Skip to main content
The Gerontologist logoLink to The Gerontologist
. 2019 May 3;60(4):607–616. doi: 10.1093/geront/gnz051

Social Relationships and Functional Impairment in Aging Cancer Survivors: A Longitudinal Social Network Study

Jennifer L Guida 1,, Cheryl L Holt 3, Cher M Dallal 2, Xin He 2, Robert Gold 3, Hongjie Liu 2
Editor: Suzanne Meeks
PMCID: PMC7368143  PMID: 31050729

Abstract

Background and Objectives

The intersection of cancer, treatment, and aging accelerates functional decline. Social networks, through the provision of social support and resources, may slow the progression of functional deterioration. Socioemotional selectivity theory posits that aging and major life events, like cancer, cause an intentional social network pruning to procure and maintain emotionally fulfilling bonds, while shedding weaker, less supportive relationships. However, it is relatively unknown if such network changes impact functional impairment in cancer survivors. This study examined the relationships between changes in the egocentric social network and functional impairment in older adult cancer survivors and a similarly aged group without cancer (older adults).

Research Design and Methods

Data were analyzed from 1,481 community dwelling older adults (n = 201 cancer survivors) aged 57–85 years, from Waves 1 and 2 (2005–2006 and 2010–2011) of the National Social Life, Health and Aging Project. Associations were analyzed with multiple logistic regression.

Results

Cancer survivors and older adults reported similar levels of functional impairment and social network change. Adding 2 new relationships exhibited protective effects against functional impairment, irrespective of cancer status (odds ratio [OR]: 0.64, 95% confidence interval [CI]: 0.41–0.99). Declines in frequent contact were associated with higher odds of functional impairment among cancer survivors (OR: 1.92, 95% CI: 1.15–3.20). Social network components were not significantly associated with functional impairment in older adults.

Discussion and Implications

Adding new relationships may reduce disability in older adults and increasing network contact may help cancer survivors remain independent. Social network interventions may improve quality of life for older adults.

Keywords: Cancer survivorship, Social support, Disability


Over the next 5 years, the number of cancer survivors living in the United States will approach 19 million individuals (DeSantis et al., 2014) and the majority of these survivors will be aged 65 and older (Parry, Kent, Mariotto, Alfano, & Rowland, 2011). The “hallmarks of aging” include a gradual decline in functional capacity, including deterioration of the cardiovascular and musculoskeletal systems (Jones, Eves, Haykowsky, Freedland, & Mackey, 2009; Sawhney, Sehl, & Naeim, 2005). The intersection of cancer, cancer treatment, and aging accelerate functional decline and mortality (Brown, Harhay, & Harhay, 2015; Henderson, Ness, & Cohen, 2014; Hurria, Jones, & Muss, 2016). Cancer survivors perceive fair or poor health (Bloom, Petersen, & Kang, 2007) and report more functional limitations in everyday life compared with healthy controls (Hewitt, Rowland, & Yancik, 2003; Keating, Nørredam, Landrum, Huskamp, & Meara, 2005). Older adult cancer survivors fatigue faster (Gresham et al., 2018), and are more likely to have comorbidities (Deimling, Arendt, Kypriotakis, & Bowman, 2009; Sogaard, Thomsen, Bossen, Sorensen, & Norgaard, 2013), including higher rates of heart conditions, lung disease, arthritis, incontinence, pain, and obesity (Keating et al., 2005); underscoring the need to design effective interventions to prevent or halt the progression of functional decline.

Adequate social network support may slow the progression of physical decline (Rottenberg et al., 2014; Unger, McAvay, Bruce, Berkman, & Seeman, 1999), while receiving less support may exacerbate disability (Stuck et al., 1999). Social networks are “the web of social relationships that surround an individual” (Kroenke, 2018). Close contacts and their attributes can have profound effects on health through social influence, norms, the perception of social support, and the flow of information and resources (Smith & Christakis, 2008). In the general population, the presence of quality social relationships has been shown to benefit physiological (Uchino, 2006; Yang, Schorpp, & Harris, 2014), physical, and mental health (Kawachi & Berkman, 2001) and increase longevity (Yang et al., 2016). Among breast cancer survivors, social isolation, or the absence of social ties, is associated with all-cause and cancer-specific mortality, and the reported effect size is of similar magnitude to established cancer risk factors, such as smoking and obesity (Hinzey, Gaudier-Diaz, Lustberg, & DeVries, 2016). Social networks may bolster physical functioning through access to information and resources, shared decision making, the provision of tangible and emotional support, and modeling positive health behaviors and coping strategies.

Socioemotional selectivity theory posits that social networks are constructed based on the perception of time. As individuals age, time is perceived to be limited, and as a result, the size and composition of the social network shifts to include a smaller number of meaningful relationships comprised primarily of family members. In this context, emotionally fulfilling bonds are procured and maintained, while weaker, less supportive relationships are pruned (English & Carstensen, 2014; Lansford, Sherman, & Antonucci, 1998). A life changing event, such as a cancer diagnosis, can also trigger the perception that end of life is near, causing cancer survivors to rearrange their social networks in similar ways (Fisher & Nussbaum, 2015). Creating a strong, supportive network may provide important advantages for long-term cancer recovery, shared decision making, survivorship care planning, psychosocial well-being, and functional independence (Thompson, Rodebaugh, Pérez, Schootman, & Jeffe, 2013). Recently, a call was put forth to understand the impact of social networks on cancer survivorship endpoints using more robust social network measures (Kroenke, 2018). To address this research gap, we conducted a secondary analysis of egocentric social network data over a 5-year follow-up period. Egocentric social networks focus on an individual, “ego” and their personal social network of close contacts (alters; Borgatti, Everett, & Johnson, 2018). Understanding how social networks evolve over time and their influence on physical decline is critical to devise strategies to address the needs of the aging cancer survivor. The objective of this study was to: (a) compare the patterns of social network change between older adult cancer survivors and a similarly aged group without cancer; and (b) investigate if changes to social networks are associated with perceived physical functioning over a 5-year period. We hypothesize that cancer survivors and older adults without cancer (older adults) will maintain frequently contacted, emotionally close, and supportive relationships over time, in accordance with socioemotional selectivity theory. Further, adding new relationships and preserving high levels of social support and network contact among enduring relationships will be protective of functional impairment in both groups, while losing specific relationships may not impact disability due to intentional network pruning.

