Smoking Cessation and 16-year Trajectories of Functional Limitations Among Dutch Older Adults: Results from the Longitudinal Aging Study Amsterdam

Smoking Cessation and 16-year Trajectories of Functional Limitations Among Dutch Older Adults:... Abstract Background This study examined whether smoking cessation in middle age and old age is associated with following a successful trajectory of functional limitations over time in Dutch older adults. Methods We used 16-year longitudinal data from 645 participants of the Longitudinal Aging Study Amsterdam. Three types of trajectories regarding functional limitations over time were defined: successful (high initial level of functioning and limited decline), late decline (high initial level of functioning and late onset of decline), and early decline (lower initial level of functioning and early onset of decline). Smoking cessation status was self-reported and categorized into: early quitters (stopped in middle age [35–40 years]), late quitters (already smoked in middle age and stopped in old age [≥55 years]), and continued smokers (smoked in middle age and still smoking in old age). Multinomial Logistic Regression Analyses were used to assess the association between smoking cessation and trajectory membership. Results The sample (55–85 years at baseline) consisted of 20.3% early quitters, 22.9% late quitters, and 56.8% continued smokers. After adjustment for confounders, the model showed that late quitters were less likely to follow an early decline trajectory instead of a successful trajectory compared to continued smokers (odds ratio [OR] = 0.48, 95% confidence interval [CI] = 0.24–0.97). After adjustment for clinically relevant level of depressive symptoms, this association remained substantial but was no longer statistically significant (OR = 0.50, 95% CI = 0.24–1.02). Conclusions Although not statistically significant in the full model, the observed associations suggest that smoking cessation in old age may have an important impact on daily functioning in old age. Functional decline, Health benefits, Longitudinal cohort study Tobacco smoking is the primary preventable cause of disability and death worldwide. The high fatality of smoking is due to its strong carcinogenic effects and its negative effect on the cardiovascular and respiratory systems (1). The health benefits of smoking cessation, such as increased life expectancy and reduced risks on the development of a range of diseases, are well documented (1–3). A health message commonly provided to smokers as encouragement to quit smoking is that it is never too late (4). However, most research regarding the health benefits of smoking cessation has focused on mortality, on clinical populations with specific diseases, and on relatively young populations (5,6). Specific evidence on the health benefits of smoking cessation in older adults from the general population is sparse (5,6). It has been shown that older adults who smoke are highly nicotine dependent and are less likely to believe that smoking harms their health (7–9). Furthermore, older adults who smoke are less likely to be advised to quit by health care providers than younger individuals (9,10). It has been suggested that more knowledge and education about the potential health benefits of smoking cessation in older adults is needed for both health care professionals and older smokers (9,10). Physical functioning is essential for older adults in maintaining independence and associated quality of life (11). Recently, Kok et al. (12) showed that trajectories of functional limitations, that is, restrictions in performing activities of daily living, differ between older individuals, in terms of the initial level of functioning (ie, high vs low), the extent of functional decline over time (ie, limited decline vs more substantial decline), and the timing of functional decline (ie, early onset of functional decline vs late onset of functional decline). It has been shown that smoking decreases cardiovascular functioning, pulmonary functioning, and physical performance in adults (1,13–15). Smoking cessation may result in less functional limitations in older adults. Supportive evidence that smoking cessation in middle age or old age is associated with having a successful trajectory in old age that is characterized by a high initial level of functioning and limited decline, may motivate smokers to quit smoking. On the other hand, supportive evidence that continuing smoking is associated with having an early decline trajectory in old age that is characterized by lower levels of functioning and an early onset of substantial decline in functioning over time, may discourage smokers to continue smoking. Recently, Artaud et al. (16) examined the association of trajectories of smoking, starting in midlife and over 20 years, with subsequent disability risk in old age. They found that never smokers and long-term ex-smokers (ie, early quitters) had lower disability risk compared to recent ex-smokers (ie, late quitters) and persistent smokers. Furthermore, they found that the risk in late quitters was lower than in current smokers, but remained elevated compared with never smokers and early quitters. How smoking cessation status is related to long-term trajectories of functional limitations has not been examined. Knowledge about the association between smoking cessation and trajectories of functional limitations over time could be used by policymakers and health care professionals to inform current smokers appropriately about the health benefits of smoking cessation. In this population-based study, we examine whether smoking cessation in middle age and old age is associated with following a successful trajectory of functional limitations over time in old age. It is hypothesized that smoking cessation in middle age and old age protects against functional decline in old age. Methods Design and Study Sample Data from the Longitudinal Aging Study Amsterdam (LASA) were used in this study. The Longitudinal Aging Study Amsterdam is an ongoing, prospective cohort study in the Netherlands on the determinants, trajectories, and consequences of physical, cognitive, emotional, and social functioning in older adults (17,18). Sampling, response, and procedures are described in detail elsewhere (18). In short, a random sample of older men and women (55–85 years), stratified for age and gender, was drawn from the population registries of eleven municipalities in three culturally distinct regions in the Netherlands. The baseline data collection was conducted in 1992/1993. Since then, follow-up measurements have been conducted approximately every 3 years until the latest measurement in 2015/2016. Data were collected in main face-to-face, computer-assisted interviews. Additionally, respondents were asked to fill out a written questionnaire and to participate in a medical interview, entailing a separate visit to administer clinical measurements and ask additional questions. Interviewers were trained and the interviews were tape-recorded for quality checks. For this study, data from the first cohort were used, including data from the LASA baseline measurement (Wave B; 1992/1993), Wave C (1995/1996), Wave D (1998/1999), Wave E (2001/2002), Wave F (2005/2006), and Wave G (2008/2009). Respondents who had data available on 16-year trajectories of functional limitations and smoking cessation status were included in this study. The analyzed sample consisted of 645 individuals. The included participants were younger and higher educated than the excluded individuals. Furthermore, the proportion of persons with a partner was higher in the included group than in the excluded group. In addition, the proportions of (very) excessive alcohol drinkers and persons with a clinically relevant level of depressive symptoms were lower in the included group compared to the excluded group. All participants completed an informed consent and the study was approved by the Ethical Review Board of the VU University medical center. Dependent Variables Trajectories of functional limitations over time Functional limitations were assessed by using a validated questionnaire concerning the degree of difficulty with performing the following activities of daily living (1): climbing stairs (2), walking 5 minutes outdoors without resting (3), getting up and sitting down in a chair (4), dressing and undressing oneself (5), using own or public transportation, and (6) cutting one’s own toenails (19,20). The response categories ranged from 1 (“No, I cannot”) to 5 (“Yes, without difficulty”), resulting in a scale score ranging from 6 to 30, with higher scores indicating less functional limitations and thus better functioning. Based on the initial level of functioning, the extent of functional decline over time and the timing of functional decline, three types of trajectories of functional limitations over time were defined (1): successful trajectory (reference category) (2), late decline trajectory (0=no, 1=yes), and (3) early decline trajectory (0=no, 1=yes). The assessment of trajectories of functional limitations in LASA-participants between Wave B (1992/1993) and Wave G (2008/2009) has been described in detail elsewhere (12). In short, Latent Class Growth Analyses were used to identify subgroups of individuals following similar trajectories of functional limitations over the 16-year period. The modelling of trajectories of functional limitations was done separately for men and women who participated at least at Wave B (1992/1993) and Wave C (1995/1996). For dropouts after Wave C, a full trajectory was estimated using Maximum Likelihood Estimation. The optimal model fit was found with four trajectories in men (Figure 1; M1-M4) and five trajectories in women (Figure 1; W1-W5). The results of the Latent Class Growth Analyses are probabilistic, that is, each individual has a probability to belong to each of the latent classes, ranging between 0 and 1. The assignment of trajectory membership to participants was based on the class with the highest probability. Since the classification quality of the models was very high (entropy was 0.87 in men and 0.85 in women), using the highest class probability introduces no bias into the subsequent regression models (21). Figure 1. View largeDownload slide Trajectories of functional limitations over time in the study sample stratified for men (M1-M4) and women (W1-W5)a,b. aHigher functional limitation scale scores represent better functioning. bThe number of participants following each trajectory was as follows: 292 (M1), 