect of the CREMAS. The main criteria used to measure SB in EMA were self-report (81%) with only 19% measuring SB using objective methods (eg, accelerometer). The main equipment to collect objective SB was the ActiGraph, and the cutoff point to define SB was <100 counts/min. Studies most commonly used a 15-min window to compare EMA and accelerometer data.ConclusionsThe majority of studies in this review met minimum CREMAS criteria for studies conducted with EMA. Most studies measured SB with EMA self-report (n=17; 81.0%), and a few studies also used objective methods (n=4; 19%). The standardization of the 15-min window criteria to compare EMA and accelerometer data would lead to a comparison between these and new studies. New studies using EMA with mobile phones should be conducted as they can be considered an attractive method for capturing information about the specific context of SB activities of young people and adults in real time or very close to it.

Using Smartphone Survey Data and Machine Learning to Identify Situational and Contextual Risk Factors for HIV Risk Behavior Among Men Who Have Sex with Men Who Are Not on PrEP.

"Just-in-time" interventions (JITs) delivered via smartphones have considerable potential for reducing HIV risk behavior by providing pivotal support at key times prior to sex. However, these programs depend on a thorough understanding of when risk behavior is likely to occur to inform the timing of JITs. It is also critical to understand the most important momentary risk factors that may precede HIV risk behavior, so that interventions can be designed to address them. Applying machine learning (ML) methods to ecological momentary assessment data on HIV risk behaviors can help answer both questions. Eighty HIV-negative men who have sex with men (MSM) who were not on PrEP completed a daily diary survey each morning and an experience sampling survey up to six times per day via a smartphone application for 30days. Random forest models achieved the highest area under the curve (AUC) values for classifying high-risk condomless anal sex (CAS). These models achieved 80% specificity at a sensitivity value of 74%. Unsurprisingly, the most important contextual risk factors that aided in classification were participants' plans and intentions for sex, sexual arousal, and positive affective states. Findings suggest that survey data collected throughout the day can be used to correctly classify about three of every four high-risk CAS events, while incorrectly classifying one of every five non-CAS days as involving high-risk CAS. A unique set of risk factors also often emerge prior to high-risk CAS events that may be useful targets for JITs.

Ecological momentary assessments among patients with cancer: A scoping review.

IntroductionEcological momentary assessment (EMA) is an emerging method to assess an individual's current thoughts, affect, behaviour, physical states and contextual factors as they occur in real‐time and in their natural environment. Whereas EMA is frequently used in mental health, little is known about the added value of EMA in oncology research. This review aimed to synthesise methodological information and results of studies that applied EMA among patients with cancer to inform future researchers about the opportunities and challenges.MethodsWe included full‐text articles on studies that: (a) were conducted among adult cancer patients; and (b) examined cancer and treatment‐related experiences by EMA. Information from selected studies was synthesised: study designs, EMA data collection methods, response‐related results and main findings.ResultsTwelve studies were included, which all applied an observational design. The EMA data collection methods varied considerably and the reporting of response‐related results were poor. Nevertheless, EMA was found feasible as no systematic patterns of problems were reported and reported response‐related results were acceptable. Furthermore, EMA was found useful as it facilitated examination of real‐time experiences and behaviour.ConclusionEcological momentary assessment is useful and feasible in oncology research. Future studies would benefit from guidelines for designing and reporting EMA studies.

Within-day time-varying associations between motivation and movement-related behaviors in older adults

ObjectivesEmerging evidence suggests both motivation and movement-related behaviors vary within and across days. Yet common data analytic approaches assume static or consistent relationships between motivation and behavior across time. Just as motivation and behavior change across time, so too might associations between motivation and behavior. This study will apply time-varying effect modeling to Ecological Momentary Assessment (EMA) data to examine time of day differences in how motivational constructs predict older adults’ subsequent movement-related behavior over the next 2 h. DesignOlder adults (n = 104) completed a 10-day EMA protocol. MethodParticipants answered up to 6 EMA prompts/day to assess momentary intentions and self-efficacy to stand or move as well as intentions and self-efficacy to limit sedentary time over the next 2 h. Participants wore an activPAL accelerometer continuously to measure time spent being upright (i.e., standing or moving) and time spent sitting. ResultsOn weekdays, intentions and self-efficacy were generally predictive of subsequent behavior in the expected direction over the majority of the day whereas these constructs predicted subsequent behavior in the expected directions over a smaller range of times on weekend days. ConclusionsThis study adds to emerging evidence that associations between motivational constructs and subsequent behavior change over the course of the day, but these time-varying associations may be different depending on the day of week. This work has implications for intervention design and the timing of intervention content delivery in approaches like just-in-time adaptive interventions.

Relationship Between Sleep Quality and Mood: Ecological Momentary Assessment Study.

BackgroundSleep disturbances play an important role in everyday affect and vice versa. However, the causal day-to-day interaction between sleep and mood has not been thoroughly explored, partly because of the lack of daily assessment data. Mobile phones enable us to collect ecological momentary assessment data on a daily basis in a noninvasive manner.ObjectiveThis study aimed to investigate the relationship between self-reported daily mood and sleep quality.MethodsA total of 208 adult participants were recruited to report mood and sleep patterns daily via their mobile phones for 6 consecutive weeks. Participants were recruited in 4 roughly equal groups: depressed and anxious, depressed only, anxious only, and controls. The effect of daily mood on sleep quality and vice versa were assessed using mixed effects models and propensity score matching.ResultsAll methods showed a significant effect of sleep quality on mood and vice versa. However, within individuals, the effect of sleep quality on next-day mood was much larger than the effect of previous-day mood on sleep quality. We did not find these effects to be confounded by the participants’ past mood and sleep quality or other variables such as stress, physical activity, and weather conditions.ConclusionsWe found that daily sleep quality and mood are related, with the effect of sleep quality on mood being significantly larger than the reverse. Correcting for participant fixed effects dramatically affected results. Causal analysis suggests that environmental factors included in the study and sleep and mood history do not mediate the relationship.