Research Design and Methods

Study Population

A secondary analysis of the National Social Life Health and Aging Project (NSHAP) was conducted to assess whether changes in the social network was associated with functional limitations 5 years later. NSHAP is a cohort of community dwelling older adults aged 57–85 years old. Wave 1 (W1) and Wave 2 (W2) data were collected in 2005–2006 and 2010–2011, respectively. The study design, methods of recruitment, and sampling have been well described elsewhere (Hayward & Wallace, 2014; O’Muircheartaigh, English, Pedlow, & Kwok, 2014; Waite, Laumann, Levinson, Lindau, & O’Muircheartaigh, 2014). Briefly, NSHAP is a complex multistage probability sample of older adults. Oversampling by age, race/ethnicity, and gender was conducted to ensure adequate representation of subgroups. The W1 sample included 3,005 participants, of whom 2,261 participated again in W2. Participants were considered cancer survivors if they indicated on the NSHAP W1 questionnaire that they had been diagnosed with cancer (excluding nonmelanoma skin cancer). Participants who were diagnosed with cancer after W1 (n = 148) were excluded because their social network at baseline would not reflect their cancer experience. Individuals with missing functional impairment data (n = 1) or missing social network data (n = 631) were excluded from the analysis, resulting in a final sample size of 1,481 men and women (Figure 1). This study was approved by the Institutional Review Board at University of Maryland, College Park.

Figure 1.

Figure 1.

Derivation of the analytic sample.

Measures

Functional Impairment

Disability is the inability to perform tasks essential for autonomous living (Cornwell & Laumann, 2015) and is predictive of future health complications and mortality (Stuck et al., 1999). The Activities of Daily Living (ADL) scale is a 7-item instrument that asks respondents to self-report difficulties performing activities lasting 3 months or more: walking across a room, dressing, bathing, eating, getting in or out of bed, using the toilet, and driving during the day. Responses ranged from 0 (no difficulty) to 3 (difficulty). Scores were summed with higher scores indicating more severe impairments. The Cronbach’s alpha for the scale was 0.84 in W1 and 0.81 in W2. We created a binary variable (1= any impairment vs 0= no impairment) because more than 70% of the sample indicated no functional impairments.

Social Network Change

The NSHAP egocentric social network module has been previously described (Cornwell, 2015; Cornwell, Schumm, Laumann, & Graber, 2009; Cornwell, Schumm, Laumann, Kim, & Kim, 2014). Strong ties were elicited from the name generator, “from time to time, most people discuss things that are important to them with others. For example, these may include good or bad things that happen to you, problems you are having, or important concerns you may have. Looking back over the last 12 months, who are the people with whom you most often discussed things that were important to you?” (Marin, 2004; Ruan, 1998). For the current study, social network change was measured in three ways: (a) network turnover, (b) changes to the characteristics of relationships that last over time, and (c) changes to the overall network structure.

Network Turnover

To distinguish between ties that were added, lost, or persisted between waves, NSHAP developed a computer-assisted personal interviewing (CAPI) exercise. During the W2 interview, NSHAP respondents were first asked the “important matters” question, as done in W1. Interviewers then presented the respondent’s W1 and W2 alters and asked participants to verify the linked matches. To capture potential differences in the direction and magnitude of the associations of interest, we created categorical variables for the numbers of lost and added ties. Lost ties were defined as alters who were named in W1, but not in W2. Categories were created based on the distribution: 0 = no lost alters (reference group), 1 = one lost alter, 2= two lost alters, and 3 = three or more lost alters. Added ties were alters that were named for the first time in W2 and were coded in the same fashion as lost ties.

Changes to the Characteristics of Persistent Ties

Understanding the attributes of ties that persist over time can help distinguish why some ties endure and why others do not. NSHAP measured emotional closeness by the question “How close do you feel is your relationship with [name]?” Response options ranged from 0 = not very close to 3 = extremely close. The frequency of contact indicates how often ego interacts with their personal network. NSHAP asked each respondent to rate on an ordinal scale how often they talked to each alter, including via telephone and email. Responses ranged from 1 = once a year to 8 = every day. Perceived social support was measured via six questions that asked participants how often they could open up to and rely on their spouse/partner, family, and friends. All responses were measured on a three-point scale (0 = hardly ever/never to 2 = often). The six social support questions were summed to calculate the total amount of support the ego received from each specific relation. Average difference scores for closeness and contact were calculated by computing the mean of each variable for each ego at both waves, and then taking the absolute difference between W1 and W2. Change in social support was computed by taking the absolute difference between waves.

Changes to the Overall Network Structure

Density is the proportion of alters within ego’s network who know each other. Each participant was asked how often each of the alters talked to each other. Alters who had never spoken to each other were considered to have no connection (no tie). Density was calculated by dividing the total number of actual ties by the number of potential ties. The number of potential ties is the proportion of [k(k − 1)/2] pairs, where k is the total number of alters in ego’s network (not including ego) and ego has at least two alters. Density ranges from 0 to 1, where higher network density signifies more connections among alters. Variations in network density over time were assessed with difference scores.

