Health-Related Quality of Life Trajectories Among Older Adults With Diabetes Mellitus: A Group-Based Modeling Approach

Introduction

Health-related quality of life (HR-QoL) in older adults with chronic diseases has emerged as an important issue in recent years. In numerous previous studies, people with diabetes mellitus have been shown to be more likely to have poorer overall quality of life compared to their peers (Lee & Kim, 2017; Speight et al., 2020). In these individuals, HR-QoL is associated with complications related to glycemic control and comorbidities associated with their disease. Several studies have reported that lifestyle factors, including nutrition, physical activity, smoking, and stress, may influence HR-QoL. Other influences include sociopsychological factors such as depression, anxiety, fatigue, social support, self-esteem, and economic status (Lim & Oh, 2013; Scollan-Koliopoulos et al., 2013; Shiu et al., 2014). The lives of older adults with diabetes mellitus are generally more complicated than those of younger individuals with this condition. Older individuals are at relatively higher risk of contracting geriatric syndromes, pharmacotherapy-related hypoglycemia, and diabetes mellitus-related complications, while also experiencing complications associated with the psychological and physiological changes induced by aging (American Diabetes Association, 2020; Rwegerera et al., 2018). In addition, older adults with diabetes mellitus have largely been considered as a single group with identical characteristics, with no study of unique characteristics between individuals. Furthermore, most studies have sought to identify the relationship between individual variables, with few taking an overall perspective based on a theoretical framework.

Individual HR-QoL may vary over time based on health status and how health conditions are managed. Indeed, it is well known that HR-QoL involves the comprehensive and dynamic perception of mental, physical, and social functioning. Therefore, for older adults with chronic diseases, HR-QoL is likely to change over time (American Diabetes Association, 2020; Kojima et al., 2016; Zaninotto et al., 2009).

Varying patterns of HR-QoL over time have been identified in previous studies for several chronic diseases, including heart failure, chronic obstructive pulmonary disease, stroke, colon cancer, coronary artery bypass graft surgery, and substance abuse using a group-based approach (Olson et al., 2014; Pancani et al., 2018; Yoo et al., 2016). Group-based approaches that classify participants into subgroups based on mutually exclusive characteristics and individual responses or patterns have become common (Nagin, 2014). In these prior studies, mean HR-QoL trajectories were presumed to differ between individuals, and the potential of differences developing over time was considered, with potential trajectory trends including the following: remain consistently good, worsen steadily from mild to moderate, move from moderate to severe symptomatology, steadily improve from moderate to mild symptomatology, and remain consistently poor.

Based on the results of previous studies, temporal changes in HR-QoL may differ between individuals depending on the progress of their condition, how it is managed, and aging (Kojima et al., 2016; Yoo et al., 2016; Zaninotto et al., 2009). However, limited information is available on longitudinal changes in HR-QoL over time among older adults with diabetes mellitus. There is also a lack of research on the factors that affect individualized HR-QoL trajectories.

Considering these gaps in the literature, the primary aim of this study was to identify distinctive patterns in temporal changes in HR-QoL using longitudinal data and a group-based modeling approach. Moreover, the predictors of HR-QoL trajectory patterns were identified from the pool of demographic, health-related, and environmental variables included in the revised Wilson and Cleary (1995) model (Ferrans et al., 2005), which provides a theoretical basis for variable selection. The HR-QoL domains in older adults with diabetes mellitus may include physical status, symptoms, functional status, general health perceptions, and overall quality of life (see Figure 1).

F1Figure 1.:

Conceptual Framework of the Study

Methods Study Design

This was an analytic, descriptive, and longitudinal analysis of population-based data sourced from the nationwide Korea Health Panel Survey (KHPS).

Study Population and Data Collection

Longitudinal data were drawn from the KHPS, which was implemented using face-to-face interviews conducted by trained professional interviewers from a nationally representative sample of the Korean population. This survey focused on topics such as the demographic characteristics, service utilization behavior, medical expenditure, and health behaviors of targeted households and all members within the selected households. The KHPS has been conducted annually since 2008. The most recent data set for this survey, the 2015 (the 10th wave) edition, was released in June 2018. HR-QoL scores have been included in the KHPS for 5 years only (2009–2013) and are readily available (Korea Institute for Health and Social Affairs, 2019).