85 (M2), 41 (M3), 15 (M4), 126 (W1), 39 (W2), 23 (W3), 16 (W4), and 8 (W5). Figure 1. View largeDownload slide Trajectories of functional limitations over time in the study sample stratified for men (M1-M4) and women (W1-W5)a,b. aHigher functional limitation scale scores represent better functioning. bThe number of participants following each trajectory was as follows: 292 (M1), 85 (M2), 41 (M3), 15 (M4), 126 (W1), 39 (W2), 23 (W3), 16 (W4), and 8 (W5). Trajectories M1/W1 represent the successful trajectory in men and women, respectively. The successful trajectory is characterized by a high initial level of functioning and a limited functional decline over time. Trajectories M2/W2 represent the late decline trajectory in men and women, respectively. The late decline trajectory is characterized by a high initial level of functioning and a late onset of substantial functional decline over time. Trajectories M3/M4 and W3-W5 represent the early decline trajectories in men and women, respectively. The early decline trajectories are characterized by lower initial levels of functioning and an early onset of substantial functional decline over time. Independent Variables Smoking cessation status Smoking status and the age that participants started/stopped smoking were assessed at each measurement wave during the medical interview. Individuals who stopped smoking in middle age (ie, 35–40 years) and who did not start smoking again before and during the study period were classified as “early quitters” (0=no, 1=yes). Individuals who smoked in middle age and stopped smoking during the study period were classified as “late quitters” (0=no, 1=yes). Participants who smoked in middle age and continued smoking (also during the study period) were classified as “continued smokers” (reference category). Respondents who could not be classified in one of these three categories were excluded from the study. Potential confounders Age, partner status, educational level, alcohol consumption, and clinically relevant level of depressive symptoms are expected to influence both smoking cessation status as well as trajectories of functional limitations, and are therefore considered as confounders in the present study (16). All confounders were assessed at baseline. Marital status was assessed by asking whether the participants were single or never been married, married or cohabitating, divorced, widowed, had a registered partnership or were living apart. Marital status was dichotomized into partner status (0=no partner, 1=partner). Level of education was categorized and dummy-coded into: Low (elementary education not completed, elementary education, or lower vocational education) (reference category), Intermediate (general intermediate education, intermediate vocational education, or secondary education) (0=no, 1=yes), and High (higher vocational education, college education, or university education (0=no, 1=yes). Alcohol consumption was measured by using the alcohol consumption index developed by Garretsen (22). This index classifies alcohol drinkers into four categories (no (reference category), light (0=no, 1=yes), moderate (0=no, 1=yes), and (very) excessive (0=no, 1=yes)) based on the number of days drinking alcohol per month and the number of alcohol consumptions each time. Clinically relevant level of depressive symptoms was measured by using the Center for Epidemiologic Studies Depression Scale (CES-D) (23). The CES-D is a self-report scale ranging from 0 to 60, with higher scores representing more depressive symptoms. A clinically relevant level of depressive symptoms was defined as present when participants had a CES-D score of 16 or higher (0=no, 1=yes). Statistical Analyses Baseline characteristics of the study sample were stratified for gender and smoking cessation status and analyzed by using descriptive statistics. Means and standard deviations were presented for normally distributed continuous variables. Frequencies and proportions were presented for categorical variables. Differences in categorical variables between men and women as well as across the three smoking cessation groups were tested using Pearson’s Chi-squared tests. Differences in normally distributed continuous variables between men and women and across smoking cessation groups were assessed using independent-sample T-tests and one-way analysis of variance, respectively. Multinomial Logistic Regression Analyses were used to examine the association between smoking cessation and trajectory membership in the full sample, and in men and women separately. The associations between smoking cessation and trajectory membership were examined in models constructed step by step. Model 1 examined the unadjusted associations. Model 2 examined the associations adjusted for age in years, partner status, educational level, and alcohol consumption. In Model 3, the associations between smoking cessation and trajectory membership were additionally adjusted for clinically relevant level of depressive symptoms at baseline. In the present study, full trajectories were estimated for drop-outs (ie, for those who died, refused, were ineligible or were not contacted) using Maximum Likelihood Estimation. Preplanned sensitivity analyses were conducted to examine the associations between smoking cessation status and trajectory membership in older adults who dropped out during the study period. In all models, the p value was set to .05. All analyses were performed in IBM SPSS Statistics, version 24 (IBM Corp, Armonk, NY). The Latent Class Growth Analyses were conducted in Mplus 7.0 (24). Results The mean age of all 645 participants at baseline was 67.8 (SD = 8.1) years with an age-range of 55–85 years. Of all participants, 212 (32.9%) were female (Table 1). In the full sample, there were 131 (20.3%) early quitters, 148 (22.9%) late quitters, and 366 (56.8%) continued smokers. The baseline characteristics differed between men and women and differed across the three smoking cessation groups (Table 1). Table 1. Baseline Characteristics of the Study Sample Stratified for Gender and Smoking Cessation Statusa, b   All Participants  Early Quitters  Late Quitters  Continued Smokers    All Participants (n = 645)  Men (n = 433)  Women (n = 212)  All Early Quitters (n = 131)  All Late Quitters (n = 148)  All Continued Smokers (n = 366)  Characteristics  Age (in years) (Mean [SD])  67.8 (8.1)  68.4 (8.3)  66.8 (7.7)  66.2 (7.3)  66.2 (7.9)  69.1 (8.3)  Partner status (yes) (%)  73.0  82.4  53.8  83.2  73.6  69.1  Educational level (%)               Low  56.0  57.2  62.6  48.8  53.4  59.6   Intermediate  29.2  26.9  27.6  33.6  30.4  27.2   High  14.8  15.9  9.8  17.6  16.2  13.2  Alcohol consumption (%)               No  14.2  12.2  18.3  11.8  13.8  15.3   Light  47.8  46.0  51.5  63.0  42.1  45.0   Moderate  30.0  31.3  27.2  22.7  32.4  31.4   (Very) excessive  8.0  10.5  3.0  2.5  11.7  8.4  Clinically relevant level of depressive symptoms (yes) (%)  11.8  9.7  16.0  5.3  8.8  15.3    All Participants  Early Quitters  Late Quitters  Continued Smokers    All Participants (n = 645)  Men (n = 433)  Women (n = 212)  All Early Quitters (n = 131)  All Late Quitters (n = 148)  All Continued Smokers (n = 366)  Characteristics  Age (in years) (Mean [SD])  67.8 (8.1)  68.4 (8.3)  66.8 (7.7)  66.2 (7.3)  66.2 (7.9)  69.1 (8.3)  Partner status (yes) (%)  73.0  82.4  53.8  83.2  73.6  69.1  Educational level (%)               Low  56.0  57.2  62.6  48.8  53.4  59.6   Intermediate  29.2  26.9  27.6  33.6  30.4  27.2   High  14.8  15.9  9.8  17.6  16.2  13.2  Alcohol consumption (%)               No  14.2  12.2  18.3  11.8  13.8  15.3   Light  47.8  46.0  51.5  63.0  42.1  45.0   Moderate  30.0  31.3  27.2  22.7  32.4  31.4   (Very) excessive  8.0  10.5  3.0  2.5  11.7  8.4  Clinically relevant level of depressive symptoms (yes) (%)  11.8  9.7  16.0  5.3  8.8  15.3  Note: aThe sample size may vary for some variables, because of missing values. bCES-D = Center for Epidemiologic Studies Depression scale; n = number of participants; SD = Standard deviation. View Large Table 1. Baseline Characteristics of the Study Sample Stratified for Gender and Smoking Cessation Statusa, b   All Participants  Early Quitters  Late Quitters  Continued Smokers    All Participants (n = 645)  Men (n = 433)  Women (n = 212)  All Early Quitters (n = 131)  All Late Quitters (n = 148)  All Continued Smokers (n = 366)  Characteristics  Age (in years) (Mean [SD])  67.8 (8.1)  68.4 (8.3)  66.8 (7.7)  66.2 (7.3)  66.2 (7.9)  69.1 (8.3)  Partner status (yes) (%)  73.0  82.4  53.8  83.2  73.6  69.1  Educational level (%)               Low  56.0  57.2  62.6  48.8  53.4  59.6   Intermediate  29.2  26.9  27.6  33.6  30.4  27.2   High  14.8  15.9  9.8  17.6  16.2  13.2  Alcohol consumption (%)               No  14.2  12.2  18.3  11.8  13.8  15.3   Light  47.8  46.0  51.5  63.0  42.1  45.0   Moderate  30.0  31.3  27.2  22.7  32.4  31.4   (Very) excessive  8.0  10.5  3.0  2.5  11.7  8.4  Clinically relevant level of depressive symptoms (yes) (%)  11.8  9.7  16.0  5.3  8.8  15.3    All Participants  Early Quitters  Late Quitters  Continued Smokers    All Participants (n = 645)  Men (n = 433)  Women (n = 212)  All Early Quitters (n = 131)  All Late Quitters (n = 148)  All Continued Smokers (n = 366)  Characteristics  Age (in years) (Mean [SD])  67.8 (8.1)  68.4 (8.3)  66.8 (7.7)  66.2 (7.3)  66.2 (7.9)  69.1 (8.3)  Partner status (yes) (%)  73.0  82.4  53.8  83.2  73.6  69.1  Educational level (%)               Low  56.0  57.2  62.6  48.8  53.4  59.6   Intermediate  29.2  26.9  27.6  33.6  30.4  27.2   High  14.8  15.9  9.8  17.6  16.2  13.2  Alcohol consumption (%)               No  14.2  12.2  18.3  11.8  13.8  15.3   Light  47.8  46.0  51.5  63.0  42.1  45.0   Moderate  30.0  31.3  27.2  22.7  32.4  31.4   (Very) excessive  8.0  10.5  3.0  2.5  11.7  8.4  Clinically relevant level of depressive symptoms (yes) (%)  11.8  9.7  16.0  5.3  8.8  15.3  Note: aThe sample size may vary for some variables, because of missing values. bCES-D = Center for Epidemiologic Studies Depression scale; n = number of participants; SD = Standard deviation. View Large Functional Decline Over Time The participants reported, on average, a functional limitation score of 28.6 (SD = 2.9) and 25.2 (SD = 5.8) at Wave B (1992/1993) and Wave G (2008/2009), respectively. The functional limitation scale score differed significantly across smoking cessation groups at baseline (p < .001) and 16-year follow-up (p = .02). In men as well as in women, the continued smokers reported, on average, the most