How Does CAM Contribute to Variations in Older Adult Depression?
An NHIS Study
Ryan Harrison, PsyD, DAAIM
Director of Resident Life & Wellness
Hillcrest Retirement Community
2705 Mountain View Drive
La Verne, CA 91750
Depression in older adults is frequently misunderstood and under-diagnosed, with poor treatment compliance and marginal efficacy when medications or psychotherapy are prescribed. Some older adults turn to Complementary and Alternative Medicine (CAM) to help treat or manage their depressive symptoms. The effects of CAM on depression and depressed older adults are not well-known, and the degree to which CAM therapies contribute to any variation in depressive symptoms in older adults has not been fully researched. This study investigated the extent to which the use of CAM therapies contributes to the variation of symptoms of depression in older adults over a 12-month period. Four distinct hierarchical multiple regression analyses were run on data collected by the Centers for Disease Control and Prevention’s 2012 National Health Interview Survey (NHIS) and its supplemental CAM questionnaire. Main outcome measures comprised adjusted R2 and beta weight values of risk factors for depression and the relative impact of CAM therapy use on incidence of depressive symptoms over a 12-month period. Analyses revealed that CAM therapy use exhibits mild protective effects against depressive symptoms, relative to the effect sizes of risk factors for older adult depression. CAM therapy use is a significant predictor of the variation in incidence of depressive symptoms in older adults. CAM therapy use appears to exert its strongest preventative effects in older adults with a history of depression.
Keywords: older adult, depression, psychology, complementary and alternative medicine, NHIS
Target Audience: Psychologists, Psychiatrists, Social Workers, DO, MD, ND, Nurses, Wellness Directors, Geriatricians
- Recognize the difficulty inherent in determining the prevalence of depression in the older adult population
- Distinguish the different classifications of Complementary and Alternative Medicine
- Recognize the differences between depression in older adults and younger populations
- List risk factors for older adult depression
- Discuss the potential contribution of Complementary and Alternative Medicine to depressed older adults’ frequency of symptoms.
Program Level: Advanced
- Practicing health or mental health professional
- Experience working with older adults in a health-related capacity
- Basic familiarity with statistical analyses
The American Association of Integrative Medicine® provides this continuing education opportunity to fulfill 1hr of Continuing Education Credit for all certified members. Certified members are required to obtain 30 hours of continuing education credits in the 3 year recertification period to maintain their certification status.
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Depression is one of the most common mood disorders experienced by older adults (Centers for Disease Control and Prevention [CDC], 2012b; Geriatric Mental Health Foundation [GMHF], n.d.; National Institute of Mental Health [NIMH], n.d.a). Although there are valid screening measures for this population, the American Psychiatric Association’s (APA, 2013a) Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) lacks specific guidelines for diagnosing depression in older adults. Additionally, older adults may present different depressive symptoms than those of younger people. This makes it difficult for researchers to determine the prevalence of depression among the elderly and can produce skewed data (GMHF, n.d.; NIMH, n.d.a; Rojas-Fernandez & Mikhail, 2012; Steinman et al., 2007).
There is ample literature describing the numerous risk factors associated with older adult depression and its multiple adverse effects. Risk factors include gender, prior depression, chronic disease, sleep disturbance, and physical limitations (Cole & Dendukuri, 2003; Fiske, Wetherell, & Gatz, 2009; Tong, Lai, Zeng, & Xu, 2011). Adverse effects of older adult depression include deleterious effects on health, such as the worsening of chronic illnesses and greater mortality, and also negative influences on financial well-being. Resources suggest there is little indication of a decline in what appears to be a steadily growing financial burden associated with depression (Bosworth, Voils, Potter, & Steffens, 2008; U.S. Department of Health and Human Services [USDHHS], 2011). Yet, diagnosing depression in the older adult population is frequently confounded by several factors, with implications for the effective treatment of the mood disorder; researchers suggest that a large number of older adults go undiagnosed, and as a result, untreated (Lakey et al., 2012; Wancata, Alexandrowicz, Marquart, Weiss, & Friedrich, 2006).