This study comprised 528 older adults with diabetes mellitus who had participated in the KHPS for five consecutive years. Inclusion criteria were being 65 years of age or older and having an official diagnosis of diabetes mellitus. The study excluded those who had responded to survey items concerning HR-QoL on fewer than two occasions. Of the 528 older adults with diabetes mellitus, 440 adults were included as the study population (see Figure 2).

F2Figure 2.:

Flowchart of the Participant Selection Process

The baseline data consisted of demographic factors, environmental factors, and health-related factors, such as physical status, symptoms, functional status, and health perceptions, based on the revised Wilson and Cleary (1995) model for HR-QoL. The design of this study was considered exempt from ethical review by the institutional review board of Yonsei University Severance Hospital (IRB Approval No. 4-2017-0901), as the collected data were deidentified.

Measures Health-related quality of life

The self-reported European Quality of Life–5 Dimensions (EQ-5D) was used to evaluate HR-QoL. This scale is composed of five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each item is scored using a Likert scale, ranging from 1 to 3, with 1 denoting no difficulty and 3 denoting extreme difficulty. The total score is the summed score for all five dimensions, which is then converted to a single index score based on the Korean valuation set developed by the Korea Centers for Disease Control and Prevention (Nam et al., 2007). In this study, the single index score for HR-QoL was used, which ranges from −0.171 to 1, with negative values indicating poor health status and values closer to 1 indicating no concern for any of the five dimensions.

EQ−5D Index=1−(0.05+0.096×M2+0.418×M3+0.046×SC2+0.136×SC3+0.051×UA2+0.208×UA3+0.037×PD2+0.151×PD3+0.043×AD2+0.158×AD3+0.050×N3)

Note that M = mobility; SC = self-care; UA = usual activities; PD = pain/discomfort; AD = anxiety/depression; 1 = no health problems; 2 = moderate health problems; 3 = extreme health problems; and N3 = number of “3”, extreme health problems.

The reliability of HR-QoL for this measure was very good in this study (Cronbach’s α = .92). HR-QoL data were collected at baseline and every year over a 4-year period, spanning 2009–2013.

Demographic factors

Demographic characteristics, including gender, age, educational level, and spousal status, were collected. Responses to the question about educational level were sorted into three categories based on the Korean obligatory curriculum: “no schooling,” “elementary or middle school graduate,” and “high school/college graduate or higher.” Educational level was classified as a continuous variable in the analysis, with lower scores indicating lower educational levels.

Environmental factors

Environmental factors included household income, residential region, years of duration of diabetes mellitus (since diagnosis), and whether medical needs were met. Household income was divided into five categories, with higher scores indicating greater financial stability. Residential region was determined using the participant’s current address, which was dichotomized into whether or not the older adults lived in a metropolitan area (i.e., Busan, Daegu, Daejeon, Kwangju, Incheon, Seoul, and Ulsan). Duration of diabetes mellitus was calculated by subtracting the age at which diabetes mellitus was first diagnosed from the participant’s current age. Information on unmet medical needs was obtained using the question: “During the past 12 months, was there ever a time when you felt you needed healthcare but did not receive it?” Availability of healthcare and accessibility problems were classified as either “yes” or “no.”

Physical status

Obesity was assessed by determining body mass index (BMI), which was calculated by dividing weight (kg) by height squared (m2). Using BMI, the participants were categorized into three groups, underweight (< 18.5 kg/m2), normal (18.5–22.9 kg/m2), and overweight (≥ 23 kg/m2), based on the guidelines published by the World Health Organization for the Asia Pacific Region, the Korean Endocrine Society, and the Korean Society for the Study of Obesity (Korean Endocrine Society, 2010; Nam et al., 2007). The number of chronic diseases participants lived with was measured using a single question: “How many chronic diseases do you have?” Data regarding hypertension and hearing, vision, and eating problems were collected and categorized as either “yes” or “no.”

Symptoms

Wilson and Cleary (1995) defined symptom status as “a patient’s perception of an abnormal physical, emotional, or cognitive state” and classified symptoms into physical symptoms, psychological symptoms, and symptoms that are not clearly physical or psychological in origin (e.g., emotional distress, fear, worry, and frustration). In this study, symptoms were measured using sleep disturbance and stress levels related to the disease, family, and finances. Data were collected and categorized as either “yes” or “no.”

