Interaction Between Self-Perceived Disease Control and Self-Management Behaviors: The Role of Subjective Life Expectancy


 Backgrounds: One of the effective ways to control chronic diseases is long-term self-management, but it is difficult to adhere to. Therefore, the understanding of how people engage in the process of self-management behavior change is necessary. In this study, we aimed to examine the dynamic relationship between self-perceived disease control and self-management behaviors in Chinese older adults with hypertension, namely, medication use, self-monitoring, physical activity, tobacco and alcohol avoidance; and to explore the mediating role of subjective life expectancy (SLE) on this relationship. Methods: Data came from a nationally representative sample of 508 older hypertensive patients (aged 45+) from 2013, 2015, and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey. A cross-lagged panel model combined with mediation analysis was used to determine the dynamic relationship between self-perceived disease control and self-management behaviors, and also clarify the mediating effect of SLE on this ascertained relationship.Results: Good self-perceived disease control predicted subsequently well medication use, self-monitoring and physical activity, and vice versa. The subjective life expectancy (SLE) partially mediated the prospective reciprocal relationships between self-perceived disease control and these self-management behaviors, accounting for 37.11%, 25.88%, 19.39% of the total effect of self-perceived disease control on medication use, self-monitoring and physical activity, respectively. These self-management behaviors further had significantly and positively feedback effect on self-perceived disease control. However, both direct and indirect effects (via SLE) of self-perceived disease control on tobacco and alcohol avoidance were not revealed.Conclusions: Positive feedback loops of present self-perceived disease control, future SLE and self-management behaviors (medication use, self-monitoring, and physical activity) help older hypertensive patients adhere to these behaviors, but useless for their addictive behaviors’ avoidance. Interventions aimed at enhancing effect perception of general self-management behaviors (e.g., medication use, self-monitoring and physical activity) on present disease control perspective and future lifespan perspective would be beneficial for consistent these self-management behaviors of older hypertensive patients. The utility of present disease control perception to these self-management behaviors was much higher than the utility of future expectations. While alternative stress relief strategies may be conducive to the long-term change of their addictive behaviors.