Treatment of older adult depression primarily comprises prescription medications or psychotherapy, although research has indicated that combining the two approaches can be significantly more effective than either used alone (Blazer, 2003; NIMH, n.d.a.; Rojas-Fernandez & Mikhail, 2012). Regardless of the treatment option, remission rates are low, and many older adults become noncompliant. In part, this reflects patients’ discomfort with the adverse effects, or perceived stigma, of standard therapy (Blazer, 2003; NIMH, n.d.a.; Rojas-Fernandez & Mikhail, 2012).
Noncompliance with standard (i.e., allopathic) care does not mean, however, that depressed older adults are not seeking treatment elsewhere. Some may turn to Complementary and Alternative Medicine (CAM) therapies as supplemental or substitute treatment options (Barnes, Bloom, & Nahin, 2008; Nemer & McCaffrey, 2010). Yet, to date, there has been no research investigating the extent to which CAM therapy may contribute to a significant variation in depressive symptoms among older adults.
Materials and Methods
The sample for this study was drawn from the 2012 National Health Interview Survey (NHIS) Sample Adult component and its supplemental CAM questionnaire. Data were used only for respondents aged 65 and older. The 2012 NHIS procedures were designed to collect data representative of the national population. As a result, participants aged 65 and older who were Black, Hispanic, or Asian had a higher likelihood of being selected as the sample adult from each household (CDC, 2012a).
Participants who were selected to complete the Sample Adult survey were also invited to participate in the supplemental CAM questionnaire (N = 34,525). Of this group, 7,935 older adults (58% female, 42% male) completed the survey and the CAM questionnaire. Whites/Caucasians made up the majority of sampled adults (44%), with African American/Black being the second most prevalent reported ethnicity (7.5%).
The NHIS is a cross-sectional household interview survey with sampling and interviewing occurring continuously throughout the year (CDC, 2012a). According to the Centers for Disease Control and Prevention (2012a), the NHIS sampling plan “follows a multistage area probability design” (para. 7) that is redesigned after every census. For the 2012 NHIS, 428 primary sampling units (PSUs, consisting of counties, contiguous counties, or metropolitan areas) were drawn from all 50 states and the District of Columbia. Within each PSU, four to 16 addresses were used. For the adult sample of the 2012 NHIS, one civilian adult per family was randomly selected to self-report responses to survey questions, including those asked as part of the CAM supplement.
Study Design and Models
This study was correlational and quantitative in design. It used hierarchical multiple regression analysis to account for distinct variations in the incidence of depressive symptoms experienced by older adults when CAM therapies were used. This study operationalized the term depression to refer to incidences of clinically diagnosed major and minor depression, as well as the experience and expression of depressive symptoms. This aligns with a majority of the studies represented in the literature. Further, the NHIS and supplemental CAM questionnaire did not screen for depression. Rather, respondents were asked whether they had ever been told by a health care practitioner that they had depression or some form of depressive disorder. A secondary approach used in the NHIS comprised several items regarding distinct depressive symptoms that may have been experienced within the prior 30 days. Thus, the term depression was used throughout this study, as it was noted to occur at various times throughout the literature to refer to all cases of identified depressive disorders, as well as to depressive symptoms as identified in the NHIS.