functional limitations at each wave (Figure 2). Figure 2. View largeDownload slide Functional limitation scale score at each wave stratified for gender and smoking cessation groupa–c. aHigher scores represent better functioning. bError bars represent one standard deviation. cThe letters on the x-axis represent the LASA-waves: B (1992/1993), C (1995/1996), D (1998/1999), E (2001/2002), F (2005/2006), and G (2008/2009). Figure 2. View largeDownload slide Functional limitation scale score at each wave stratified for gender and smoking cessation groupa–c. aHigher scores represent better functioning. bError bars represent one standard deviation. cThe letters on the x-axis represent the LASA-waves: B (1992/1993), C (1995/1996), D (1998/1999), E (2001/2002), F (2005/2006), and G (2008/2009). Trajectory Membership In total, there were 418 (64.8%), 124 (19.2%), and 103 (16.0%) participants who followed a successful trajectory, a late decline trajectory, and an early decline trajectory, respectively (Figure 1). In the full sample, proportions of trajectory membership were significantly different across smoking cessation groups (p < .01). The proportion of participants following a successful trajectory was highest in early quitters (71.8%) and lowest in continued smokers (59.8%). The proportion of participants following an early decline trajectory was higher in continued smokers (20.8%) than in early quitters (9.9%) and late quitters (9.5%). The proportions of trajectory membership were significantly different across smoking cessation groups in men (p = .02), but not in women (p = .18) (Table 2). Table 2. Trajectory Membership Regarding Functional Limitations Stratified for Gender and Smoking Cessation Groups   Early Quitters  Late Quitters  Continued Smokers  p Value  Men and women  Trajectory membership (%)        <.01   Successful trajectory  71.8  70.9  59.8     Late decline trajectory  18.3  19.6  19.4     Early decline trajectory  9.9  9.5  20.8    Men  Trajectory membership (%)        .02   Successful trajectory  75.3  71.4  63.3     Late decline trajectory  19.5  21.0  19.1     Early decline trajectory  5.2  7.6  17.5    Women  Trajectory membership (%)        .18   Successful trajectory  66.6  69.7  52.2     Late decline trajectory  16.7  16.3  20.0     Early decline trajectory  16.7  14.0  27.8      Early Quitters  Late Quitters  Continued Smokers  p Value  Men and women  Trajectory membership (%)        <.01   Successful trajectory  71.8  70.9  59.8     Late decline trajectory  18.3  19.6  19.4     Early decline trajectory  9.9  9.5  20.8    Men  Trajectory membership (%)        .02   Successful trajectory  75.3  71.4  63.3     Late decline trajectory  19.5  21.0  19.1     Early decline trajectory  5.2  7.6  17.5    Women  Trajectory membership (%)        .18   Successful trajectory  66.6  69.7  52.2     Late decline trajectory  16.7  16.3  20.0     Early decline trajectory  16.7  14.0  27.8    View Large Table 2. Trajectory Membership Regarding Functional Limitations Stratified for Gender and Smoking Cessation Groups   Early Quitters  Late Quitters  Continued Smokers  p Value  Men and women  Trajectory membership (%)        <.01   Successful trajectory  71.8  70.9  59.8     Late decline trajectory  18.3  19.6  19.4     Early decline trajectory  9.9  9.5  20.8    Men  Trajectory membership (%)        .02   Successful trajectory  75.3  71.4  63.3     Late decline trajectory  19.5  21.0  19.1     Early decline trajectory  5.2  7.6  17.5    Women  Trajectory membership (%)        .18   Successful trajectory  66.6  69.7  52.2     Late decline trajectory  16.7  16.3  20.0     Early decline trajectory  16.7  14.0  27.8      Early Quitters  Late Quitters  Continued Smokers  p Value  Men and women  Trajectory membership (%)        <.01   Successful trajectory  71.8  70.9  59.8     Late decline trajectory  18.3  19.6  19.4     Early decline trajectory  9.9  9.5  20.8    Men  Trajectory membership (%)        .02   Successful trajectory  75.3  71.4  63.3     Late decline trajectory  19.5  21.0  19.1     Early decline trajectory  5.2  7.6  17.5    Women  Trajectory membership (%)        .18   Successful trajectory  66.6  69.7  52.2     Late decline trajectory  16.7  16.3  20.0     Early decline trajectory  16.7  14.0  27.8    View Large Smoking Cessation and Trajectory Membership In the full sample, the crude model (Table 3; Model 1) showed that early quitters and late quitters are less likely to follow an early decline trajectory than a successful trajectory compared to continued smokers (odds ratio [OR]early = 0.40, 95% confidence interval [CI] = 0.21–0.75; ORlate = 0.38, 95% CI = 0.21–0.71). The crude model further showed that early quitters and late quitters are also less likely to follow a late decline trajectory than a successful trajectory compared to continued smokers (ORearly = 0.79, 95% CI = 0.47–1.33; ORlate = 0.85, 95% CI = 0.52–1.39). Similar findings were observed in the crude stratified analyses for men and women (Table 3; Model 1). Table 3. Associations Between Smoking Cessation and Trajectory Membership Regarding Functional Limitations in the Full Sample and in Men and Women Separatelya,b   Men and Women   Men   Women     Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Model 1c  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  0.79 (0.47–1.33)  0.40 (0.21–0.75)*  0.86 (0.45–1.65)  0.25 (0.09–0.72)*  0.65 (0.27–1.56)  0.47 (0.20–1.09)  Late quitters  0.85 (0.52–1.39)  0.38 (0.21–0.71)*  0.97 (0.55–1.73)  0.39 (0.17–0.86)*  0.61 (0.24–1.58)  0.38 (0.14–0.99)*  Model 2d  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  1.00 (0.56–1.80)  0.67 (0.32–1.38)  1.30 (0.61–2.76)  0.36 (0.10–1.27)  0.72 (0.27–1.93)  0.82 (0.28–2.43)  Late quitters  1.03 (0.61–1.74)  0.48 (0.24–0.97)*  1.20 (0.65–2.22)  0.40 (0.17–0.98)*  0.58 (0.20–1.75)  0.59 (0.17–2.06)  Model 3e  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  1.02 (0.56–1.83)  0.80 (0.38–1.67)  1.31 (0.61–2.79)  0.47 (0.13–1.68)  0.72 (0.27–1.92)  0.90 (0.30–2.66)  Late quitters  1.05 (0.62–1.78)  0.50 (0.24–1.02)  1.21 (0.65–2.24)  0.46 (0.18–1.14)  0.59 (0.20–1.78)  0.52 (0.14–1.93)    Men and Women   Men   Women     Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Model 1c  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  0.79 (0.47–1.33)  0.40 (0.21–0.75)*  0.86 (0.45–1.65)  0.25 (0.09–0.72)*  0.65 (0.27–1.56)  0.47 (0.20–1.09)  Late quitters  0.85 (0.52–1.39)  0.38 (0.21–0.71)*  0.97 (0.55–1.73)  0.39 (0.17–0.86)*  0.61 (0.24–1.58)  0.38 (0.14–0.99)*  Model 2d  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  1.00 (0.56–1.80)  0.67 (0.32–1.38)  1.30 (0.61–2.76)  0.36 (0.10–1.27)  0.72 (0.27–1.93)  0.82 (0.28–2.43)  Late quitters  1.03 (0.61–1.74)  0.48 (0.24–0.97)*  1.20 (0.65–2.22)  0.40 (0.17–0.98)*  0.58 (0.20–1.75)  0.59 (0.17–2.06)  Model 3e  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  1.02 (0.56–1.83)  0.80 (0.38–1.67)  1.31 (0.61–2.79)  0.47 (0.13–1.68)  0.72 (0.27–1.92)  0.90 (0.30–2.66)  Late quitters  1.05 (0.62–1.78)  0.50 (0.24–1.02)  1.21 (0.65–2.24)  0.46 (0.18–1.14)  0.59 (0.20–1.78)  0.52 (0.14–1.93)  Note: aOR = Odds ratio; CI = Confidence interval. bThe reference category of the outcome measure is successful trajectory. c The associations in Model 1 are unadjusted. d The associations in Model 2 are adjusted for age in years, partner status, educational level and alcohol consumption. These potential confounders were assessed at baseline. e The associations in Model 3 are additionally adjusted for clinically relevant level of depressive symptoms at baseline. *p < .05. View Large Table 3. Associations Between Smoking Cessation and Trajectory Membership Regarding Functional Limitations in the Full Sample and in Men and Women Separatelya,b   Men and Women   Men   Women     Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Model 1c  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  0.79 (0.47–1.33)  0.40 (0.21–0.75)*  0.86 (0.45–1.65)  0.25 (0.09–0.72)*  0.65 (0.27–1.56)  0.47 (0.20–1.09)  Late quitters  0.85 (0.52–1.39)  0.38 (0.21–0.71)*  0.97 (0.55–1.73)  0.39 (0.17–0.86)*  0.61 (0.24–1.58)  0.38 (0.14–0.99)*  Model 2d  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  1.00 (0.56–1.80)  0.67 (0.32–1.38)  1.30 (0.61–2.76)  0.36 (0.10–1.27)  0.72 (0.27–1.93)  0.82 (0.28–2.43)  Late quitters  1.03 (0.61–1.74)  0.48 (0.24–0.97)*  1.20 (0.65–2.22)  0.40 (0.17–0.98)*  0.58 (0.20–1.75)  0.59 (0.17–2.06)  Model 3e  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  1.02 (0.56–1.83)  0.80 (0.38–1.67)  1.31 (0.61–2.79)  0.47 (0.13–1.68)  0.72 (0.27–1.92)  0.90 (0.30–2.66)  Late quitters  1.05 (0.62–1.78)  0.50 (0.24–1.02)  1.21 (0.65–2.24)  0.46 (0.18–1.14)  0.59 (0.20–1.78)  0.52 (0.14–1.93)    Men and Women   Men   Women     Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Model 1c  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  0.79 (0.47–1.33)  0.40 (0.21–0.75)*  0.86 (0.45–1.65)  0.25 (0.09–0.72)*  0.65 (0.27–1.56)  0.47 (0.20–1.09)  Late quitters  0.85 (0.52–1.39)  0.38 (0.21–0.71)*  0.97 (0.55–1.73)  0.39 (0.17–0.86)*  0.61 (0.24–1.58)  0.38 (0.14–0.99)*  Model 2d  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  1.00 (0.56–1.80)  0.67 (0.32–1.38)  1.30 (0.61–2.76)  0.36 (0.10–1.27)  0.72 (0.27–1.93)  0.82 (0.28–2.43)  Late quitters  1.03 (0.61–1.74)  0.48 (0.24–0.97)*  1.20 (0.65–2.22)  0.40 (0.17–0.98)*  0.58 (0.20–1.75)  0.59 (0.17–2.06)  Model 3e  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  1.02 (0.56–1.83)  0.80 (0.38–1.67)  1.31 (0.61–2.79)  0.47 (0.13–1.68)  0.72 (0.27–1.92)  0.90 (0.30–2.66)  Late quitters  1.05 (0.62–1.78)  0.50 (0.24–1.02)  1.21 (0.65–2.24)  0.46 (0.18–1.14)  0.59 (0.20–1.78)  0.52 (0.14–1.93)  Note: aOR = Odds ratio; CI = Confidence interval. bThe reference category of the outcome measure is successful trajectory. c The associations in Model 1 are unadjusted. d The associations in Model 2 are adjusted for age in years, partner status, educational level and alcohol consumption. These potential confounders were assessed at baseline. e The associations in Model 3 are additionally adjusted for clinically relevant level of depressive symptoms at baseline. *p < .05. View Large After adjustment for all confounders, the associations between smoking cessation status and trajectory membership were no longer statistically significant in the full sample (Table 3; Model 3). Although not statistically significant, the associations particularly suggest that late quitters are less likely to follow an early decline trajectory than a successful trajectory compared