Sample

The Irish Longitudinal Study on Ageing (TILDA) is a large prospective cohort study of the social, economic and health circumstances of community-dwelling older people in Ireland. Analyses are based on the first wave of data collected between January 2009 and July 2011 and follow-up data collected between March and December 2012. The mean time difference between interviews was 744.12 days (24 months) (inter-quartile range = 114). The Irish Geodirectory—a listing of all residential addresses in the Republic of Ireland—was used as a sampling frame to randomly select households in which individuals aged 50+ lived. The full procedure has been described in detail elsewhere [16]. Household residents aged 50 or over and their spouses/partners (of any age) were eligible to participate in the study. The response rate was 62% leading to a final sample size of 8,504 participants. Ethical approval was obtained from the Trinity College Dublin Research Ethics Committee and all participants provided written informed consent.

The study design has been described in detail elsewhere [16]. Briefly, data collection involved (i) a computer-assisted personal interview carried out by trained social interviewers that included detailed questions on socio-demographics, wealth, health, lifestyle, social support and participation and use of health and social care (ii) a self-completion questionnaire which included questions about perceptions of aging, social networks and social participation (iii) a health assessment carried out by research nurses. Participants who were unwilling or unable to attend a health assessment in a designated health centre were offered a home-based health assessment. In wave 2 the computer-assisted personal interview and key elements of the health assessment were combined into a home interview and assessment carried out by trained interviewers.

In total, 8,175 individuals aged 50 years and over were interviewed at baseline (N = 8,504 includes partners younger than 50) and 5,895 (72%) of these individuals also completed a health assessment. Follow up data for wave 2 was available for 6,995 (86%) participants of which 78 (.01%) were proxy interviews and were thus not included in the sample for this analysis. Follow up data was unavailable for 1,180 (14%) participants due to death (n = 205); refusal (n = 809) or no contact (n = 166). We also had a number of missing data values which are outlined in Table 1. Exclusion criteria for the present analysis included a doctor’s diagnosis of Parkinson’s disease (N = 35), a history of stroke (N = 91) or an MMSE of <18 (N = 20) at baseline or between waves 1 and 2. Our final sample included participants who had completed both a wave 1 health assessment and a wave 2 follow up, who had returned the self-completion questionnaire and who did not meet any of the exclusion criteria. The final sample size was 4,803 (for a flow chart of sample selection see Fig 1).

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Table 1. Descriptive Statistics^{a, b, a, b}.

https://doi.org/10.1371/journal.pone.0123260.t001

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Fig 1. Flow Chart.

Flow Chart Indicating Participants Remaining From Waves 1–2.

https://doi.org/10.1371/journal.pone.0123260.g001

Data Collection

Timed Up-and-Go

The Timed Up-and-Go (TUG) task was measured in both waves. Participants were asked to rise from a chair (seat height 46cm), walk 3m at a normal pace, turn around, walk back to the chair and sit down again [14]. The time taken from the command “Go” to when the participant was sitting with their back resting against the back of the chair was recorded using a stopwatch. TUG was assessed using the same procedure in the home assessment and in wave 2. Researchers in the home assessments in wave 2 sought and conducted the test using a hard-backed chair that matched the health centre chair as closely as possible in height (46cm). TUG is a measure of walking speed, balance and coordination [14]. Higher scores on the TUG task (in seconds) indicate slower walking speed.

Aging Perceptions Questionnaire (B-APQ)

The short form of the Aging Perceptions Questionnaire (B-APQ) comprises 17 Likert scale items that ask participants to rate their level of agreement with questions about the aging experience and their expectations about aging in the future. The model was based on Leventhal’s self-regulation model of ill health which proposes that the way people think about their own health or illness can be categorised into different themes [19]. The questions are categorised into 5 domains: timeline (e.g. “I am conscious of getting older all of the time”); positive consequences (e.g. “As I get older I continue to grow as a person”); positive control (e.g. “As I get older there is much I can do to maintain my independence”); negative consequences and control (e.g. “Slowing down with age is not something I can control”); and emotional representations (e.g. “I get depressed when I think about getting older”) [20]. Participants rated their level of agreement with each statement as strongly disagree, disagree, neither, agree or strongly agree (range 1–5). The score for each of the five domains was calculated as the average rating of questions within each domain. Cronbach’s alpha showed good internal consistency for each of the domains (Timeline α = 0.75; Positive Control α = 0.85; Negative Control and Consequences α = 0.80; Positive Consequences α = 0.77; Emotional Representations α = 0.75).

Statistical Analysis

Descriptive statistics were first ascertained for all main variables. TUG time had a skewed distribution which was best corrected with a log transformation. Rather than conducting the analysis on a log-transformed TUG variable, which would have provided output only in the log scale, we conducted a linear regression analysis with a link log function as this allows the predictor to act on the log of the outcome variable while providing marginal mean output in the original scale. This allows for easier interpretation of results.

