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

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.

本数据集说明包含使用R语言（R programming language）编写的代码，以及一篇研究论文中呈现的已处理数据与研究结果（详见参考文献）。本数据集未提供原始数据，且公开数据无法与研究参与者建立关联。

研究样本纳入308名符合入组条件的参与者中的180名，均为瑞典慢性疾病成年初级医疗照护患者，且在两个时间点完成了瑞典语网络问卷调研。本研究采用确认性因子分析（confirmatory factor analysis），基于34个问卷条目计算了9个心理构念的潜在因子得分（latent factor scores）。本数据集包含上述潜在因子得分与潜在剖面分析（latent profile analysis）结果。尽管未共享原始数据，但我们提供了全部问卷条目及其作答量表。同时附有所用的潜在因子得分与潜在剖面分析结果计算代码。

本研究作为一项科研项目的子课题开展，旨在探索慢性疾病照护场景中电子健康服务（eHealth services）的使用对患者与医疗照护人员之间互动与协作的影响。本研究的核心目的是依据DoCCA量表（Distribution of Co-Care Activities Scale，von Thiele Schwarz, 2021）所测量的共照体验，识别出具有相似特征的初级医疗照护患者亚组。

基线数据采集于患者首次接触用于支持其自我照护及与医疗照护人员开展数字化沟通的电子健康服务之后，随访数据则在7个月后完成采集。所有研究对象均在瑞典斯德哥尔摩地区的同一家初级医疗照护中心接受诊疗服务。

引用文献：von Thiele Schwarz U, Roczniewska M, Pukk Härenstam K, Karlgren K, Hasson H, Menczel S, Wannheden C. 慢性病照护工作：共照活动分配（DoCCA）量表的编制与心理测量学验证. BMC Health Services Research (2021) 21:480. doi: 10.1186/s12913-021-06455-8

本数据集包含两个数据文件："factorscores_docca.csv"与"latent-profile-analysis-results_docca.csv"。

* "factorscores_docca.csv"：该数据文件包含180个样本（行）与18个变量（列）。变量涵盖两个时间点（T1与T2）的潜在因子，变量取值为对应的潜在因子得分。用于计算潜在因子得分的问卷数据包含20个条目，基于DoCCA量表测量患者与医疗照护人员的协作体验，这些条目同时被纳入潜在剖面分析。此外，本研究还计算了反映以下维度的潜在因子得分：自我照护感知自我效能（6个条目）、医疗照护满意度（2个条目）、自评健康状况（2个条目）以及电子健康服务感知影响（4个条目），上述得分用于比较潜在剖面分析得到的不同患者亚组。变量定义详见单独附带文件（见下文）。

* "latent-profile-analysis-results_docca.csv"：该数据文件包含180个样本（行）与14个变量（列）。变量包含已识别患者亚组的分类编号与标签，以及每个样本归属各亚组的后验分类概率：T1时间点共识别出4个亚组，T2时间点共识别出5个亚组。由于T1与T2的亚组未达到结构不变性，因此未计算亚组转换概率，故本文件未提供相关转换概率数据。

本数据集附带的支撑文档包含1份中英双语变量定义文件，以及4份使用R语言（R programming language）编写的脚本文件：

* "variable-definitions_swe-eng.xlsx"：该文件包含4个工作表。工作表1（scale-items_original_swedish）列出了用于计算潜在因子得分的瑞典语原文问卷条目及其作答量表。工作表2（scale-items_translated_english）提供了工作表1中问卷条目与作答量表的英文翻译版本。工作表3（factorscores_docca）定义了"factorscores_docca.csv"数据集的全部变量。工作表4（latent-profile-analysis-results）定义了"latent-profile-analysis-results_docca.csv"数据集的全部变量。

* "R-script_Step-0_Factor-scores.R"：该R脚本文件包含用于计算潜在因子得分的代码。由于伦理审查限制，原始数据未公开共享，因此本脚本仅可在获取原始数据的前提下运行，其核心目的在于实现代码透明化。此外，本脚本展示了确认性因子分析（CFA）所用的模型设定。数据缺失值采用全信息极大似然估计（Full Information Maximum Likelihood, FIML）进行处理。

* "R-script_Step-1_Latent-profile-analysis.R"：该R脚本文件包含用于在T1与T2时间点开展潜在剖面分析并生成亚组可视化绘图的代码。本脚本可通过本数据集提供的"factorscores_docca.csv"文件运行，其生成的分析结果即为"latent-profile-analysis-results_docca.csv"文件中的数据。

* "R-script_Step-2_Non-parametric-tests.R"：该R脚本文件包含用于开展非参数检验以比较T1与T2时间点各亚组间外生变量差异的代码。本脚本使用以下两个数据文件："factorscores_docca.csv"与"latent-profile-analysis-results_docca.csv"。

* "R-script_Step-3_Class-transitions.R"：该R脚本文件包含用于绘制桑基图（sankey diagram）以展示患者亚组转换情况的代码。本脚本使用以下数据文件："latent-profile-analysis-results_docca.csv"。

软件运行要求：运行本数据集附带的代码需安装R软件环境以及脚本文件中指定的R软件包（均为开源软件）。本脚本基于R 4.2.1版本编写。