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Correspondence: Address correspondence to Hans-Werner Wahl, PhD, German Center for Research on Ageing at the University of Heidelberg, Bergheimer Str. 20, D-69115 Heidelberg. E-mail: wahl{at}dzfa.uni-heidelberg.de
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Abstract |
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Key Words: Age-related low vision • Age-related macular degeneration • Assistive device use • Control theory • Psychological factors
Visual impairment offers a specific situation in terms of research on assistive devices, because few other chronic conditions can be found for which there is such a rich scope of devices aimed at counteracting negative day-to-day consequences. Examples of such devices include special eyeglasses, long canes, phones with enlarged buttons, magnifying glasses, large-print books, newspapers, and journals read on cassettes, optoelectronic reading systems (i.e., video magnifiers), or even spatial orientation and information aids based on the global positioning system (Rosenthal & Williams, 2000). It is also important to note that about 20% of individuals older than 65 years and about 25% of those older than 75 years suffer from severe vision loss (Lighthouse Research Institute, 1995). Given the important supportive role of assistive devices in day-to-day quality of life (Horowitz, Brennan, Reinhardt, & MacMillan, 2003), learning more about the role of psychological factors with respect to assistive device use in visually impaired older adults adds to a major public health issue in general.
The role of psychological factors related to assistive device use in visually impaired elders has not been well addressed so far, and this is particularly true when it comes to respective theoretical reasoning and empirical research. Gitlin (2002) has argued that psychological control theory may be a particularly good model for use as a theoretical driver in assistive device research. The fundamental argument is such that, in the situation of disablement and frailty, assistive devices become an important means to regulate control and self-efficacy (Schulz, Heckhausen, & O'Brien, 1994). Maintaining control, particularly maintaining what Heckhausen and Schulz (1995) have coined in their life-span theory of control as primary control, is crucial for adaptation as people age. Assistive devices may take over a major role in maintaining primary control, especially when other personal capabilities to underfeed primary control become quite limited as the result of the occurrence of severe competence loss.
Assistive device use in the context of loss of vision in later life is a particularly important case in which to apply the life-span theory of control (Wahl, Becker, Burmedi, & Schilling, 2004). On the basic adaptation level, the co-occurrence of old age and low vision can be expected to become a major threat for psychological control because the visual system plays such a crucial role in the performance of day-to-day behavior and the attainment of important goals in old age, such as preserving autonomy for as long as possible (e.g., Goldstein, 1989). Additional qualitative research that explicitly addresses the subjective experience of low vision after decades of a "seeing life" also underscores the notion that loss of control is a core experience of becoming visually impaired in old age (e.g., Ainlay, 1988). Going further, Heckhausen and Schulz (1995) have argued that, by means of selective primary control, individuals invest internal resources such as effort, time, and ability in order to attain important goals. Conversely, compensatory primary control is aimed at finding external resources such as help from others or making use of technical aids in order to facilitate goal attainment. Selective secondary control operates at the level of cognitive strategies and serves to increase motivational commitment toward desired goals. Finally, compensatory secondary control involves, for example, the substitution or self-serving alteration of goals that are no longer achievable.
Translating the fourfold control distinction of the Heckhausen and Schulz (1995) model to assistive device use, we arrive at the following hypothesis regarding visually impaired older adults: With respect to selective primary control, assistive devices frequently require an investment in effort, time, and ability, and such investment may become a critical pathway to "still" attain subjectively important life goals such as going out for a stroll, reading the newspaper, or handling one's financial issues. Therefore, we expect a positive relation between selective primary control and assistive device use. Furthermore, assistive devices should become a direct means to exert compensatory primary control. Therefore, we expect a positive association between compensatory primary control and assistive device use. For the concept of selective secondary control, two opposing components are assumed to be of importance for assistive device use. On the one hand, high motivational commitment directed toward oneself of being able to achieve important life goals by use of one's own resources and capabilities may undermine the use of assistive devices. On the other hand, emphasizing the subjective intention of goal attainment on the cognitive level, as is the case with selective secondary control, supports at the same time the efforts of selective primary control and therefore may be related an increased use of assistive devices. Finally, compensatory secondary control is operating the most strongly of all four control strategies on the level of adapting the self to the level of remaining functioning. Therefore, it seems unlikely that compensatory secondary control efforts should be related to assistive device use.
