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Effect of a Lifestyle-Based Intervention on Health-Related Quality of Life in Older Adults with Hypertension

Effect of a Lifestyle-Based Intervention on Health-Related Quality of Life in Older Adults with... Hindawi Journal of Aging Research Volume 2018, Article ID 6059560, 8 pages https://doi.org/10.1155/2018/6059560 Research Article Effect of a Lifestyle-Based Intervention on Health-Related Quality of Life in Older Adults with Hypertension 1,2 3 4 4 5 Mei-Lan Chen , Jie Hu, Thomas P. McCoy , Susan Letvak, and Luba Ivanov Byrdine F. Lewis College of Nursing and Health Professions, Georgia State University, Atlanta, GA 30303, USA Gerontology Institute, Georgia State University, Atlanta, GA 30303, USA College of Nursing, &e Ohio State University, Columbus, OH 43210, USA School of Nursing, University of North Carolina at Greensboro, Greensboro, NC 27402, USA College of Nursing, Chamberlain University, Downers Grove, IL 60515, USA Correspondence should be addressed to Mei-Lan Chen; mchen13@gsu.edu Received 9 February 2018; Accepted 2 April 2018; Published 7 May 2018 Academic Editor: Carmela R. Balistreri Copyright © 2018 Mei-Lan Chen et al. (is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (e purpose of this study was to examine the effect of a six-month lifestyle-based intervention on health-related quality of life (HRQOL) in older adults with hypertension. A secondary analysis of a randomized controlled trial was conducted to test the differences between the intervention and control groups on HRQOL (N � 196). (e results indicated that there were no sta- tistically significant differences between the intervention and control groups on change in HRQOL, but the final regression models were statistically significant. SF-36 mental component summary (MCS) score at baseline, stress at baseline, and change in stress were significant predictors for predicting change in the SF-36 MCS. SF-36 physical component summary (PCS) at baseline and change in stress were significant predictors for predicting change in the SF-36 PCS. (e findings suggest that the development of an effective intervention in improving HRQOL should be considered within individual, interpersonal, societal, and cultural factors for future research and clinical practice. percentage of adults ≥60 years of age who had controlled 1. Introduction hypertension was only 49.4% during 2015-2016 [6]. Hence, (e older population is growing significantly in the United high prevalence and poor control of high blood pressure States and global society. In 2014, the number of Americans remain critical issues for older Americans. aged 65 and above was 46 million, representing 15% of the (e World Health Organization (WHO) has emphasized total U.S. population; by 2030, the older population is es- the importance of assessing and promoting people’s quality timated to be about 21% of the total population and one in 13 of life [7]. One of the goals of Healthy People 2020 is will be older than 85 [1–3]. Older adults frequently expe- promoting health-related quality of life (HRQOL) and well- being across all life stages [8]. It is referred to as HRQOL rience aging-related functional declines and chronic diseases such as hypertension. Overall, one in three adults, an esti- when quality of life is considered in the health-related mated 75 million adults, has hypertension in the United context. HRQOL is a subjective and multidimensional States and only about half have high blood pressure under concept which is related to physical, mental, emotional, and control [4]. Hypertension is more prevalent among older social functioning [8]. (e term HRQOL is frequently used adults than young adults. Among those 45 to 54 years of age, to measure the effects of interventions and treatments on the prevalence of hypertension was 34.7%; the prevalence of health benefits in older adults. Trevisol et al. pointed out that hypertension was 64.7% among those 65 to 74 years of age; patients with hypertension are likely to have lower quality of and the prevalence was 77.3% in older adults≥75 years of age life than normotensive adults [9]. Studies also indicated that [5]. 82.9% of adults ≥60 years of age self-reported that they low HRQOL was associated with lower levels of treatment were taking antihypertensive medication [5]. However, the adherence in older adults with hypertension [10–12]. Hence, 2 Journal of Aging Research developing effective interventions to promote better colleagues [27, 28, 30, 31]. Briefly, in the original study, HRQOL in older adults with hypertension is essential. a convenience sample was recruited from the urban Los Studies suggested that stress and social support may Angeles area in California [28, 30, 31]. Independent-living impact HRQOL in older adults [13–16]. Frias and Whyne’s older Americans who spoke either English or Spanish were study indicated that stress was negatively associated with included in the study. Participants were excluded from the HRQOL [13]. In addition, Gerber (2012) found that social study if they had signs of psychosis or were not able to support was not significantly associated with physical complete the assessment battery. (ere were 460 older adults HRQOL, but lower levels of social support were significantly aged 60–95 enrolled in the study. Participants were ran- associated with lower levels of mental HRQOL [14]. Older domly assigned into the 6-month lifestyle intervention adults can be more vulnerable to stressful life events. It is group or the no-treatment control group. After 6 months, critically important to examine the influence of stress and its the control group received a delayed intervention (the same relation to HRQOL in older adults with hypertension. 6-month lifestyle intervention as the intervention group). However, there is little research in this area. Studies also have (e 6-month lifestyle-based intervention included weekly 2- shown that social support can impact older adults’ HRQOL hour group meetings, 10 individual 1-hour sessions in [17–20]. Based on existing literature, few studies have in- homes or community settings, and monthly community cluded a measure of both stress and social support in this outings [27, 30]. (e modular content of the intervention population [21–25]. Healthy aging is largely determined by comprised “impact of everyday activity on health, time individual lifestyle choices [1, 26]. Lifestyle interventions spending and energy conservation, transportation utiliza- have been found to promote physical functioning and tion, home and community safety, social relationship, cul- mental health in older adults [27, 28]. However, few research tural awareness, goal setting, and changing routines and studies tested the effects of lifestyle interventions in older habits” (30, p. 783; 31). adults with hypertension. (e current study performed In the present secondary analysis, we used only data secondary data analysis to investigate the effect of a lifestyle- collected during the first 6 months of the study (baseline and based intervention on HRQOL in this population. the first 6-month time point). Participants who self-reported taking blood pressure medication at baseline in the Well Elderly 2 Study were selected as subjects. 2. Conceptual Framework and Hypotheses (e conceptual framework guiding the current study was 3.2. Measures derived from the Social Cognitive (eory [29] and literature review. (is study assumes that there is a relationship be- 3.2.1. Demographic Data and Medical History. Demographic tween person (e.g., stress), environment (e.g., social sup- characteristics and medical history obtained by partici- port), and the outcome (e.g., HRQOL). Demographic factors pants’ self-report included gender, age, race, educational (age, race, gender, education, and income) may determine level, prescription medications, over-the-counter medicine, stress (person) and social support (environment) and can and diagnosis. influence HRQOL (outcome). Lifestyle-based interventions would significantly improve changes in stress (person), social support (environment), and HRQOL (outcome). 