Functional status

Activities of daily living (ADL) were assessed using Katz’s (1983) six-item index, which was designed to examine respondent abilities related to transferring, dressing, using a toilet, bathing, feeding, and continence. Instrumental ADL (IADL) were assessed using the Lawton–Brody Scale, which was designed to assess the ability to use a telephone, go shopping, prepare food, perform housekeeping, do laundry, and handle finances as well as the mode of transportation used and the respondent’s ability to independently manage their own medications (Lawton & Brody, 1969). For statistical analysis, the scores for each item (both ADL and IADL) were calculated and recoded dichotomously as either independent or dependent. If an item was checked as dependent (even one item of ADL or IADL), the item was identified as dependent. Self-reported medication compliance was determined and classified as either “yes” or “no.” Physical activity levels were assessed using the Korean standardized short form of the International Physical Activity Questionnaire (IPAQ; Kim, 2006). This questionnaire contains items related to vigorous physical activity, moderate physical activity, walking, and sitting. Responses were converted into metabolic equivalent tasks in terms of minutes per week following the IPAQ scoring protocol (Chun, 2012). Physical activity levels were categorized into three categories: inactive, minimally active, and active.

Health perception

Self-rated health was assessed using one question: “How do you feel about your health?” This question was scored on a scale of 0 (poor health) to 4 (excellent health), with higher scores associated with a higher self-rated health score on the 36-item Short-Form Health Survey (Ware, 2005).

Data Analysis

Data analysis was performed using IBM SPSS Statistics Version 24.0 (IBM, Inc., Armonk, NY, USA) and STATA 14 (StataCorp, College Station, TX, USA). Descriptive statistics (means, standard deviations, and proportions), independent t tests, chi-square tests, and analyses of variance tests, including post hoc analysis, were used to determine participant characteristics and compare trajectories.

Latent class growth analysis (LCGA) was used to determine whether there were subgroups or classes within the sample that exhibited different initial levels and different patterns of growth of HR-QoL over time (Yoo et al., 2016). LCGA is a semiparametric statistical technique that is used to analyze longitudinal data under the basic assumption that latent classes of trajectories exist in sample populations. The aim of the LCGA is to select a model with the optimal number of distinct patterns and appropriate polynomial order to best represent the heterogeneity of the trajectories (Yoo et al., 2016). The optimal number of latent classes was determined using the adjusted Bayesian information criterion (BIC), which has been shown to be a robust indicator of class enumeration with categorical outcomes (Muthén & Muthén, 2000). The adjusted BIC was used to compare several plausible models, with the lowest values indicating the best-fitting model.

Subsequently, multinomial logistic regressions were used to identify factors that affected the different trajectories over time. Results were presented in terms of relative risk ratio (RRR) with 95% confidence intervals.

Results

A total of 440 older adults with diabetes mellitus participated in the KHPS on more than two occasions during the 5-year period. Their mean age was 72.2 (SD = 4.9) years, and 56.6% were women. The mean duration of diabetes mellitus in the study sample was 10.8 (SD = 7.9) years; 69.1% of the participants lived with a spouse; and 13.2% had an education level of “no schooling.” At baseline and at the first, second, third, and fourth follow-ups, missing data for the primary outcome variable, HR-QoL, comprised 3.9%, 2.0%, 8.4%, 14.3%, and 19.1% of the responses, respectively. Mean HR-QoL scores increased from baseline to the first two follow-ups and decreased minimally thereafter, representing significant change (F = 4.587, p < .001). The descriptive statistics of the HR-QoL subdomain scores (unweighted score) indicated significant improvement from baseline and through each follow-up point in terms of the mobility, self-care, usual activity, and anxiety/depression subdomain trajectories (p < .05). However, the pain/discomfort subdomain trajectory was relatively stable, with a high score at baseline and at the four subsequent follow-up points (p < .242).

Health-Related Quality of Life Trajectories

Using group-based modeling, four distinct classes of HR-QoL trajectories were determined based on the BIC. The BIC decreased continuously from the one-class model to the six-class model (BIC1 = −410.69, BIC2 = −267.39, BIC3 = −199.77, BIC4 = −189.03, BIC5 = −178.55, BIC6 = −187.58). The five-class model showed the maximum point of the BIC curves, which comprised the class with less than 1% of cases. The four-class model was reviewed and selected as it contained the most prominent and substantively meaningful number of classes. The four-class model contained a group with a low HR-QoL pattern of 1.3%, and each group was examined in terms of their conceptual and clinical explanatory feasibility.