Covariates

Confounders were identified a priori from the literature. Age was categorized as 57–64 (reference), 65–74, 75–85 because older age groups have different levels of risk for functional impairments (Jette & Branch, 1981). Body mass index (BMI, kg/m2) was categorized based on established cut points (BMI: <18.5 = underweight, 18.5–24.9 = normal, 25–29.9 = overweight, >30 = obese; Williams, Pham-Kanter, & Leitsch, 2009). Underweight and normal categories were combined due to small sample size and were used as the reference group. Race (white [reference], black, and other), gender (male [reference] vs female), education (high school education or less vs some college or more [reference]), marital status (married/cohabitating partner [reference] vs unmarried), smoking (yes/no [reference]), and the number of comorbidities were also controlled for in the analysis. Comorbidities were assessed using a modified version of the Charlson Comorbidity Index (Vasilopoulos et al., 2014) including 12 chronic conditions (hypertension, heart attack/myocardial infarction, congestive heart failure, stroke, any procedure for coronary artery disease, depression, diabetes, COPD/asthma, arthritis, Alzheimer’s disease/dementia, urinary/stool incontinence, and other urinary problems). Scores were summed for a total of 12 possible points.

Statistical Analysis

Means and proportions were used to describe the distributions of variables by cancer status. Descriptive characteristics of the individual ADL items are presented in Supplementary Table 1. Functional impairment and social networks by cancer type is described in Supplementary Table 2. Simple linear regression and chi-squared tests were used to examine differences between cancer survivors and older adults for continuous and categorical variables, respectively. All p values are two-sided. Multiple logistic regression was used to assess whether the change in social network components were associated with functional impairment in W2, adjusting for existing impairments in W1 and all covariates. A priori, power analyses indicated that we would have sufficient power to detect an effect size of 0.15 with at least 175 participants in each of the two groups (power = 0.80, alpha = 0.05). We analyzed the reported reasons alters were “lost” between waves of data collection to provide context to the results (data not shown). Additionally, we separated tangible and emotional support in logistic regression models to assess the individual contribution of the different support types by relation (Supplementary Table 3). The complex survey design was taken into account using the survey procedures in SAS version 9.3 (SAS, Inc., Cary, NC) and by utilizing the W1 survey weights adjusted for nonresponse (Cornwell et al., 2014).

Results

The final analytic sample included 1,481 participants, of whom 13.8% (n = 201) indicated that they had a cancer diagnosis. About half the sample was aged 57–64 years (47.7%) and female (57.6%). The majority were white (83.8%), married (71.7%), overweight or obese (75.4%), nonsmokers (88.2%), and had received some college or more (62.4%) in W1. Overall, 26.7% of the sample reported impairments in their activities of daily living in W2, which was an increase from W1 (21.2%; Table 1). Cancer survivors were statistically significantly older (p < .01) and unmarried (p = .02) compared with older adults, but did not differ in terms of functional impairment (cancer survivors: W1 = 22.6%, W2 = 29.4%; older adults: W1 = 21.0%, W2 = 26.3%; p = .57). Cancer survivors reported statistically significantly higher percentages of difficulty bathing in W2 compared with older adults (11.9% vs 7.3%; Supplementary Table 1). Most cancer survivors either had breast (n = 48), prostate (n = 40), or gynecological cancer (n = 35). Leukemia/lymphoma survivors reported the highest levels of functional impairment (39.5%; Supplementary Table 2).

Table 1.

Characteristics of Cancer Survivors and Older Adults

Sociodemographic and social network characteristics Overall (n = 1,481) Cancer survivor (n = 201) Older adults (n = 1,280)
N % N % N % p Valuea
Age (years)
 57–64 592 47.7 49 33.4 543 50.0 <.01
 65–74 561 35.5 89 44.6 472 34.0
 75–85 328 16.8 63 22.0 265 16.0
Gender
 Male 628 42.4 82 41.7 546 42.5 .82
 Female 853 57.6 119 58.3 734 57.5
Race
 White 1,099 83.8 167 89.5 932 82.9 .07
 Black 214 8.4 24 5.8 190 8.8
 Hispanic/other 162 7.9 10 4.7 152 8.4
Education
 Less than high school 235 12.1 23 10.6 212 12.4 .16
 High school 373 25.5 42 23.5 331 25.8
 Some college 477 33.6 82 41.4 395 32.3
 Bachelor’s degree or higher 396 28.8 54 24.5 342 29.4
Married/cohabitating partner
 Yes 970 71.7 120 65.1 850 72.8 .02
 No 511 28.3 81 34.9 430 27.2
Smoking
 Nonsmoker 1,303 88.2 183 90.4 1,120 87.8 .42
 Smoker 177 11.8 18 9.6 159 12.2
BMI
 Underweight 2 0.1 0 0.0 2 0.1 .72
 Normal 344 24.4 44 25.0 300 24.4
 Overweight 506 35.1 66 32.5 440 35.5
 Obese 556 40.3 81 42.4 475 40.0
 Comorbidity Index (mean, SE) 2.14 0.05 2.20 0.14 2.13 0.05 .65
Functional impairment W2
 No impairment 1,058 73.3 139 70.6 919 73.7 .80
 1–2 impairments 202 13.3 26 12.8 176 13.4
 3–4 impairments 114 6.7 23 9.6 91 6.2
 5–6 impairments 54 3.2 7 3.3 47 3.2
 6–7 impairments 23 1.7 3 1.1 20 1.8
 8 or more impairments 30 1.8 3 2.6 27 1.7
Functional impairment W1
 No impairment 1,034 72.8 137 71.7 897 73.0 .93
 1–2 impairments 270 16.6 43 18.2 227 16.3
 3–4 impairments 100 5.7 15 5.7 85 5.6
 5–6 impairments 39 3.0 3 2.4 36 3.1
 6–7 impairments 18 1.0 1 0.3 17 1.1
 8 or more impairments 19 1.0 2 1.7 17 0.9
Change in functional impairment score (mean, SE) 0.18 0.05 0.46 0.26 0.13 0.04 .22
 % Declined 272 18.0 41 20.0 231 17.7 .62
 % Stable 932 65.5 126 66.0 806 65.4
 % Improved 277 16.5 34 14.1 243 16.9
Lost alters between W1 and W2
 No lost alters 238 15.4 25 12.5 213 15.9 .74
 Lost one alter 414 27.2 65 29.4 349 26.8
 Lost two alters 388 27.2 47 26.7 341 27.2
 Lost three or more alters 441 30.3 64 31.4 377 30.1
Added alters between W1 and W2
 No added alters 319 21.5 38 21.5 281 21.5 .28
 Added one alter 391 27.0 61 29.6 330 26.5
 Added two alters 396 27.5 45 20.4 351 28.6
 Added three or more alters 375 24.1 57 28.5 318 23.4

