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  • Participants were enrolled in the Adult Changes in Thought (ACT) study, a population based prospective cohort study of incident dementia and Alzheimer's disease, previously described [30]. Briefly, participants were cognitively intact older adults randomly sampled from Group Health Cooperative members aged 65 and older in the Seattle area. The original cohort of 2,581 participants was enrolled between 1994 and 1996. Between 2000 and 2002 an additional 811 participants were enrolled, and in 2004 continuous enrollment began to replace participants who dropped out, developed dementia, or died. Individuals with dementia at baseline were not enrolled in the ACT study. All participants were followed biennially until time of dementia diagnosis, death, or drop-out. At baseline and biennial follow-up evaluations, data collected included demographic characteristics, medical history, cognitive function, memory functioning, blood pressure, depression, and physical functioning. The research protocol for this study followed the Helsinki declaration and was reviewed and approved by the Group Health and University of Washington institutional review boards. Written informed consent was obtained from all participants. The current analysis included ACT participants aged 65–89 years old who were followed between 1994 and July 2011. The sample was restricted to participants with complete data on SRH, PPF, and covariates at one or more visits. For this study, we defined the baseline visit as the first visit with non-missing SRH, PPF, and covariate information. Follow-up of participants ended at time of dementia diagnosis, death or drop-out; and did not include the visit at which a participant was diagnosed with dementia due to concern that dementia may influence SRH and PPF measures. SRH was assessed with a single-item question in the ACT study. This item is similar to SRH questions used in other questionnaires, particularly in the U.S. [1], such as the first question in the SF-36 health survey [31]. Participants were asked at each evaluation “In general, how would you rate your health at this time” with response options of excellent, very good, good, fair, and poor. The SRH question was asked before the physical function tests were performed. Although prior studies have used a variety of different wording and response options for SRH, they are considered to represent the same underlying variable [1], and concordance between different response options is good [32]. Prior studies have found dose-response relationships between original levels of SRH and adverse health outcomes, indicating the validity of SRH [7], [9]. In analyses when SRH was the primary exposure, we used SRH at baseline retaining the original five categories. In analyses where SRH was the outcome, responses were dichotomized into excellent, very good, or good (“healthy”) SRH vs. fair or poor (“unhealthy”) [33], [34]. The PPF score was created following methods developed by Wang and colleagues (2002) [28] for the ACT study population. The PPF score consisted of four performance measures that evaluate upper and lower extremity function: 10-foot timed walks (walking speed), five repeated chair stand time (chair rises), standing balance, and grip strength (in kilograms). These tests were chosen based on previously published research [35]–[37] and study logistics. Walking speed was tested by asking participants to walk a 10-foot distance at their usual speed, using assistive devices if needed. The average of two walks was recorded. Ability to rise from a chair was assessed by instructing participants to stand up from a straight-backed chair with their arms across their chest. Participants successful with one chair rise were then asked to stand up and sit down five times, as fast as possible. They were timed from the first sitting position to the final standing position. To test standing balance, participants were asked to stand close to a wall and were timed for how long they could stand with their feet side-by-side before touching the wall for support. Participants who were able to stand with their feet in the side-by-side position for 10 seconds were next asked to stand with their feet in a semi-tandem position for 10 seconds. Those able to maintain the semi-tandem position were then asked to stand with their feet in a full tandem position for 10 seconds. Grip strength was evaluated using a handheld dynamometer [JAMAR hydraulic hand dynamometer] and measured to the nearest 0.1 kg. Participants were asked to grip the handle as hard as possible using their dominant hand. The average of three attempts was recorded. A score of 0 to 4 was determined for walking speed, chair rises, standing balance, and grip strength tests. For all tests a score of 0 was given if the participant could not complete the test. Scores of 1–4 for walking speed, chair rises, and grip strength tests were based on previously published sex-specific cutoffs, which corresponded to sex-specific quartiles in the ACT population [28]. Scores of 1–4 for standing balance were categorized based on ability to maintain standing in each position for at least 10 seconds. Score cutoffs are summarized in Table 1. Table data removed from full text. Table identifier and caption: 10.1371/journal.pone.0111761.t001 Sex-specific Cutoffs for Scores (0 to 4) for Walking Speed, Chair Rises, Standing Balance, and Grip Strength Tests Based on Previously Published Scoring. The sum of the four test scores determined an individual's PPF score (range: 0–16); higher scores corresponded to better performance. In