to continued smokers (ORearly = 0.80, 95% CI = 0.38–1.67; ORlate = 0.50, 95% CI = 0.24–1.02). Similar results were found in the fully adjusted analyses for men and women (Table 3; Model 3). The sensitivity analyses, in which the associations between smoking cessation status and trajectory membership were assessed while excluding those who dropped out during the study period, revealed similar results as observed in the main analyses. After adjustment for all confounders, early quitters and late quitters were found to be less likely to follow an early decline trajectory instead of a successful trajectory compared to continued smokers (ORearly = 0.15, 95% CI = 0.02–1.33; ORlate = 0.26, 95% CI = 0.04–1.84). The sex-stratified sensitivity analyses also revealed similar results as observed in the main analyses (results not shown). Discussion This study examined whether smoking cessation in middle age and old age is associated with following a successful trajectory of functional limitations over time in Dutch older adults. The results particularly suggest that individuals who quit smoking in old age are more likely to follow a successful trajectory instead of an early decline trajectory compared to continued smokers. An important strength of this study is the assessment of trajectories of functional limitations in Dutch older adults from the general population, based on 16-year longitudinal data from the LASA cohort study. Another strength of this study is that we could adjust for a wide range of relevant confounders. Some limitations have to be acknowledged as well. First, the study sample was fairly small, which resulted in limited statistical power. This made it difficult to gauge the true size of associations in the present study, and this might explain that we could not demonstrate a relationship of functional limitations with early quitting. The small number of cases among women makes it difficult to draw definite conclusions regarding this group. Second, smoking cessation was based on self-reports. This might have caused recall bias, especially regarding the exact age at which participants quit smoking. Third, social desirability bias, related to feelings of shame, may have led to unwillingness to report correct information about smoking behavior. This may have resulted in some misclassification regarding the smoking cessation status and underestimation of the associations with functional limitations. Fourth, selection bias might have influenced the results. If the relationship between smoking cessation and trajectories of functional limitations are stronger in unhealthy individuals, the observed associations in the present study might be slightly underestimated due to the inclusion of healthier persons. Finally, late quitting was defined as smoking cessation during the study period, and it is not clear from the design which happened first: smoking cessation or decline in functional limitations. The results suggest that individuals who quit smoking in middle age and old age were better able to maintain a high level of functioning over time in old age than continued smokers. An explanation for this could be that smoking cessation improves cardiovascular functioning and pulmonary functioning (ie, improved oxygen carrying capacity of blood and improved blood circulation), resulting in less fatigue during daily activities (1,13). Furthermore, it has been shown that smoking cessation is associated with improved physical performance in terms of increased muscle strength, better agility and coordination, and improved gait and balance (14). Previous studies have shown that older adults who smoke are less likely to believe that smoking harms their health (7–9) and are also less likely to receive advice to quit from health care providers than younger individuals (9,10). The current findings suggest that older adults who quit smoking are more likely to follow a successful trajectory than an early decline trajectory compared to continued smokers. This finding particularly highlights the need to inform older current smokers and health care providers about the potential benefits of smoking cessation in terms of long-term trajectories of functional limitations over time in old age. The results imply that smoking cessation should be a priority in healthy aging policy that aims to increase quality of life, social participation, and independence in older adults. In conclusion, the results of this study particularly suggest that smoking cessation in old age increases the likelihood of following a successful trajectory of functional limitations, that is characterized by high levels of initial functioning and limited functional decline over time. The supporting evidence that smoking cessation in old age provides health benefits in older adults may help to motivate older current smokers to quit smoking. Funding This work was supported by the Amsterdam Public Health Research Institute in Amsterdam, the Netherlands. The Longitudinal Aging Study Amsterdam is supported by a grant from the Netherlands Ministry of Health Welfare and Sports, Directorate of Long-Term Care. References 1. United States Department of Health Human Services. The Health Consequences of Smoking-50 years of Progress: A Report of the Surgeon General . Atlanta, GA: United States Department of Health and Human Services, Center for Diseases Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2014. 2. Doll R, Peto R, Boreham J, Sutherland I. Mortality in relation to smoking: 50 years’ observations on male British doctors. BMJ . 2004; 328: 1519. doi: 10.1136/bmj.38142.554479.AE Google Scholar CrossRef Search ADS PubMed  3. Kenfield SA, Stampfer MJ, Rosner BA, Colditz GA. Smoking and smoking cessation in relation to mortality in women. JAMA . 2008; 299: 2037– 2047. doi: 10.1001/jama.299.17.2037 Google Scholar CrossRef Search ADS PubMed  4. Taylor DHJr, Hasselblad V, Henley SJ, Thun MJ, Sloan FA. Benefits of smoking cessation for longevity. Am J Public Health . 2002; 92: 990– 996. doi: 10.2105/AJPH.92.6.990 Google Scholar CrossRef Search ADS PubMed  5. Gellert C, Schöttker B, Brenner H. Smoking and all-cause mortality in older people: systematic review and meta-analysis. Arch Intern Med . 2012; 172: 837– 844. doi: 10.1001/archinternmed.2012.1397 Google Scholar CrossRef Search ADS PubMed  6. Hirdes JP, Maxwell CJ. Smoking cessation and quality of life outcomes among older adults in the Campbell’s Survey on Well-Being. Can J Public Health . 1994; 85: 99– 102. Google Scholar PubMed  7. Abdullah AS, Ho LM, Kwan YH, Cheung WL, McGhee SM, Chan WH. Promoting smoking cessation among the elderly: what are the predictors of intention to quit and successful quitting? J Aging Health . 2006; 18: 552– 564. doi: 10.1177/0898264305281104 Google Scholar CrossRef Search ADS PubMed  8. Honda K. Psychosocial correlates of smoking cessation among elderly ever-smokers in the United States. Addict Behav . 2005; 30: 375– 381. doi: 10.1016/j.addbeh.2004.05.009 Google Scholar CrossRef Search ADS PubMed  9. Schmitt EM, Tsoh JY, Dowling GA, Hall SM. Older adults’ and case managers’ perceptions of smoking and smoking cessation. J Aging Health . 2005; 17: 717– 733. doi: 10.1177/0898264305280995 Google Scholar CrossRef Search ADS PubMed  10. Maguire CP, Ryan J, Kelly A, O’Neill D, Coakley D, Walsh JB. Do patient age and medical condition influence medical advice to stop smoking? Age Ageing . 2000; 29: 264– 266. doi: 10.1093/ageing/29.3.264 Google Scholar CrossRef Search ADS PubMed  11. Manini TM, Pahor M. Physical activity and maintaining physical function in older adults. Br J Sports Med . 2009; 43: 28– 31. doi: 10.1136/bjsm.2008.053736 Google Scholar CrossRef Search ADS PubMed  12. Kok AAL, Aartsen MJ, Deeg DJHet al.   Capturing the diversity of successful aging: an operational definition based on 16-year trajectories of functioning. The Gerontologist . 2017; 57: 240– 251. doi: 10.1093/geront/gnv127 Google Scholar PubMed  13. LaCroix AZ, Omenn GS. Older adults and smoking. Clin Geriatr Med . 1992; 8: 69– 87. Google Scholar PubMed  14. Rapuri PB, Gallagher JC, Smith LM. Smoking is a risk factor for decreased physical performance in elderly women. J Gerontol A . 2007; 62: 93– 99. doi: 10.1093/gerona/62.1.93 Google Scholar CrossRef Search ADS   15. Strand BH, Mishra G, Kuh Det al.   Smoking history and physical performance in midlife: results from the British 1946 Birth Cohort. J Gerontol A . 2011; 66: 142– 149. doi: 10.1093/gerona/glq199 Google Scholar CrossRef Search ADS   16. Artaud F, Sabia S, Dugravot Aet al.   Trajectories of unhealthy behaviors in midlife and risk of disability at older ages in the Whitehall II Cohort Study. J Gerontol A . 2016; 71: 1500– 1506. doi: 10.1093/gerona/glw060 Google Scholar CrossRef Search ADS   17. Hoogendijk EO, Deeg DJ, Poppelaars Jet al.   The Longitudinal Aging Study Amsterdam: cohort update 2016 and major findings. Eur J Epidemiol . 2016; 31: 927– 945. doi: 10.1007/s10654-016-0192-0 Google Scholar CrossRef Search ADS PubMed  18. Huisman M, Poppelaars J, van der Horst Met al.   Cohort profile: the Longitudinal Aging Study Amsterdam. Int J Epidemiol . 2011; 40: 868– 876. doi: 10.1093/ije/dyq219 Google Scholar CrossRef Search ADS PubMed  19. Kriegsman DMW, Deeg DJH, Stalman WAB. Comorbidity of somatic chronic diseases and decline in physical functioning. The Longitudinal Aging Study Amsterdam. J Clin Epidemiol . 2004; 57: 55– 65. doi: 10.1016/S0895-4356(03)00258-0 Google Scholar CrossRef Search ADS PubMed  20. Van Sonsbeek JLA. Methodological and substantial aspects of the OECD indicator of chronic functional limitations. Maandbericht Gezondheid (Statistics Netherlands)  1988; 88: 4– 17. 21. Clark SL, Muthén B. Relating Latent Class Analysis Results to Variables not Included in the Analysis. Retrieved from www.statmodel.com/download/relatinglca.pdf. Accessed March 29, 2017. 22. Garretsen HFL. Probleemdrinken: Prevalentiebepaling, beïnvloedende factoren en Prevalentiemogelijkheden; Theoretische overwegingen en onderzoek in Rotterdam. (Dissertation in Dutch) . Lisse, the Netherlands: Swets & Zeitlinger; 1983. 23. Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas . 1977; 3: 385– 401. doi: 10.1177/014662167700100306 Google Scholar CrossRef Search ADS   24. Muthén LK, Muthén BO. Mplus User’s Guide. 7th ed . Los Angeles, CA: Muthén & Muthén; 1998–2002. © The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences Oxford University Press

Smoking Cessation and 16-year Trajectories of Functional Limitations Among Dutch Older Adults: Results from the Longitudinal Aging Study Amsterdam