The smallest group (n = 5, 1.1%), Class 1, was labeled “low increase” (LI). This group exhibited steadily worsening HR-QoL (from medium to severe) and had increased 4 years after estimation. Class 2 (n = 157, 35.7%) was labeled “medium stable” (MS) because, although this group exhibited a slight decrease in HR-QoL over time, the average HR-QoL level was “medium” for the 5-year period. The largest group (n = 232, 52.7%), Class 3, was labeled “high stable” (HS) because the individuals in this group demonstrated a consistently high HR-QoL level across the 5-year period. Finally, Class 4 (n = 46, 10.5%) was labeled “high decrease” (HD) because its members maintained a stable HR-QoL score that, although initially the highest among the four trajectory groups, began to decrease at the third follow-up point (see Figure 3 and Table 1).

F3Figure 3.:

Health-Related Quality of Life (HR-QoL) Trajectories for the Four Latent Classes of Older Adults With Diabetes

Table 1. - Intercept and Slope of the Four Latent Trajectory Classes (N = 440) Class HR-QoL Classes Intercept (SE) Linear (SE) Quadratic (SE) n % (SE) p Class 1 Low increase 0.652 (0.161) −0.338 (0.129) 0.053 (0.022) 5 1.1 (0.553) .024 Class 2 Medium stable 0.753 (0.010) — — 157 35.7 (3.979) < .001 Class 3 High stable 0.944 (0.010) — — 232 52.7 (3.917) < .001 Class 4 High decrease 1.712 (0.206) −0.137 (0.043) — 46 10.5 (1.802) < .001

Note. HR-QoL = health-related quality of life; SE = standard error.

A summary of the comparison of the sample proportion is provided in Table 2.

Table 2. - Differences in the Characteristics of the Four Latent Classes (N = 440) Variable LI Class
(n = 5) MS Class
(n = 157) HS Class
(n = 232) HD Class
(n = 46) F/χ2 (p) n or M (SD) n or M (SD) n or M (SD) n or M (SD) Demographic factors  Gender (male/female) 2/3 43/114 108/124 38/8 47.577 (< .001)  Age (years) 71.6ab (6.62) 73.5b (5.29) 71.8a (4.59) 69.8a (3.54) 8.449 (< .001)  Educational level (none/elementary,  middle/high school or higher) 0/5/0 33/108/16 24/138/70 1/23/22 44.618 (< .001)  Spouse (yes/no) 4/1 94/63 172/60 34/12 9.454 (.019) Environmental factors  Household income (score, 1–5) 1.60 ab (0.89) 1.76 a (0.99) 2.19 b (1.18) 1.89 ab (3.16) 7.575 (< .001)  Residential region (metro/nonmetro) 0/5 48/109 95/137 15/31 7.135 (.058)  Duration of diabetes mellitus (years) 15.2 (7.98) 10.9 (8.16) 10.8 (7.85) 10.2 (6.84) 0.623 (.601)  Whether medical needs were met (yes/no/missing) 1/4 33/122/2 29/194/9 1/39 11.319 (.009) Health-related factors  Physical status   Body mass index 22.2 (2.75) 23.5 (3.89) 24.0 (2.90) 24.2 (3.04) 1.527 (.207)   Obesity (overweight/normal/underweight/missing) 1/2/2 52/84/19/2 73/143/15/1 15/26/3 9.028 (.144)   Number of chronic diseases 5.00 (1.41) 5.52 (2.61) 4.28 (2.00) 3.57 (1.95) 13.900 (< .001)   Hypertension (yes/no) 4/1 121/36 164/68 32/14 2.373 (.494)   Hearing problem (yes/no) 3/2 62/95 65/167 5/41 17.865 (< .001)   Vision problem (yes/no) 4/1 95/62 97/135 8/38 33.246 (< .001)   Eating problem (yes/no/missing) 1/4 23/134 29/202/1 14397 1.044 (.775)  Symptoms   Sleep disturbance (yes/no/missing) 2/3 92/65 132/99/1 21/24 2.641 (.468)   Stress regarding disease (yes/no/missing) 2/3 148/7/2 175/48/9 24/16 37.722 (< .001)   Stress regarding family (yes/no/missing) 2/3 65/87/5 92/129/11 5/32 12.705 (.004)   Stress regarding finances (yes/no/missing) 4/1 122/33/2 154/69/9 21/19 11.411 (.008)  Functional status   ADL (independent/dependent) 0/5 82/75 193/39 147/63 59.499 (< .001)   IADL (independent/dependent) 1/4 137/20 231/1 46/0 51.189 (< .001)   IPAQ (inactive/minimally active/active/missing) 5/0/0 93/53/11 90/106/35/1 14/22/9 26.493 (< .001)   Medication compliance (high/low) 5/0 145/12 205/27 45/1 4.725 (.175) Health perception  Self-rated health (score, 0–4) 0.25 a (0.50) 1.35 a (0.930) 2.04 b (0.940) 2.47 c (0.842) 32.467 (< .001)