In addition, we assume in a more process-oriented perspective that the association between selective primary control and assistive device use should exist only for persons in earlier and less severe stages of age-related vision loss, in which adaptation to the vision impairment can still be successfully handled by investing time and effort. With ongoing progression of the disease, an investing-time-and-effort attitude becomes more and more maladaptive. Thus, in a sample covering participants at varying stages, including a substantial portion of persons with rather short duration of the vision loss at Time 1 (T1), the importance of selective primary control should decrease after a considerable period of time after T1 such as a 1-year period, which would then result in a decreased importance of selective primary control in the total sample at Time 2 (T2). However, independent of this effect, compensatory primary control as a driver for assistive device use should remain important across time.
The present study provides a first empirical test of these research hypotheses based on a sample of 71 visually impaired elders followed across one year. In addition, all elders included in the sample suffer from age-related macular degeneration (ARMD; e.g., Holz, Pauleikhoff, Spaide, & Bird, 2003). ARMD is the leading cause of severe visual impairment in old age and affects nearly every fifth person between 65 and 74 years of age and nearly every third person beyond the age of 75 (Fine, Berger, Maguire, & Ho, 2000). It also has been estimated that ARMD accounts for approximately 50% of all cases of age-related low vision (Evans, 1995). Currently there are very few medical treatment options that can halt the progression of ARMD (Holz et al.). As a consequence, the aging individual faced with ARMD is forced to adapt to an ongoing and hard-to-influence kind of visual loss; assistive device use is particularly significant for this important subgroup of visually impaired older adults (Dahlin-Ivanoff & Sonn, 2005).
Besides our emphasis on psychological control as a means to predict assistive device use in visually impaired elders, we also explore the role of sociostructural and vision-loss-related variables. With respect to the former, we address age, gender, and education; with respect to the latter, we check for the role of (remaining) visual acuity, functional vision loss, and duration of the vision loss. Given the characteristics of the course of ARMD, additional loss in visional functioning must be expected across the 1-year interval, which in consequence may be of relevance for changes in assistive device use. Therefore, duration of vision loss may be important for assistive device use over time, as assistive device use might be less developed with less progression of vision loss, and duration may thus predict change in assistive device use across the observation interval. However, it is likely that the speed of progression also varies between individuals, so that the absolute duration of the disease in itself may be too rough of an indicator of progression. Instead, a direct indication of ARMD progression, such as a visual acuity measure, may be more useful as an objective indicator for the stage of age-related vision loss experienced at the time of measurement.
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Methods |
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Our original study sample consisted of 90 older community residents (26 men, 64 women) with a mean age of 79.5 years (range = 61–93 years; see Table 1). All participants were diagnosed by ophthalmologists as suffering from various forms of ARMD (Holz et al., 2003). In addition, they all met the criterion of a far visual acuity of equal or worse than 20/70 in the better eye, which is generally regarded as an indication of low vision (see, e.g., Orr, 1992).
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In order to reach the original sample size of 90 at T1, a total of 123 patients had to be contacted by the ophthalmologists, leading to a participation rate of 73%. The most frequently mentioned reasons for refusal were "feeling too sick" (35%), and "I don't want somebody to visit me at home" (27%).
We then followed participants across a 1-year interval. This allowed us to address change in assistive device use. Furthermore, we hypothesized that the relation between control strategies, especially selective primary and compensatory primary control and assistive device use, is not constant over time, but changes with the development of the vision loss and its functional consequences in day-to-day life. We expected a 1-year observational period to be a time span long enough to cover such hypothesized changes in the control and assistive device relation. As an empirical indicator, we have observed in our sample across the 1-year period not only a significantly meaningful decrease in visual functioning but also a decrease in functional capacity in activities of daily living (Wahl & Becker, 2004).
As we expected, the number of participants dropped between the first and second measurement occasions from 90 to 71. The main reasons were refusals and relocation to an unknown new residency; one person died. As we can see in Table 1, the differences between the individuals followed from T1 to T2 and those dropped between T1 and T2 were not statistically significant with respect to sociostructural, vision-related, and health-related variables. However, we should acknowledge that the rather low sample size constrains the detection of statistical significance in that drop-outs tended to be older and female, and they showed a shorter duration in their vision loss.
In order to have comparable samples for both measurement points, we included only those individuals participating at both measurement occasions (N = 71; age, M = 78.9 years at T1) in the analyses presented in this article.