proofs also proved that in disease control, avoidance of complications, and improvements in patients' long-term health and quality of life, effective self-management plays a key role [4][5][6][7]. The idea of selfmanagement originates from responsible stewardship of one's health [8], which encourages patients to be the active participants in their own day-to-day management of chronic conditions over the course of illness [9]. Self-management is not only the practice of providing information and increasing patient knowledge, but also demonstrates its potential as an effective paradigm across the prevention spectrum by teaching individuals to actively identify challenges and working in partnership with others to solve problems to ensure that the illness is under control [4,10,11].
The core challenge of self-management for patients with chronic diseases is persistence because the progression of chronic diseases is generally slow and lasts for a long time [12], although many prevalent chronic conditions are unique in their own attributes and demands [1]. This is because momentary performance tends to fail to be generalized into routinized adherence. However, approximately 50% of patients have di culty in self-managing their long-term condition, so adherence to self-managing behaviors is usually low [13,14]. These low levels of adherence inevitably undermine the effectiveness of treatments, and lead to a deterioration of illness. As a result, it will increase the preventable morbidity, members of hospitalization, burdens of diseases and healthcare costs [7].
Researchers have advocated the application of social cognition theories to identify the determinants of self-management, because of behavioral decisions and guide subsequent behavior later than individuals' social-cognition factors [15,16]. It is widely believed that a continuous, dynamic, and non-linear process that people undertake every day can be called health behavior change in chronic disease management [3]. Leventhal et al. (2016) proposed a dynamic framework based on the common-sense model of self-regulation (CSM), believing that illness-related perception and daily experience can lead to consistent self-management results [17]. When confronted with a threat to their health, people construct perception about their disease control to change their behavior associated with disease management [18]. By monitoring and testing the performances and outcomes of self-management behaviors to see if they meet their expectations, patients' perception of disease control is further changed. Self-management behavior can become coherent and automatic through a two-way continuous update: the patient's perceived illness is controlled rst, and the patient continues to adhere to the selfmanagement behavior [5,19,20]. This continuous present perception changing established basis on one's own practice can rmly promote people to believe in relevant idea or thing and practice it. Thus, we propose the rst hypothesis that there is bidirectional causality between self-perceived disease control and self-management behavior. However, several systematic reviews have found that the relationship between the dimensions of disease-related perceptions and individuals' self-management persistence behavior is weak [4,21]. Other factors may have in uenced potential associations with subsequent selfmanagement behaviors. Social cognitive theory argues that the closest predictor of target behavior is motivation, and that it creates a connection between cognition and related behavior with an inherent "energy" force. Future time perspective is seen as primary motivational space in human self-regulation of their future behaviors [22] and maybe act as an inner motivation between patients' self-perceived disease control and their self-management behaviors. Usually, the estimates of subjective (self-rated) life expectancy (SLE) re ect the future time perspective [23,24]. It is an individual's expectation regarding the perceived extent of his or her remaining years [24,25], and has implications for health across the life course, especially in mid-and later life within an adjusted time frame [26]. Previous studies demonstrated that SLE preceded health behavior [27,28], and became a critical indicator of providing guidance for apportioning health-related behavior [29,30]. Meanwhile, individuals always took their present genetic health and functional status and risk factors into account in their subjective evaluation of life expectancy [24,31]. In this sense, self-perceived disease control may be an indicator for patients, and not surprisingly, patients with low sense of disease control perception are tended to be less future oriented [32][33][34].
Therefore, we propose a second hypothesis that the mediator between self-perceived disease control and subsequent self-managed behavior can be performed by SLE.
It is worth noting that the relationship between SLE and health-related behaviors is fuzzy. The vast majority of studies believed that adults who had low survival expectations were less motivated to do things that would promote their longevity and were reluctant to engage in health-related behavior even more likely to participate in a range of risky behaviors [2,27,28,30,35]. But pessimistic expectations for survival may encourage people to live more carefully [36], unrealistic optimism may prompt recklessness and risk seeking [37], and longer SLE may lead to adverse self-management adherence behaviors. In a study of African Americans, Irby-Shasanmi (2012) reported the relationships between longer SLE and risky behaviors on participants' social networks is small-sized, but positive [38]. Thus, we propose that the individual's future self-management behavior may also be interrupted by longer SLE, the mediating effect of SLE on self-perceived disease control and subsequent self-management behaviors is ambiguous.
Although some previous predictive analyses are valuable for identifying predictors of self-management and provide insight into mediational factors and pathways to outcomes, on account of the cross sectional-based research design, these studies do not evaluate the complex interactions among speci c variables in the dynamics of self-management processes [15,16]. In terms of support for selfmanagement behavior, patients were not effectively understood [4]. Therefore, the longitudinal data were analyzed in this study for the two purposes, (i) to examine previously unexplored causality between selfperceived disease control and self-management behaviors, and (ii) to assess whether the SLE mediated the relationship between self-perceived disease control and self-management behaviors. Compared to current cross-sectional studies, this longitudinal study may make two contributions to current literatures.
First, we further re ne and verify the Leventhal et al. (2016)'s dynamic framework based on the commonsense model of self-regulation (CSM) [17]. In addition, we might clarify the short-term and long-term cognitive mechanisms that persist in self-management behavior by conducting a more thorough analysis of the relationship between individual illness related perception domains and self-management behavior. Second, we will examine the prospective relationship between individual disease-related perception domains, SLE and self-management behavior more vigorously, by using longitudinal data to form may rmer conclusions than previously.  [12], and the core component of their care considered to be self-management [14]. Given the prevalence of hypertension and our aging population are increasing, the impact of poor adherence to self-management behaviors on the health of the population are likely to become worse increasingly [4]. Therefore, we choose hypertensive patients as the participants in our study.
The current study used CHARLS's last three waves of the data in 2013, 2015 and 2018 (recorded as T1, T2 and T3). We selected a total of 2,801 hypertensive patients from 14,277 elders who participated in all three-wave investigation, excluding 429 of them with mental retardation and physical disabilities, and 1864 of them with missing values on analysis variables; the nal sample for analyses, therefore, consisted of 508 respondents.