3.2.2. Stress. Stress was measured using the adapted Per- (e purpose of this study was to test the effects of a 6- ceived Stress Scale [32]. (e Perceived Stress Scale (PSS) is month lifestyle-based intervention on HRQOL in older one of the most widely used scales to examine levels of American adults with hypertension, accounting for stress perceived stress in older adults [22, 33]. (is instrument and social support as mediating variables. After receiving examines to what extent participants perceive the degree of a 6-month lifestyle-based intervention, the intervention their lives to be uncontrollable, unpredictable, and over- group was hypothesized to significantly improve in person loaded during the past month. (e adapted PSS is an 18-item (stress) and environment (social support) (H1) and outcome scale, and all items are rated on a 5-point Likert scale ranging (HRQOL) (H2) in older adults with hypertension from from 1 (never) to 5 (very often). Scores theoretically range pretest (baseline) to posttest (6 months) compared to the from 18 to 90; higher scores indicate higher levels of per- control group. ceived stress. In this study, Cronbach’s alpha coefficient of the adapted PSS was 0.85. 3. Materials and Methods 3.2.3. Social Support. Social support was assessed using the 3.1.DataSourceandSample. (is study was approved by the Institutional Review Board (IRB) of the University of North Lubben Social Network Scale (LSNS) [34]. (e LSNS is one Carolina at Greensboro (study#: 14-0428). (e sample in the of most commonly used instruments to measure perceived current study was drawn from the Well Elderly 2 Study. social support in older adults [35–37]. It is a 10-item scale (e data from the Well Elderly 2 Study were provided by that assesses the level of perceived support received from the Inter-university Consortium for Political and Social family, friends, and neighbors. Scores of the LSNS range Research [28]. (e research design, methods, and the in- from 0 to 50; higher scores indicate higher levels of social support. In this study, Cronbach’s alpha coefficient of the tervention of the Well Elderly 2 Study have been previously described in detail by Clark, Carlson, Jackson, and their LSNS was 0.75. Journal of Aging Research 3 Table 1: Characteristics of the sample at baseline (N � 196). Characteristic/variables Total (N � 196) Intervention group (n � 103) Control group (n � 93) p Education, n (%) 0.284 Less than high school graduate 57 (29) 36 (35) 21 (23) High school graduate 48 (25) 24 (23) 24 (26) Some college or technical school 71 (36) 33 (32) 38 (41) Four years of college or more 20 (10) 10 (10) 10 (11) Gender, n (%) 0.083 Male 72 (37) 32 (31) 40 (43) Female 124 (63) 71 (69) 53 (57) Race, n (%) 0.275 White 64 (33) 31 (30) 33 (36) African American 79 (40) 46 (45) 33 (36) Hispanic/Latino 33 (17) 19 (18) 14 (15) Asian 5 (3) 3 (3) 2 (2) Others 14 (7) 4 (4) 10 (11) Age (years), mean±SD 74.8± 7.7 74.2± 7.7 75.3± 7.7 0.304 Monthly income, n (%) 0.564 $0–$999 104 (54) 56 (54) 48 (53) $1,000–$1,999 44 (23) 20 (19) 24 (27) $2,000–$2,999 24 (12) 15 (15) 9 (10) $3,000 or more 21 (11) 12 (12) 9 (10) Other medications used, n (%) Diabetes medication 62 (32) 33 (32) 29 (31) 0.898 Antidepressant medication 21 (11) 14 (14) 7 (8) 0.170 Antipsychotic medication 39 (20) 22 (21) 17 (18) 0.590 Cholesterol reducer medication 95 (49) 49 (48) 46 (50) 0.792 Stress (PSS) 43.7± 10.7 43.8± 11.0 43.6± 10.5 0.885 Social support (LSNS) 27.2± 9.1 27.1± 8.5 27.3± 9.7 0.907 HRQOL : MCS 46.7± 11.4 46.1± 12.3 47.5± 10.4 0.405 HRQOL : PCS 39.6± 10.1 39.1± 10.1 40.2± 10.0 0.459 Note. SD: standard deviation; PSS: perceived stress scale; LSNS: Lubben Social Network Scale; HRQOL: health-related quality of life; MCS: mental component summary; PCS: physical component summary. 3.2.4. Health-Related Quality of Life (HRQOL). HRQOL was support at baseline, and the intervention group as in- measured using the 36-Item Short-Form Health Survey (SF- dependent variables were entered for separately modeling 36, version 2.0) [38, 39]. (e SF-36 is frequently used to the change in social support. Finally, the change in measure HRQOL in older adults [10, 40, 41]. It is a multi- HRQOL was modeled using independent variables in- domain that measures physical and mental components of cluding demographic variables, HRQOL at baseline, stress HRQOL with eight subscales. (e 8 subscales contribute to at baseline, change in stress, social support at baseline, two resulting component summaries: a mental component change in social support, and the intervention group summary (MCS) and a physical component summary (PCS). through hierarchical multiple regression analysis. A two- sided p value< 0.05 was considered statistically significant. Both PCS and MCS scores range from 0 to 100, representing worst to best health. Higher scores indicate better HRQOL All analyses were performed using SPSS version 23 (IBM [42]. In this study, reliability coefficients of the SF-36 PCS corp., Armonk, IL). and MCS were 0.83 and 0.85, respectively. 4. Results 3.3. Data Analysis. Descriptive statistics were initially cal- 4.1. Sample Characteristics. (ere were a total of 196 par- culated using means and standard deviations or frequencies ticipants in this study. Of the 196 participants, 103 were and percentages. Continuous variables were checked for randomly assigned to the intervention group and 93 to the outliers and normality in univariate analysis. To test hy- control group. Table 1 presents baseline characteristics of the potheses H1 and H2, multiple linear regression using a hi- participants and descriptive statistics. At baseline, the mean erarchical regression model building approach was age of participants was 74.8± 7.7 years; 63% were women. performed to test the effect of the intervention and make Most participants were White (33%) and African American predictions on criterion variables [43, 44]. Demographic (40%); the majority reported having a high school education variables (age, race, gender, education, and income), stress at or more (71%). Also, more than half (54%) reported baseline, and the intervention group (intervention versus a monthly income less than $1,000. In addition to taking control) as independent variables were entered for modeling hypertension medication, 32% of the participants reported the change in stress. Similarly, demographic variables, social that they also took diabetes medication and 49% used 4 Journal of Aging Research Table 2: Hierarchical multiple regression analyses predicting Table 3: Hierarchical multiple regression analyses predicting change in stress (post – baseline) after lifestyle-based intervention change in social support (post – baseline) after lifestyle-based in- (N � 169). tervention (N � 168). 