The impact of PTSD clusters on cannabis use in a racially diverse trauma-exposed sample: An analysis from ecological momentary assessment

BACKGROUND Background: The use of ecological momentary assessment (EMA) to study youth experiencing homelessness (YEH) behaviors is an emerging area of research. Despite high rates of participation and potential clinical utility, few studies have investigated best practices and recommendations for EMA from the YEH perspective. OBJECTIVE This study aimed to describe the perceived benefits, usability, acceptability, and barriers to the use of EMA from the homeless youth perspective. METHODS YEH were recruited from a larger EMA study. Semi-structured exit interviews were performed using an interview guide that focused on the YEH experience with the EMA app, including perceived barriers and recommendations for future studies. Data analyses employed an inductive approach with thematic analysis to identify major themes and subthemes. RESULTS A total of 18 YEH aged 19-24 participated in individual and group exit interviews. EMA was highly acceptable to YEH and they found the app and survey easy to navigate. Perceived benefits included increased behavioral and emotional awareness with some YEH reporting a decrease in their high-risk behaviors as a result of participation. Another significant perceived benefit was the ability to use the phones for social support and make connections to family, friends, and potential employers. Barriers were primarily survey and technology related. Survey-related barriers included the redundancy of questions, the lack of customizable responses, and the timing of survey administration. Technology-related barriers included the “freezing” of the app, battery problems, and connectivity issues. Recommendations for future studies included the need to provide real-time mental health support for symptomatic youth, creating individually customized questions, and testing the use of personalized motivational messages that respond to the EMA data in real-time. CONCLUSIONS YEH are highly receptive to the use of EMA methodology. Further studies are warranted to assess whether participation improves behavior change. More research is needed to understand the impact of EMA on YEH behaviors. Incorporating the YEH perspective in the design and implementation of EMA studies may help minimize barriers, increase acceptability, and improve participation rates in this hard-to-reach, disconnected population.

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Exploring the impact of efficacy messages on cessation-related outcomes using Ecological Momentary Assessment

Tobacco Induced Diseases ◽

10.18332/tid/94460 ◽

2018 ◽

Vol 16 (September) ◽

Author(s):

Victoria Lambert ◽

Stuart Ferguson ◽

Jeff Niederdeppe ◽

David Hammond ◽

James Hardin ◽

...

Keyword(s):

Ecological Momentary Assessment ◽

Ecological Momentary ◽

The Impact ◽

Momentary Assessment

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Investigating the impact of a smart growth community on the contexts of children's physical activity using Ecological Momentary Assessment

Health & Place ◽

10.1016/j.healthplace.2011.07.007 ◽

2012 ◽

Vol 18 (1) ◽

pp. 76-84 ◽

Cited By ~ 27

Author(s):

Genevieve F. Dunton ◽

Stephen S. Intille ◽

Jennifer Wolch ◽

Mary Ann Pentz

Keyword(s):

Physical Activity ◽

Ecological Momentary Assessment ◽

Smart Growth ◽

Ecological Momentary ◽

The Impact ◽

Momentary Assessment ◽

Children’S Physical Activity

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User Experience Related Biases in Data Collected Via Technology-Enabled Ecological Momentary Assessment (Preprint)

10.2196/preprints.13758 ◽

2019 ◽

Author(s):

Si Sun ◽

Zhiguo Li ◽

Chandramouli Maduri ◽

Tian Hao ◽

Xinxin Zhu

Keyword(s):

User Experience ◽

Ecological Momentary Assessment ◽

User Interaction ◽

Physiological Signals ◽

Main Effect ◽

Forest Models ◽

Reported Data ◽

Ecological Momentary ◽

The Impact ◽

Momentary Assessment

BACKGROUND Technology-enabled ecological momentary assessment (EMA) facilitates the calibration of physiological signals against self-reported data and contexts. However, research using this method rarely considers the impact that user experience (UX) has on the quality of data. OBJECTIVE The purpose of this study is to explore the biases that UX factors induce in self-reported data and physiological signals collected through EMA and the UX factors that have the largest impact on the data. METHODS A retrospective analysis on data from a field feasibility study is conducted. The study uses an application on a smartwatch device to measure heart rate variability (HRV) and collect self-reported stress levels. We collected data on event types, age, sex, personality traits, and engineered 66 UX features (e.g., number of screens viewed, perception of notification frequency). We use a series of random forest models, conditional forest models, linear regression models, and correlation analysis to predict self-reported stress, HRV, and their discrepancies. We then use iterated comparative analysis to confirm the effects of UX factors. RESULTS Analysis on 1240.6 hours of data from 29 participants reveal that self-reported stress is correlated with the HRV signal collected after EMA notification (HRV2) but not with the HRV signal collected before the notification (HRV1) or after user interaction starts (HRV3). UX factors explain 6.6% - 10% (P < .001) of the variation in self-reported stress. UX factors do not significantly predict HRV signals but explain 63.8% (P < .001) of the difference between self-reported stress and the HRV signal collected after the EMA notification. In addition, UX factors have a significant but smaller delayed effect on self-reported stress and HRV signals collected in the next user interaction cycle. In almost all models, UX features rank higher in terms of feature importance than the other confounding factors (i.e., age, sex, personality traits) and in some models rank higher than the main effect (i.e., event types). We discuss specific symptoms of UX-induced biases related to EMA instrument design and study design, mere measurement effect and observer effect, and propose topics of examination for future studies. CONCLUSIONS User experience may induce biases in data collected through technology-enabled EMA method. In some cases, the impact of the biases may be larger than that of the main effect, other confounding factors, and the corresponding data used for calibration.

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Geographic Imputation of Missing Activity Space Data from Ecological Momentary Assessment (EMA) GPS Positions

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph15122740 ◽

2018 ◽

Vol 15 (12) ◽

pp. 2740 ◽

Cited By ~ 3

Author(s):

Jeremy Mennis ◽

Michael Mason ◽

Donna L. Coffman ◽

Kevin Henry

Keyword(s):

Ecological Momentary Assessment ◽

Census Bureau ◽

Activity Space ◽

Future Research ◽

Listwise Deletion ◽

Spatial Imputation ◽

Space Data ◽

Ecological Momentary ◽

The Impact ◽

Momentary Assessment

This research presents a pilot study to develop and compare methods of geographic imputation for estimating the location of missing activity space data collected using geographic ecological momentary assessment (GEMA). As a demonstration, we use data from a previously published analysis of the effect of neighborhood disadvantage, captured at the U.S. Census Bureau tract level, on momentary psychological stress among a sample of 137 urban adolescents. We investigate the impact of listwise deletion on model results and test two geographic imputation techniques adapted for activity space data from hot deck and centroid imputation approaches. Our results indicate that listwise deletion can bias estimates of place effects on health, and that these impacts are mitigated by the use of geographic imputation, particularly regarding inflation of the standard errors. These geographic imputation techniques may be extended in future research by incorporating approaches from the non-spatial imputation literature as well as from conventional geographic imputation and spatial interpolation research that focus on non-activity space data.

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