Outcome variables
Self-management behaviors were the interaction of health behaviors and their related processes that patients and families engage in were to care for a chronic condition [39]. On the basis of relevant literatures [7,10,11,40] and CHARLS questionnaires, self-management behaviors in this study encompassed both pharmacological and non-pharmacological management behaviors, including medication use, self-monitoring, physical activity, and tobacco and alcohol avoidance.
Medication use: A single question "Are you now taking any of the following medications to treat or control your hypertension?" was administered to assess participants' medication use status. Each option that taking Chinese traditional medicine or taking Western modern medicine was scored 1 when the answer was yes and 0 for none of the above, so higher scores indicated better medication use.
Self-monitoring: A question of "During last year (last 12 months), how many times have you had blood pressure examination?" was inquired to inform the patients' self-monitoring behavior. 0 time of examination was scored by 0, 1 for [1,6), 2 for [6,12), and ≥12 for 3. Higher scores indicated better selfmonitoring behavior.
Physical activity: The participation of physical activities (PA) were binary answers to the questions of whether an individual took vigorous physical activities (VPA), participated in moderate physical activities (MPA), or walked for at least 10 minutes continuously every week (WALK). CHARLS de ned VPA as activities which made people breathe much harder than normal and might include heavy lifting, digging, plowing, aerobics, fast bicycling, and cycling with a heavy load; MPA as activities which made individuals breathe somewhat harder than normal might include carrying light loads, bicycling at a regular pace, or mopping the oor; WALK as walking those individuals might do solely for recreation, sport, exercise, or leisure. PA in the database was divided into four levels according to exercise intensity, exercise volume and exercise time. The PA standard of level 1 was more than once a week with no less than 30 minutes of VPA each time, more than 3 times a week with no less than 30 minutes of MPA each time, or more than 5 times a week with no less than 30 minutes' WALK each time. Level 2 PA was no less than 30 minutes of MPA three times a week or no less than 30 minutes of WALK of four or ve times a week; Level 3 PA was for no less than 3 times a week with at least 30 minutes' WALK each time; Level 4 PA was no participating in physical exercise. Scored 0-4 points were from level 4 PA to level 1 PA, and a higher score indicated better physical activity taking.
Tobacco avoidance: To understand patients' tobacco avoidance status, we used questions of "Have you ever chewed tobacco, smoked a pipe, smoked self-rolled cigarettes, or smoked cigarettes/cigars?" and "Do you still have the habit or have you totally quit?" to investigate the patients' smoking history. The option of quitting or never smoking was scored for 1, and 0 for still smoking.
Alcohol avoidance: The question of "Did you drink any alcoholic beverages, such as beer, wine, or liquor in the past year? How often?" was used to understand the patients' alcohol avoidance. The response of having ever drunk was scored for 0, and never drinking was scored for 1.

Independent variable
Self-perceived disease control Self-perceived disease control of hypertensive patients was reported by a question "Compared to when we interviewed you in R's LAST IW MONTH, YEAR, is your condition better, about the same as it was then or worse?". When the patient answered better, he or she was scored for 1, while -1 for worse and 0 for same.