2 a 2 a Independent variable ΔR β Independent variable ΔR β Step 1: demographic variables 0.05 Step 1: demographic variables 0.07 Education Education b b Less than high school graduate Less than high school graduate High school graduate 0.07 High school graduate 0.07 Some college or technical school 0.11 Some college or technical school 0.05 Four years of college or more 0.22 Four years of college or more −0.05 Gender Gender b b Male Male Female 0.08 Female −0.07 Race Race b b White White African American 0.11 African American 0.15 Hispanic/Latino 0.20 Hispanic/Latino 0.07 Asian −0.03 Asian −0.02 Others 0.10 Others 0.13 Age (years) −0.04 Age (years) −0.03 Monthly income Monthly income b b $0–$999 $0–$999 $1,000–$1,999 −0.08 $1,000–$1,999 0.13 $2,000–$2,999 −0.04 $2,000–$2,999 0.06 $3,000 or more 0.09 $3,000 or more 0.01 ∗∗∗ ∗∗∗ Step 2 0.20 Step 2 0.11 ∗∗∗ ∗∗∗ Stress at baseline (points) −0.49 Social support at baseline (points) −0.37 Step 3 <0.01 Step 3 <0.01 b b Control group Control group Intervention group 0.03 Intervention group −0.06 2 ∗∗∗ 2 ∗∗ Total R 0.25 Total R 0.18 a b a b ∗ ∗∗ ∗ ∗∗ Note. β shown is for the last step. Reference category. p< 0.05; p< 0.01; Note. β shown is for the last step. Reference category. p< 0.05; p< 0.01; ∗∗∗ ∗∗∗ p< 0.001. p< 0.001. antihyperlipidemic agents. (e intervention and control demographic variables based on the second step of the hi- groups did not statistically significantly differ on any sample erarchical model building (ΔR � 0.20, p< 0.001). Four characteristics (all p≥ 0.05). years of college or more, Hispanic/Latino versus White At baseline, the average stress score was 43.7± 10.7, and Americans, and stress at baseline were significant predictors. the average of social support score was 27.2± 9.1. (e mean Adjusting for all other factors, participants who received scores of the SF-36 mental component summary (MCS) and four years of college or more education were associated with physical component summary (PCS) were 46.7± 11.4 and a 0.22 increase in standard deviation (SD) units of predicted 39.6± 10.1, respectively. As shown in Table 1, there were no change in stress compared to participants who received less significant differences between the intervention and control than high school; Hispanic/Latino participants were asso- groups on these measures at baseline (p≥ 0.05). ciated with a 0.20 increase in SD units of predicted change in stress compared to White participants. For every 1 SD in- crease in stress at baseline, the predicted mean decrease in 4.2. Effects of a Lifestyle-Based Intervention in Changes in change in stress was 0.49 SD units, adjusting for all other Stress and Social Support. Table 2 indicates the results of the factors. predictor variables at each step and in the final model for Table 3 presents the results for the predictor variables at predicting change in stress (post – baseline) after lifestyle- each step and in the final model for predicting change in based intervention. (ere was not a statistically significant social support (post – baseline) after lifestyle-based in- difference between the intervention and control groups on tervention. As shown in Table 3, there was no statistically change in stress, but the final regression model was statis- significant difference between the intervention and control tically significant (p< 0.001). In the final hierarchical re- groups on change in social support, but the final regression gression model, demographic variables (education, gender, model was statistically significant (p< 0.01). In the final race, age, and monthly income), stress at baseline, and in- hierarchical regression model, demographic variables, social tervention versus control significantly accounted for 25% of support at baseline, and intervention versus control sig- the variance in change in stress (R � 0.25). In addition, nificantly accounted for 18% of the variance in change in stress at baseline accounted for a significant amount of social support (R � 0.18). Additionally, social support at variance in change in stress after controlling for the effect of baseline accounted for a significant amount of variance in Journal of Aging Research 5 Table 4: Hierarchical multiple regression analyses predicting Table 5: Hierarchical multiple regression analyses predicting change in health-related quality of life (MCS; post – baseline) change in health-related quality of life (PCS; post – baseline) according to lifestyle-based intervention, stress, and social support according to lifestyle-based intervention, stress, and social support (N � 167). (N � 167). a a 2 2 Independent variable ΔR β Independent variable ΔR β Step 1: demographic variables 0.05 Step 1: demographic variables 0.03 Education Education b b Less than high school graduate Less than high school graduate High school graduate 0.03 High school graduate −0.04 Some college or technical school 0.13 Some college or technical school −0.04 Four years of college or more 0.10 Four years of college or more −0.07 Gender Gender b b Male Male Female −0.13 Female −0.03 Race Race b b White White African American 0.02 African American −0.01 Hispanic/Latino −0.01 Hispanic/Latino −0.01 Asian 0.01 Asian −0.03 Others 0.01 Others 0.03 Age (years) 0.06 Age (years) 0.06 Monthly income Monthly income b b $0–$999 $0 - $999 $1,000–$1,999 0.03 $1,000 - $1,999 −0.01 $2,000–$2,999 0.00 $2,000 - $2,999 0.12 $3,000 or more −0.01 $3,000 or more 0.04 ∗∗∗ ∗∗∗ Step 2 0.26 Step 2 0.10 ∗∗∗ ∗∗∗ MCS at baseline (points) −0.66 PCS at baseline (points) −0.38 Step 3 <0.01 Step 3 <0.01 b b Control group Control group Intervention group 0.07 Intervention group 0.04 ∗∗ Step 4 0.08 Step 4 0.05 ∗∗ Stress at baseline (points) −0.27 Stress at baseline (points) −0.13 ∗∗ ∗ Change in stress −0.28 Change in stress −0.18 Social support at baseline (points) 0.07 Social support at baseline (points) 0.12 Change in social support 0.05 Change in social support 0.07 2 ∗∗∗ 2 ∗ Total R 0.39 Total R 0.18 a a Note. MCS: mental component summary. β shown is for the last step. Note. PCS: physical component summary. β shown is for the last step. b ∗ ∗∗ ∗∗∗ b ∗ ∗∗ ∗∗∗ Reference category. p< 0.05; p< 0.01; p< 0.001. Reference category. p< 0.05; p< 0.01; p< 0.001. change in social support, after controlling for the effect of change in the SF-36 MCS (R � 0.39). (e SF-36 MCS at demographic variables (ΔR � 0.11, p< 0.001). (e only baseline accounted for a significant amount of variance in significant predictor was social support at baseline, where for change in the SF-36 MCS, after controlling for the effect of every 1 SD increase in social support at baseline, the pre- demographic variables in the second step of modeling dicted mean decrease in change in social support was 0.37 (ΔR � 0.26, p< 0.001). In the last step, stress at baseline, SD units, adjusting for all other factors. change in stress, social support at baseline, and change in social support accounted for a significant amount of vari- ance in change in the SF-36 MCS after controlling for the 4.3. Effects of a Lifestyle-Based Intervention in Changes in effect of demographic variables, SF-36 MCS score at base- HRQOL. Table 4 indicates the results of the associations line, and the effect of intervention (ΔR � 0.08, p< 0.01). with the predictor variables at each step in predicting change (e SF-36 MCS score at baseline, stress at baseline, and in the SF-36 mental component summary (MCS; post – change in stress were significant predictors in the final baseline) according to lifestyle-based intervention, stress, model. For every 1 SD increase in the SF-36 MCS at baseline, and social support. (ere was no statistically significant the predicted mean decrease in the change in the SF-36 MCS difference between the intervention and control groups on was 0.66 SD units; for every 1 SD increase in stress at change in the SF-36 MCS, but the final regression model was baseline, the predicted mean decrease in change in the SF-36 statistically significant (p< 0.001). In the final hierarchical MCS was 0.27 SD units; for every 1 SD increase in change in regression model, demographic variables, SF-36 MCS score stress, the predicted mean decrease in change in the SF-36 at baseline, intervention versus control, stress at baseline, MCS was 0.28 SD units, adjusting for all other factors. change in stress, social support at baseline, and change in Table 5 shows the results for the associations with the social support significantly accounted for 39% of variance in predictor variables at each step for predicting change in the 6 Journal of Aging Research SF-36 physical component summary (PCS; post – baseline) reduce stress and increase social support for older adults according to lifestyle-based