Note: For continuous variables, the mean is presented with the standard error of the mean. For categorical variables, the number and percentage are presented. Responses are weighted for nonresponse. BMI= body mass index; SE = standard error of the mean.

a p Value is testing the difference between cancer survivors and older adults with chi-squared or simple linear regression.

Most egocentric networks experienced some network turnover, with both cancer survivors and older adults reporting similar percentages of lost (p = .74) and added (p = .28) alters. Among stable relationships over the 5-year period, the frequency of contact, emotional closeness, density, and social support from spouses and friends declined slightly over time and the patterns of change were similar between the two groups (Table 2). Results from the multiple logistic regression model are presented in Table 3. In the overall sample, adding two new alters to the network was associated with lower odds of impairment, adjusting for W1 functional impairments and other confounding factors (odds ratio [OR]: 0.64, 95% confidence interval [CI]: 0.41–0.99). Among cancer survivors, declines in frequent contact were associated with higher odds of impairment (OR: 1.92, 95% CI: 1.15–3.20). No social network components were associated with functional impairments among the older adults. The stratified results did not reach statistical significance after adjusting for multiple comparisons using the Bonferroni method. Additionally, tangible and emotional support were not associated with functional impairment when modeled separately (Supplementary Table 3).

Table 2.

Network Characteristics Among Persistent Ties Among Cancer Survivors and Older Adults

Social network characteristics of persistent ties Cancer survivors (n = 201) Older adults (n = 1,280)
Wave 1 Wave 2 Change Wave 1 Wave 2 Change p Valuea
Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE
Mean frequency of contact 7.08 0.07 6.95 0.07 −0.13 0.06 7.05 0.04 6.99 0.04 −0.06 0.03 0.22
Mean closeness 2.33 0.05 2.27 0.04 −0.05 0.04 2.33 0.02 2.29 0.02 −0.04 0.02 0.73
Density 0.80 0.05 0.74 0.04 −0.05 0.07 0.81 0.02 0.75 0.01 −0.06 0.02 0.96
Social support
 Spouse 2.37 0.12 2.12 0.15 −0.25 0.11 2.64 0.06 2.38 0.06 −0.26 0.04 0.97
 Family 3.22 0.07 3.12 0.08 −0.11 0.07 3.07 0.03 3.12 0.03 0.04 0.03 0.05
 Friends 2.51 0.08 2.38 0.14 −0.13 0.16 2.47 0.04 2.38 0.04 −0.09 0.04 0.77

Note: Responses are weighted for nonresponse. SE= standard error of the mean.

a p Value is testing the difference in network change between cancer survivors and older adults using simple linear regression to take the complex survey design into account.

Table 3.

Adjusted Associations for Social Network Characteristics and Functional Impairment in W2 by Cancer Statusa

Social network characteristics Overall Cancer survivors Older adults
OR (95% CI) OR (95% CI) OR (95% CI)
Lost ties between W1 and W2
 No lost alters (ref.)
 Lost one alter 1.47 (0.85–2.54) 1.57 (0.36–6.76) 1.46 (0.82–2.60)
 Lost two alters 1.10 (0.62–1.94) 1.26 (0.26–6.02) 1.05 (0.57–1.94)
 Lost three or more alters 1.77 (0.90–3.48) 4.32 (0.68–27.33) 1.48 (0.73–3.02)
Added ties between W1 and W2
 No added alters (ref.)
 Added one alter 0.67 (0.41–1.09) 0.39 (0.11–1.39) 0.75 (0.42–1.33)
 Added two alters 0.64 (0.41–1.00)* 0.27 (0.07–1.11) 0.74 (0.45–1.22)
 Added three or more alters 0.63 (0.37–1.08) 0.31 (0.08–1.17) 0.70 (0.39–1.25)
Change in closeness 0.82 (0.60–1.12) 0.69 (0.25–1.91) 0.84 (0.60–1.17)
Change in frequency of contact 1.16 (0.98–1.37) 1.92 (1.15–3.20)* 1.09 (0.91–1.30)
Change in density 1.02 (0.73–1.41) 0.63 (0.26–1.51) 1.09 (0.75–1.58)
Change in social support
 Spouse 1.05 (0.95–1.16) 1.15 (0.82–1.61) 1.05 (0.94–1.17)
 Family 1.06 (0.94–1.20) 0.97 (0.65–1.45) 1.08 (0.96–1.22)
 Friends 0.99 (0.87–1.13) 1.24 (0.90–1.72) 0.94 (0.83–1.07)

Note: Responses are weighted for nonresponse. OR = odds ratio; CI = confidence interval.

*p < .05. **p < .01. ***p < .001.

aModel adjusted for age, gender, race, marital status, education, BMI, number of comorbidities, smoking, and functional impairment in Wave 1.

