OBJECTIVE: The present article proposes a general framework and a set of specific methodological steps for conducting person-specific dynamic assessments, which yield information about syndrome structures and states that can be used to provide actionable information for the formulation of personalized interventions. It is proposed that researchers should (a) determine the relevant constituent inputs for a diagnostic system; (b) measure these inputs with as much detail as possible; (c) assess the correlational structure of system inputs via factor-analytic methods within individuals; and (d) subject the individual-level, latent dimension time series to dynamic analyses such as the dynamic factor model (Molenaar, 1985) to discern the time-dependent, dynamic relationships within and between system components. METHOD: An exemplar is provided wherein 10 individuals with clinically diagnosed generalized anxiety disorder completed surveys related to generalized anxiety disorder symptomatology for at least 60 consecutive days. These data were then subjected to person-specific exploratory and confirmatory factor analyses for the identification of latent symptom dimensions. Finally, dynamic factor models were used to model the dynamic interrelationships within and between symptom domains on a person-by-person basis. RESULTS: Person-specific factor analyses returned models with 3 (n = 8) or 4 (n = 2) latent factors, all with excellent fit. Dynamic factor modeling successfully revealed the contemporaneous correlations and time-lagged predictive relationships between factors, providing prescriptive information for the formulation of targeted interventions. CONCLUSIONS: The proposed approach has the potential to inform the construction and implementation of personalized treatments by delineating the idiosyncratic structure of psychopathology on a person-by-person basis. (c) 2015 APA, all rights reserved).
OBJECTIVE: The present article proposes a general framework and a set of specific methodological steps for conducting person-specific dynamic assessments, which yield information about syndrome structures and states that can be used to provide actionable information for the formulation of personalized interventions. It is proposed that researchers should (a) determine the relevant constituent inputs for a diagnostic system; (b) measure these inputs with as much detail as possible; (c) assess the correlational structure of system inputs via factor-analytic methods within individuals; and (d) subject the individual-level, latent dimension time series to dynamic analyses such as the dynamic factor model (Molenaar, 1985) to discern the time-dependent, dynamic relationships within and between system components. METHOD: An exemplar is provided wherein 10 individuals with clinically diagnosed generalized anxiety disorder completed surveys related to generalized anxiety disorder symptomatology for at least 60 consecutive days. These data were then subjected to person-specific exploratory and confirmatory factor analyses for the identification of latent symptom dimensions. Finally, dynamic factor models were used to model the dynamic interrelationships within and between symptom domains on a person-by-person basis. RESULTS:Person-specific factor analyses returned models with 3 (n = 8) or 4 (n = 2) latent factors, all with excellent fit. Dynamic factor modeling successfully revealed the contemporaneous correlations and time-lagged predictive relationships between factors, providing prescriptive information for the formulation of targeted interventions. CONCLUSIONS: The proposed approach has the potential to inform the construction and implementation of personalized treatments by delineating the idiosyncratic structure of psychopathology on a person-by-person basis. (c) 2015 APA, all rights reserved).
Authors: Asif Salekin; Jeremy W Eberle; Jeffrey J Glenn; Bethany A Teachman; John A Stankovic Journal: Proc ACM Interact Mob Wearable Ubiquitous Technol Date: 2018-06