intervention, stress, and social with hypertension. In this study, stress at baseline and change in stress were support. As shown in Table 5, there was no statistically significant difference between the intervention and control significant predictors in predicting the mental component of groups on change in the SF-36 PCS, but the final regression HRQOL; change in stress was a significant predictor in model was statistically significant (p< 0.05). In the final predicting the physical component of HRQOL. Gerber also hierarchical regression model, demographic variables, SF-36 found that higher perceived stress was significantly associ- PCS score at baseline, intervention versus control, stress at ated with poorer mental HRQOL in older adults [14]. In baseline, change in stress, social support at baseline, and addition, Frias and Whyne revealed that stress was nega- change in social support significantly accounted for 18% of tively associated with HRQOL in community-dwelling older the variance in change in the SF-36 PCS (R � 0.18). (e SF- adults [13]. Gerber indicated that there were significant interactions between perceived stress and social support on 36 PCS score at baseline accounted for a significant amount of variance in the change in the SF-36 PCS after controlling mental HRQOL [14]. However, synergistic effects of stress and social support on HRQOL remain unclear. (ese for the effect of demographic variables (ΔR � 0.10, p< 0.001). (e SF-36 PCS at baseline and change in stress findings suggest that stress should be considered as a sig- were significant predictors of change in PCS scores in the nificant predictor for changes on HRQOL in older adults final model. For every 1 SD increase in the SF-36 PCS at with hypertension. baseline, the predicted mean decrease in the change in the Aging is a multifaceted process and is related to reduced SF-36 PCS was 0.38 SD units; for every 1 SD increase in functional capacity and chronic diseases [47–49]. Many change in stress, the predicted mean decrease in change in older adults have at least one chronic disease such as hy- the SF-36 PCS was 0.18 SD units, adjusting for all other pertension, diabetes, or cardiovascular diseases. However, factors. there has been little research to investigate the effects of lifestyle-based interventions in older adults with chronic diseases. (e result of this study showed the presence of 5. Discussion comorbidities in participants with hypertension. (e effects (is secondary analysis examined the effectiveness of of comorbidities on HRQOL remain unclear. (us, comorbidities should be considered as a factor for future a lifestyle-based intervention on HRQOL in older adults with hypertension and investigated stress and social support studies, and the conceptual framework should be expanded as mediating variables. As many older adults suffer from to include comorbidities. Finally, there is no common hypertension, developing effective interventions to enhance language on what is the dose-response effect of lifestyle- older adults’ HRQOL is necessary for healthy aging. (e based programs in older adults with hypertension. How results of this analysis provide empirical evidence, advance much is enough for older adults with hypertension? Further the scientific knowledge, and propose intervention recom- research should focus on older adults with hypertension in exposure to lifestyle interventions and racial differences in mendations for future research and clinical practice in older adults with hypertension. response to lifestyle interventions. (e current study has several limitations. First, the effect (e findings of the study indicated that there were no statistically significant intervention effects on stress, social of the lifestyle-based intervention was tested from pretest (baseline) to posttest (the 6-month time point). Hence, the support, and HRQOL, but the final regression models were statistically significant in the last step of the hierarchical lifestyle intervention may not have significant short-term multiple regression analysis. According to Baron and Kenny effects on change in social support, stress, and HRQOL (1986) criteria for a mediation analysis, social support and [28, 31, 50, 51]. Second, some confounding factors were not stress failed to function as mediators in the current study available in the dataset that may impact the intervention [45]. (is result is inconsistent with previous research. effect on HRQOL, such as frailty, chronic pain, and sleep Previous studies revealed that social support and stress can quality. Also, details on the hypertensive status of patients were not available in the dataset. Additionally, most par- mediate lifestyle practices and health-related quality of life in older adults [17–20]. Additionally, in the original study, the ticipants were women and reported low income. Lastly, the sample was urban, community-dwelling older adults and 6-month intervention was an activity-based lifestyle in- tervention which emphasized the importance of activity cannot be generalized to older adults who live in rural areas participation and developing new health-related habits (28; and nursing homes. 31, p. 92). However, for older adults, stress and lack of social support can come from chronic illness, financial difficulties, 6. Conclusions retirement, change in living situation, family problems, or aging-related physical impairments [20, 24, 46]. (erefore, (ere is limited research to test the effects of lifestyle in- this lifestyle intervention may not have significant effects on terventions on HRQOL in older adults with hypertension. In changes in stress, social support, and HRQOL. Also, many this study, the results revealed that the regression model is stressors are chronic and long term in older adults [24, 46]. statistically significant in predicting changes in HRQOL (e 6-month duration of the intervention may not be according to lifestyle-based intervention, stress, and social sufficient for changing stress and HRQOL. Hence, this study support. Educational levels, race, stress at baseline are sig- suggests that further interventions should consider how to nificant predictors for predicting change in stress; social Journal of Aging Research 7 Data Brief, 289, National Center for Health Statistics, support at baseline is the significant predictor for predicting Hyattsville, MD, USA, 2017. change in social support. In addition, SF-36 MCS score at [7] World Health Organization, “(e World Health Organization baseline, stress at baseline, and change in stress are signif- Quality of Life Assessment (WHOQOL): position paper from icant predictors of change in MCS scores in the final model. the World Health Organization,” Social Science and Medicine, SF-36 PCS score at baseline and change in stress are sig- vol. 41, no. 10, pp. 1403–1409, 2005. nificant predictors for predicting change in the SF-36 PCS. [8] Office of Disease Prevention and Health Promotion, Healthy As many older adults have high blood pressure and reduced People 2020: Health-Related Quality of Life and Well-Being, HRQOL, developing effective interventions in promoting Office of Disease Prevention and Health Promotion, Wash- hypertension self-management and improving HRQOL for ington, DC, USA, 2015, https://www.healthypeople.gov/2020/ older adults with hypertension is essential. (is secondary about/foundation-health-measures/Health-Related-Quality-of- analysis suggests that stress management and social support Life-and-Well-Being. resources should be included in the lifestyle intervention for [9] D. J. Trevisol, L. B. Moreira, A. Kerkhoff, S. C. Fuchs, and F. Fuchs, “Health- related quality of life and hypertension: future research and clinical practice. (e results indicate that a systematic review and meta-analysis of observational studies,” the development of an effective intervention in improving Journal of Hypertension, vol. 29, no. 2, pp. 179–188, 2011. HRQOL should be considered within individual, in- [10] E. W. Holt, P. Muntner, C. J. Joyce, L. Webber, and terpersonal, societal, and cultural factors when imple- M. A. Krousel-Wood, “Health-related quality of life and menting the lifestyle-based interventions. antihypertensive medication adherence among older adults,” Age and Ageing, vol. 39, no. 4, pp. 481–487, 2010. Data Availability [11] D. J. 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Effect of a Lifestyle-Based Intervention on Health-Related Quality of Life in Older Adults with Hypertension