Discussion and Implications

The objective of this study was to describe the evolving social environment among cancer survivors and older adults and its association with functional impairment. The proportion of functional impairments was similar in the two groups, and is consistent with estimates from other studies of cancer survivors (Gewandter et al., 2013; Goodwin, Hunt, & Samet, 1991), but higher than reported national estimates (CDC, NCBDDD, 2017; Chavan, Kedia, & Yu, 2017). Our findings suggest that the patterns of social network change in older adults with and without cancer were quite similar, in line with the socioemotional selectivity theory. Adding new relationships was protective of disability in the overall sample. Declines in the frequency of contact were associated with having at least one functional impairment among cancer survivors, controlling for impairments at baseline and other confounding factors.

Although both groups reported high levels of contact with network members, and the level of decline was relatively small over the 5-year period, frequent contact may be especially important for cancer survivors for two reasons. First cancer survivors are more vulnerable to functional decline due to the genotoxic and cytotoxic effects of cancer therapies (Henderson et al., 2014). Second, frequent contact with network members is especially important to arrange tangible assistance to complete activities of daily living. Cancer survivors may require additional assistance due to ongoing or intermittent treatments and have follow-up care needs that those without cancer do not have to endure. Additionally, informational support may facilitate resources for individual cancer patient needs, such as rehabilitation services (Kroenke, 2018) that may help to stabilize or mitigate cancer- and treatment-related physical decline. Therefore, diminishing contact may suggest a weakening of ties over time and/or reduced network support available to the cancer survivor, which may impact physical functioning.

Our study indicates that both cancer survivors and individuals without cancer were both adding and losing at least one alter over time. Adding two alters to the network was associated with lower disability in the overall NSHAP sample. Network loss was not associated with functional impairment in both groups. Losing network members over time may impact functional impairment because it eliminates potential sources of support required to sustain an independent lifestyle (Avlund et al., 2004; de Leon, Gold, Glass, Kaplan, & George, 2001). Although different measures were used to evaluate social network connectedness, our results are consistent with Michael and colleagues’ (2002) study, indicating that changes in social network integration were not associated with disability in cancer survivors (Michael, Berkman, Colditz, Holmes, & Kawachi, 2002). However, the relatively high and unchanged levels of emotional closeness and social support from family members among lasting ties suggests that strong relationships were maintained for both groups, providing support for the socioemotional selectivity theory. Among participants who lost alters, the majority reported that the alter moved, died, or they lost touch for other reasons, suggesting that the specific relationships lost over time may have been weaker ties less likely to provide help.

To expand on this point, studies on aging suggest that older adults maintain a close-knit network consisting of mainly familial ties that provide social support (Antonucci & Akiyama, 1987; Carstensen, Isaacowitz, & Charles, 1999). Van Tilberg’s 4-year longitudinal study in older Dutch adults showed that seniors tend to preserve a strong circle of familial ties and lose more friend ties over time (Van Tilburg, 1998). Keeping a close network may be one strategy to ameliorate some of the challenges of aging and cope with the transitions to come (Southwick, Litz, Charney, & Friedman, 2011). While losing certain network members to death, conflict, or other reasons may be a stressful situation to endure, a wealth of literature suggests that the process of aging itself can help older adults become resilient to future stressors (Helgeson, Snyder, & Seltman, 2004; Southwick et al., 2011).

The majority of cancer survivors in our sample were long-term survivors (e.g., surviving >5 years) and therefore, their social networks may reflect resilience to life challenges as they transition to the permanent survivorship phase. First, cancer survivors are also aging, which may cause them to arrange their networks into similar patterns as their cancer-free counterparts. Second, network loss may have little impact functional impairment because new relationships are created and support and resources from existing network members remain relatively stable (Southwick et al., 2011). Moreover, the relationships lost are intentionally pruned in favor of deep, supportive relationships, irrespective of age at diagnosis (Pinquart & Silbereisen, 2006). Our findings indicate that this preference is maintained among older cancer survivors many years after a cancer diagnosis. Future studies should replicate these findings in large-scale epidemiologic studies.

Given the importance of frequent contact for cancer survivors, interventions focusing on strengthening communication may improve quality of life. Family-level public health interventions have also been shown to improve access to resources among individuals with disabilities (Heller, Gibbons, & Fisher, 2015). Web- and mobile-based technologies hold promise for functionally impaired older adults as a means to create new connections or enhance existing social relationships, although their efficacy has yet to be established (McAlpine, Joubert, Martin-Sanchez, Merolli, & Drummond, 2015). For example, a randomized trial of a technology application among seniors demonstrated enhanced social connectivity and reduced feelings of loneliness (Czaja, Boot, Charness, Rogers, & Sharit, 2018). Further, a multicomponent trial targeting cancer survivors with high levels of distress showed that an online social networking and coping-based program significantly reduced cancer-related fatigue (Owen, O’Carroll Bantum, Pagano, & Stanton, 2017), a common posttreatment symptom associated with poor functional outcomes (Giacalone et al., 2013; Hofman, Ryan, Figueroa-Moseley, Jean-Pierre, & Morrow, 2007).

The strengths of our study include utilizing a prospective egocentric social network framework over a 5-year period from a large cohort of older adults with the ability to evaluate differences amongst cancer survivors and older adults. The limitations of the study include the small number of cancer survivors in the NSHAP dataset, limiting the ability to assess differences by cancer type. Prognostic features such as cancer stage and treatment information were not collected by NSHAP and the sample consisted of community dwelling older adults. Therefore, it is possible that the cancer survivors in our study may be healthier and/or diagnosed at earlier stages with better prognosis than other cancer samples. Additionally, the provision of social support from ego to network members was not collected, but offers important context, since higher levels of impairment may hinder one’s ability to provide support. The measures of disability in our study were self-reported measures of ADL and individuals with low levels of social support and network contact may rate their impairments as more severe. Finally, regression analyses can only explore associations among factors and does not imply causation.

