Introduction: Individuals with osteoarthritis (OA) show increased morbidity and mortality. Telomere length, a measure of cellular aging, predicts increased morbidity and mortality. Telomeres shorten with persisting biological and psychosocial stress. Living with chronic OA pain is stressful. Previous research exploring telomere length in people with OA has produced inconsistent results. Considering pain severity may clarify the relationship between OA and telomeres.

Objectives: We hypothesized that individuals with high OA chronic pain severity would have shorter telomeres than those with no or low chronic pain severity.

Methods: One hundred thirty-six adults, ages 45 to 85 years old, with and without symptomatic knee OA were included in the analysis. Peripheral blood leukocyte telomere length was measured, and demographic, clinical, and functional data were collected. Participants were categorized into 5 pain severity groups based on an additive index of frequency, intensity, time or duration, and total number of pain sites (FITT). Covariates included age, sex, race or ethnicity, study site, and knee pain status.

Results: The no or low chronic pain severity group had significantly longer telomeres compared with the high pain severity group, P50.025. A significant chronic pain severity dose response emerged for telomere length, P50.034. The FITT chronic pain severity index was highly correlated with the clinical and functional OA pain measures. However, individual clinical and functional measures were not associated with telomere length.

Conclusion: Results demonstrate accelerated cellular aging with high knee OA chronic pain severity and provide evidence for the potential utility of the FITT chronic pain severity index in capturing the biological burden of chronic pain.

Objectives: To determine the off-shift sleep strategies of bi-ethnic night-shift nurses, the relationship between these sleep strategies and adaptation to shift work, and identify the participant-level characteristics associated with a given sleep strategy.

Methods: African-American and non-Hispanic White female, night-shift nurses from an academic hospital were recruited to complete a survey on sleep–wake patterns (n = 213). Participants completed the standard shiftwork index and the biological clocks questionnaire to determine sleep strategies and adaptation to night-shift work. In addition, chronotype was determined quantitatively with a modified version of the Munich ChronoType Questionnaire. Most participants worked ~3 consecutive 12-h night-shifts followed by several days off.

Results: Five sleep strategies used on days off were identified: (a) night stay, (b) nap proxy, (c) switch sleeper, (d) no sleep, and (e) incomplete switcher. Nap proxy and no sleep types were associated with poorer adaptation to night-shift work. The switch sleeper and incomplete switcher types were identified as more adaptive strategies that were associated with less sleep disturbance, a later chronotype, and less cardiovascular problems.

Conclusion: Behavioral sleep strategies are related to adaptation to a typical night-shift schedule among hospital nurses. Nurses are crucial to the safety and well-being of their patients. Therefore, adoption of more adaptive sleep strategies may reduce sleep/wake dysregulation in this population, and improve cardiovascular outcomes.

Graph pebbling is a network optimization model for transporting discrete resources that are consumed in transit: the movement of 2 pebbles across an edge consumes one of the pebbles. The pebbling number of a graph is the fewest number of pebbles t so that, from any initial configuration of t pebbles on its vertices, one can place a pebble on any given target vertex via such pebbling steps. It is known that deciding whether a given configuration on a particular graph can reach a specified target is NP-complete, even for diameter 2 graphs, and that deciding whether the pebbling number has a prescribed upper bound is Π[P over 2]-complete. On the other hand, for many families of graphs there are formulas or polynomial algorithms for computing pebbling numbers; for example, complete graphs, products of paths (including cubes), trees, cycles, diameter 2 graphs, and more. Moreover, graphs having minimum pebbling number are called Class 0, and many authors have studied which graphs are Class 0 and what graph properties guarantee it, with no characterization in sight. In this paper we investigate an important family of diameter 3 chordal graphs called split graphs; graphs whose vertex set can be partitioned into a clique and an independent set. We provide a formula for the pebbling number of a split graph, along with an algorithm for calculating it that runs in O(n[superscript β]) time, where β = 2ω/(ω + 1) [= over ∼] 1.41 and ω [= over ∼] 2.376 is the exponent of matrix multiplication. Furthermore we determine that all split graphs with minimum degree at least 3 are Class 0.