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Copyright © 2018 Mei-Lan Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Hindawi Journal of Aging Research Volume 2018, Article ID 6059560, 8 pages https://doi.org/10.1155/2018/6059560 Research Article Effect of a Lifestyle-Based Intervention on Health-Related Quality of Life in Older Adults with Hypertension 1,2 3 4 4 5 Mei-Lan Chen , Jie Hu, Thomas P. McCoy , Susan Letvak, and Luba Ivanov Byrdine F. Lewis College of Nursing and Health Professions, Georgia State University, Atlanta, GA 30303, USA Gerontology Institute, Georgia State University, Atlanta, GA 30303, USA College of Nursing, &e Ohio State University, Columbus, OH 43210, USA School of Nursing, University of North Carolina at Greensboro, Greensboro, NC 27402, USA College of Nursing, Chamberlain University, Downers Grove, IL 60515, USA Correspondence should be addressed to Mei-Lan Chen; mchen13@gsu.edu Received 9 February 2018; Accepted 2 April 2018; Published 7 May 2018 Academic Editor: Carmela R. Balistreri Copyright © 2018 Mei-Lan Chen et al. (is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (e purpose of this study was to examine the effect of a six-month lifestyle-based intervention on health-related quality of life (HRQOL) in older adults with hypertension. A secondary analysis of a randomized controlled trial was conducted to test the differences between the intervention and control groups on HRQOL (N � 196). (e results indicated that there were no sta- tistically significant differences between the intervention and control groups on change in HRQOL, but the final regression models were statistically significant. SF-36 mental component summary (MCS) score at baseline, stress at baseline, and change in stress were significant predictors for predicting change in the SF-36 MCS. SF-36 physical component summary (PCS) at baseline and change in stress were significant predictors for predicting change in the SF-36 PCS. (e findings suggest that the development of an effective intervention in improving HRQOL should be considered within individual, interpersonal, societal, and cultural factors for future research and clinical practice. percentage of adults ≥60 years of age who had controlled 1. Introduction hypertension was only 49.4% during 2015-2016 [6]. Hence, (e older population is growing significantly in the United high prevalence and poor control of high blood pressure States and global society. In 2014, the number of Americans remain critical issues for older Americans. aged 65 and above was 46 million, representing 15% of the (e World Health Organization (WHO) has emphasized total U.S. population; by 2030, the older population is es- the importance of assessing and promoting people’s quality timated to be about 21% of the total population and one in 13 of life [7]. One of the goals of Healthy People 2020 is will be older than 85 [1–3]. Older adults frequently expe- promoting health-related quality of life (HRQOL) and well- being across all life stages [8]. It is referred to as HRQOL rience aging-related functional declines and chronic diseases such as hypertension. Overall, one in three adults, an esti- when quality of life is considered in the health-related mated 75 million adults, has hypertension in the United context. HRQOL is a subjective and multidimensional States and only about half have high blood pressure under concept which is related to physical, mental, emotional, and control [4]. Hypertension is more prevalent among older social functioning [8]. (e term HRQOL is frequently used adults than young adults. Among those 45 to 54 years of age, to measure the effects of interventions and treatments on the prevalence of hypertension was 34.7%; the prevalence of health benefits in older adults. Trevisol et al. pointed out that hypertension was 64.7% among those 65 to 74 years of age; patients with hypertension are likely to have lower quality of and the prevalence was 77.3% in older adults≥75 years of age life than normotensive adults [9]. Studies also indicated that [5]. 82.9% of adults ≥60 years of age self-reported that they low HRQOL was associated with lower levels of treatment were taking antihypertensive medication [5]. However, the adherence in older adults with hypertension [10–12]. Hence, 2 Journal of Aging Research developing effective interventions to promote better colleagues [27, 28, 30, 31]. Briefly, in the original study, HRQOL in older adults with hypertension is essential. a convenience sample was recruited from the urban Los Studies suggested that stress and social support may Angeles area in California [28, 30, 31]. Independent-living impact HRQOL in older adults [13–16]. Frias and Whyne’s older Americans who spoke either English or Spanish were study indicated that stress was negatively associated with included in the study. Participants were excluded from the HRQOL [13]. In addition, Gerber (2012) found that social study if they had signs of psychosis or were not able to support was not significantly associated with physical complete the assessment battery. (ere were 460 older adults HRQOL, but lower levels of social support were significantly aged 60–95 enrolled in the study. Participants were ran- associated with lower levels of mental HRQOL [14]. Older domly assigned into the 6-month lifestyle intervention adults can be more vulnerable to stressful life events. It is group or the no-treatment control group. After 6 months, critically important to examine the influence of stress and its the control group received a delayed intervention (the same relation to HRQOL in older adults with hypertension. 6-month lifestyle intervention as the intervention group). However, there is little research in this area. Studies also have (e 6-month lifestyle-based intervention included weekly 2- shown that social support can impact older adults’ HRQOL hour group meetings, 10 individual 1-hour sessions in [17–20]. Based on existing literature, few studies have in- homes or community settings, and monthly community cluded a measure of both stress and social support in this outings [27, 30]. (e modular content of the intervention population [21–25]. Healthy aging is largely determined by comprised “impact of everyday activity on health, time individual lifestyle choices [1, 26]. Lifestyle interventions spending and energy conservation, transportation utiliza- have been found to promote physical functioning and tion, home and community safety, social relationship, cul- mental health in older adults [27, 28]. However, few research tural awareness, goal setting, and changing routines and studies tested the effects of lifestyle interventions in older habits” (30, p. 783; 31). adults with hypertension. (e current study performed In the present secondary analysis, we used only data secondary data analysis to investigate the effect of a lifestyle- collected during the first 6 months of the study (baseline and based intervention on HRQOL in this population. the first 6-month time point). Participants who self-reported taking blood pressure medication at baseline in the Well Elderly 2 Study were selected as subjects. 