title: Cluster analysis of intake, output, and voiding habits collected from diary data.
abstract: BACKGROUND
  Data that incorporate the full complexity of healthy beverage intake and voiding frequency do not exist; therefore, clinicians reviewing bladder habits or voiding diaries for continence care must rely on expert opinion recommendations.
  
  
  OBJECTIVE
  The aims of this study were to use data-driven cluster analyses to reduce complex voiding diary variables into discrete patterns or data cluster profiles, descriptively name the clusters, and perform validity testing.
  
  
  METHODS
  Participants were 352 community women who filled out a 3-day voiding diary. Six variables (void frequency during daytime hours, void frequency during nighttime hours, modal output, total output, total intake, and body mass index) were entered into cluster analyses. The clusters were analyzed for differences by continence status, age, race (Black women, n= 196; White women, n = 156), and, for those who were incontinent, by leakage episode severity.
  
  
  RESULTS
  Three clusters emerged, labeled descriptively as "conventional," "benchmark," and "superplus." The conventional cluster (68% of the sample) demonstrated a mean daily intake of 1320 ± 375 ml, a mean daily output of 1069 ± 434 ml, mean daily voids of 5 ± 2 times, mean modal daytime output of 290 ± 144 ml, and mean nighttime voids of 1 ± 1 times. The superplus cluster (7% of the sample) showed double or triple these values across the five variables, and the benchmark cluster (25%) showed values consistent with current popular recommendations on intake and output (e.g., meeting or exceeding the 8 × 8 fluid intake rule of thumb). The clusters differed significantly (p < .05) by age, race, amount of bladder irritant beverages consumed, and incontinence.
  
  
  CONCLUSIONS
  Identification of three discrete clusters provides for a potential parsimonious but data-driven means of classifying individuals for additional epidemiological or clinical study. The clinical utility rests with potential for intervening to move an individual from a high-risk to low-risk cluster with regard to incontinence.
authors.affiliation: School of Nursing, University of Michigan, Ann Arbor, MI 48109-5482, USA. janismm@umich.edu
authors.forename: Janis M
authors.initials: JM
authors.lastname: Miller
authors.forename: Ying
authors.initials: Y
authors.lastname: Guo
authors.forename: Sarah Becker
authors.initials: SB
authors.lastname: Rodseth
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D015982:Bias; D044383:Black People / Q000706:statistics & numerical data; D016022:Case-Control Studies; D016009:Chi-Square Distribution; D016000:Cluster Analysis; D003625:Data Collection / Q000379:methods; D004327:Drinking Behavior / Q000502:physiology; D005260:Female; D006184:Habits; D006801:Humans; D008499:Medical Records; D008824:Michigan / Q000453:epidemiology; D008875:Middle Aged; D018570:Risk Assessment; D057566:Self Report; D013997:Time Factors; D014549:Urinary Incontinence / Q000453:epidemiology* / Q000503:physiopathology; D014554:Urination / Q000502:physiology; D044465:White People / Q000706:statistics & numerical data; D001741:Black or African American