In general, CAM comprises non-mainstream therapies that are either used in conjunction with standard care (i.e., complementary) or used in its place (i.e., alternative). Since CAM’s scope is wide, it is important to operationalize the term complementary and alternative medicine such that researchers are aware which therapies are considered CAM, and which are not, within a given study. For this study, the term CAM reflected what appears to be the most widely accepted definition, which has been put forward by the National Center for Complementary and Alternative Medicine (NCCAM, 2013a). This definition divides CAM into five distinct classifications: (a) alternative medical systems, (b) mind-body medicine, (c) biological-based therapies, (d) manipulative and body-based therapies, and (e) energy-related therapies (Bomar, 2013; NCCAM, 2013a; Park, 2013). In sum, these five classifications capture most, if not all, of the nonstandard therapies considered CAM. In particular, this definition of CAM coincides with the therapies included in the 2012 NHIS’s CAM supplemental questionnaire. The term CAM was further operationalized to refer to the 23 specific CAM therapies included in the 2012 NHIS supplemental CAM questionnaire. These 23 therapies (chiropractic/osteopathic medicine, massage, acupuncture, energy healing, naturopathy, hypnosis, biofeedback, Ayurveda, chelation therapy, craniosacral therapy, herbs/non-vitamin supplements, homeopathic treatment, meditation, mindfulness based therapy, guided imagery, progressive relaxation, yoga, Tai Chi, Qi Gong, Feldenkrais, Alexander technique, Pilates, and Trager psychophysical integration) constituted the CAM independent variable of the hierarchical multiple regression model (Fig. 1).
Four distinct hierarchical multiple regressions were performed based on a regression model (Fig. 1), with risk factors and CAM therapy use variables used to predict the incidence of depressive symptoms among older adults. Gender, sleep disturbance, chronic disease, prior depression, and physical limitation were presented as controlled variables and entered as the first and second blocks of the regression. These variables were generated through the summation of NHIS items related to each risk factor (Table 1). CAM therapy, indicated by a positive response to any of the 23 NHIS items regarding use of specific CAM therapies over a 12-month period, was entered in the third block as the independent variable. The dependent variable was the reported incidence of depressive symptoms provided by the sum of five NHIS items related to symptoms of depression.
The first hierarchical multiple regression was performed according to the original model, with data from all older adult respondents (n = 7,935). In the interest of providing additional clarity, the sample was then split into two subgroups: older adults with a prior history of depression (n = 1,158) and those without (n = 6,773). Regression analyses were performed on each of these groups separately, with the control factor “prior depression” removed from the model. Finally, a fourth hierarchical multiple regression was run on a sample of older adults who reported at least one depressive symptom and the use of at least one CAM therapy within the prior 12-month period (n = 1,131).
| Dependent variable
||The sum of five items coded on a 5-point Likert scale measuring symptoms of depression as coded in the NHIS, with 1 = all of the time, 5 = none of the time. Items are: During the past 30 days, how often did you feel… so sad that nothing could cheer you up?… hopeless?… that everything was an effort?… worthless?; and Altogether, how much did these feelings interfere with your life or activities? (Reverse scored such that high scores indicate higher levels of depression)
||One item, coded on a binary scale, measuring gender with 0 = male, 1 = female
||The sum of eight items related to chronic diseases, coded on a binary scale, measuring incidence of chronic diseases with 1 = yes, 0 = no. Items are: Have you ever been told by a doctor or other health professional that you had… coronary heart disease?… stroke? … emphysema?… COPD?… asthma?… cancer?… diabetes?… arthritis?
||The sum of two items coded on a binary scale, measuring disturbance in sleep with 1 = yes, 0 = no. Items are: During the past 12 months, have you… regularly had excessive sleepiness during the day?… regularly had insomnia or trouble sleeping?
||The sum of 12 items related to physical limitation coded on a 5-point Likert scale with 0 = not at all difficult, 4 = can’t do at all. Items are: By yourself, and without using any special equipment, how difficult is it for you to… walk a quarter of a mile? … walk up 10 steps without resting?… stand or be on your feet for about two hours? … sit for about two hours?… stoop, bend, or kneel?… reach up over your head?… use your fingers to grasp or handle small objects?… lift or carry something as heavy as 10 pounds such as a full bag of groceries?… push or pull large objects like a living room chair?… go out to things like shopping, movies, or sporting events?… participate in social activities?… do things to relax at home or for leisure?
||One item coded on a binary scale measuring prior depression with 1 = yes, 0 = no: Have you ever been told by a doctor or other health professional that you had depression?
|Complementary and alternative medicine
||The sum of 23 items measuring use and frequency of CAM therapy within the past 30 days to 12 months; items are coded variously, using either Likert scales or binary scales, and are enumerated in the appendix.