To test the effect of aging perceptions on change in TUG speed we estimated a second linear regression with follow-up TUG as the outcome, controlling for TUG at baseline as well as all baseline covariates and change in covariates between waves. To better understand the effect of aging perceptions on TUG time we estimated and plotted marginal mean scores of the TUG for five points of the Aging Perceptions scale (1, 2, 3, 4 and 5) which represent the level of agreement with the domain (1 = strongly disagree, 2 = disagree, 3 = neither, 4 = agree, 5 = strongly agree). Marginal means represent the mean in the outcome variable after accounting for the confounding effect of all variables which have been controlled for in the model.

Attrition weights were used due to differences between respondents who participated in both wave 1 and 2 and respondents who participated only in wave 1 on key variables (see Table 1). Attrition weights were calculated as the inverse of the probability that the respondent participated in wave 2 given their participation in wave 1 and their survival up to wave 2. We calculated this using logistic regression in which the dependent variable was whether a respondent had returned in wave 2 or dropped out of the study and the independent variables were multiple measures taken at baseline to try to explain the probability of attrition including measures of baseline mood, cognitive function, physical health, health behaviours and sociodemographic factors. This is multiplied by an initial inverse probability weight reflecting the probability that a member of the Irish population aged 50 years and older participated in the study. This method has previously been reported as an appropriate strategy to minimise bias due to attrition in survey data [21].

Health Behaviours

Previous work has shown that those who blame ‘old age’ as the reason for a chronic condition over any other cause show a decrease in health maintenance behaviours such as having a nutritious diet, participating in exercise, visiting health professionals and getting consistent levels of sleep [27]. Stewart et al. suggested that as aging is uncontrollable, those who blame their condition on old age also see their health as uncontrollable and thus do not act even when the action may be beneficial. Our results indicate that even in a relatively healthy cohort, and after controlling for the presence of chronic conditions, it is the control and expectations for negative consequences aspects of aging perceptions which affect physical function. It is thus possible that these are the same people who blame any health deficits on ‘old age’ and who are less likely to stay physical active, maintain a healthy diet and visit the doctor. Previous work on aging perceptions has shown that adults with positive aging perceptions engage in more exercise, have healthier diets and are more likely to take medications as prescribed than those with negative perceptions regardless of health status [28, 29]. These types of health behaviours are activities which are carried out through the lifespan and not just in older age. It seems unlikely, therefore, that people with negative perceptions of aging make a conscious decision to stop engaging in health maintenance as soon as they reach a certain age. Instead, attentional and confirmatory biases likely reinforce negative perceptions as they age and result in a gradual removal from activities. Psychologically, humans work towards stability in their world view and will give more credence and attention to information which confirms an attitude than to information which disproves it [30]. Adults who have negative perceptions of aging will therefore look for information which confirms that aging is unpleasant. For example, an older adult who believes that severe physical decline is immediately inevitable will have an attentional bias towards the information which confirms this point of view. If they go for a walk and feel more tired than usual after it they will likely attribute this to ‘old age’ rather than considering alternative explanations such as a lack of sleep or a busy week. The negative response to the walk on this occasion may be taken as a sign that walking will always be unpleasant from now on and they will gradually disengage from the activity. As their sedentary behaviour will result in real threats to health, their negative beliefs will be confirmed and the behavioural risks will increase

Psychological Factors and Coping Mechanisms

The pathway between negative perceptions of aging and declining physical function may not be limited to behavioural explanations but also to psychological responses. We might have expected that the negative emotions elicited by holding negative perceptions of aging would affect walking speed. After all, the effects of depression on walking speed are well documented [31, 32]. Surprisingly, however, although depressed mood was associated with decline in TUG performance, the effect of aging perceptions remained significant after controlling for depression. Furthermore, the emotional representations domain of the B-APQ was not statistically significantly associated with change in TUG in the longitudinal models.

Stress is a potential biological mediator of this effect. Feelings of a lack of control and expectations for negative events are individually associated with increased stress responses both psychologically and physiologically [26, 33]. When a stressful event occurs it activates the hypothalamic-pituitary-adrenal axis which results in spikes in cortisol secretion. Over-activity of this process and resulting sustained high levels of cortisol are damaging to muscle mass [33]. Thus older adults who believe that negative consequences are forthcoming and inevitable, and who believe that they have no control over events, likely experience increased stress and spikes in cortisol secretion which may reduce muscle mass as they age resulting in slower walking speed.