Measures
Assistive Device Use
We based our assessment of assistive device use on a given list of devices including the following visual aids: magnifying glasses, long cane, special glasses, optoelectronic reading systems (i.e., video magnifier), large-print books, books read on cassettes (books on tape), touch watch, phone with enlarged buttons, books in Braille, and Braille typewriters. We did not consider other assistive devices such as walking and hearing aids, since the everyday dynamics of using these is likely to be quite different from the assistive devices related to the vision loss. For example, the use of a walking cane can aid a mobility problem not connected to the vision loss, or it may simply help an individual feel safer even without a direct severe walking or vision problem. We aggregated all the information into a sum score indicating the number of assistive devices used. We assessed assistive device use at both measurement occasions. It should be emphasized again that we refer to assistive device use as the target variable, not the possession of assistive devices, because only the active use of visual aids most directly reflects behavior by an individual who is motivated to deal with the vision-related difficulties in everyday life.
Control-Theory-Related Variables
We assessed control strategies at T1 and T2 by using the German version of the Optimization in Primary and Secondary Control Scale (OPS; Heckhausen, Schulz, & Wrosch, 1999). Each dimension included eight items to be rated on a 5-point scale (0 = never true to 4 = almost always true), leading to a theoretical range from 0 to 32, with higher scores indicating higher use of the control strategy in question. Typical items regarding the four control strategies are (a) selective primary control, such as "Once I decide on a goal I do whatever I can to achieve it"; (b) compensatory primary control, such as "When I cannot solve a problem by myself I ask others for help"; (c) selective secondary control, such as "When I have decided on a goal, I always keep in mind its benefits"; (d) compensatory secondary control, such as "When something becomes too difficult, I can put it out of my thoughts." Cronbach's alphas for the control-related scales at T1 were 
= 0.81 (selective primary control), = 0.68 (compensatory primary control), = 0.70 (selective secondary control), and = 0.59 (compensatory secondary control). It should be noted that the use of assistive devices is not explicitly mentioned in any of the items of the compensatory primary control subscale, although Heckhausen and Schulz (1995) argued that this may become a major means to exert this kind of control.
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Results |
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Relations with sociostructural variables and duration of the vision loss are shown in Table 3. As we can see, age was not related to assistive device use, but female participants and those with more education were more likely to use assistive devices at T1. This relation also holds with respect to gender for T2. Duration of vision loss was positively related with assistive device use at T1 and T2, with an even stronger relation at T2 than at T1. In addition, education also was significantly linked with change in the use of assistive devices between T1 and T2; that is, those with more education showed a tendency to reduce the number of devices used over the 1-year interval.
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When the same set of predictors at T1 and at T2 was tested in their relation to assistive device use, the simultaneous control for assistive device use in the dependent variable at T1 no longer revealed any statistically meaningful role of the control strategies. That is, control strategies were not able to predict change in assistive device use over the 1-year observation period. The only significant predictor was assistive device use at T1, which indicates the amount of stability of the dependent variable over time. In terms of stability, the squared semipartial correlation seems quite low, though statistically significant, which indicates that only 15% of the T2 variance is uniquely due to the T1 variance in assistive device use. Finally, it should be mentioned that we also checked in an exploratory manner (not shown in Table 5) whether change in control strategies from T1 to T2, in addition to control strategies at T1, were related to assistive device use at T2. We found no significant association in these analyses.
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Discussion |
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However, the predictive value of both control strategies differed depending on the measurement point, with selective primary control playing a role at T1 and compensatory primary control at T2. This provides empirical support for our process-related hypothesis operating against a control framework. According to this interpretation, at earlier stages of ARMD, persons are trying to attain their previously important goals predominantly by investing effort, time, and abilities. Among other things, the use of assistive devices may serve this purpose well, while also leading to a substantial link to selective primary control. With the progression of the vision loss, investing time and effort may no longer be enough for goal attainment, and the focus seems to shift toward assistive device use predominantly as a means to exert compensatory primary control. In our sample, 16 participants could be considered to be at an early stage, with outbreak of the disease occurring no longer than 1 year before the first measurement occasion. Thus, we assume that the observed T1–T2 change in the role of primary selective and primary compensatory control is primarily due to this subgroup in our sample. Indeed, running the regression analyses on this subsample in an exploratory manner revealed still a more pronounced decrease in the importance of selective primary control in terms of explained variance than in the full sample between T1 and T2. However, this result may be rather spurious because of to our low sample size.