Our results suggest that the social networks of cancer survivors and older adults have similar impacts on functional impairments, albeit a few subtle differences. Maintaining frequent contact and adding new relationships may be strategies to halt the progression of functional decline. Recommendations for future research include the use of a sample with greater variability in functional impairment and a personal network approach to describe the social networks of older adult cancer survivors. The health promoting aspects of social relationships should be further studied to optimize independence in late life.

Funding

This work was supported by the University of Maryland, College Park.

Supplementary Material

gnz051_suppl_Supplementary_Tables

Acknowledgments

We would like to acknowledge and thank the investigators at National Social Life, Health, and Aging Project and the National Opinion Research Center for their support.

Conflict of Interest

None reported.

References

  1. Antonucci T. C., & Akiyama H (1987). Social networks in adult life and a preliminary examination of the convoy model. Journal of Gerontology, 42, 519–527. doi:10.1093/geronj/42.5.519 [DOI] [PubMed] [Google Scholar]
  2. Avlund K., Lund R., Holstein B. E., Due P., Sakari-Rantala R., & Heikkinen R. L (2004). The impact of structural and functional characteristics of social relations as determinants of functional decline. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 59, 44–51. doi:10.1037/0003-066X.54.3.165 [DOI] [PubMed] [Google Scholar]
  3. Bloom J. R., Petersen D. M., & Kang S. H (2007). Multi-dimensional quality of life among long-term (5+ years) adult cancer survivors. Psycho-Oncology, 16, 691–706. doi:10.1002/pon.1208 [DOI] [PubMed] [Google Scholar]
  4. Borgatti S. P., Everett M. G., & Johnson J. C (2018). Analyzing social networks. Thousand Oaks, CA: Sage. [Google Scholar]
  5. Brown J. C., Harhay M. O., & Harhay M. N (2015). Physical function as a prognostic biomarker among cancer survivors. British Journal of Cancer, 112, 194–198. doi:10.1038/bjc.2014.568 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Carstensen L. L., Isaacowitz D. M., & Charles S. T (1999). Taking time seriously. A theory of socioemotional selectivity. The American Psychologist, 54, 165–181. doi:10.1037/0003-066X.54.3.165 [DOI] [PubMed] [Google Scholar]
  7. CDC, NCBDDD (2017). Disability and health Retrieved September 23, 2016, from http://www.cdc.gov/ncbddd/disabilityandhealth/dhds.html
  8. Chavan P. P., Kedia S. K., & Yu X (2017). Physical and functional limitations in US older cancer survivors. Journal of Palliative Care Medicine, 7, 312. doi:10.4172/2165-7386.1000312 [Google Scholar]
  9. Cornwell B. (2015). Social disadvantage and network turnover. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 70, 132–142. doi:10.1093/geronb/gbu078 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cornwell B., & Laumann E. O (2015). The health benefits of network growth: New evidence from a national survey of older adults. Social Science & Medicine (1982), 125, 94–106. doi:10.1016/j.socscimed.2013.09.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Cornwell B., Schumm L. P., Laumann E. O., & Graber J (2009). Social networks in the NSHAP Study: Rationale, measurement, and preliminary findings. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 64(Suppl. 1), i47–i55. doi:10.1016/j.socnet.2008.10.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Cornwell B., Schumm L. P., Laumann E. O., Kim J., & Kim Y.-J (2014). Assessment of social network change in a national longitudinal survey. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 69(Suppl. 2), S75–S82. doi:10.1093/geronb/gbu037 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Czaja S. J., Boot W. R., Charness N., Rogers W. A., & Sharit J (2018). Improving social support for older adults through technology: Findings from the PRISM randomized controlled trial. The Gerontologist, 58, 467–477. doi:10.1093/geront/gnw249 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Deimling G. T., Arendt J. A., Kypriotakis G., & Bowman K. F (2009). Functioning of older, long-term cancer survivors: The role of cancer and comorbidities. Journal of the American Geriatrics Society, 57(Suppl. 2), S289–S292. doi:10.1111/j.1532-5415.2009.02515.x [DOI] [PubMed] [Google Scholar]
  15. DeSantis C. E., Lin C. C., Mariotto A. B., Siegel R. L., Stein K. D., Kramer J. L.,…Jemal A (2014). Cancer treatment and survivorship statistics, 2014. CA: A Cancer Journal for Clinicians, 64, 252–271. doi:10.3322/caac.21235 [DOI] [PubMed] [Google Scholar]
  16. English T., & Carstensen L. L (2014). Selective narrowing of social networks across adulthood is associated with improved emotional experience in daily life. International Journal of Behavioral Development, 38, 195–202. doi:10.1177/0165025413515404 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Fisher C. L., & Nussbaum J. F (2015). Maximizing wellness in successful aging and cancer coping: The importance of family communication from a socioemotional selectivity theoretical perspective. Journal of Family Communication, 15, 3–19. doi:10.1080/15267431.2014.946512 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Gewandter J. S., Fan L., Magnuson A., Mustian K., Peppone L., Heckler C.,…Mohile S. G (2013). Falls and functional impairments in cancer survivors with chemotherapy-induced peripheral neuropathy (CIPN): A University of Rochester CCOP study. Supportive Care in Cancer, 21, 2059–2066. doi:10.1007/s00520-013-1766-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Giacalone A., Quitadamo D., Zanet E., Berretta M., Spina M., & Tirelli U (2013). Cancer-related fatigue in the elderly. Supportive Care in Cancer, 21, 2899–2911. doi:10.1007/s00520-013-1897-1 [DOI] [PubMed] [Google Scholar]