Control beliefs can take two forms: primary control is the belief that behaviours will generate desired effects while secondary control is the belief that one can shape one’s cognitive, emotional or motivational responses to events in the environment [34]. Primary control decreases with age while secondary control increases [34]. Older adults become continuously better at shaping their response to the environment. These coping strategies include selecting and adapting relevant goals in the face of adverse change. For example, an older adult who used to go to the gym daily but who develops a serious illness which prevents them from going could either give up the goal of fitness or could adapt it such that they may aim to be able to walk up a flight of stairs once a day instead. Previous work has shown that older adults with positive perceptions of aging are better at using these goal adaption strategies in the face of serious health events and have higher life satisfaction as a result [7]. Previous work has also shown that older adults primed with negative stereotypes about aging are more likely to dissociate themselves from their peer group [35]. Although this protects self-esteem in the short-term it may also have damaging psychological effects as part of the identity is essentially rejected [6]. Weiss and colleagues suggest that age dissociation may prevent people from reaching important developmental stages in older age such as advance preparation for potential adverse events. This may be related to goal adaptation such that older adults who hold negative perceptions about aging, and who dissociate themselves from their age group as a result, may initially be less likely to prepare themselves for potential adverse events and then less willing to use goal adaptation strategies if adverse events do occur. We thus propose that those adults with positive perceptions of aging are better able to use adaptive strategies to overcome difficulties and are therefore less likely to experience the surge in psychological and physiological stress responses that can affect physical function. It is curious that we did not see a protective effect of the positive control or positive consequences domains on walking speed considering there was a detrimental effect of negative control and consequences. Having examined the individual questions within the domains, however, we suspect that this may be due to the nature of the questions. The positive consequences domain includes statements such as “As I get older I get wiser” which may tap into a different construct than beliefs in the inevitability of physical decline. Similarly, the positive control domain is mostly constructed of questions about the quality of relationships (e.g. “The quality of my relationships with others in later life depends on me”) which, particularly with close family and friends, may not be expected to decline merely due to age.

Strengths and Limitations

The strength of this study lies in its large, population representative sample and longitudinal data collection. The extent of the measures collected also allowed us to control for a large number of potential confounders which might otherwise explain the association between aging perceptions and physical outcomes, although it is possible that some unmeasured confounding remained. There was some attrition between waves and those who refused at wave 2 had slower walking speed and stronger beliefs in the negative perceptions of aging than those who participated in both waves. However, we attempted to control for the confounding effects of this using attrition weights.

One potential limitation of our study is the missing data values spread across variables. These were small numbers for the majority of variables (for example N = 1 for education and N = 7 for age) but larger for the aging perceptions domains. This is likely due to the nature of the self-completion questionnaire which respondents had to fill in and return to the study themselves. Missing data values were more likely in adults who were older, less educated and less physically able. These, however, are the same variables which the attrition weights attempt to control for and thus some bias due to missing values will have been removed while controlling for attrition although additional confounding may remain. We also imputed the missing data for the aging perceptions domains based on multiple chained imputations but did not find any difference in our results. In fact, the results were slightly stronger in the multiple imputation model suggested that our original model may be conservative. A second limitation is that we only had one measure of aging perceptions at baseline and not at follow-up. This allowed us to assess the independent effect of aging perceptions on TUG speed at wave 2 but not vice versa. It is thus still possible that there could be a reverse causality with an already declining walking speed prior to wave 1 affecting perceptions of aging. We will, however, be taking measures of aging perceptions in future waves of TILDA and thus will be able to update the analysis at a later date in order to assess the change in aging perceptions against change in walking speed.

This study provides evidence for the damaging role of negative perceptions of aging on an objective health outcome and highlights the need for awareness of these potentially modifiable effects. Two previous studies have explored the possibility of changing attitudes towards aging in order to change behaviour (in both cases physical activity). The first study included attribution retraining and physical activity classes in combination. Participants were taught that becoming sedentary is not an inevitable part of aging and that failure to be physically active should be attributed to controllable causes rather than to old age. 3 weeks after the intervention participants were walking more and had increased expectations for aging [36]. The second study was a randomised controlled trial in which adults were categorised into a ‘physical activity only’ or a ‘physical activity with views on aging’ group. The latter group were engaged in sessions which challenged misconceptions about aging and taught participants to recognise and challenge their own automatic negative thoughts. They found that those in the latter group had higher rates of physical activity at follow up than those in the ‘physical activity only’ group [37].

These studies highlight the promising possibility of changing behaviour through changing perceptions of aging. Both studies used a dual model in which general misconceptions of aging were challenged at the same time as changing individual’s attributions about their own experiences of aging. It would be interesting to know which of these components were more effective in changing behaviour and what elements of perceptions of aging they acted upon. For example, it would be interesting to know whether the participants felt more in control of their health, more hopeful about the future or just thought about aging less. Future research could also explore whether the behaviour and attitude change results were long-lasting and what factors determine this. Sarkisian and colleagues, in the 2007 study, found that participants reported improved mood, decreased pain, improved energy and improved sleep following the intervention but they were only followed up for a further 3 weeks. Bodily states and physical perceptions are known to be important predictors of evaluation and attitude formation so it would be interesting to determine whether the improved bodily states of participants changed their experience of how old they felt and resulting in improved self-perceptions of aging in general [36].

In conclusion, slow walking speed, a good marker of overall health among older people, is often seen as a physical, and sometimes inevitable, consequence of old age. Our study indicates that psychological factors, specifically attitudes to aging, could play a significant role in determining the development of this physical symptom.