2. Conceptual Framework and Hypotheses (e conceptual framework guiding the current study was 3.2. Measures derived from the Social Cognitive (eory [29] and literature review. (is study assumes that there is a relationship be- 3.2.1. Demographic Data and Medical History. Demographic tween person (e.g., stress), environment (e.g., social sup- characteristics and medical history obtained by partici- port), and the outcome (e.g., HRQOL). Demographic factors pants’ self-report included gender, age, race, educational (age, race, gender, education, and income) may determine level, prescription medications, over-the-counter medicine, stress (person) and social support (environment) and can and diagnosis. influence HRQOL (outcome). Lifestyle-based interventions would significantly improve changes in stress (person), social support (environment), and HRQOL (outcome). 3.2.2. Stress. Stress was measured using the adapted Per- (e purpose of this study was to test the effects of a 6- ceived Stress Scale [32]. (e Perceived Stress Scale (PSS) is month lifestyle-based intervention on HRQOL in older one of the most widely used scales to examine levels of American adults with hypertension, accounting for stress perceived stress in older adults [22, 33]. (is instrument and social support as mediating variables. After receiving examines to what extent participants perceive the degree of a 6-month lifestyle-based intervention, the intervention their lives to be uncontrollable, unpredictable, and over- group was hypothesized to significantly improve in person loaded during the past month. (e adapted PSS is an 18-item (stress) and environment (social support) (H1) and outcome scale, and all items are rated on a 5-point Likert scale ranging (HRQOL) (H2) in older adults with hypertension from from 1 (never) to 5 (very often). Scores theoretically range pretest (baseline) to posttest (6 months) compared to the from 18 to 90; higher scores indicate higher levels of per- control group. ceived stress. In this study, Cronbach’s alpha coefficient of the adapted PSS was 0.85. 3. Materials and Methods 3.2.3. Social Support. Social support was assessed using the 3.1.DataSourceandSample. (is study was approved by the Institutional Review Board (IRB) of the University of North Lubben Social Network Scale (LSNS) [34]. (e LSNS is one Carolina at Greensboro (study#: 14-0428). (e sample in the of most commonly used instruments to measure perceived current study was drawn from the Well Elderly 2 Study. social support in older adults [35–37]. It is a 10-item scale (e data from the Well Elderly 2 Study were provided by that assesses the level of perceived support received from the Inter-university Consortium for Political and Social family, friends, and neighbors. Scores of the LSNS range Research [28]. (e research design, methods, and the in- from 0 to 50; higher scores indicate higher levels of social support. In this study, Cronbach’s alpha coefficient of the tervention of the Well Elderly 2 Study have been previously described in detail by Clark, Carlson, Jackson, and their LSNS was 0.75. Journal of Aging Research 3 Table 1: Characteristics of the sample at baseline (N � 196). Characteristic/variables Total (N � 196) Intervention group (n � 103) Control group (n � 93) p Education, n (%) 0.284 Less than high school graduate 57 (29) 36 (35) 21 (23) High school graduate 48 (25) 24 (23) 24 (26) Some college or technical school 71 (36) 33 (32) 38 (41) Four years of college or more 20 (10) 10 (10) 10 (11) Gender, n (%) 0.083 Male 72 (37) 32 (31) 40 (43) Female 124 (63) 71 (69) 53 (57) Race, n (%) 0.275 White 64 (33) 31 (30) 33 (36) African American 79 (40) 46 (45) 33 (36) Hispanic/Latino 33 (17) 19 (18) 14 (15) Asian 5 (3) 3 (3) 2 (2) Others 14 (7) 4 (4) 10 (11) Age (years), mean±SD 74.8± 7.7 74.2± 7.7 75.3± 7.7 0.304 Monthly income, n (%) 0.564 $0–$999 104 (54) 56 (54) 48 (53) $1,000–$1,999 44 (23) 20 (19) 24 (27) $2,000–$2,999 24 (12) 15 (15) 9 (10) $3,000 or more 21 (11) 12 (12) 9 (10) Other medications used, n (%) Diabetes medication 62 (32) 33 (32) 29 (31) 0.898 Antidepressant medication 21 (11) 14 (14) 7 (8) 0.170 Antipsychotic medication 39 (20) 22 (21) 17 (18) 0.590 Cholesterol reducer medication 95 (49) 49 (48) 46 (50) 0.792 Stress (PSS) 43.7± 10.7 43.8± 11.0 43.6± 10.5 0.885 Social support (LSNS) 27.2± 9.1 27.1± 8.5 27.3± 9.7 0.907 HRQOL : MCS 46.7± 11.4 46.1± 12.3 47.5± 10.4 0.405 HRQOL : PCS 39.6± 10.1 39.1± 10.1 40.2± 10.0 0.459 Note. SD: standard deviation; PSS: perceived stress scale; LSNS: Lubben Social Network Scale; HRQOL: health-related quality of life; MCS: mental component summary; PCS: physical component summary. 3.2.4. Health-Related Quality of Life (HRQOL). HRQOL was support at baseline, and the intervention group as in- measured using the 36-Item Short-Form Health Survey (SF- dependent variables were entered for separately modeling 36, version 2.0) [38, 39]. (e SF-36 is frequently used to the change in social support. Finally, the change in measure HRQOL in older adults [10, 40, 41]. It is a multi- HRQOL was modeled using independent variables in- domain that measures physical and mental components of cluding demographic variables, HRQOL at baseline, stress HRQOL with eight subscales. (e 8 subscales contribute to at baseline, change in stress, social support at baseline, two resulting component summaries: a mental component change in social support, and the intervention group summary (MCS) and a physical component summary (PCS). through hierarchical multiple regression analysis. A two- sided p value< 0.05 was considered statistically significant. Both PCS and MCS scores range from 0 to 100, representing worst to best health. Higher scores indicate better HRQOL All analyses were performed using SPSS version 23 (IBM [42]. In this study, reliability coefficients of the SF-36 PCS corp., Armonk, IL). and MCS were 0.83 and 0.85, respectively. 4. Results 3.3. Data Analysis. Descriptive statistics were initially cal- 4.1. Sample Characteristics. (ere were a total of 196 par- culated using means and standard deviations or frequencies ticipants in this study. Of the 196 participants, 103 were and percentages. Continuous variables were checked for randomly assigned to the intervention group and 93 to the outliers and normality in univariate analysis. To test hy- control group. Table 1 presents baseline characteristics of the potheses H1 and H2, multiple linear regression using a hi- participants and descriptive statistics. At baseline, the mean erarchical regression model building approach was age of participants was 74.8± 7.7 years; 63% were women. performed to test the effect of the intervention and make Most participants were White (33%) and African American predictions on criterion variables [43, 44]. Demographic (40%); the majority reported having a high school education variables (age, race, gender, education, and income), stress at or more (71%). Also, more than half (54%) reported baseline, and the intervention group (intervention versus a monthly income less than $1,000. In addition to taking control) as independent variables were entered for modeling hypertension medication, 32% of the participants reported the change in stress. Similarly, demographic variables, social that they also took diabetes medication and 49% used 4 Journal of Aging Research Table 2: Hierarchical multiple regression analyses predicting Table 3: Hierarchical multiple regression analyses predicting change in stress (post – baseline) after lifestyle-based intervention change in social support (post – baseline) after lifestyle-based in- (N � 169). tervention (N � 168). 