Mediating variable
Usually, individual subjective life expectancy was gathered by subjective probability of survival for a de ned age or self-rated life expectancy. To calculate the subjective residua life (SLE), we refer to Spaenjers & Spira (2015)'s study and calculate it as a proxy variable of SLE [41]. The calculation formula is as follows: Subjective residual life = expected age at death − current age (1) Expected age at death = average life expectancy + (target age − average life under the same probability) The "current age" refers to the age of the respondents at the time of each survey.
The "average life under the same probability" was searched in the China Life Insurance Mortality Table  (2010-2013) by the individuals' ages and genders, and the same probability refers to an individual's subjective survival probability (SPS) which was investigated by the CHARLS. The question is: "Suppose there are 5 options, where the lowest option represents the smallest chance and the highest option represents the highest chance, on what option do you think is your chance of reaching the age of [...]?" So, the response options for this item were 1=almost impossible, 2=not very likely, 3=maybe, 4=very likely, 5=almost certain, which correspond to the SPS of 0%, 25 %, 50 %, 75 % and 100 %, respectively.
The "target age" was determined by the current age of the interviewee, as shown in Table 1. We included a number of covariates that were known to be associated with self-perceived disease control and self-management behaviors were controlled in our statistical analyses, in order to minimize the disturbing possibility of other variables and to maximize the parsimony of our analytic model [11,42,43]. The covariates including gender (0=male, 1=female), age (continuous variable), marital status (1=married, 2=unmarried, 3=others (divorced and widowed)(reference)), Hukou types (0=agricultural Hukou, 1=non-agricultural Hukou), medical insurance (1=urban employee medical insurance, 2=urban and rural resident medical insurance, 3=other medical insurance (reference)), education (1=illiterate(reference), 2=primary school and below, 3= Junior school and above), living arrangement (0=living alone, 1=living with others), comorbidity (Whether the individual have other chronic diseases? (1=Yes, 0=No), these chronic diseases were dyslipidemia (elevation of low density lipoprotein, triglycerides, and total cholesterol, or a low high density lipoprotein level); diabetes or high blood sugar; cancer or malignant tumor (excluding minor skin cancers); chronic lung diseases (such as chronic bronchitis); emphysema (excluding tumors, or cancer); liver disease (except fatty liver, tumors, and cancer); heart attack, coronary heart disease, angina, congestive heart failure, or other heart problems; stroke; kidney disease (except for tumor or cancer); stomach or other digestive disease (except for tumor or cancer); emotional, nervous, or psychiatric problems; memory-related disease; arthritis or rheumatism; asthma). Life satisfaction was assessed by a single question: "Please think about your life-as-a-whole.
How satis ed are you with it? (1 =Not satis ed at all; 2=Not very satis ed; 3=Somewhat satis ed; 4=Very satis ed; 5 =Completely satis ed). Social participation was assessed by a single question: "Have you participated in the following social activity in the past month?" for which there were 10 activities (interacted with friend; played mahjong, chess, or cards, or went to community club; provided help to family, friends, or neighbors who did not live with you and did not pay you for the help; went to a sport, social, or other kind of club; took part in a community-related organization; did voluntary or charity work; cared for a sick or disabled adult who did not live with you and who did not pay you for the help; attended an educational or training course; stocked investment; used the Internet.); participating in each activity was scored by 1, otherwise, it was 0; and the range of total score was 0-10 [44]. Self-rated health was evaluated by a question of "What do you feel about your health status?" (1 =Very poor, 2 =Poor, 3 =Very good, 4 =Good, 5 =Fair). Medical intervention status was investigated by the question "Have you ever received any medical intervention?" The intervention items contained blood pressure examination, weight control, physical exercise advice, diet advice, and smoking control. The answer "yes" was scored for 1, ranging from 0 to 5. Depression was determined by using a short form of the Center for Epidemiologic Studies Depression Scale (CES-D10) developed by Andresen et al. (1994). Lei et al. (2014) tested the reliability and validity of CES-D10 by using CHARLS data to con rm the validity of CES-D10 through Chinese population studies, and CESD-10 covered a range of depressive symptomatology with emphasis on current levels of depressive affect [45,46]. Items were weighted by frequency of symptom occurrence in the last week, using a 4-point Likert-type response format, and each item was rated from 0 (rarely or no time) to 3 (most or all the time). Individuals were divided into three groups based on these ranges identi ed by Andresen et al. (1994) [45]: 1=depression (score≥10), 0=non-depression (score<10).
Activities of daily living (ADL) functional status was an index that indicated individual functional status of middle-aged and elders when they dealt with ADL on their own. According to the international standard ADL index developed by Katz (1963), ADL functional status contained six indices, which were the functional status of eating, dressing, transferring, bathing, using the toilet, and continence [47]. Each item was independently completed with 1 point, otherwise was 0, and the range of total score was 0-6.
Statistical approach SPSS v26 (IBM Corp 2019) was used to test the demographic differences among self-management behaviors (independent-sample t-test and one-way ANOVA) and the correlation among self-perceived diseases control, SLE, and self-management behaviors (Pearson correlation analysis and multilinear test).
Using two types of separate autoregressive cross-lagged models in Mplus v7.4 (Muthen & Muthen 1998-2015) to estimate the main hypotheses [48]. The rst model examined the bidirectional association between self-perceived disease control and self-management behaviors, and the second model added SLE into the rst model to test its role in bidirectional mediation. Both two types of full models included stability paths within variables across time (i.e., autoregressive paths), concurrent associations among variables within each assessment wave, and associations among variables across time (i.e., cross-lagged paths). All analyses used the robust maximum likelihood (ML) estimator because the data was in nonnormal distribution which was determined by the K-S normality test. Model t was evaluated by using criterions proposed by Hu and Bentler (1998;, which used multiple t indices comprising root mean square error of approximation (RMSEA) [49,50], standardized root mean square residual (SRMR) and the ratio of chi-square to degrees of freedom (χ 2 /df). Model t is good when RMSEA<0.06, SRMR<0.08, χ 2 /df <3.
Furthermore, because the data distribution of variables was skewed, we used the bootstrapping method, an approach for implementing statistical tests and constructing con dence intervals without the use of the traditional statistical assumption of normality, to test the statistical signi cance of the paths and to compute an estimation of the indirect effect with a 95% CI. The indirect effect was deemed to be signi cant when the con dence interval did not include zero. Footnote: [*] The China Life Insurance Mortality Table (

