Examining the Interplay of Processes Across Multiple Time-Scales: Illustration With the Intraindividual Study of Affect, Health, and Interpersonal Behavior (iSAHIB)

Ram, Nilam; Conroy, David E.; Pincus, Aaron L.; Lorek, Amy E.; Rebar, Amanda L.; Roche, Michael J.; Coccia, Michael; Morack, Jennifer; Feldman, Josh; Gerstorf, Denis

doi:10.1080/15427609.2014.906739

Cited by 101 publications

(110 citation statements)

References 30 publications

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“…Interested readers are encouraged to consult [75] for further detail regarding this survey. These sets of questionnaires can be categorized by their frequencies of response, namely once (background information), burst (pre/post burst visits), daily (daily information), and event-contingent (interaction/conversation information).…”

Section: Methodsmentioning

confidence: 99%

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How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information

Tuarob

Tucker

Kumara

et al. 2017

Journal of Biomedical Informatics

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It is believed that anomalous mental states such as stress and anxiety not only cause suffering for the individuals, but also lead to tragedies in some extreme cases. The ability to predict the mental state of an individual at both current and future time periods could prove critical to healthcare practitioners. Currently, the practical way to predict an individual’s mental state is through mental examinations that involve psychological experts performing the evaluations. However, such methods can be time and resource consuming, mitigating their broad applicability to a wide population. Furthermore, some individuals may also be unaware of their mental states or may feel uncomfortable to express themselves during the evaluations. Hence, their anomalous mental states could remain undetected for a prolonged period of time. The objective of this work is to demonstrate the ability of using advanced machine learning based approaches to generate mathematical models that predict current and future mental states of an individual. The problem of mental state prediction is transformed into the time series forecasting problem, where an individual is represented as a multivariate time series stream of monitored physical and behavioral attributes. A personalized mathematical model is then automatically generated to capture the dependencies among these attributes, which is used for prediction of mental states for each individual. In particular, we first illustrate the drawbacks of traditional multivariate time series forecasting methodologies such as vector autoregression. Then, we show that such issues could be mitigated by using machine learning regression techniques which are modified for capturing temporal dependencies in time series data. A case study using the data from 150 human participants illustrates that the proposed machine learning based forecasting methods are more suitable for high-dimensional psychological data than the traditional vector autoregressive model in terms of both magnitude of error and directional accuracy. These results not only present a successful usage of machine learning techniques in psychological studies, but also serve as a building block for multiple medical applications that could rely on an automated system to gauge individuals’ mental states.

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Section: Methodsmentioning

confidence: 99%

“…The ground-truth validation data was drawn from the Intra-individual Study of Affect, Health, and Interpersonal Behavior [75]. The data collection campaign was conducted with 150 participants from the Pennsylvania State University community, ages range from 18 – 90 years (mean = 47.10, standard deviation = 18.76).…”

Section: Case Studymentioning

confidence: 99%

How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information

Tuarob

Tucker

Kumara

et al. 2017

Journal of Biomedical Informatics

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“…Comprehensive descriptions of the National Survey of Daily Experiences (NSDE; Almeida, McGonagle, & King, 2009), and the Intraindividual Study of Affect, Health, and Interpersonal Behavior (iSAHIB; Ram et al, 2014) can be found in the noted references, while specific details relevant to the present study are given below.…”

Section: Methodsmentioning

confidence: 99%

Stressor diversity: Introduction and empirical integration into the daily stress model.

Koffer¹,

Ram²,

Conroy³

et al. 2016

Psychology and Aging

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The present study examined if and how stressor diversity, the extent to which one’s stressor events are spread across multiple types of stressors, contributes to daily affective well-being through the adult life span. Stressor diversity was examined as a unique predictor of daily affect and as a moderator of stressor exposure and stressor reactivity effects. Analyses span two independent studies of daily stress: the National Study of Daily Experiences with N=2,022 adults, aged 33–85, assessed over T=8 days, and the Intraindividual Study of Affect, Health, and Interpersonal Behavior with N=150 adults, aged 18–89, assessed over T=63 days. Across both studies, older age was associated with less stressor diversity. Additionally, multivariate multilevel models indicated higher stressor diversity was linked with better affective well-being. Age, however, was not a consistent moderator of such associations. The combination of low stressor diversity and high stressor exposure is discussed as an operationalization of chronic stressors, and this combination was associated with particularly high negative affect and low positive affect. We believe further work will benefit from including both the frequency and diversity of stressor experiences in analyses in order to better characterize individuals’ stressor experiences.

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“…The Intraindividual Study of Affect, Health, and Interpersonal Behavior (iSAHIB; Ram et al, 2014) worked with 150 adults (51% women) recruited from the Pennsylvania State University and surrounding community, and purposively stratified by gender and across the adult life span in 18–24 ( n =22), 25–34 ( n =27), 35–49 ( n =30), 50–64 ( n =41), 65+ ( n =30) year age-bins. Participants were between 19 and 89 years of age ( M = 47.10, SD = 18.76), had obtained between 2 and 24 years of formal education ( M = 16.36, SD = 3.90), and had between 0 and 6 children ( M = 1.5, SD = 1.41).…”

Section: Methodsmentioning

confidence: 99%

The within-person association between alcohol use and sleep duration and quality in situ: An experience sampling study

Lydon

Ram

Conroy

et al. 2016

Addictive Behaviors

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Objective Despite evidence for detrimental effects of alcohol on sleep quality in laboratory studies, alcohol is commonly used as a self-prescribed sleep aid. This study examined the within-person associations of alcohol use with sleep duration and quality in everyday life to gain insight into the ecological validity of laboratory findings on the association between sleep and alcohol. Method A sample of 150 adults (age 19–89 years) were followed for 60+ days as part of an intensive experience sampling study wherein participants provided daily reports of their alcohol use, sleep duration, and sleep quality. Within-person and between-person associations of daily sleep duration and quality with alcohol use were examined using multilevel models. Results A significant, negative within-person association was observed between sleep quality and alcohol use. Sleep quality was lower on nights following alcohol use. Sleep duration did not vary as a function of within-person variation in alcohol use. Conclusions In line with laboratory assessments, alcohol use was associated with low sleep quality but was not associated with sleep duration, suggesting that laboratory findings generalize to everyday life. This examination of individuals’ daily lives suggests that alcohol does not systematically improve sleep quality or duration in real life.

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Examining the Interplay of Processes Across Multiple Time-Scales: Illustration With the Intraindividual Study of Affect, Health, and Interpersonal Behavior (iSAHIB)

Cited by 101 publications

References 30 publications

How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information

How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information

Stressor diversity: Introduction and empirical integration into the daily stress model.

The within-person association between alcohol use and sleep duration and quality in situ: An experience sampling study

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