2 a 2 a Independent variable ΔR β Independent variable ΔR β Step 1: demographic variables 0.05 Step 1: demographic variables 0.07 Education Education b b Less than high school graduate Less than high school graduate High school graduate 0.07 High school graduate 0.07 Some college or technical school 0.11 Some college or technical school 0.05 Four years of college or more 0.22 Four years of college or more −0.05 Gender Gender b b Male Male Female 0.08 Female −0.07 Race Race b b White White African American 0.11 African American 0.15 Hispanic/Latino 0.20 Hispanic/Latino 0.07 Asian −0.03 Asian −0.02 Others 0.10 Others 0.13 Age (years) −0.04 Age (years) −0.03 Monthly income Monthly income b b $0–$999 $0–$999 $1,000–$1,999 −0.08 $1,000–$1,999 0.13 $2,000–$2,999 −0.04 $2,000–$2,999 0.06 $3,000 or more 0.09 $3,000 or more 0.01 ∗∗∗ ∗∗∗ Step 2 0.20 Step 2 0.11 ∗∗∗ ∗∗∗ Stress at baseline (points) −0.49 Social support at baseline (points) −0.37 Step 3 <0.01 Step 3 <0.01 b b Control group Control group Intervention group 0.03 Intervention group −0.06 2 ∗∗∗ 2 ∗∗ Total R 0.25 Total R 0.18 a b a b ∗ ∗∗ ∗ ∗∗ Note. β shown is for the last step. Reference category. p< 0.05; p< 0.01; Note. β shown is for the last step. Reference category. p< 0.05; p< 0.01; ∗∗∗ ∗∗∗ p< 0.001. p< 0.001. antihyperlipidemic agents. (e intervention and control demographic variables based on the second step of the hi- groups did not statistically significantly differ on any sample erarchical model building (ΔR � 0.20, p< 0.001). Four characteristics (all p≥ 0.05). years of college or more, Hispanic/Latino versus White At baseline, the average stress score was 43.7± 10.7, and Americans, and stress at baseline were significant predictors. the average of social support score was 27.2± 9.1. (e mean Adjusting for all other factors, participants who received scores of the SF-36 mental component summary (MCS) and four years of college or more education were associated with physical component summary (PCS) were 46.7± 11.4 and a 0.22 increase in standard deviation (SD) units of predicted 39.6± 10.1, respectively. As shown in Table 1, there were no change in stress compared to participants who received less significant differences between the intervention and control than high school; Hispanic/Latino participants were asso- groups on these measures at baseline (p≥ 0.05). ciated with a 0.20 increase in SD units of predicted change in stress compared to White participants. For every 1 SD in- crease in stress at baseline, the predicted mean decrease in 4.2. Effects of a Lifestyle-Based Intervention in Changes in change in stress was 0.49 SD units, adjusting for all other Stress and Social Support. Table 2 indicates the results of the factors. predictor variables at each step and in the final model for Table 3 presents the results for the predictor variables at predicting change in stress (post – baseline) after lifestyle- each step and in the final model for predicting change in based intervention. (ere was not a statistically significant social support (post – baseline) after lifestyle-based in- difference between the intervention and control groups on tervention. As shown in Table 3, there was no statistically change in stress, but the final regression model was statis- significant difference between the intervention and control tically significant (p< 0.001). In the final hierarchical re- groups on change in social support, but the final regression gression model, demographic variables (education, gender, model was statistically significant (p< 0.01). In the final race, age, and monthly income), stress at baseline, and in- hierarchical regression model, demographic variables, social tervention versus control significantly accounted for 25% of support at baseline, and intervention versus control sig- the variance in change in stress (R � 0.25). In addition, nificantly accounted for 18% of the variance in change in stress at baseline accounted for a significant amount of social support (R � 0.18). Additionally, social support at variance in change in stress after controlling for the effect of baseline accounted for a significant amount of variance in Journal of Aging Research 5 Table 4: Hierarchical multiple regression analyses predicting Table 5: Hierarchical multiple regression analyses predicting change in health-related quality of life (MCS; post – baseline) change in health-related quality of life (PCS; post – baseline) according to lifestyle-based intervention, stress, and social support according to lifestyle-based intervention, stress, and social support (N � 167). (N � 167). a a 2 2 Independent variable ΔR β Independent variable ΔR β Step 1: demographic variables 0.05 Step 1: demographic variables 0.03 Education Education b b Less than high school graduate Less than high school graduate High school graduate 0.03 High school graduate −0.04 Some college or technical school 0.13 Some college or technical school −0.04 Four years of college or more 0.10 Four years of college or more −0.07 Gender Gender b b Male Male Female −0.13 Female −0.03 Race Race b b White White African American 0.02 African American −0.01 Hispanic/Latino −0.01 Hispanic/Latino −0.01 Asian 0.01 Asian −0.03 Others 0.01 Others 0.03 Age (years) 0.06 Age (years) 0.06 Monthly income Monthly income b b $0–$999 $0 - $999 $1,000–$1,999 0.03 $1,000 - $1,999 −0.01 $2,000–$2,999 0.00 $2,000 - $2,999 0.12 $3,000 or more −0.01 $3,000 or more 0.04 ∗∗∗ ∗∗∗ Step 2 0.26 Step 2 0.10 ∗∗∗ ∗∗∗ MCS at baseline (points) −0.66 PCS at baseline (points) −0.38 Step 3 <0.01 Step 3 <0.01 b b Control group Control group Intervention group 0.07 Intervention group 0.04 ∗∗ Step 4 0.08 Step 4 0.05 ∗∗ Stress at baseline (points) −0.27 Stress at baseline (points) −0.13 ∗∗ ∗ Change in stress −0.28 Change in stress −0.18 Social support at baseline (points) 0.07 Social support at baseline (points) 0.12 Change in social support 0.05 Change in social support 0.07 2 ∗∗∗ 2 ∗ Total R 0.39 Total R 0.18 a a Note. MCS: mental component summary. β shown is for the last step. Note. PCS: physical component summary. β shown is for the last step. b ∗ ∗∗ ∗∗∗ b ∗ ∗∗ ∗∗∗ Reference category. p< 0.05; p< 0.01; p< 0.001. Reference category. p< 0.05; p< 0.01; p< 0.001. change in social support, after controlling for the effect of change in the SF-36 MCS (R � 0.39). (e SF-36 MCS at demographic variables (ΔR � 0.11, p< 0.001). (e only baseline accounted for a significant amount of variance in significant predictor was social support at baseline, where for change in the SF-36 MCS, after controlling for the effect of every 1 SD increase in social support at baseline, the pre- demographic variables in the second step of modeling dicted mean decrease in change in social support was 0.37 (ΔR � 0.26, p< 0.001). In the last step, stress at baseline, SD units, adjusting for all other factors. change in stress, social support at baseline, and change in social support accounted for a significant amount of vari- ance in change in the SF-36 MCS after controlling for the 4.3. Effects of a Lifestyle-Based Intervention in Changes in effect of demographic variables, SF-36 MCS score at base- HRQOL. Table 4 indicates the results of the associations line, and the effect of intervention (ΔR � 0.08, p< 0.01). with the predictor variables at each step in predicting change (e SF-36 MCS score at baseline, stress at baseline, and in the SF-36 mental component summary (MCS; post – change in stress were significant predictors in the final baseline) according to lifestyle-based intervention, stress, model. For every 1 SD increase in the SF-36 MCS at baseline, and social support. (ere was no statistically significant the predicted mean decrease in the change in the SF-36 MCS difference between the intervention and control groups on was 0.66 SD units; for every 1 SD increase in stress at change in the SF-36 MCS, but the final regression model was baseline, the predicted mean decrease in change in the SF-36 statistically significant (p< 0.001). In the final hierarchical MCS was 0.27 SD units; for every 1 SD increase in change in regression model, demographic variables, SF-36 MCS score stress, the predicted mean decrease in change in the SF-36 at baseline, intervention versus control, stress