Perceptions and predictors of health-related quality of life among aging adults who stutter: a first glimpse

ABSTRACTQuality of life among adults who stutter (AWS) is well-studied but little is known about health-related quality of life, an index of successful aging. The study’s aim was to begin documenting perceptions and predictors of physical and mental health quality of life (PH-, MH-QoL) among aging AWS. Forty AWS (50-84 years old) in the United States completed the SF-36 survey of PH- and MH-QoL, and were surveyed on potential explanatory variables including resilience, social resources, health-promoting behavior, socioeconomic status, perceptions of aging, social risk, identity management, neuroticism, stuttering severity, and difficulty communicating. The prevalence of very low PH- and MH-QoL scores was compared against age- and gender-graded population norms. Finally, PH- and MH-QoL scores were regressed onto explanatory variables. Relatively few aging AWS had very low PH-QoL scores. A non-trivial subset of aging AWS had very low MH-QoL scores. Greater awareness of aging-related loss predicted lower PH- and MH-QoL. Greater neuroticism also predicted lower MH-QoL. Greater resilience predicted better MH-QoL. If replicable, results would suggest few aging AWS are at-risk for very low PH-QoL while more aging AWS are at-risk for very low MH-QoL. Predictors of PH- and MH-QoL may inform strategies for successful aging among AWS.KEYWORDS: Stutteringagingphysical healthmental healthquality of life AcknowledgementsAmanda Kelly contributed to the study concept and design. The study survey was advertised by the National Stuttering Association, the Stuttering Community page on Facebook, the r/Stutter group on Reddit, and several Osher Lifelong Learning Institutes across the United States. I appreciate the participation of all adults who stutter who responded to the survey.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 Adults in the U.S. reach full retirement age between 66 and 67 years old. Physical limitations impacting the ability to live independently, and perceived quality of life, tend not appear until this age in more advantaged adults (Kramarow, Lubitz, Lentzner, & Gorina, Citation2007; Manton, Gu, & Lowrimore, Citation2008). However, the prevalence of such physical limitations is greater in less advantaged groups as young as age 50 (Holmes, Powell-Griner, Lethbridge-Cejku, & Heyman, Citation2009). Thus, this study focused on AWS age 50 years or older.2 Due to unbalanced data, some explanatory variables were excluded including household income (28 participants had a household income >200%FPL), education level (35 participants had at least some college education), relationship status (31 participants were partnered/married), substance non-use (32 participants were substance non-users), and participation in speech therapy (only 2 participants reported attending speech therapy in the past year).3 One reviewer questioned whether the sample size (n = 40) was adequate for the conducted regression analyses. Published sample size recommendations for regression analysis range from two subjects per variable (Austin & Steyerberg, Citation2015), to five subjects per variable (Tabachnick, Fidell, & Ullman, Citation2013), to 10 subjects per variable (Wampold & Freund, Citation1987; Harris, Citation2001). In the current study, each regression analysis involved five variables (one outcome variable, one explanatory variable, and three covariates). Given the sample size of 40 subjects, this results in a ratio of eight subjects per variable, which is closer to the high side of the cited sample size recommendations. According to Green (Citation1991), determining an appropriate sample size for regression analysis should incorporate some consideration of effect size. Based on a rule proposed by Green (Citation1991), a total sample size of 28 subjects would be needed to detect predictors with large effect sizes given five variables (equating to 5.6 subjects per variable). Thus, the current sample size was more than adequate for detecting predictors with large effect sizes given five variables in each analysis.4 It also seems worth noting that more frequent participation in stuttering support groups predicted higher MH-QoL scores, but this effect was not statistically significant after type-1 error correction was applied.