Health Benefits of Increased Walking for Sedentary, Generally Healthy Older Adults: Using Longitudinal Data to Approximate an Intervention Trial

PHYSICAL activity is recommended for persons of all ages (1). Because walking is the most commonly reported leisure-time physical activity in the United States and is relatively common among the elderly adults (2,3), older adults are often advised to walk more. But the few long-term randomized trials of the health benefits of walking for older adults have had equivocal results (4), and the recommendations to walk more have been based primarily on results from observational studies. Such studies have found a strong correlation between the amount walked and survival and incident cardiovascular disease (CVD) in older men (5,6) and an inverse association between walking intensity and cardiovascular events in older women (7). Among adult diabetics, walking at least 2 hours per week was correlated with lower all-cause mortality (39%) and cardiovascular mortality (34%) over 8 years of follow-up (8). However, when isolated from overall physical activity, walking showed no significant association with either mortality or CVD in the Shanghai Women's Health Study (9). As discussed below, the health benefits of walking estimated from observational data may be biased. We propose to use longitudinal data to make the analysis of observational data more like the analysis of a randomized trial. Suppose the goal is to estimate the benefit of increased walking for healthy sedentary persons. Ideally, a randomized controlled trial (RCT) would be conducted. The RCT might randomize persons who had been healthy and sedentary (according to the trial's eligibility criteria) for some amount of time to one of three walking groups: sedentary, somewhat active, or active. The health of the three groups might be compared 2 years later. Differences among the groups would be unequivocal estimates of the benefit of the walking program for healthy sedentary persons. Health at randomization would be unassociated with treatment group because of randomization. The analysis might control for measures of health status at baseline anyway to adjust for any small imbalances among the groups and perhaps increase power. If the group of interest included older adults, the health outcome measure should account for death (10). Note that the RCT design has four important features: (a) it addresses the question of interest (the effect of walking on the health of sedentary persons). (b) the time when the walking program started is known, (c) the health at the start was equivalent in the three groups because of randomization, and (d) any covariates used in the model were measured before anyone started to exercise and so cannot have been in the causal pathway between exercise and health. Most often, however, the benefits of walking have been estimated from observational cohort studies that measured physical activity only once, at baseline, and followed participants prospectively for health events (here called the “typical design”) (5–9,11–14). A typical analysis might classify participants’ walking level at the study's baseline and examine their health 2 years later, controlling by regression for baseline differences in the health of the groups. Note that this analysis has none of the features of the RCT just mentioned: (a) it does not effectively address the question of interest because analysis is not restricted to healthy sedentary older adults, (b) the time when persons started to exercise is unknown and may have been long before baseline, (c) persons’ health when they started to exercise is unknown and cannot be adjusted for, and (d) all the covariates for the treatment group were measured after exercise was already initiated and so may be in the causal pathway between exercise and health. (Controlling for health variables that have already been improved by exercise is likely to bias the effects toward the null.) A different analysis is possible in cohort studies exemplified by the Cardiovascular Health Study (CHS; described below), which measured physical activity every year and health status every 6 months. This limited-bias design, diagrammed in Figure 1, mimics an RCT by restricting analysis to persons who were healthy and sedentary in the first 2 years of data collection. By Year 3, referred to here as the analytic baseline, some persons will have increased their walking level, which may have affected their health in Year 5. Health at Year 5, as defined by the study, would be regressed on walking level in Year 3. Health variables measured at Year 2 (when all were sedentary) and perhaps at Year 2.5 are used as covariates. Figure 1. Schematic diagram of the limited-bias design (Year 4 omitted). Analysis is restricted to persons who were sedentary and healthy in Years 1 and 2. The primary analysis regresses Y (a binary variable indicating whether the person is healthy at Year 5) on ... This design has several features in common with the RCT: (a) it does address the question of interest because it is restricted to previously sedentary and healthy persons, (b) the time when exercise commenced is known to be sometime between Years 2 and 3, (c) health status when exercise started is known to within 6 months and so can be controlled for approximately, and (d) all covariates but one (health at Year 2.5) were measured at Year 2, before any exercise started, and therefore are not in the causal pathway. The Year 2.5 health measure was included as a “compromise covariate.” For persons whose health changed between Years 2 and 2.5, who then started to exercise, and who received benefits of exercise after Year 2.5, this analysis is appropriate because it controls for health at Year 2.5. The design may overcontrol for variables in the causal pathway for any persons who both started to exercise and then changed their health between Years 2 and 2.5. The design may undercontrol for health at the start of exercise for persons who both changed their health and then started to exercise between Years 2.5 and 3.0. Because this design is more similar to the RCT than is the typical design and because the possibilities of over- and undercorrection are limited to the probably small number of persons who had all their exercise and health changes within a 6-month period, we refer to it as the “limited-bias” design. In this study, we implemented the limited-bias design to estimate the health benefit of moving from sedentary to active for healthy older adults. Ten different definitions of health were used, and all health measures were coded to account for death. Results were compared with those of the “typical” analysis.