Note: Adapted from 2012 National Health Interview Survey Sample Adult questionnaire and complementary and alternative medicine questionnaire.
The first regression, based on the original model, produced a significant equation, F(6, 1124) = 86.521, p < .001, with an adjusted R2 value of .312, signifying that approximately 31.2% of the variation in depressive symptoms was explained by the model. This included the use of CAM therapies. This R2 value was 30.5% better than gender alone and 0.3% better than addition of all other risk factors, represented in Block 2.
The second regression, using the subsample of older adults with no prior history of depression, did not produce a significant equation, F(5, 899) = 31.686, p < .001. Although the model accounted for roughly 14.5% of the variation in incidence of depressive symptoms in this sample, the adjusted R2 value was not significant due to the addition of CAM; prior to its block entering the equation, the model accounted for 14.6% of the variation, at a significance value of p < .001.
The third regression analysis, based on data from older adults with a prior history of depression, did produce a significant equation, F(5, 219) = 17.014, p < .001, that explained approximately 26.3% of the variation in incidence of depressive symptoms (Adj R2 = .263). This was 26.7% more predictive than gender alone and 2.9% more than gender, and the other risk factors, combined.
The final regression, using data from older adults who reported at least one depressive symptom and use of at least one CAM therapy over the prior year, also produced a significant equation, F(6, 1123) = 102.670, p < .001, demonstrating that approximately 35.1% of the variation in incidence of depressive symptoms was accounted for by the model (Adj R2 = .351). The small difference in adjusted R2 values between Blocks 2 and 3 suggested, however, that CAM use explained only a negligible amount of the variation, at less than 1%. Although the overall model was found to be significant at the p < .05 value, the effect size indicated that CAM’s contribution to the predictive power of the model was relatively trivial.
Extant literature emphasizes both the commonness and the severity of depression in the older adult population (CDC, 2012b; GMHF, n.d.; NIMH, n.d.a). Further, treatment for older adult depression is impeded by a variety of factors that complicate not only depression management but also compliance with treatment and its efficacy (Bosworth et al., 2008; Chapman & Perry, 2008; Feliciano & Arean, 2007; Ivanova et al., 2011; NIMH, n.d.a).
At the same time, it is possible that a number of older adults with depression selectively use CAM, either in conjunction with or in lieu of standard allopathic care. There is scant research, however, to suggest the extent to which such CAM therapy use may account for any variation in symptoms of depression experienced within this population. This study was undertaken to elucidate the complex relationship between older adult depression and CAM. The purpose of the research was to examine the extent to which self-reported CAM use could predict variations in depressive symptoms experienced by older adults.
The major findings of this study show that CAM therapies have a place in the treatment or maintenance of older adults who experience depressive symptoms, although there is insufficient evidence to suggest that CAM use produces large effects as a predictive measure of depressive symptomatology. An initial regression revealed that 31.2% of older adults’ incidence of depressive symptoms could be accounted for by the original model. However, the largest effect was seen within the second block, with 30.9% of the predictive value due to the combined risk factors of gender, chronic disease, sleep disturbance, physical limitation, and prior depression. CAM use contributed less than half of 1% (i.e., 0.3%) toward the overall model’s predictive value.
This small effect size was seen repeatedly in three additional, but distinct, regressions. The second and third regressions used subsamples of older adults; data from older adults without a prior history of depression were analyzed in comparison with data from older adults who had experienced depression earlier in life. In the former analysis, only the second block was found to be significant, with the largest adjusted R2 value (.146) in the model. Indeed, the addition of CAM use to the model lowered this value, indicating a lower overall predictive ability than without CAM (R2 = .145). In the third analysis, involving older adults who had experienced prior depression, the model was found to be stronger overall, accounting for 26.3% of the variation in incidence of depression within the prior year. However, of this amount, only 2.9% was directly attributable to CAM therapy use; 23.4% was accounted for by the risk factors included in the second block.