  20. Goodwin J. S., Hunt W. C., & Samet J. M (1991). A population-based study of functional status and social support networks of elderly patients newly diagnosed with cancer. Archives of Internal Medicine, 151, 366–370. doi:10.1001/archinte.1991.00400020114022 [PubMed] [Google Scholar]
  21. Gresham G., Dy S. M., Zipunnikov V., Browner I. S., Studenski S. A., Simonsick E. M.,…Schrack J. A (2018). Fatigability and endurance performance in cancer survivors: Analyses from the Baltimore Longitudinal Study of Aging. Cancer, 124, 1279–1287. doi:10.1002/cncr.31238 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hayward M. D., & Wallace R. B (2014). Wave 2 of the National Social Life, Health, and Aging Project: An overview. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 69(Suppl. 2), 1–3. doi:10.1093/geronb/gbu126 [DOI] [PubMed] [Google Scholar]
  23. Helgeson V. S., Snyder P., & Seltman H (2004). Psychological and physical adjustment to breast cancer over 4 years: Identifying distinct trajectories of change. Health Psychology, 23, 3–15. doi:10.1037/0278-6133.23.1.3 [DOI] [PubMed] [Google Scholar]
  24. Heller T., Gibbons H. M., & Fisher D (2015). Caregiving and family support interventions: Crossing networks of aging and developmental disabilities. Intellectual and Developmental Disabilities, 53, 329–345. doi:10.1352/1934-9556-53.5.329 [DOI] [PubMed] [Google Scholar]
  25. Henderson T. O., Ness K. K., & Cohen H. J (2014). Accelerated aging among cancer survivors: From pediatrics to geriatrics. American Society of Clinical Oncology Educational Book. American Society of Clinical Oncology. Annual Meeting, 2014, e423–430. doi:10.14694/EdBook_AM.2014.34.e423 [DOI] [PubMed] [Google Scholar]
  26. Hewitt M., Rowland J. H., & Yancik R (2003). Cancer survivors in the United States: Age, health, and disability. The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 58, 82–91. doi:10.1093/gerona/58.1.M82 [DOI] [PubMed] [Google Scholar]
  27. Hinzey A., Gaudier-Diaz M. M., Lustberg M. B., & DeVries A. C (2016). Breast cancer and social environment: Getting by with a little help from our friends. Breast Cancer Research, 18, 54. doi:10.1186/s13058-016-0700-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hofman M., Ryan J. L., Figueroa-Moseley C. D., Jean-Pierre P., & Morrow G. R (2007). Cancer-related fatigue: The scale of the problem. The Oncologist, 12(Suppl. 1), 4–10. doi:10.1634/theoncologist.12-S1-4 [DOI] [PubMed] [Google Scholar]
  29. Hurria A., Jones L., & Muss H. B (2016). Cancer treatment as an accelerated aging process: Assessment, biomarkers, and interventions. American Society of Clinical Oncology Educational Book. American Society of Clinical Oncology. Annual Meeting, 35, e516–e522. doi:10.1200/EDBK_156160 [DOI] [PubMed] [Google Scholar]
  30. Jette A. M., & Branch L. G (1981). The Framingham Disability Study: II. Physical disability among the aging. American Journal of Public Health, 71, 1211–1216. doi:10.2105/AJPH.71.11.1211 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Jones L. W., Eves N. D., Haykowsky M., Freedland S. J., & Mackey J. R (2009). Exercise intolerance in cancer and the role of exercise therapy to reverse dysfunction. The Lancet. Oncology, 10, 598–605. doi:10.1016/S1470-2045(09)70031-2 [DOI] [PubMed] [Google Scholar]
  32. Kawachi I., & Berkman L. F (2001). Social ties and mental health. Journal of Urban Health: Bulletin of the New York Academy of Medicine, 78, 458–467. doi:10.1093/jurban/78.3.458 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Keating N. L., Nørredam M., Landrum M. B., Huskamp H. A., & Meara E (2005). Physical and mental health status of older long-term cancer survivors. Journal of the American Geriatrics Society, 53, 2145–2152. doi:10.1111/j.1532-5415.2005.00507.x [DOI] [PubMed] [Google Scholar]
  34. Kroenke C. H. (2018). A conceptual model of social networks and mechanisms of cancer mortality, and potential strategies to improve survival. Translational Behavioral Medicine, 8, 629–642. doi:10.1093/tbm/ibx061 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Lansford J. E., Sherman A. M., & Antonucci T. C (1998). Satisfaction with social networks: An examination of socioemotional selectivity theory across cohorts. Psychology and Aging, 13, 544–552. doi:10.1037//0882-7974.13.4.544 [DOI] [PubMed] [Google Scholar]
  36. Marin A. (2004). Are respondents more likely to list alters with certain characteristics? Implications for name generator data. Social Networks, 26, 289–307. doi:10.1016/j.socnet.2004.06.001 [Google Scholar]
  37. McAlpine H., Joubert L., Martin-Sanchez F., Merolli M., & Drummond K. J (2015). A systematic review of types and efficacy of online interventions for cancer patients. Patient Education and Counseling, 98, 283–295. doi:10.1016/j.pec.2014.11.002 [DOI] [PubMed] [Google Scholar]
  38. Mendes de Leon C. F., Gold D. T., Glass T. A., Kaplan L., & George L. K (2001). Disability as a function of social networks and support in elderly African Americans and Whites: The Duke EPESE 1986–1992. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 56, 179–190. doi:10.1093/geronb/56.3.S179 [DOI] [PubMed] [Google Scholar]
  39. Michael Y. L., Berkman L. F., Colditz G. A., Holmes M. D., & Kawachi I (2002). Social networks and health-related quality of life in breast cancer survivors: A prospective study. Journal of Psychosomatic Research, 52, 285–293. doi:10.1016/S0022-3999(01)00270-7 [DOI] [PubMed] [Google Scholar]