What Are the Experiences of Men Affected by Prostate Cancer Participating in an Ecological Momentary Assessment Study?

Within the cluster of self-report methodologies, ecological momentary assessment (EMA) is a method used in health services research whereby a participant repeatedly reports on affect, behaviors, symptoms, and cognitions as they occur in real time in the participant's natural environment. However, little is known about the impact of participating in an EMA study on individuals' experiences who are affected by prostate cancer. The aims of this study were to explore the lived experiences of men affected by prostate participating in an EMA study and assess whether their participation in EMA alters their representation of their condition. Participants (n = 12) were purposively recruited from 2 university teaching hospitals in Scotland. Semistructured interviews were conducted with men affected by prostate cancer following the completion of EMA data collection. Data were analyzed using thematic analysis. The lived experience of prostate cancer included 6 superordinate themes: isolation in the healthcare system, lack of shared care plans, informational support, coping with prostate cancer, fear of death and dying, and vocational rehabilitation. The organizing theme electronic diary as an intervention included 4 superordinate themes: changing self-management behaviors, habitual experience, changing perceptions, and diary in daily life. We observed that men participating in the EMA study described several methodological complexities, which need to be addressed through future research. Importantly, there is a need for the health system to prioritize research and develop a more holistic approach to prostate cancer care in line with men's preferences and needs in the future.

3048 Measuring the Autonomic Nervous System for Translational Research: Identification of Non-invasive Methods

OBJECTIVES/SPECIFIC AIMS: The objective of this study is to identify and categorize non-invasive measurement methods for autonomic nervous system (ANS) symptoms that develop in hypoglycemic episodes. METHODS/STUDY POPULATION: We first reviewed literature for hypoglycemia symptomology. We then performed a selective literature review of Google Scholar, PubMed and Scopus for an ANS symptom and/or synonyms and the words ‘sensor’ or ‘detection’, e.g. ‘sweat sensor’ and ‘tremor detection’, studies utilizing non-invasive measurements in DM, and datasets of non-invasive measurements in DM. Measurement methods were then organized based on the ANS symptoms and existing metadata models for harmonizing sensors and surveys. RESULTS/ANTICIPATED RESULTS: We identified several measurement methods to for ANS symptoms during hypoglycemic events: thermometer, accelerometer, electrocardiogram (ECG), galvanic skin response (GSR), image processing, infrared imaging, thermal actuator, and ecological momentary assessment (EMA). The stage of implementation varied across the measurement methods from under development, to use in research and clinical settings, and even commercially available consumer products. Measurement methods that could be worn as wrist-band wearables or as film-based epidermal sensors would be capable of automatically gathering data with little to no effort required of the person wearing the device. Image-based methods would require the individual to actively engage in generating a photograph for analysis. In the case of EMA’s, a message containing a question is sent to the individual, often via text message, soliciting short and immediate responses. It is anticipated that one sensor alone would not be sufficient to measure ANS responses to hypoglycemia, but rather several data points would be required. For example, if the GSR was the only signal, sweat in response to vigorous exercise or a warm environment would inject noise into the signal. Including the accelerometer data would allow for the identification of body movement which would indicate exercise, while an ECG signal could confirm the exercise. DISCUSSION/SIGNIFICANCE OF IMPACT: Impaired awareness of hypoglycemia (IAH) is a complication that develops in about 30% of type 1 DM and 10% type 2 DM populations. In individuals with intact awareness of hypoglycemia, the ANS leads to symptoms which includes: shaking, trembling, anxiety, nervousness, palpitation (i.e. change in heart rate and/or function), clamminess, sweating, dry mouth, hunger, pallor (i.e. drop in blood flow and/or skin-surface temperature), and pupil dilation. IAH is defined as the onset of hypoglycemia before the appearance of autonomic warning symptoms. IAH is caused by repeated exposures to low blood glucose levels, which reduces the body’s ability to sense hypoglycemia, and therefore it is difficult for patients to recognize and self-treat. Individuals with IAH are six times more likely to experience severe hypoglycemia, an emergent condition which can lead to unconsciousness, seizure, coma, and death. Clinical investigators are developing interventions that aim to improve awareness of hypoglycemia. Surveys, observations by clinicians, and laboratory tests, often carried out in highly controlled in-patient settings, are currently used to assess the severity of IAH and the ANS’s ability to respond to hypoglycemia. In other disease states, for example heart disease and Parkinson’s disease, electrocardiograms and accelerometers have been used to assess heart function and tremor, respectively. However, there is currently a barrier to examining the efficacy of IAH interventions in real world settings as there are no established objective and non-invasive means to measure ANS symptoms due to hypoglycemia. This work encompasses the first important step necessary to direct translational researchers interested in testing the efficacy of IAH interventions and developing diagnostic tools for IAH in real-world studies outside the clinic. Next steps include evaluating these sensors and specifying EMA surveys, designing studies, and integration and assimilation of these data streams to identify true events of IAH by leveraging informatics platform such as the Utah PRISMS Informatics Ecosystem. Investigators would then be able to conduct studies that aim to develop and validate models that take sensor and EMA data as the input to detect and assess the severity of IAH.