Finally, the last hierarchical multiple regression analysis was run on data contributed only by older adults who had experienced at least one symptom of depression and self-reported any amount of CAM use within the previous year. In this case, 35.1% of the variation in incidence of depressive symptoms was accounted for by this model. Of that amount, however, 34.8% was attributable to the risk factors for depression (i.e., Block 2). Although CAM use was significant at the p < .05 level, its beta weight (-.059) indicated that its overall effect size was negligible.
This finding is not surprising and is supported by the literature. As noted by several researchers, there are distinct risk factors for depression, such as those used as control variables in Block 2 (CDC & NACDD, 2008b; Chapman & Perry, 2008; Cole & Dendukuri, 2003; Gellis & McCracken, 2009; Hamer et al., 2011; NIMH, n.d.a; USDHHS, 2011). Further, it is possible that the “complex interactions among genetic vulnerabilities, cognitive diathesis, age-associated neurobiological changes, and stressful events” (Fiske et al., 2009, p. 363), such as those correlated with the control variables in Block 2, have a greater synergistic effect on older adults toward depressive symptoms than CAM use might exert in a preventative manner.
This was seen, at least marginally, via the beta weights resulting from the data analysis. In the third regression model, in which CAM use had the largest beta weight (-.182), both sleep disturbance and physical limitation had larger beta weights (.294 and .342, respectively). Thus, although CAM use may exert preventative effects, these may not be strong enough to outweigh the contributions to depression produced by a combination of common risk factors.
It appears that CAM use may exhibit its greatest efficacy toward the ability to predict variation in the incidence of depressive symptoms in older adults who have experienced prior depression. This is illustrated in the marked difference in adjusted R2 values between the regressions run on data from older adults with no history of depression versus those with prior incidence. In the former, the regression model accounted for approximately 14.6% of incidence; when CAM use was added to the regression, the predictive ability decreased to a statistically non-significant 14.5%. When the same regression model was applied to data provided by older adults with incidence of prior depression, however, it accounted for 26.3% of incidence, with a significance that exceeded the p < .05 level.
Additionally, the highest beta weight of CAM use was achieved in the analysis of older adults with a history of depression. In this case, the beta weight of -.182 was higher than even the beta weight of -.059 produced by the strongest of the hierarchical multiple regression models, run on older adults who had used CAM within the prior year. This suggests that CAM use’s ability to predict a variation in incidence of depressive symptoms is strongest in connection with older adults who have experienced prior depression.
The literature supports this possibility. For example, several studies have revealed that individuals with depression, and perhaps older adults in particular, are more likely to use CAM than their non-depressed counterparts (Adams, Sibbritt, & Lui, 2012; Elkins, Rajab, & Marcus, 2005; Grzywacz et al., 2006; Montazeri, Sajadian, Ebrahimi, & Akbari, 2005). Although this does not suggest that CAM therapies are more effective as treatment for depressed persons than for those without depression, it could suggest that individuals with depression may seek out CAM therapies more readily or that CAM therapies exert a greater effect on persons with a history of depressive symptoms. If so, some of the biopsychosocial dynamics involved in CAM therapies may synergistically potentiate various CAM therapies’ helpful effects. That is, depressed patients who seek CAM therapies may respond better to them than those people who are not depressed and who do not actively seek CAM therapy.
If this is the case, then the helpful effects cannot be solely attributed to the placebo effect; a meta-analysis conducted by M. P. Freeman et al. (2010) found that depressed persons receiving CAM were less likely to have a placebo response than those who were using standard antidepressant medications. Rather, it is probable that the synergistic effects of multiple CAM therapies produce a preventative outcome built on a range of biopsychosocial dynamics. There may be a precedent in the literature for this position. Sarris (2011a) found that several CAM therapies, when used with conventional medication therapy, resulted in better outcomes and reduced relapse rates in clinical depression than were found with standard care alone.