Note. Post hoc was analyzed with the Bonferroni method. LI = low increase; MS = medium stable; HS = high stable; HD = high decrease; ADL = activities of daily living; IADL = instrumental activities of daily living; IPAQ = International Physical Activity Questionnaire.

a, b, c are group names given when the mean difference between groups is significant, and the order is a < b < c.


Baseline Characteristics of the Four Trajectory Classes

Information about the demographic, environmental, and health-related factors of the four latent classes is presented in Table 2. Differences in the demographic variables of gender, age, educational level, and spousal status were found among the four trajectory groups. The HD group was younger, the MS group included more women, and the HD group had more men. Whereas environmental factors, residential region, and duration of diabetes mellitus were largely similar among the four groups, group differences in physical status in terms of the number of chronic diseases, hearing problems, and vision problems were found. However, BMI and eating problems were homogeneous among the groups. Intergroup differences in terms of stress were observed for disease, family, and finance. Participants in all groups reported experiencing sleep disturbance issues. Functional status variables such as ADL, IADL, and physical activity differed across the groups, and all four groups complied with medication well. In addition, health perception differed across the four groups, and the HD group reported better self-rated health at baseline.

Predictors of Health-Related Quality of Life Trajectories

Multinomial logistic regression was used to identify the predictors of trajectory class group assignment (Table 3). The HD class was used as the reference group for the model, and its characteristics were compared with those of the MS and HS classes at baseline. The LI class was excluded from this analysis because its small number of members could not generate valid statistical difference results.

Table 3. - Class Comparison (Multinomial Logistic Regression) Variable Trajectory Class MS Class vs. HD Class (Ref.) HS Class vs. HD Class (Ref.) RRR 95% CI p RRR 95% CI p Demographic factors  Gender (male) 0.02 [0.00, 0.89] < .001 0.03 [0.01, 0.17] < .001  Age (years) 1.26 [1.09, 1.46] .002 1.23 [1.08, 1.40] .002  Educational level 0.29 [0.10, 0.82] .020 0.55 [0.22, 1.38] .201  Spouse (yes) 16.36 [2.90, 92.39] .002 17.20 [3.32, 89.03] .001 Environmental factors  Household income 0.97 [0.68, 1.39] .881 1.19 [0.90, 1.58] .215  Residential region (metro) 0.97 [0.30, 3.13] .957 1.62 [0.58, 4.50] .354  Duration of diabetes mellitus 0.99 [0.92, 1.08] .858 0.99 [0.92, 1.07] .824  Whether medical needs were met (yes) 12.22 [1.15, 129.96] .038 8.14 [0.84, 79.17] .071 Health-related factors  Physical status   Body mass index 0.99 [0.69, 1.45] .983 1.07 [0.78, 1.49] .668   Obesity (underweight) 2.75 [0.07, 115.44] .595 2.70 [0.10, 70.56] .551   Obesity (normal) 0.51 [0.07, 3.58] .498 1.05 [0.19, 5.65] .959   Number of chronic diseases 1.38 [1.04, 1.85] .027 1.09 [0.83, 1.42] .542   Hypertension (yes) 0.94 [0.27, 3.22] .915 1.17 [0.39, 3.50] .783   Hearing problem(s) (yes) 1.69 [0.38, 7.55] .494 2.25 [0.56, 9.10] .254   Vision problem(s) (yes) 5.84 [1.50, 22.66] .011 4.41 [1.25, 15.55] .021   Eating problem(s) (yes) 0.89 [0.17, 4.74] .894 0.86 [0.19, 3.96] .850  Symptoms   Sleep disturbance (yes) 1.45 [0.50, 4.25] .495 1.50 [0.59, 3.85] .395

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