Descriptive analyses
The distribution of the study variables measured in 2013, 2015, 2018 are presented in Table 2. At baseline, 68.9% of total respondents felt that their hypertensions status had not changed; only 18.5% or 12.6 % of total felt that their hypertensions were getting "better" or "worse".  Table 3 Additional le 1. Both within waves and across waves, self-perceived disease control, SLE and self-management behaviors were signi cantly and positively associated with each other.

Reciprocal Associations Between Self-perceived Disease Control And Self-management Behaviors
Longitudinal cross-lagged models (Model 1-5) between self-perceived disease control and selfmanagement behaviors (medication use, self-monitoring, physical activity, tobacco and alcohol avoidance) after controlling for covariates, with the auto-regressive and concurrent estimates as well as cross-lagged paths, illustrated in Figure 1

Longitudinal Indirect Effect Of Sle In The Bidirectional Association
As Figure 6-10 presents, we tested the longitudinal mediation cross-lagged models by adding SLE as the mediator after adjusting for control variables. These models tted the data well (RMSEA = 0.042~0.058, SRMR=0.026~0.037, χ 2 /df = 1.88~2.73) (Supplementary Table 2  While the reverse cross-lagged direct effect but not indirect effect from self-management behaviors (medication use, self-monitoring, and physical activity) to self-perceived disease control were showed over time. Meanwhile, these mediation model demonstrated partial effects accounted for 37.11%, 25.88%, 19.39% of the total effect of self-perceived disease control on medication use, self-monitoring and physical activity, respectively. Moreover, this mediation path was not observed between self-perceived disease control and tobacco/alcohol avoidance.

