The respiratory inductance plethysmograph (RIP) has recently gained popularity in both the research and clinical arenas for measuring tidal volume (VT) and changes in functional residual capacity (delta FRC). It is important however, to define the likelihood that individual RIP measurements of VT and delta FRC would be acceptably accurate (+/- 10%) for clinical and investigational purposes in spontaneously breathing individuals on continuous positive airway pressure (CPAP). Additionally, RIP accuracy has not been compared in these regards after calibration by two commonly employed techniques, the least squares (LSQ) and the quantitative diagnostic calibration (QDC) methods. We compared RIP with pneumotachographic (PTH) measurements of delta FRC and VT during spontaneous mouth breathing on 0-10 cmH2O CPAP. Comparisons were made after RIP calibration with both the LSQ (6 subjects) and QDC (7 subjects) methods. Measurements of delta FRC by RIPLSQ and RIPQDC were highly correlated with PTH measurements (r = 0.94 +/- 0.04 and r = 0.98 +/- 0.01 (SE), respectively). However, only an average of 30% of RIPQDC determinations per subject and 31.4% of RIPLSQ determinations per subject were accurate to +/- 10% of PTH values. An average of 55.2% (QDC) and 68.8% (LSQ) of VT determinations per subject were accurate to +/- 10% of PTH values. We conclude that in normal subjects, over a large number of determinations, RIP values for delta FRC and VT at elevated end-expiratory lung volume correlate well with PTH values. However, regardless of whether QDC or LSQ calibration is used, only about one-third of individual RIP determinations of delta FRC and one-half of two-thirds of VT measurements will be sufficiently accurate for clinical and investigational use.

Introduction Previously, active phone use at bedtime has been implicated in disrupted sleep and related complaints. To improve sleep, a recommendation following such findings is limiting phone use before and during bedtime. However, for those with the characteristic of “nomophobia”, fear of being out of mobile phone contact, this recommendation could exacerbate anxiety at and around bedtime and disrupt, rather than improve, sleep. In 2012, an estimated 77% of 18-24-year-olds could be identified as nomophobic. Because of the prevalence of nomophobia and its possible interaction with sleep, we explored the existence of nomophobia in a college-age population and its relationship to sleep, sleepiness, and sleep hygiene behaviors. Methods 327 university students (age: M=19.7 years, SD=3.78) recruited from introductory psychology courses and campus newsletters were given extra credit or a chance to win $25 gift cards for participation. Participants completed demographic information, the Nomophobia Questionnaire (NMP-Q), the Epworth Sleepiness Scale (ESS), the Pittsburgh Sleep Quality Index, questions regarding associated features of inadequate sleep hygiene, and the Sleep Hygiene Index. Additional sleep hygiene questions assessed frequency of active and passive technology use during sleep time. Results 89.4% of the participants had moderate or severe nomophobia. Greater nomophobia was significantly related to greater daytime sleepiness (ESS) (r(293)=.150, p<.05), associated features of poor sleep (daytime sleepiness: r(297)=.097, p<.05, and avolition: r(297)=.100, p<.05), more maladaptive sleep hygiene behaviors including active technology use during sleep time (r(298)=.249, p<.05), long daytime naps, inconsistent wake and bed times, using bed for non-sleep purposes, uncomfortable bed, and bedtime cognitive rumination (r’s=0.097 to 0.182). Conclusion Most participants experienced moderate to severe nomophobia with greater nomophobia associated with greater sleepiness, avolition, and poorer sleep hygiene. Nomophobia is likely to be an important consideration when treating sleep disorders and/or making any sleep hygiene recommendations. Support Hendrix College Charles Brewer Fund for Psychology

Introduction We examined the relationship between bedtime active and passive social technology use (self and bedpartner) and daytime sleepiness/sleep. We generated questions to differentiate participants with and without bedpartners and updated passive personal, active bedpartner, and passive bedpartner social technology questions of the Sleep Hygiene Index. Methods 327 students (age: M=19.7 years, SD=3.78) recruited through psychology courses and campus newsletters received extra credit or chances to win $25 gift cards. Participants completed demographic information, the Epworth Sleepiness Scale (ESS), the Pittsburgh Sleep Quality Index, questions regarding associated features of inadequate sleep hygiene, and the Sleep Hygiene Index. Five questions assessed active and passive social technology use, presence of a bedpartner, and awareness of bedpartner active and passive social technology use during sleep time. Results 61.8% and 62.7% of students reported frequently or always using active and passive bedtime social technology, respectively; and 23.5% and 29.1% reported noticing a partner’s active or passive use. More frequent active technology use was significantly related to greater daytime sleepiness (ESS) (r(305)=.193, p<.05), sleep disturbances (PSQI-global: r(302)=.120, p<.05), and associated features of inadequate sleep hygiene (daytime sleepiness, worry about sleep, mood disturbance, avolition, and reduced cognition (r(306)=.212, p<.05)). Neither passive use nor passive or active partner use was significantly related to any sleep/sleepiness variables. Conclusion We continue to find students are frequent users of bedtime social technology which is related to daytime sleepiness, disrupted sleep, and related complaints. Passive and partner active/passive bedtime technology use may not have a significant impact on daytime sleepiness. It is possible younger participants are not good judges of passive or partner technology use or this younger population is resilient to these disruptions. Support none