Time Series Analysis for Psychological Research

Velicer, Wayne F.; Molenaar, Peter

doi:10.1002/9781118133880.hop202022

Cited by 30 publications

(31 citation statements)

References 93 publications

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“…Fourth, we would like to extend our predictive models to incorporate intervention information, in a way similar to that in interrupted time series models or intervention models [ 60 ]. Our current models consider no intervention information.…”

Section: Discussionmentioning

confidence: 99%

Predicting asthma control deterioration in children

Luo

Stone

Fassl

et al. 2015

BMC Med Inform Decis Mak

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BackgroundPediatric asthma affects 7.1 million American children incurring an annual total direct healthcare cost around 9.3 billion dollars. Asthma control in children is suboptimal, leading to frequent asthma exacerbations, excess costs, and decreased quality of life. Successful prediction of risk for asthma control deterioration at the individual patient level would enhance self-management and enable early interventions to reduce asthma exacerbations. We developed and tested the first set of models for predicting a child’s asthma control deterioration one week prior to occurrence.MethodsWe previously reported validation of the Asthma Symptom Tracker, a weekly asthma self-monitoring tool. Over a period of two years, we used this tool to collect a total of 2912 weekly assessments of asthma control on 210 children. We combined the asthma control data set with patient attributes and environmental variables to develop machine learning models to predict a child’s asthma control deterioration one week ahead.ResultsOur best model achieved an accuracy of 71.8 %, a sensitivity of 73.8 %, a specificity of 71.4 %, and an area under the receiver operating characteristic curve of 0.757. We also identified potential improvements to our models to stimulate future research on this topic.ConclusionsOur best model successfully predicted a child’s asthma control level one week ahead. With adequate accuracy, the model could be integrated into electronic asthma self-monitoring systems to provide real-time decision support and personalized early warnings of potential asthma control deteriorations.

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Section: Discussionmentioning

confidence: 99%

Predicting asthma control deterioration in children

Luo

Stone

Fassl

et al. 2015

BMC Med Inform Decis Mak

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“…In brief, an AR model of a variable presents a temporary dependence of previous values of the same variable, so in order to forecast the value of an AR variable, we only need to know the value of the previous record of that variable for any given subject. To describe the autoregressive model of salivary behaviour, if we suppose that a person’s sAA level at a certain time is a function of the amount of sAA shown by that same person in the previous p hr ( sAA j , t = f(sAA j , t-1 , sAA j , t-2 , … , sAAs j , t-p ), then where sAA is the value of the variable sAA for participant j at hr t ; subscript j represents each participant ( j : 1 , 2 , … , 19 ); t is the moment of measurement, thus if we consider a value of sAA for a person j at a particular hr t (or sAA j , t ), then the sAA value of this person j at the previous hr ( t-1) will be sAA j , t-1 , and so on until sAA j , t-p , p being the number of significant explanatory lags of sAA; the terms b and e are as in Eqs 1 and 2, though obviously they are different coefficients in the two equations because their statistical relationships are different; evidently, b 0j in Eq 3 is multilevel, its formulation being as in Eq 2 [15, 25, 28, 45–50].…”

Section: Introductionmentioning

confidence: 99%

Intensive longitudinal modelling predicts diurnal activity of salivary alpha-amylase

et al. 2019

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Salivary alpha-amylase (sAA) activity has been widely used in psychological and medical research as a surrogate marker of sympathetic nervous system activation, though its utility remains controversial. The aim of this work was to compare alternative intensive longitudinal models of sAA data: (a) a traditional model, where sAA is a function of hour (hr) and hr squared (sAAj,t = f(hr, hr2), and (b) an autoregressive model, where values of sAA are a function of previous values (sAAj,t = f(sAA j,t-1, sAA j,t-2, …, sAA j,t-p). Nineteen normal subjects (9 males and 10 females) participated in the experiments and measurements were performed every hr between 9:00 and 21:00 hr. Thus, a total of 13 measurements were obtained per participant. The Napierian logarithm of the enzymatic activity of sAA was analysed. Data showed that a second-order autoregressive (AR(2)) model was more parsimonious and fitted better than the traditional multilevel quadratic model. Therefore, sAA follows a process whereby, to forecast its value at any given time, sAA values one and two hr prior to that time (sAA j,t = f(SAAj,t-1, SAAj,t-2) are most predictive, thus indicating that sAA has its own inertia, with a “memory” of the two previous hr. These novel findings highlight the relevance of intensive longitudinal models in physiological data analysis and have considerable implications for physiological and biobehavioural research involving sAA measurements and other stress-related biomarkers.

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“…Alternatively, a proxy variable that is sensitive to the same weekly cycle can be included as a time‐varying covariate. This covariate is used to remove the effect of the cycle from X t (Velicer & Molenaar, ). Dummy and indicator variables . To represent weekly cycles, six dummy variables corresponding to days of the week can be used with the seventh day chosen as the reference day (e.g., Armeli et al, ).…”

Section: Estimating Between‐ and Within‐person Relationshipsmentioning

confidence: 99%

“…Everyday experience methods—research methods that study ongoing behaviors, psychological states, and experiences as they occur in their natural context in everyday life—have gained great popularity and have become “standard tools in social‐personality psychology” (Reis, Gable, & Maniaci, , p. 373). Among the diverse procedures and measures, some methods are already well known and widely used, for example diary methods, experience sampling, and ecological momentary assessment, to name a few (for a detailed description, see Reis et al, ); new intensive methods of monitoring are emerging such as automatic sensing of behavior or physiological states (Nusser, Intille, & Maitra, ; Velicer & Molenaar, ) and content analysis of electronic media sampled over time (e.g., Facebook posts; Ivcevic & Ambady, ).…”

mentioning

confidence: 99%

Weekly Cycles in Daily Report Data: An Overlooked Issue

Liu

West

2015

Journal of Personality

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Daily diaries and other everyday experience methods are increasingly used to study relationships between two time-varying variables X and Y. Although daily data potentially often have weekly cyclical patterns (e.g., stress may be higher on weekdays and lower on weekends), the majority of daily diary studies have ignored this possibility. In this study, we investigated the effect of ignoring existing weekly cycles. We reanalyzed an empirical dataset (stress and alcohol consumption) and performed Monte Carlo simulations to investigate the impact of omitting weekly cycles. In the empirical dataset, ignoring cycles led to the inference of a significant within-person X-Y relation whereas modeling cycles suggested that this relationship did not exist. Simulation results indicated that ignoring cycles that existed in both X and Y led to bias in the estimated within-person X-Y relationship. The amount and direction of bias depended on the magnitude of the cycles, magnitude of the true within-person X-Y relation, and synchronization of the cycles. We encourage researchers conducting daily diary studies to address potential weekly cycles in their data. We provide guidelines for detecting and modeling cycles to remove their influence and discuss challenges of causal inference in daily experience studies.

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Time Series Analysis for Psychological Research

Cited by 30 publications

References 93 publications

Predicting asthma control deterioration in children

Predicting asthma control deterioration in children

Intensive longitudinal modelling predicts diurnal activity of salivary alpha-amylase

Weekly Cycles in Daily Report Data: An Overlooked Issue

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