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Abstract

Abstract Background This study examined whether smoking cessation in middle age and old age is associated with following a successful trajectory of functional limitations over time in Dutch older adults. Methods We used 16-year longitudinal data from 645 participants of the Longitudinal Aging Study Amsterdam. Three types of trajectories regarding functional limitations over time were defined: successful (high initial level of functioning and limited decline), late decline (high initial level of functioning and late onset of decline), and early decline (lower initial level of functioning and early onset of decline). Smoking cessation status was self-reported and categorized into: early quitters (stopped in middle age [35–40 years]), late quitters (already smoked in middle age and stopped in old age [≥55 years]), and continued smokers (smoked in middle age and still smoking in old age). Multinomial Logistic Regression Analyses were used to assess the association between smoking cessation and trajectory membership. Results The sample (55–85 years at baseline) consisted of 20.3% early quitters, 22.9% late quitters, and 56.8% continued smokers. After adjustment for confounders, the model showed that late quitters were less likely to follow an early decline trajectory instead of a successful trajectory compared to continued smokers (odds ratio [OR] = 0.48, 95% confidence interval [CI] = 0.24–0.97). After adjustment for clinically relevant level of depressive symptoms, this association remained substantial but was no longer statistically significant (OR = 0.50, 95% CI = 0.24–1.02). Conclusions Although not statistically significant in the full model, the observed associations suggest that smoking cessation in old age may have an important impact on daily functioning in old age. Functional decline, Health benefits, Longitudinal cohort study Tobacco smoking is the primary preventable cause of disability and death worldwide. The high fatality of smoking is due to its strong carcinogenic effects and its negative effect on the cardiovascular and respiratory systems (1). The health benefits of smoking cessation, such as increased life expectancy and reduced risks on the development of a range of diseases, are well documented (1–3). A health message commonly provided to smokers as encouragement to quit smoking is that it is never too late (4). However, most research regarding the health benefits of smoking cessation has focused on mortality, on clinical populations with specific diseases, and on relatively young populations (5,6). Specific evidence on the health benefits of smoking cessation in older adults from the general population is sparse (5,6). It has been shown that older adults who smoke are highly nicotine dependent and are less likely to believe that smoking harms their health (7–9). Furthermore, older adults who smoke are less likely to be advised to quit by health care providers than younger individuals (9,10). It has been suggested that more knowledge and education about the potential health benefits of smoking cessation in older adults is needed for both health care professionals and older smokers (9,10). Physical functioning is essential for older adults in maintaining independence and associated quality of life (11). Recently, Kok et al. (12) showed that trajectories of functional limitations, that is, restrictions in performing activities of daily living, differ between older individuals, in terms of the initial level of functioning (ie, high vs low), the extent of functional decline over time (ie, limited decline vs more substantial decline), and the timing of functional decline (ie, early onset of functional decline vs late onset of functional decline). It has been shown that smoking decreases cardiovascular functioning, pulmonary functioning, and physical performance in adults (1,13–15). Smoking cessation may result in less functional limitations in older adults. Supportive evidence that smoking cessation in middle age or old age is associated with having a successful trajectory in old age that is characterized by a high initial level of functioning and limited decline, may motivate smokers to quit smoking. On the other hand, supportive evidence that continuing smoking is associated with having an early decline trajectory in old age that is characterized by lower levels of functioning and an early onset of substantial decline in functioning over time, may discourage smokers to continue smoking. Recently, Artaud et al. (16) examined the association of trajectories of smoking, starting in midlife and over 20 years, with subsequent disability risk in old age. They found that never smokers and long-term ex-smokers (ie, early quitters) had lower disability risk compared to recent ex-smokers (ie, late quitters) and persistent smokers. Furthermore, they found that the risk in late quitters was lower than in current smokers, but remained elevated compared with never smokers and early quitters. How smoking cessation status is related to long-term trajectories of functional limitations has not been examined. Knowledge about the association between smoking cessation and trajectories of functional limitations over time could be used by policymakers and health care professionals to inform current smokers appropriately about the health benefits of smoking cessation. In this population-based study, we examine whether smoking cessation in middle age and old age is associated with following a successful trajectory of functional limitations over time in old age. It is hypothesized that smoking cessation in middle age and old age protects against functional decline in old age. Methods Design and Study Sample Data from the Longitudinal Aging Study Amsterdam (LASA) were used in this study. The Longitudinal Aging Study Amsterdam is an ongoing, prospective cohort study in the Netherlands on the determinants, trajectories, and consequences of physical, cognitive, emotional, and social functioning in older adults (17,18). Sampling, response, and procedures are described in detail elsewhere (18). In short, a random sample of older men and women (55–85 years), stratified for age and gender, was drawn from the population registries of eleven municipalities in three culturally distinct regions in the Netherlands. The baseline data collection was conducted in 1992/1993. Since then, follow-up measurements have been conducted approximately every 3 years until the latest measurement in 2015/2016. Data were collected in main face-to-face, computer-assisted interviews. Additionally, respondents were asked to fill out a written questionnaire and to participate in a medical interview, entailing a separate visit to administer clinical measurements and ask additional questions. Interviewers were trained and the interviews were tape-recorded for quality checks. For this study, data from the first cohort were used, including data from the LASA baseline measurement (Wave B; 1992/1993), Wave C (1995/1996), Wave D (1998/1999), Wave E (2001/2002), Wave F (2005/2006), and Wave G (2008/2009). Respondents who had data available on 16-year trajectories of functional limitations and smoking cessation status were included in this study. The analyzed sample consisted of 645 individuals. The included participants were younger and higher educated than the excluded individuals. Furthermore, the proportion of persons with a partner was higher in the included group than in the excluded group. In addition, the proportions of (very) excessive alcohol drinkers and persons with a clinically relevant level of depressive symptoms were lower in the included group compared to the excluded group. All participants completed an informed consent and the study was approved by the Ethical Review Board of the VU University medical center. Dependent Variables Trajectories of functional limitations over time Functional limitations were assessed by using a validated questionnaire concerning the degree of difficulty with performing the following activities of daily living (1): climbing stairs (2), walking 5 minutes outdoors without resting (3), getting up and sitting down in a chair (4), dressing and undressing oneself (5), using own or public transportation, and (6) cutting one’s own toenails (19,20). The response categories ranged from 1 (“No, I cannot”) to 5 (“Yes, without difficulty”), resulting in a scale score ranging from 6 to 30, with higher scores indicating less functional limitations and thus better functioning. Based on the initial level of functioning, the extent of functional decline over time and the timing of functional decline, three types of trajectories of functional limitations over time were defined (1): successful trajectory (reference category) (2), late decline trajectory (0=no, 1=yes), and (3) early decline trajectory (0=no, 1=yes). The assessment of trajectories of functional limitations in LASA-participants between Wave B (1992/1993) and Wave G (2008/2009) has been described in detail elsewhere (12). In short, Latent Class Growth Analyses were used to identify subgroups of individuals following similar trajectories of functional limitations over the 16-year period. The modelling of trajectories of functional limitations was done separately for men and women who participated at least at Wave B (1992/1993) and Wave C (1995/1996). For dropouts after Wave C, a full trajectory was estimated using Maximum Likelihood Estimation. The optimal model fit was found with four trajectories in men (Figure 1; M1-M4) and five trajectories in women (Figure 1; W1-W5). The results of the Latent Class Growth Analyses are probabilistic, that is, each individual has a probability to belong to each of the latent classes, ranging between 0 and 1. The assignment of trajectory membership to participants was based on the class with the highest probability. Since the classification quality of the models was very high (entropy was 0.87 in men and 0.85 in women), using the highest class probability introduces no bias into the subsequent regression models (21). Figure 1. View largeDownload slide Trajectories of functional limitations over time in the study sample stratified for men (M1-M4) and women (W1-W5)a,b. aHigher functional limitation scale scores represent better functioning. bThe number of participants following each trajectory was as follows: 292 (M1), 85 (M2), 41 (M3), 15 (M4), 126 (W1), 39 (W2), 23 (W3), 16 (W4), and 8 (W5). Figure 1. View largeDownload slide Trajectories of functional limitations over time in the study