Data and code for: Better self-care through co-care? A latent profile analysis of primary care patients’ experiences of e-health-supported chronic care management - Factor scores and results from a latent profile analysis of co-care experiences

This data description contains code (written in the R programming language), as well as processed data and results presented in a research article (see references). No raw data are provided and the data that are made available cannot be linked to study participants. The sample consists of 180 of 308 eligible participants (adult primary care patients in Sweden, living with chronic illness) who responded to a Swedish web-based questionnaire at two time points. Using a confirmatory factor analysis, we calculated latent factor scores for 9 constructs, based on 34 questionnaire items. In this dataset, we share the latent factor scores and the latent profile analysis results. Although raw data are not shared, we provide the questionnaire item, including response scales. The code that was used to produce the latent factor scores and latent profile analysis results is also provided.

The study was performed as part of a research project exploring how the use of eHealth services in chronic care influence interaction and collaboration between patients and healthcare. The purpose of the study was to identify subgroups of primary care patients who are similar with respect to their experiences of co-care, as measured by the DoCCA scale (von Thiele Schwarz, 2021). Baseline data were collected after patients had been introduced to an eHealth service that aimed to support them in their self-care and digital communication with healthcare; follow-up data were collected 7 months later. All patients were treated at the same primary care center, located in the Stockholm Region in Sweden.

Cited reference: von Thiele Schwarz U, Roczniewska M, Pukk Härenstam K, Karlgren K, Hasson H, Menczel S, Wannheden C. The work of having a chronic condition: Development and psychometric evaluation of the Distribution of Co-Care Activities (DoCCA) Scale. BMC Health Services Research (2021) 21:480. doi: 10.1186/s12913-021-06455-8

The DATASET consists of two files: factorscores_docca.csv and latent-profile-analysis-results_docca.csv.

* factorscores_docca.csv: This file contains 18 variables (columns) and 180 cases (rows). The variables represent latent factors (measured at two time points, T1 and T2) and the values are latent factor scores. The questionnaire data that were used to produce the latent factor scores consist of 20 items that measure experiences of collaboration with healthcare, based on the DoCCA scale. These items were included in the latent profile analysis. Additionally, latent factor scores reflecting perceived self-efficacy in self-care (6 items), satisfaction with healthcare (2 items), self-rated health (2 items), and perceived impact of e-health (4 items) were calculated. These items were used to make comparisons between profiles resulting from the latent profile analysis. Variable definitions are provided in a separate file (see below).

* latent-profile-analysis-results_docca.csv: This file contains 14 variables (columns) and 180 cases (rows). The variables represent profile classifications (numbers and labels) and posterior classification probabilities for each of the identified profiles, 4 profiles at T1 and 5 profiles at T2. Transition probabilities (from T1 to T2 profiles) were not calculated due to lacking configural similarity of profiles at T1 and T2; hence no transition probabilities are provided.

The ASSOCIATED DOCUMENTATION consists of one file with variable definitions in English and Swedish, and four script files (written in the R programming language):

* variable-definitions_swe-eng.xlsx: This file consists of four sheets. Sheet 1 (scale-items_original_swedish) specifies the questionnaire items (in Swedish) that were used to calculate the latent factor scores; response scales are included. Sheet 2 (scale-items_translated_english) provides an English translation of the questionnaire items and response scales provided in Sheet 1. Sheet 3 (factorscores_docca) defines the variables in the factorscores_docca.csv dataset. Sheet 4 (latent-profile-analysis-results) defines the variables in the latent-profile-analysis-results_docca.csv dataset.

* R-script_Step-0_Factor-scores.R: R script file with the code that was used to calculate the latent factor scores. This script can only be run with access to the raw data file which is not publicly shared due to ethical constraints. Hence, the purpose of the script file is code transparency. Also, the script shows the model specification that was used in the confirmatory factor analysis (CFA). Missingness in data was accounted for by using Full Information Maximum Likelihood (FIML).

* R-script_Step-1_Latent-profile-analysis.R: R script file with the code that was used to run the latent profile analyses at T1 and T2 and produce profile plots. This code can be run with the provided dataset factorscores_docca.csv. Note that the script generates the results that are provided in the latent-profile-analysis-results_docca.csv dataset.

* R-script_Step-2_Non-parametric-tests.R: R script file with the code that was used to run non-parametric tests for comparing exogenous variables between profiles at T1 and T2. This script uses the following datasets: factorscores_docca.csv and latent-profile-analysis-results_docca.csv.

* R-script_Step-3_Class-transitions.R: R script file with the code that was used to create a sankey diagram for illustrating class transitions. This script uses the following dataset: latent-profile-analysis-results_docca.csv.

Software requirements: To run the code, the R software environment and R packages specified in the script files need to be installed (open source). The scripts were produced in R version 4.2.1. Show less..