at baseline, MCS was 0.28 SD units, adjusting for all other factors. change in stress, social support at baseline, and change in Table 5 shows the results for the associations with the social support significantly accounted for 39% of variance in predictor variables at each step for predicting change in the 6 Journal of Aging Research SF-36 physical component summary (PCS; post – baseline) reduce stress and increase social support for older adults according to lifestyle-based intervention, stress, and social with hypertension. In this study, stress at baseline and change in stress were support. As shown in Table 5, there was no statistically significant difference between the intervention and control significant predictors in predicting the mental component of groups on change in the SF-36 PCS, but the final regression HRQOL; change in stress was a significant predictor in model was statistically significant (p< 0.05). In the final predicting the physical component of HRQOL. Gerber also hierarchical regression model, demographic variables, SF-36 found that higher perceived stress was significantly associ- PCS score at baseline, intervention versus control, stress at ated with poorer mental HRQOL in older adults [14]. In baseline, change in stress, social support at baseline, and addition, Frias and Whyne revealed that stress was nega- change in social support significantly accounted for 18% of tively associated with HRQOL in community-dwelling older the variance in change in the SF-36 PCS (R � 0.18). (e SF- adults [13]. Gerber indicated that there were significant interactions between perceived stress and social support on 36 PCS score at baseline accounted for a significant amount of variance in the change in the SF-36 PCS after controlling mental HRQOL [14]. However, synergistic effects of stress and social support on HRQOL remain unclear. (ese for the effect of demographic variables (ΔR � 0.10, p< 0.001). (e SF-36 PCS at baseline and change in stress findings suggest that stress should be considered as a sig- were significant predictors of change in PCS scores in the nificant predictor for changes on HRQOL in older adults final model. For every 1 SD increase in the SF-36 PCS at with hypertension. baseline, the predicted mean decrease in the change in the Aging is a multifaceted process and is related to reduced SF-36 PCS was 0.38 SD units; for every 1 SD increase in functional capacity and chronic diseases [47–49]. Many change in stress, the predicted mean decrease in change in older adults have at least one chronic disease such as hy- the SF-36 PCS was 0.18 SD units, adjusting for all other pertension, diabetes, or cardiovascular diseases. However, factors. there has been little research to investigate the effects of lifestyle-based interventions in older adults with chronic diseases. (e result of this study showed the presence of 5. Discussion comorbidities in participants with hypertension. (e effects (is secondary analysis examined the effectiveness of of comorbidities on HRQOL remain unclear. (us, comorbidities should be considered as a factor for future a lifestyle-based intervention on HRQOL in older adults with hypertension and investigated stress and social support studies, and the conceptual framework should be expanded as mediating variables. As many older adults suffer from to include comorbidities. Finally, there is no common hypertension, developing effective interventions to enhance language on what is the dose-response effect of lifestyle- older adults’ HRQOL is necessary for healthy aging. (e based programs in older adults with hypertension. How results of this analysis provide empirical evidence, advance much is enough for older adults with hypertension? Further the scientific knowledge, and propose intervention recom- research should focus on older adults with hypertension in exposure to lifestyle interventions and racial differences in mendations for future research and clinical practice in older adults with hypertension. response to lifestyle interventions. (e current study has several limitations. First, the effect (e findings of the study indicated that there were no statistically significant intervention effects on stress, social of the lifestyle-based intervention was tested from pretest (baseline) to posttest (the 6-month time point). Hence, the support, and HRQOL, but the final regression models were statistically significant in the last step of the hierarchical lifestyle intervention may not have significant short-term multiple regression analysis. According to Baron and Kenny effects on change in social support, stress, and HRQOL (1986) criteria for a mediation analysis, social support and [28, 31, 50, 51]. Second, some confounding factors were not stress failed to function as mediators in the current study available in the dataset that may impact the intervention [45]. (is result is inconsistent with previous research. effect on HRQOL, such as frailty, chronic pain, and sleep Previous studies revealed that social support and stress can quality. Also, details on the hypertensive status of patients were not available in the dataset. Additionally, most par- mediate lifestyle practices and health-related quality of life in older adults [17–20]. Additionally, in the original study, the ticipants were women and reported low income. Lastly, the sample was urban, community-dwelling older adults and 6-month intervention was an activity-based lifestyle in- tervention which emphasized the importance of activity cannot be generalized to older adults who live in rural areas participation and developing new health-related habits (28; and nursing homes. 31, p. 92). However, for older adults, stress and lack of social support can come from chronic illness, financial difficulties, 6. Conclusions retirement, change in living situation, family problems, or aging-related physical impairments [20, 24, 46]. (erefore, (ere is limited research to test the effects of lifestyle in- this lifestyle intervention may not have significant effects on terventions on HRQOL in older adults with hypertension. In changes in stress, social support, and HRQOL. Also, many this study, the results revealed that the regression model is stressors are chronic and long term in older adults [24, 46]. statistically significant in predicting changes in HRQOL (e 6-month duration of the intervention may not be according to lifestyle-based intervention, stress, and social sufficient for changing stress and HRQOL. Hence, this study support. Educational levels, race, stress at baseline are sig- suggests that further interventions should consider how to nificant predictors for predicting change in stress; social Journal of Aging Research 7 Data Brief, 289, National Center for Health Statistics, support at baseline is the significant predictor for predicting Hyattsville, MD, USA, 2017. change in social support. In addition, SF-36 MCS score at [7] World Health Organization, “(e World Health Organization baseline, stress at baseline, and change in stress are signif- Quality of Life Assessment (WHOQOL): position paper from icant predictors of change in MCS scores in the final model. the World Health Organization,” Social Science and Medicine, SF-36 PCS score at baseline and change in stress are sig- vol. 41, no. 10, pp. 1403–1409, 2005. nificant predictors for predicting change in the SF-36 PCS. [8] Office of Disease Prevention and Health Promotion, Healthy As many older adults have high blood pressure and reduced People 2020: Health-Related Quality of Life and Well-Being, HRQOL, developing effective interventions in promoting Office of Disease Prevention and Health Promotion, Wash- hypertension self-management and improving HRQOL for ington, DC, USA, 2015, https://www.healthypeople.gov/2020/ older adults with hypertension is essential. 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