sample stratified for men (M1-M4) and women (W1-W5)a,b. aHigher functional limitation scale scores represent better functioning. bThe number of participants following each trajectory was as follows: 292 (M1), 85 (M2), 41 (M3), 15 (M4), 126 (W1), 39 (W2), 23 (W3), 16 (W4), and 8 (W5). Trajectories M1/W1 represent the successful trajectory in men and women, respectively. The successful trajectory is characterized by a high initial level of functioning and a limited functional decline over time. Trajectories M2/W2 represent the late decline trajectory in men and women, respectively. The late decline trajectory is characterized by a high initial level of functioning and a late onset of substantial functional decline over time. Trajectories M3/M4 and W3-W5 represent the early decline trajectories in men and women, respectively. The early decline trajectories are characterized by lower initial levels of functioning and an early onset of substantial functional decline over time. Independent Variables Smoking cessation status Smoking status and the age that participants started/stopped smoking were assessed at each measurement wave during the medical interview. Individuals who stopped smoking in middle age (ie, 35–40 years) and who did not start smoking again before and during the study period were classified as “early quitters” (0=no, 1=yes). Individuals who smoked in middle age and stopped smoking during the study period were classified as “late quitters” (0=no, 1=yes). Participants who smoked in middle age and continued smoking (also during the study period) were classified as “continued smokers” (reference category). Respondents who could not be classified in one of these three categories were excluded from the study. Potential confounders Age, partner status, educational level, alcohol consumption, and clinically relevant level of depressive symptoms are expected to influence both smoking cessation status as well as trajectories of functional limitations, and are therefore considered as confounders in the present study (16). All confounders were assessed at baseline. Marital status was assessed by asking whether the participants were single or never been married, married or cohabitating, divorced, widowed, had a registered partnership or were living apart. Marital status was dichotomized into partner status (0=no partner, 1=partner). Level of education was categorized and dummy-coded into: Low (elementary education not completed, elementary education, or lower vocational education) (reference category), Intermediate (general intermediate education, intermediate vocational education, or secondary education) (0=no, 1=yes), and High (higher vocational education, college education, or university education (0=no, 1=yes). Alcohol consumption was measured by using the alcohol consumption index developed by Garretsen (22). This index classifies alcohol drinkers into four categories (no (reference category), light (0=no, 1=yes), moderate (0=no, 1=yes), and (very) excessive (0=no, 1=yes)) based on the number of days drinking alcohol per month and the number of alcohol consumptions each time. Clinically relevant level of depressive symptoms was measured by using the Center for Epidemiologic Studies Depression Scale (CES-D) (23). The CES-D is a self-report scale ranging from 0 to 60, with higher scores representing more depressive symptoms. A clinically relevant level of depressive symptoms was defined as present when participants had a CES-D score of 16 or higher (0=no, 1=yes). Statistical Analyses Baseline characteristics of the study sample were stratified for gender and smoking cessation status and analyzed by using descriptive statistics. Means and standard deviations were presented for normally distributed continuous variables. Frequencies and proportions were presented for categorical variables. Differences in categorical variables between men and women as well as across the three smoking cessation groups were tested using Pearson’s Chi-squared tests. Differences in normally distributed continuous variables between men and women and across smoking cessation groups were assessed using independent-sample T-tests and one-way analysis of variance, respectively. Multinomial Logistic Regression Analyses were used to examine the association between smoking cessation and trajectory membership in the full sample, and in men and women separately. The associations between smoking cessation and trajectory membership were examined in models constructed step by step. Model 1 examined the unadjusted associations. Model 2 examined the associations adjusted for age in years, partner status, educational level, and alcohol consumption. In Model 3, the associations between smoking cessation and trajectory membership were additionally adjusted for clinically relevant level of depressive symptoms at baseline. In the present study, full trajectories were estimated for drop-outs (ie, for those who died, refused, were ineligible or were not contacted) using Maximum Likelihood Estimation. Preplanned sensitivity analyses were conducted to examine the associations between smoking cessation status and trajectory membership in older adults who dropped out during the study period. In all models, the p value was set to .05. All analyses were performed in IBM SPSS Statistics, version 24 (IBM Corp, Armonk, NY). The Latent Class Growth Analyses were conducted in Mplus 7.0 (24). Results The mean age of all 645 participants at baseline was 67.8 (SD = 8.1) years with an age-range of 55–85 years. Of all participants, 212 (32.9%) were female (Table 1). In the full sample, there were 131 (20.3%) early quitters, 148 (22.9%) late quitters, and 366 (56.8%) continued smokers. The baseline characteristics differed between men and women and differed across the three smoking cessation groups (Table 1). Table 1. Baseline Characteristics of the Study Sample Stratified for Gender and Smoking Cessation Statusa, b   All Participants  Early Quitters  Late Quitters  Continued Smokers    All Participants (n = 645)  Men (n = 433)  Women (n = 212)  All Early Quitters (n = 131)  All Late Quitters (n = 148)  All Continued Smokers (n = 366)  Characteristics  Age (in years) (Mean [SD])  67.8 (8.1)  68.4 (8.3)  66.8 (7.7)  66.2 (7.3)  66.2 (7.9)  69.1 (8.3)  Partner status (yes) (%)  73.0  82.4  53.8  83.2  73.6  69.1  Educational level (%)               Low  56.0  57.2  62.6  48.8  53.4  59.6   Intermediate  29.2  26.9  27.6  33.6  30.4  27.2   High  14.8  15.9  9.8  17.6  16.2  13.2  Alcohol consumption (%)               No  14.2  12.2  18.3  11.8  13.8  15.3   Light  47.8  46.0  51.5  63.0  42.1  45.0   Moderate  30.0  31.3  27.2  22.7  32.4  31.4   (Very) excessive  8.0  10.5  3.0  2.5  11.7  8.4  Clinically relevant level of depressive symptoms (yes) (%)  11.8  9.7  16.0  5.3  8.8  15.3    All Participants  Early Quitters  Late Quitters  Continued Smokers    All Participants (n = 645)  Men (n = 433)  Women (n = 212)  All Early Quitters (n = 131)  All Late Quitters (n = 148)  All Continued Smokers (n = 366)  Characteristics  Age (in years) (Mean [SD])  67.8 (8.1)  68.4 (8.3)  66.8 (7.7)  66.2 (7.3)  66.2 (7.9)  69.1 (8.3)  Partner status (yes) (%)  73.0  82.4  53.8  83.2  73.6  69.1  Educational level (%)               Low  56.0  57.2  62.6  48.8  53.4  59.6   Intermediate  29.2  26.9  27.6  33.6  30.4  27.2   High  14.8  15.9  9.8  17.6  16.2  13.2  Alcohol consumption (%)               No  14.2  12.2  18.3  11.8  13.8  15.3   Light  47.8  46.0  51.5  63.0  42.1  45.0   Moderate  30.0  31.3  27.2  22.7  32.4  31.4   (Very) excessive  8.0  10.5  3.0  2.5  11.7  8.4  Clinically relevant level of depressive symptoms (yes) (%)  11.8  9.7  16.0  5.3  8.8  15.3  Note: aThe sample size may vary for some variables, because of missing values. bCES-D = Center for Epidemiologic Studies Depression scale; n = number of participants; SD = Standard deviation. View Large Table 1. Baseline Characteristics of the Study Sample Stratified for Gender and Smoking Cessation Statusa, b   All Participants  Early Quitters  Late Quitters  Continued Smokers    All Participants (n = 645)  Men (n = 433)  Women (n = 212)  All Early Quitters (n = 131)  All Late Quitters (n = 148)  All Continued Smokers (n = 366)  Characteristics  Age (in years) (Mean [SD])  67.8 (8.1)  68.4 (8.3)  66.8 (7.7)  66.2 (7.3)  66.2 (7.9)  69.1 (8.3)  Partner status (yes) (%)  73.0  82.4  53.8  83.2  73.6  69.1  Educational level (%)               Low  56.0  57.2  62.6  48.8  53.4  59.6   Intermediate  29.2  26.9  27.6  33.6  30.4  27.2   High  14.8  15.9  9.8  17.6  16.2  13.2  Alcohol consumption (%)               No  14.2  12.2  18.3  11.8  13.8  15.3   Light  47.8  46.0  51.5  63.0  42.1  45.0   Moderate  30.0  31.3  27.2  22.7  32.4  31.4   (Very) excessive  8.0  10.5  3.0  2.5  11.7  8.4  Clinically relevant level of depressive symptoms (yes) (%)  11.8  9.7  16.0  5.3  8.8  15.3    All Participants  Early Quitters  Late Quitters  Continued Smokers    All Participants (n = 645)  Men (n = 433)  Women (n = 212)  All Early Quitters (n = 131)  All Late Quitters (n = 148)  All Continued Smokers (n = 366)  Characteristics  Age (in years) (Mean [SD])  67.8 (8.1)  68.4 (8.3)  66.8 (7.7)  66.2 (7.3)  66.2 (7.9)  69.1 (8.3)  Partner status (yes) (%)  73.0  82.4  53.8  83.2  73.6  69.1  Educational level (%)               Low  56.0  57.2  62.6  48.8  53.4  59.6   Intermediate  29.2  26.9  27.6  33.6  30.4  27.2   High  14.8  15.9  9.8  17.6  16.2  13.2  Alcohol consumption (%)               No  14.2  12.2  18.3  11.8  13.8  15.3   Light  47.8  46.0  51.5  63.0  42.1  45.0   Moderate  30.0  31.3  27.2  22.7  32.4  31.4   (Very) excessive  8.0  10.5  3.0  2.5  11.7  8.4  Clinically relevant level of depressive symptoms (yes) (%)  11.8  9.7  16.0  5.3  8.8  15.3  Note: aThe sample size may vary for some variables, because of missing values. bCES-D = Center for Epidemiologic Studies Depression scale; n = number of participants; SD = Standard deviation. View Large Functional Decline Over Time The participants reported, on average, a functional limitation score of 28.6 (SD = 2.9) and 25.2 (SD = 5.8) at Wave B (1992/1993) and Wave G (2008/2009), respectively. The functional limitation scale score differed significantly across smoking cessation groups at baseline (p < .001) and 16-year follow-up (p = .02). In men as well as in women, the continued smokers reported, on average, the most functional limitations at each wave (Figure 2). Figure 2. View largeDownload slide Functional limitation scale score at each wave stratified for gender and smoking cessation