Discussion
This study examined the reciprocal association between self-perceived disease control and selfmanagement behaviors in the old hypertensive patients, as well as the underlying mechanism of SLE in explaining the process. The current longitudinal study documented the differentiated causal relationships among self-perceived disease control, SLE and various self-management behaviors at three waves over a 5-year period. These relationships were more complex than previously thought from cross-sectional research and had their implications for adherence of old patients' self-management behaviors.
In this study, we found between self-perceived disease control and medication use, self-monitoring, and physical activity have a positive bidirectional association respectively. It demonstrated that people would continue to implement self-management behaviors when they believed these behaviors should be good for their disease-control [18], and these experiences would come to pervade daily life by validating and updating by daily life. The prediction of illness-related perceptions to the self-management behaviors for chronic disease management has been con rmed by previous cross-sectional studies [4,20]. However, there was no clear evidence to prove the reverse prediction, the importance role of past behaviors for illness-related perceptions and consistent self-management behaviors has been certi ed by various studies [4,21,51]. In support of cognitive behavioral theories, past good experience was bene cial for individuals to study self-management knowledge and to perceive positive feelings in disease control simultaneously [4, 18, 52, 53,], which might enhance their self-perceived disease control to enable them to perform complex self-management activities [2,54,55,56,57]. From the point of biological irritability and adaptability, as Gray's reinforcement sensitivity theory (RST) pointed out, the perception of disease control effect resulting from past self-management experiences might create rewards and punishments as stimulators, motivate their behavior approach response and behavioral avoidance response, then help them with continuance of self-management behaviors under repeated stimulation [53,58].
Moreover, the positive regulatory role of SLE among self-perceived disease control and medication use, self-monitoring, and physical activity was separately identi ed in our study. It indicated that when individuals perceived their disease control better, they were preferred with a more expansive time perspective to anticipate more positive and less negative outcomes, investing more self-regulatory effort (i.e., planning) and report high level self-management behaviors. According to socioemotional selectivity theory [59,60], individuals monitored their life expectancy based on their present health status and made a difference decision in health self-regulation. An expansive time perspective was assumed to lead to a preference for goals aimed at optimizing the future (e.g., adopting a health behavior), whereas a restricted future time perspective will be assumed to be related to a preference for emotionally meaningful goals, and pay more attention to present payoff than before [27,36,61].
We also found an interesting thing that our results didn't show reverse mediating effects, but these behaviors were found to feedback into self-perceived disease control, forming a potential feedback loop or spiral which could impact on an individual's adherence to medication use, self-monitoring and physical activity trajectory. Even though SLE has been shown to be associated with health behaviors, smoking status, alcohol status and physical activity through several studies [26,30], this study further proved that SLE was a powerful predictor of behaviors.
Furthermore, we discovered that in contrast to medication use, self-monitoring, and physical activity, the current study found no consistent patterns in self-perceived disease control and their addictive behaviors' avoidance (tobacco and alcohol avoidance). Self-perceived disease control signi cantly in uenced SLE, but it did not affect tobacco and alcohol avoidance directly or indirectly through SLE. Also, avoidance of addictive behavior did not lead to better self-perceived disease control or SLE. High levels of social isolation and feelings of personal uselessness are more common among the elderly, leading to further exacerbations of chronic diseases. Except for the need for social interaction, tobacco and alcohol are also good mechanisms for coping with stress in older patients [14], which could help them to relax, feel good, relieve tension or anxiety, and pass the time

Conclusion
Long-term self-management is an effective chronic disease-coping way. This longitudinal study attempted to shed light on the underlying mechanisms linking self-perceived disease control and selfmanagement behaviors among old patients, and the results were of great interest as they had implications for the development of future intervention strategies for self-management behaviors. The present study indicated a bidirectional causality between self-perceived disease control and medication use, self-monitoring and physical activity, and the individual perception of "how long he/she will live partly" partially mediated the predictive of self-perceived disease control on these self-management behaviors (medication use, self-monitoring and physical activity) by forming a potential feedback loop or spiral. Individual adherence to medication use, self-monitoring and physical activity may be affected by these. But this predicative relationship was not con rmed in tobacco and alcohol avoidance behaviors. Based on these ndings, in establishing their trajectories of health, the old person's present self-perceived disease control and future lifespan expectations played important roles, and this might become pathways to shape their self-management behaviors. It might be helpful to stress the notion of plasticity (i.e., chronic disease can be largely controlled by self-management behaviors in the present and gains in life span can be achieved in this way in the future). Especially it might also encourage those people with a limited time perspective to try to engage in thinking about short-term effect of self-management behaviors for disease control and long-term effect on lifespan. However, because addictive behaviors (smoking and drinking alcohol) are di cult to change by changing the present self-perceived disease control and the future time perspective, it might bene t from promoting the use of adaptive coping strategies (e.g., exercise, and talking to family and friends) to actively cope with stress.

Limitations
There are also some limitations to this study. First, autoregressive cross-lagged path analyses can't separate the between-person effects from the within-person effects. There is a need for future studies to use the random intercept cross-lagged panel model focusing on within-person longitudinal associations to further test out results. Second, the study sample only comprised the old patients with hypertension in China. It is unknown whether our ndings will generalize to other cultural contexts and any other chronic disease patients. The results should be replicated in diverse social environments and patient populations.
Third, there is a lack of analysis of other self-management behaviors (e.g., sleeping, dieting) due to the limitation of data. Other constructs of the illness belief domains, or other mediators which have been well documented in empirical studies should be examined in further, too. Fourth, the complex associations between self-perceived disease control and self-management behaviors could be moderated by other factors (e.g., social support, regional medical level, personal health literacy, etc). Future studies should elucidate the protective factors to strengthen the self-perceived disease control or SLE to adherent selfmanagement behaviors.

Declarations
Ethics approval and consent to participate The study used secondary data from CHARLS which are publicly available.