Several implications for theory and research result from this study. First, the results of the data analysis suggest that CAM use can help predict the variation in incidence of depressive symptoms in older adults, particularly those with a history of depression. Further, the inverse relationship between CAM therapy use and depressive symptoms within this subpopulation implies that CAM therapies may be helpful for inhibiting recurrence or prolonging the remission of depressive symptoms.
Second, this study provided a unique look at an aspect of the complex relationship between CAM therapy use and depression in older adults living in the United States. Although there is a large body of literature exploring depression and specific CAM therapies’ utility in treating depressive symptoms, to the author’s knowledge, this is the first study of its kind to explore the unique contribution of CAM therapies in predicting the variation in incidence of older adult depressive symptoms over a 12-month period. This research contributes to the literature in a meaningful way, as it increases the degree of our general understanding of how CAM and older adult depression intersect.
Third, the results of the data analysis posited a distinct difference in CAM therapies’ utility as a predictive (and preventative) agent between older adults with no history of depression and those with prior history. This finding may provide clues for further research into the mechanisms of different CAM therapies within a biopsychosocial framework. This framework is large, encompassing several factors including those of biological, psychological, social, and environmental parameters (Garcia-Toro & Aguirre, 2007; Nemade et al., 2007). Although additional research would be necessary to discern, from within a biopsychosocial framework, precisely how older adults without prior depression differ from those with prior depression, the finding that CAM therapy use may be preventative for the latter group indicates that some CAM therapies may have a significant biopsychosocial influence on certain people.
As a result of this correlational study, there are several recommendations for further research. For example, this research revealed CAM’s potential preventative effects in particular for older adults with a history of depression. Although the effect sizes of some risk factors for depression were stronger than CAM’s influence overall, future research could reveal the degree of CAM’s preventative effects in the absence of such risk factors. This would represent an important discovery of CAM therapies’ preventative effects when unimpeded by countervailing dynamics.
Additional studies could also elucidate more precise relationships between CAM therapies that depressed older adults use and the efficacy of these therapies as either a preventive or treatment measure. Randomized, controlled, double-blind studies of distinct CAM therapies used for preventing recurrence of depressive symptoms or for treating depression in older adults have been conducted. However, sample sizes tend to be small, and such studies typically suffer from weaknesses in research design. Additionally, the operationalization of CAM for this study did not include a number of emotionally supportive nutrients including vitamins, minerals, essential fatty acids, or amino acids, which are widely marketed and available to older adults. Correcting for these limitations would constitute meaningful research. Further, conducting a comparative analysis of the results of similarly methodologically strong studies could help determine the relative utility of some CAM therapies over others in regard to older adult depression.
Additionally, although this study determined a significant relationship between CAM therapy use and predicted variation in incidence of older adult depressive symptoms, it did not explore why such use has apparent preventative effects. This determination could be made through rigorous clinical trials exploring the biological, psychological, and social (i.e., biopsychosocial) aspects of distinct CAM therapy usage by older adults with depressed mood. Although this would represent a large undertaking involving the measurement of various biomarkers, standardized depression scales or screens, and both qualitative and quantitative research methods, such a multidimensional study could go far toward elucidating the precise mechanisms of various CAM therapies’ utility in treating or otherwise mitigating older adult depression.
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About the Author
Dr. Ryan Harrison, PsyD, DAAIM, is the Director of Resident Life & Wellness at Hillcrest, a continuing care retirement community in southern California. After having practiced privately as a board certified health and wellness consultant for over ten years, Ryan completed his doctorate in Health & Wellness Psychology at the University of the Rockies, where he focused his research on the intersection of older adult health and well-being and Complementary and Alternative Medicine. He currently leads a team of dedicated staff to optimize older adult health, fitness, and wellness.