groupa–c. aHigher scores represent better functioning. bError bars represent one standard deviation. cThe letters on the x-axis represent the LASA-waves: B (1992/1993), C (1995/1996), D (1998/1999), E (2001/2002), F (2005/2006), and G (2008/2009). Figure 2. View largeDownload slide Functional limitation scale score at each wave stratified for gender and smoking cessation groupa–c. aHigher scores represent better functioning. bError bars represent one standard deviation. cThe letters on the x-axis represent the LASA-waves: B (1992/1993), C (1995/1996), D (1998/1999), E (2001/2002), F (2005/2006), and G (2008/2009). Trajectory Membership In total, there were 418 (64.8%), 124 (19.2%), and 103 (16.0%) participants who followed a successful trajectory, a late decline trajectory, and an early decline trajectory, respectively (Figure 1). In the full sample, proportions of trajectory membership were significantly different across smoking cessation groups (p < .01). The proportion of participants following a successful trajectory was highest in early quitters (71.8%) and lowest in continued smokers (59.8%). The proportion of participants following an early decline trajectory was higher in continued smokers (20.8%) than in early quitters (9.9%) and late quitters (9.5%). The proportions of trajectory membership were significantly different across smoking cessation groups in men (p = .02), but not in women (p = .18) (Table 2). Table 2. Trajectory Membership Regarding Functional Limitations Stratified for Gender and Smoking Cessation Groups   Early Quitters  Late Quitters  Continued Smokers  p Value  Men and women  Trajectory membership (%)        <.01   Successful trajectory  71.8  70.9  59.8     Late decline trajectory  18.3  19.6  19.4     Early decline trajectory  9.9  9.5  20.8    Men  Trajectory membership (%)        .02   Successful trajectory  75.3  71.4  63.3     Late decline trajectory  19.5  21.0  19.1     Early decline trajectory  5.2  7.6  17.5    Women  Trajectory membership (%)        .18   Successful trajectory  66.6  69.7  52.2     Late decline trajectory  16.7  16.3  20.0     Early decline trajectory  16.7  14.0  27.8      Early Quitters  Late Quitters  Continued Smokers  p Value  Men and women  Trajectory membership (%)        <.01   Successful trajectory  71.8  70.9  59.8     Late decline trajectory  18.3  19.6  19.4     Early decline trajectory  9.9  9.5  20.8    Men  Trajectory membership (%)        .02   Successful trajectory  75.3  71.4  63.3     Late decline trajectory  19.5  21.0  19.1     Early decline trajectory  5.2  7.6  17.5    Women  Trajectory membership (%)        .18   Successful trajectory  66.6  69.7  52.2     Late decline trajectory  16.7  16.3  20.0     Early decline trajectory  16.7  14.0  27.8    View Large Table 2. Trajectory Membership Regarding Functional Limitations Stratified for Gender and Smoking Cessation Groups   Early Quitters  Late Quitters  Continued Smokers  p Value  Men and women  Trajectory membership (%)        <.01   Successful trajectory  71.8  70.9  59.8     Late decline trajectory  18.3  19.6  19.4     Early decline trajectory  9.9  9.5  20.8    Men  Trajectory membership (%)        .02   Successful trajectory  75.3  71.4  63.3     Late decline trajectory  19.5  21.0  19.1     Early decline trajectory  5.2  7.6  17.5    Women  Trajectory membership (%)        .18   Successful trajectory  66.6  69.7  52.2     Late decline trajectory  16.7  16.3  20.0     Early decline trajectory  16.7  14.0  27.8      Early Quitters  Late Quitters  Continued Smokers  p Value  Men and women  Trajectory membership (%)        <.01   Successful trajectory  71.8  70.9  59.8     Late decline trajectory  18.3  19.6  19.4     Early decline trajectory  9.9  9.5  20.8    Men  Trajectory membership (%)        .02   Successful trajectory  75.3  71.4  63.3     Late decline trajectory  19.5  21.0  19.1     Early decline trajectory  5.2  7.6  17.5    Women  Trajectory membership (%)        .18   Successful trajectory  66.6  69.7  52.2     Late decline trajectory  16.7  16.3  20.0     Early decline trajectory  16.7  14.0  27.8    View Large Smoking Cessation and Trajectory Membership In the full sample, the crude model (Table 3; Model 1) showed that early quitters and late quitters are less likely to follow an early decline trajectory than a successful trajectory compared to continued smokers (odds ratio [OR]early = 0.40, 95% confidence interval [CI] = 0.21–0.75; ORlate = 0.38, 95% CI = 0.21–0.71). The crude model further showed that early quitters and late quitters are also less likely to follow a late decline trajectory than a successful trajectory compared to continued smokers (ORearly = 0.79, 95% CI = 0.47–1.33; ORlate = 0.85, 95% CI = 0.52–1.39). Similar findings were observed in the crude stratified analyses for men and women (Table 3; Model 1). Table 3. Associations Between Smoking Cessation and Trajectory Membership Regarding Functional Limitations in the Full Sample and in Men and Women Separatelya,b   Men and Women   Men   Women     Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Model 1c  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  0.79 (0.47–1.33)  0.40 (0.21–0.75)*  0.86 (0.45–1.65)  0.25 (0.09–0.72)*  0.65 (0.27–1.56)  0.47 (0.20–1.09)  Late quitters  0.85 (0.52–1.39)  0.38 (0.21–0.71)*  0.97 (0.55–1.73)  0.39 (0.17–0.86)*  0.61 (0.24–1.58)  0.38 (0.14–0.99)*  Model 2d  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  1.00 (0.56–1.80)  0.67 (0.32–1.38)  1.30 (0.61–2.76)  0.36 (0.10–1.27)  0.72 (0.27–1.93)  0.82 (0.28–2.43)  Late quitters  1.03 (0.61–1.74)  0.48 (0.24–0.97)*  1.20 (0.65–2.22)  0.40 (0.17–0.98)*  0.58 (0.20–1.75)  0.59 (0.17–2.06)  Model 3e  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  1.02 (0.56–1.83)  0.80 (0.38–1.67)  1.31 (0.61–2.79)  0.47 (0.13–1.68)  0.72 (0.27–1.92)  0.90 (0.30–2.66)  Late quitters  1.05 (0.62–1.78)  0.50 (0.24–1.02)  1.21 (0.65–2.24)  0.46 (0.18–1.14)  0.59 (0.20–1.78)  0.52 (0.14–1.93)    Men and Women   Men   Women     Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Model 1c  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  0.79 (0.47–1.33)  0.40 (0.21–0.75)*  0.86 (0.45–1.65)  0.25 (0.09–0.72)*  0.65 (0.27–1.56)  0.47 (0.20–1.09)  Late quitters  0.85 (0.52–1.39)  0.38 (0.21–0.71)*  0.97 (0.55–1.73)  0.39 (0.17–0.86)*  0.61 (0.24–1.58)  0.38 (0.14–0.99)*  Model 2d  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  1.00 (0.56–1.80)  0.67 (0.32–1.38)  1.30 (0.61–2.76)  0.36 (0.10–1.27)  0.72 (0.27–1.93)  0.82 (0.28–2.43)  Late quitters  1.03 (0.61–1.74)  0.48 (0.24–0.97)*  1.20 (0.65–2.22)  0.40 (0.17–0.98)*  0.58 (0.20–1.75)  0.59 (0.17–2.06)  Model 3e  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  1.02 (0.56–1.83)  0.80 (0.38–1.67)  1.31 (0.61–2.79)  0.47 (0.13–1.68)  0.72 (0.27–1.92)  0.90 (0.30–2.66)  Late quitters  1.05 (0.62–1.78)  0.50 (0.24–1.02)  1.21 (0.65–2.24)  0.46 (0.18–1.14)  0.59 (0.20–1.78)  0.52 (0.14–1.93)  Note: aOR = Odds ratio; CI = Confidence interval. bThe reference category of the outcome measure is successful trajectory. c The associations in Model 1 are unadjusted. d The associations in Model 2 are adjusted for age in years, partner status, educational level and alcohol consumption. These potential confounders were assessed at baseline. e The associations in Model 3 are additionally adjusted for clinically relevant level of depressive symptoms at baseline. *p < .05. View Large Table 3. Associations Between Smoking Cessation and Trajectory Membership Regarding Functional Limitations in the Full Sample and in Men and Women Separatelya,b   Men and Women   Men   Women     Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Model 1c  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  0.79 (0.47–1.33)  0.40 (0.21–0.75)*  0.86 (0.45–1.65)  0.25 (0.09–0.72)*  0.65 (0.27–1.56)  0.47 (0.20–1.09)  Late quitters  0.85 (0.52–1.39)  0.38 (0.21–0.71)*  0.97 (0.55–1.73)  0.39 (0.17–0.86)*  0.61 (0.24–1.58)  0.38 (0.14–0.99)*  Model 2d  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  1.00 (0.56–1.80)  0.67 (0.32–1.38)  1.30 (0.61–2.76)  0.36 (0.10–1.27)  0.72 (0.27–1.93)  0.82 (0.28–2.43)  Late quitters  1.03 (0.61–1.74)  0.48 (0.24–0.97)*  1.20 (0.65–2.22)  0.40 (0.17–0.98)*  0.58 (0.20–1.75)  0.59 (0.17–2.06)  Model 3e  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  1.02 (0.56–1.83)  0.80 (0.38–1.67)  1.31 (0.61–2.79)  0.47 (0.13–1.68)  0.72 (0.27–1.92)  0.90 (0.30–2.66)  Late quitters  1.05 (0.62–1.78)  0.50 (0.24–1.02)  1.21 (0.65–2.24)  0.46 (0.18–1.14)  0.59 (0.20–1.78)  0.52 (0.14–1.93)    Men and Women   Men   Women     Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Late decline trajectory OR (95% CI)  Early decline trajectory OR (95% CI)  Model 1c  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  0.79 (0.47–1.33)  0.40 (0.21–0.75)*  0.86 (0.45–1.65)  0.25 (0.09–0.72)*  0.65 (0.27–1.56)  0.47 (0.20–1.09)  Late quitters  0.85 (0.52–1.39)  0.38 (0.21–0.71)*  0.97 (0.55–1.73)  0.39 (0.17–0.86)*  0.61 (0.24–1.58)  0.38 (0.14–0.99)*  Model 2d  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  1.00 (0.56–1.80)  0.67 (0.32–1.38)  1.30 (0.61–2.76)  0.36 (0.10–1.27)  0.72 (0.27–1.93)  0.82 (0.28–2.43)  Late quitters  1.03 (0.61–1.74)  0.48 (0.24–0.97)*  1.20 (0.65–2.22)  0.40 (0.17–0.98)*  0.58 (0.20–1.75)  0.59 (0.17–2.06)  Model 3e  Continued smokers (reference group)  1.00  1.00  1.00  1.00  1.00  1.00  Early quitters  1.02 (0.56–1.83)  0.80 (0.38–1.67)  1.31 (0.61–2.79)  0.47 (0.13–1.68)  0.72 (0.27–1.92)  0.90 (0.30–2.66)  Late quitters  1.05 (0.62–1.78)  0.50 (0.24–1.02)  1.21 (0.65–2.24)  0.46 (0.18–1.14)  0.59 (0.20–1.78)  0.52 (0.14–1.93)  Note: aOR = Odds ratio; CI = Confidence interval. bThe reference category of the outcome measure is successful trajectory. c The associations in Model 1 are unadjusted. d The associations in Model 2 are adjusted for age in years, partner status, educational level and alcohol consumption. These potential confounders were assessed at baseline. e The associations in Model 3 are additionally adjusted for clinically relevant level of depressive symptoms at baseline. *p < .05. View Large After adjustment for all confounders, the associations between smoking cessation status and trajectory membership were no longer statistically significant in the full sample (Table 3; Model 3). Although not statistically significant, the associations particularly suggest that late quitters are less likely to follow an early decline trajectory than a successful trajectory compared to continued smokers (ORearly = 0.80, 95% CI = 0.38–1.67; ORlate = 0.50, 95% CI = 0.24–1.02). Similar results were found in the fully adjusted analyses for men and women (Table 3; Model 3). The sensitivity analyses, in which the associations between smoking cessation status and trajectory membership were assessed while excluding those who dropped out during the study period, revealed similar results as observed in the main analyses. After adjustment for all confounders, early quitters and late quitters were found to be less likely to follow an early decline trajectory instead of a successful trajectory compared to continued smokers (ORearly = 0.15, 95% CI = 0.02–1.33; ORlate = 0.26, 95% CI = 0.04–1.84). The sex-stratified sensitivity analyses also revealed similar results as observed in the main analyses (results not shown). Discussion This study examined whether smoking cessation in middle age and old age is associated with following a successful trajectory of functional limitations over time in Dutch older adults. The results particularly suggest that individuals who quit smoking in old age are more likely to follow a successful trajectory instead of an early decline trajectory compared to continued smokers. An important strength of this study is the assessment of trajectories of functional limitations in Dutch older adults from the general population, based on 16-year longitudinal data from the LASA cohort study. Another strength of this study is that we could adjust for a wide range of relevant confounders. Some limitations have to be acknowledged as well. First, the study sample was fairly small, which resulted in limited statistical power. This made it difficult to gauge the true size of associations in the present study, and this might explain that we could not demonstrate a relationship of functional limitations with early quitting. The small number of cases among women makes it difficult to draw definite conclusions regarding this group. Second, smoking cessation was based on self-reports. This might have caused recall bias, especially regarding the exact age at which participants quit smoking. Third, social desirability bias, related to feelings of shame, may have led to unwillingness to report correct information about smoking behavior. This may have resulted in some misclassification regarding the smoking cessation status and underestimation of the associations with functional limitations. Fourth, selection bias might have influenced the results. If the relationship between smoking cessation and trajectories of functional limitations are stronger in unhealthy individuals, the observed associations in the present study might be slightly underestimated due to the inclusion of healthier persons. Finally, late quitting was defined as smoking cessation during the study period, and it is not clear from the design which happened first: smoking cessation or decline in functional limitations. The results suggest that individuals who quit smoking in middle age and old age were better able to maintain a high level of functioning over time in old age than continued smokers. An explanation for this could be that smoking cessation improves cardiovascular functioning and pulmonary functioning (ie, improved oxygen carrying capacity of blood and improved blood circulation), resulting in less fatigue during daily activities (1,13). Furthermore, it has been shown that smoking cessation is associated with improved physical performance in terms of increased muscle strength, better agility and coordination, and improved gait and balance (14). Previous studies have shown that older adults who smoke are less likely to believe that smoking harms their health (7–9) and are also less likely to receive advice to quit from health care providers than younger individuals (9,10). The current findings suggest that older adults who quit smoking are more likely to follow a successful trajectory than an early decline trajectory compared to continued smokers. This finding particularly highlights the need to inform older current smokers and health care providers about the potential benefits of smoking cessation in terms of long-term trajectories of functional limitations over time in old age. The results imply that smoking cessation should be a priority in healthy aging policy that aims to increase quality of life, social participation, and independence in older adults. In conclusion, the results of this study particularly suggest that smoking cessation in old age increases the likelihood of following a successful trajectory of functional limitations, that is characterized by high levels of initial functioning and limited functional decline over time. The supporting evidence that smoking cessation in old age provides health benefits in older adults may help to motivate older current smokers to quit smoking. Funding This work was supported by the Amsterdam Public Health Research Institute in Amsterdam, the Netherlands. The Longitudinal Aging Study Amsterdam is supported by a grant from the Netherlands Ministry of Health Welfare and Sports, Directorate of Long-Term Care. References 1. United States Department of Health Human Services. The Health Consequences of Smoking-50 years of Progress: A Report of the Surgeon General . Atlanta, GA: United States Department of Health and Human Services, Center for Diseases Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health; 2014. 2. Doll R, Peto R, Boreham J, Sutherland I. Mortality in relation to smoking: 50 years’ observations on male British doctors. BMJ . 2004; 328: 1519. doi: 10.1136/bmj.38142.554479.AE Google Scholar CrossRef Search ADS PubMed  3. Kenfield SA, Stampfer MJ, Rosner BA, Colditz GA. Smoking and smoking cessation in relation to mortality in women. JAMA . 2008; 299: 2037– 2047. doi: 10.1001/jama.299.17.2037 Google Scholar CrossRef Search ADS PubMed  4. Taylor DHJr, Hasselblad V, Henley SJ, Thun MJ, Sloan FA. Benefits of smoking cessation for longevity. Am J Public Health . 2002; 92: 990– 996. doi: 10.2105/AJPH.92.6.990 Google Scholar CrossRef Search ADS PubMed  5. Gellert C, Schöttker B, Brenner H. Smoking and all-cause mortality in older people: systematic review and meta-analysis. Arch Intern Med . 2012; 172: 837– 844. doi: 10.1001/archinternmed.2012.1397 Google Scholar CrossRef Search ADS PubMed  6. Hirdes JP, Maxwell CJ. Smoking cessation and quality of life outcomes among older adults in the Campbell’s Survey on Well-Being. Can J Public Health . 1994; 85: 99– 102. Google Scholar PubMed  7. Abdullah AS, Ho LM, Kwan YH, Cheung WL, McGhee SM, Chan WH. Promoting smoking cessation among the elderly: what are the predictors of intention to quit and successful quitting? J Aging Health . 2006; 18: 552– 564. doi: 10.1177/0898264305281104 Google Scholar CrossRef Search ADS PubMed  8. Honda K. Psychosocial correlates of smoking cessation among elderly ever-smokers in the United States. Addict Behav . 2005; 30: 375– 381. doi: 10.1016/j.addbeh.2004.05.009 Google Scholar CrossRef Search ADS PubMed  9. Schmitt EM, Tsoh JY, Dowling GA, Hall SM. Older adults’ and case managers’ perceptions of smoking and smoking cessation. J Aging Health . 2005; 17: 717– 733. doi: 10.1177/0898264305280995 Google Scholar CrossRef Search ADS PubMed  10. Maguire CP, Ryan J, Kelly A, O’Neill D, Coakley D, Walsh JB. Do patient age and medical condition influence medical advice to stop smoking? Age Ageing . 2000; 29: 264– 266. doi: 10.1093/ageing/29.3.264 Google Scholar CrossRef Search ADS PubMed  11. Manini TM, Pahor M. Physical activity and maintaining physical function in older adults. Br J Sports Med . 2009; 43: 28– 31. doi: 10.1136/bjsm.2008.053736 Google Scholar CrossRef Search ADS PubMed  12. Kok AAL, Aartsen MJ, Deeg DJHet al.   Capturing the diversity of successful aging: an operational definition based on 16-year trajectories of functioning. The Gerontologist . 2017; 57: 240– 251. doi: 10.1093/geront/gnv127 Google Scholar PubMed  13. LaCroix AZ, Omenn GS. Older adults and smoking. Clin Geriatr Med . 1992; 8: 69– 87. Google Scholar PubMed  14. Rapuri PB, Gallagher JC, Smith LM. Smoking is a risk factor for decreased physical performance in elderly women. J Gerontol A . 2007; 62: 93– 99. doi: 10.1093/gerona/62.1.93 Google Scholar CrossRef Search ADS   15. Strand BH, Mishra G, Kuh Det al.   Smoking history and physical performance in midlife: results from the British 1946 Birth Cohort. J Gerontol A . 2011; 66: 142– 149. doi: 10.1093/gerona/glq199 Google Scholar CrossRef Search ADS   16. Artaud F, Sabia S, Dugravot Aet al.   Trajectories of unhealthy behaviors in midlife and risk of disability at older ages in the Whitehall II Cohort Study. J Gerontol A . 2016; 71: 1500– 1506. doi: 10.1093/gerona/glw060 Google Scholar CrossRef Search ADS   17. Hoogendijk EO, Deeg DJ, Poppelaars Jet al.   The Longitudinal Aging Study Amsterdam: cohort update 2016 and major findings. Eur J Epidemiol . 2016; 31: 927– 945. doi: 10.1007/s10654-016-0192-0 Google Scholar CrossRef Search ADS PubMed  18. Huisman M, Poppelaars J, van der Horst Met al.   Cohort profile: the Longitudinal Aging Study Amsterdam. Int J Epidemiol . 2011; 40: 868– 876. doi: 10.1093/ije/dyq219 Google Scholar CrossRef Search ADS PubMed  19. Kriegsman DMW, Deeg DJH, Stalman WAB. Comorbidity of somatic chronic diseases and decline in physical functioning. The Longitudinal Aging Study Amsterdam. J Clin Epidemiol . 2004; 57: 55– 65. doi: 10.1016/S0895-4356(03)00258-0 Google Scholar CrossRef Search ADS PubMed  20. Van Sonsbeek JLA. Methodological and substantial aspects of the OECD indicator of chronic functional limitations. Maandbericht Gezondheid (Statistics Netherlands)  1988; 88: 4– 17. 21. Clark SL, Muthén B. Relating Latent Class Analysis Results to Variables not Included in the Analysis. Retrieved from www.statmodel.com/download/relatinglca.pdf. Accessed March 29, 2017. 22. Garretsen HFL. Probleemdrinken: Prevalentiebepaling, beïnvloedende factoren en Prevalentiemogelijkheden; Theoretische overwegingen en onderzoek in Rotterdam. (Dissertation in Dutch) . Lisse, the Netherlands: Swets & Zeitlinger; 1983. 23. Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas . 1977; 3: 385– 401. doi: 10.1177/014662167700100306 Google Scholar CrossRef Search ADS   24. Muthén LK, Muthén BO. Mplus User’s Guide. 7th ed . Los Angeles, CA: Muthén & Muthén; 1998–2002. © The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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The Journals of Gerontology Series A: Biomedical Sciences and Medical SciencesOxford University Press

Published: Feb 15, 2018

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