  40. O’Muircheartaigh C., English N., Pedlow S., & Kwok P. K (2014). Sample design, sample augmentation, and estimation for Wave 2 of the NSHAP. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 69(Suppl. 2), S15–S26. doi:10.1093/geronb/gbu053 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Owen J. E., O’Carroll Bantum E., Pagano I. S., & Stanton A (2017). Randomized trial of a social networking intervention for cancer-related distress. Annals of Behavioral Medicine, 51, 661–672. doi:10.1007/s12160-017-9890-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Parry C., Kent E. E., Mariotto A. B., Alfano C. M., & Rowland J. H (2011). Cancer survivors: A booming population. Cancer Epidemiology, Biomarkers & Prevention, 20, 1996–2005. doi:10.1158/1055-9965.EPI-11-0729 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Pinquart M., & Silbereisen R. K (2006). Socioemotional selectivity in cancer patients. Psychology and Aging, 21, 419–423. doi:10.1037/0882-7974.21.2.419 [DOI] [PubMed] [Google Scholar]
  44. Rottenberg Y., Litwin H., Manor O., Paltiel A., Barchana M., & Paltiel O (2014). Prediagnostic self-assessed health and extent of social networks predict survival in older individuals with cancer: A population based cohort study. Journal of Geriatric Oncology, 5, 400–407. doi:10.1016/j.jgo.2014.08.001 [DOI] [PubMed] [Google Scholar]
  45. Ruan D. (1998). The content of the General Social Survey discussion networks: An exploration of General Social Survey discussion name generator in a Chinese context. Social Networks, 20, 247–264. doi:10.1016/S0378-8733(98)00004-5 [Google Scholar]
  46. Sawhney R., Sehl M., & Naeim A (2005). Physiologic aspects of aging: Impact on cancer management and decision making, part I. Cancer Journal (Sudbury, Mass.), 11, 449–460. doi:10.1097/00130404-200511000-00004 [DOI] [PubMed] [Google Scholar]
  47. Smith K. P., & Christakis N. A (2008). Social networks and health. Annual Review of Sociology, 34, 405–429. doi:10.1146/annurev.soc.34.040507.134601 [Google Scholar]
  48. Sogaard M., Thomsen R. W., Bossen K. S., Sorensen H. T., & Norgaard M (2013). The impact of comorbidity on cancer survival: A review. Clinical Epidemiology, 5(Suppl. 1), 3–29. doi:10.2147/CLEP.S47150 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Southwick S. M., Litz B. T., Charney D., & Friedman M. J (2011). Resilience and mental health: Challenges across the lifespan. Cambridge, United Kingdom: Cambridge University Press. doi:10.1017/CBO9780511994791 [Google Scholar]
  50. Stuck A. E., Walthert J. M., Nikolaus T., Büla C. J., Hohmann C., & Beck J. C (1999). Risk factors for functional status decline in community-living elderly people: A systematic literature review. Social Science & Medicine (1982), 48, 445–469. doi:10.1016/S0277-9536(98)00370-0 [DOI] [PubMed] [Google Scholar]
  51. Thompson T., Rodebaugh T. L., Pérez M., Schootman M., & Jeffe D. B (2013). Perceived social support change in patients with early stage breast cancer and controls. Health Psychology, 32, 886–895. doi:10.1037/a0031894 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. van Tilburg T. (1998). Losing and gaining in old age: Changes in personal network size and social support in a four-year longitudinal study. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 53, 313–323. doi:10.1093/geronb/53B.6.S313 [DOI] [PubMed] [Google Scholar]
  53. Uchino B. N. (2006). Social support and health: A review of physiological processes potentially underlying links to disease outcomes. Journal of Behavioral Medicine, 29, 377–387. doi:10.1007/s10865-006-9056-5 [DOI] [PubMed] [Google Scholar]
  54. Unger J. B., McAvay G., Bruce M. L., Berkman L., & Seeman T (1999). Variation in the impact of social network characteristics on physical functioning in elderly persons: MacArthur Studies of Successful Aging. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 54, 245–251. doi:10.1093/geronb/54B.5 [DOI] [PubMed] [Google Scholar]
  55. Vasilopoulos T., Kotwal A., Huisingh-Scheetz M. J., Waite L. J., McClintock M. K., & Dale W (2014). Comorbidity and chronic conditions in the National Social Life, Health and Aging Project (NSHAP), wave 2. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 69(Suppl. 2), 154–165. doi:10.1093/geronb/gbu025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Waite L. J., Laumann E. O., Levinson W., Lindau S. T., & O’Muircheartaigh C. A (2014). National Social Life, Health, and Aging Project (NSHAP): Wave 1. ICPSR20541-v6. Ann Arbor, MI: Inter-University Consortium for Political and Social Research [Distributor] doi:10.3886/ICPSR20541.v8 [Google Scholar]
  57. Williams S. R., Pham-Kanter G., & Leitsch S. A (2009). Measures of chronic conditions and diseases associated with aging in the National Social Life, Health, and Aging Project. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 64(Suppl. 1), i67–i75. doi:10.1093/geronb/gbn015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Yang Y. C., Boen C., Gerken K., Li T., Schorpp K., & Harris K. M (2016). Social relationships and physiological determinants of longevity across the human life span. Proceedings of the National Academy of Sciences of the United States of America, 113, 578–583. doi:10.1073/pnas.1511085112 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Yang Y. C., Schorpp K., & Harris K. M (2014). Social support, social strain and inflammation: Evidence from a national longitudinal study of US adults. Social Science & Medicine, 107, 124–135. doi:10.1016/j.socscimed.2014.02.013 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

gnz051_suppl_Supplementary_Tables

Articles from The Gerontologist are provided here courtesy of Oxford University Press

RESOURCES