In addition to these findings, our empirical findings add to the assumed complex nature of the relation between selective secondary control and assistive device use. It is important to note that our interpretation of a significant negative relation only in the multivariate case and only at T1, as an indication of a suppressor effect, must not be seen, necessarily, as a methodological artifact. As has already been argued in the development of the theoretical background of the study, a possible content-related explanation for the suppressing effect may be that two different and opposing factors may underlie the concept of selective secondary control. The first component, emphasizing the subjective intention of goal attainment on the cognitive level, may show a strong relation to selective primary control and may be positively associated with the use of assistive devices. The second component may reflect a strong motivational commitment toward continuity of activities at the same level of functioning, such as the period prior to the onset of vision loss. This component would then be explicitly directed against using assistive devices. The detection of this negative effect may, however, only be detectable when selective primary control is controlled for simultaneously, as we did in the multivariate analyses. It also deserves mentioning that the simple correlation between selective primary and selective secondary control was rather high and positive (.70; p <.001), and, if this overlapping variance in the positive direction was controlled for, a negative relation seemed to emerge. No significant correlation of selective secondary control with assistive device use can be found on the bivariate level, because here the two factors act against each other concerning the use of assistive devices. Going further, and consistent with our finding of a differential role of selective primary and compensatory primary control that is dependent on the vision loss stage, we observed the suppressor effect only at T1, where selective primary control was assumed to be of particular importance for assistive device use and particularly for those still not suffering too long from the vision loss. This was no longer the case at T2.
On the practical and rehabilitation-related level, such a content-related interpretation of the suppressor effect also coincides with the observation of a frequent ambiguity in the use of assistive devices in chronically disabled elders in general. On the one hand, assistive devices are helpful means to support the attainment of important goals; on the other hand and concurrently, assistive devices are a clear symbol of competence loss to oneself and others, which may nurture a tendency not to use them.
Caution is nevertheless needed when it comes to the application of our findings, because relationships generally appeared as rather weak. In addition, the internal consistency, especially for the compensatory secondary control measure, was relatively low. Furthermore, control strategies were not able to predict the change in assistive device use over time. After we controlled for the status of the dependent variable (i.e., assistive device use at T1), only this variable remained a statistically meaningful variable in terms of predicting assistive device use at T2. Those higher in assistive device use at T1 tended to also use more assistive devices after 1 year. Thus, it seems that device use was quite stable over time; however, a close look at the magnitude of this significant effect reveals that only a smaller proportion of the T2 variance is uniquely due to the T1 device use, reflecting the fact that there have been substantial changes uncorrelated with the T1 use. Finally, in this context, it also seems clear that there are—besides psychological variables such as control strategies—other drivers of assistive device use and the respective change over time, such as gender and education, but these relations remained in the low-to-medium range in terms of correlation heights.
Although we generally acknowledge the limited strength of our findings, we nevertheless see a potential in our results for the practical field. Particularly, we found assistive device use as a means for the optimization of control (Heckhausen & Schulz, 1995) to be motivated by different control strategies over the time course with respect to decreasing functional ability. These findings also have implications for the refinement of educational and intervention programs striving toward best practice with regard to the implementation of assistive devices in visually impaired elders. To enhance assistive device use among visually impaired elders and to secure full use of the positive potential of assistive device use for maximum possible independent living, it might not be sufficient to focus predominantly on how to use assistive devices properly, which is the common practice at present. Instead, consideration of an individual's control strategies, particularly selective primary (at early stages of the vision loss experience) and compensatory primary control (at later stages), may contribute to the successful implementation of assistive device use in light of a person's personal goals and remaining competencies.
Such implications gain even more importance when backed by our finding that "classic" variables frequently considered in the vision rehabilitation field, such as objective and subjective (functional) vision loss measures as well as age, did not reveal any significant relation to assistive device use. Also worth emphasizing is that we find a slight tendency for visually impaired women as well as those with more education to use assistive devices more frequently.
The present study is limited by the low sample size and the rather short observation interval of only 1 year. Furthermore, future research on the role of psychological factors related to assistive device use in visually impaired older adults may also consider alternative theoretical models such as the theory of planned behavior (Ajzen & Fishbein, 1980; Roelands, van Oost, Depoorter, & Buysse, 2002) in addition to control theory to explore the potentially differential predictive power of different models.
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Footnotes |
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1 Institute of Gerontology, University of Heidelberg, Germany.
2 German Center for Research on Ageing, University of Heidelberg, Germany.
Decision Editor: Linda S. Noelker, PhD
Received for publication July 15, 2004. Accepted for publication June 14, 2005.
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