analyses with PPF as the outcome, we used the total PPF score at each visit as a continuous variable. In analyses where PPF was the primary exposure, we categorized the baseline PPF score based on the study sample quartiles. We used baseline PPF quartiles because the relationship between baseline PPF and change in SRH may not be linear. In exploratory analyses, individual PPF components were dichotomized based on study sample quartiles into scores of 3–4 (“better” function), which generally corresponded to 75% of the participants vs. scores of 0–2; except balance, which had a highly skewed distribution, and “better” function was limited to a score of 4. At study baseline and follow-up visits, information was collected on demographics, health status, and chronic health conditions. Demographic factors measured included age, sex, self-reported race, and years of education. Health status-related covariates included cognitive functioning, functional status, depression, and body mass index, exercise (total number of occasions per week on which at least 15 minutes were performed for 8 activities), smoking (never, past, current), and alcohol use (never, past, current). Cognitive function was evaluated using the Cognitive Abilities Screening Instrument (CASI) [38], a 40-item test of global cognitive functioning, scaled such that the entire ACT cohort at baseline had a mean of 100 and standard deviation (SD) of 15. Self-reported functional status was measured using number of limitations in ADLs and instrumental activities of daily living (IADLs). Total number of ADL limitations was based on the participant's reported difficulty performing six ADLs (walking around inside the home, bathing/showering, dressing themselves, getting out of bed or a chair, feeding themselves, and using a toilet). Total number of IADL limitations was based on the participants reported difficulty in five IADLs (shopping, doing light housework, preparing meals, using a telephone, and managing money). Depressive symptoms were measured using the Center for Epidemiologic Studies Depression scale (CES-D) [39]. Scores were based on a standardized 10-question version [40]; each question contributed 0–3 points for possible range of 0 to 30 points. Height and weight were measured at each study visit. Body mass index was calculated from weight and height (kg/m2) and categorized as underweight (<18.5), normal (18.5 to 24.9), overweight (25–29.9), and obese (30+). Participants were asked whether a doctor had ever told them they had cancer, cardiovascular disease, cerebrovascular disease, diabetes, hypertension, or rheumatoid or osteoarthritis. We did not obtain information on number of medications. We used descriptive statistics to characterize the study population according to SRH. We described trends in PPF and SRH via their association with age. First, we estimated associations between baseline SRH and age-related change in PPF using linear mixed models with random intercepts to account for within-subject correlation. The primary exposures were age, baseline SRH, and their interactions. The outcome was the PPF score at each study visit. The interaction between age and baseline SRH allowed us to make inference on modifications to the relationship between age and PPF that were attributable to SRH. As sensitivity analyses, we re-ran models with missing PPF imputed first as the lowest PPF score and then as the highest PPF score. We also explored whether baseline SRH was associated with individual component measures of PPF. Next, we estimated associations between baseline PPF and age-related change in SRH, repeating analyses but using a logistic link function in a generalized linear mixed model. Healthy SRH was the outcome and the primary exposures were age, quartiles of baseline PPF, and their interactions. Inference was based on the interaction between age and SRH, which described the extent to which the age-related differences in odds of healthy SRH were modified by baseline PPF. We also re-ran models with missing SRH values imputed as unhealthy SRH and then as healthy SRH. We investigated three levels of time-varying covariate adjustment: Model 1 was adjusted for participant age at baseline; Model 2 was additionally adjusted for sex, race, education, cognitive functioning, limitations in ADLs and IADLS, depressive symptoms, body mass index, exercise, smoking status, and alcohol use; and Model 3 was additionally adjusted for chronic health conditions (cancer, cardiovascular disease, cerebrovascular disease, diabetes, hypertension, or arthritis). Potential confounders were selected a priori as factors previously found to be associated with SRH and PPF [21], [28]. Values of SRH adjusted for other health indicators may reflect subjective and contextual evaluations and response styles [1]. We pre-specified Model 2 as the primary analysis. To describe longitudinal trajectories across follow-up we report the estimated mean outcome for each level of the primary exposure at age 75 (corresponding to the average age at baseline for the sample) and the estimated annual change in the outcome for each level of the primary exposure from each model. In Models 2 and 3, we estimated the mean at age 75 based on regression model estimates using indirect standardization to account for possible confounding by other covariates included in the model. We used graphical analyses to illustrate estimated age-related trends in SRH and PPF. Model fit was assessed with residual plots. Analyses were conducted using R (version 2.12.1). All tests were two-sided with α = 0.05.
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