Spatial attention has been postulated to facilitate perceptual processing via several different mechanisms. For instance, attention can amplify neural responses in sensory areas (sensory gain), ...mediate neural variability (noise modulation), or alter the manner in which sensory signals are selectively read out by postsensory decision mechanisms (efficient readout). Even in the context of simple behavioral tasks, it is unclear how well each of these mechanisms can account for the relationship between attention-modulated changes in behavior and neural activity because few studies have systematically mapped changes between stimulus intensity, attentional focus, neural activity, and behavioral performance. Here, we used a combination of psychophysics, event-related potentials (ERPs), and quantitative modeling to explicitly link attention-related changes in perceptual sensitivity with changes in the ERP amplitudes recorded from human observers. Spatial attention led to a multiplicative increase in the amplitude of an early sensory ERP component (the P1, peaking ∼80-130 ms poststimulus) and in the amplitude of the late positive deflection component (peaking ∼230-330 ms poststimulus). A simple model based on signal detection theory demonstrates that these multiplicative gain changes were sufficient to account for attention-related improvements in perceptual sensitivity, without a need to invoke noise modulation. Moreover, combining the observed multiplicative gain with a postsensory readout mechanism resulted in a significantly poorer description of the observed behavioral data. We conclude that, at least in the context of relatively simple visual discrimination tasks, spatial attention modulates perceptual sensitivity primarily by modulating the gain of neural responses during early sensory processing.
Abstract
Introduction
The Psychomotor Vigilance Test is a well-validated measure of sustained attention used to assess daytime alertness in sleep research studies.1 It is commonly used in a variety ...of research settings due to its high sensitivity to sleep loss and absence of learning effects,2 making it an ideal tool to assess objective alertness. As some types of sleep research transition out of controlled laboratory environments, tools like the PVT require modification to maximize their reliability. The validation of the 3-minute version (PVT-B) against the 10-minute PVT is an example of this modification.3 However, considerable work is needed to improve trust in the utility of the PVT-B in and outside of traditional laboratory settings.
Methods
We carefully analyzed data from a mobile-based version of the PVT-B, noting responses that occurred during the interstimulus interval which were termed “wrong taps.” Wrong taps indicated that participants were not performing the task as instructed. In some cases, wrong taps occurred across multiple trials of the same PVT block, indicative of participants repeatedly tapping the screen throughout the task to minimize response times. A comprehensive examination of wrong taps was carried out in order to identify instances where this pattern emerged.
Results
A total of 1,338,538 PVT-B trials from 7,028 participants were examined to determine the number of wrong taps present across all trials. While 91.7% of PVT-B trials were free of wrong taps, 8.3% of PVT-B trials contained 1 or more wrong taps and 5.2% contained 2 or more wrong taps. It appears that a maximum of one wrong tap per trial is acceptable and trials containing 2 or more should be excluded to maximize PVT data quality.
Conclusion
Utilizing a metric like wrong taps can help identify individuals taking the PVT-B who are tapping the screen multiple times prior to stimulus display. Closely examining this metric can help to ensure the validity of PVT-B administrations. Two possible uses of the metric could be to provide feedback during training trials and to remove trials where this strategy was employed.
Support (if any)
This analysis was supported by the VA San Diego Healthcare System Research Service.
Conducting biomedical research using smartphones is a novel approach to studying health and disease that is only beginning to be meaningfully explored. Gathering large-scale, real-world data to track ...disease manifestation and long-term trajectory in this manner is quite practical and largely untapped. Researchers can assess large study cohorts using surveys and sensor-based activities that can be interspersed with participants' daily routines. In addition, this approach offers a medium for researchers to collect contextual and environmental data via device-based sensors, data aggregator frameworks, and connected wearable devices. The main aim of the SleepHealth Mobile App Study (SHMAS) was to gain a better understanding of the relationship between sleep habits and daytime functioning utilizing a novel digital health approach. Secondary goals included assessing the feasibility of a fully-remote approach to obtaining clinical characteristics of participants, evaluating data validity, and examining user retention patterns and data-sharing preferences. Here, we provide a description of data collected from 7,250 participants living in the United States who chose to share their data broadly with the study team and qualified researchers worldwide.
Abstract
Introduction
The CPAP recall of 2021 has highlighted an inherent problem that occurs when a field of medicine is overly dependent on a single class of medical devices to treat a condition. ...The global shortage of CPAP devices has led to numerous individuals with OSA being unable to obtain a CPAP machine to treat their condition, including those with severe OSA. CPAP is well known to be efficacious in treating OSA, but has limited effectiveness, particularly for mild-to-moderate cases. It has been reported that there are nearly 200 different medical devices approved by the FDA to treat OSA. The goal of this project was to search the FDA databases to investigate the number of devices currently on file with the FDA. A secondary goal was to examine the range of FDA product categories for the treatment of OSA.
Methods
An FDA database (AccessGUDID; https://accessgudid.nlm.nih.gov) with a release date of December 1, 2021 was searched for devices that are approved for the treatment of sleep apnea. The text string “sleep apnea” was used for the search. Diagnostic devices, duplicate versions of the same treatment devices, and device accessories were excluded from the total counts. The FDA classifies medical devices into three categories (I, II, III), with a higher classification level indicating greater risk to patients.
Results
The FDA AccessGUDID database search returned 166 results, which resulted in 72 unique devices across 10 product code categories. 9 of the 10 product codes in the FDA database were class II (medium risk) and 1/10 was classified as III (high risk). 65 of the devices were reported to be in commercial distribution at the time of the search and 7 were not.
Conclusion
This analysis found that a relatively large number of FDA-approved devices exist for the treatment of OSA across a range of product categories. The field is encouraged to develop a better understanding of which subgroups of OSA patients could benefit from alternative forms of treatment in an effort to diversify treatment options and reduce the field’s reliance on a single type of device, particularly for patients with mild-to-moderate OSA.
Support (If Any)
VA IIR 16-277
Abstract
Introduction
Research study recruitment has been profoundly affected by the COVID-19 pandemic, demonstrated by significant delays or pauses. Various guidelines pertaining to in-person visits ...have applied to research. Some call for exclusion of participants that the CDC has labeled “at increased risk”.1 For obstructive sleep apnea (OSA) studies, these guidelines have caused a sharp decrease in the number of new participants. This decrease is due to high rates of OSA comorbidities including obesity and diabetes. New evidence-based risk scores have been developed using individual- and community-level factors. The use of more refined COVID-19 risk scores can help protect patient safety while allowing research to continue.
Methods
The risk score assessment used for this study (COVID-19 Mortality Risk Calculator; Johns Hopkins University, Baltimore, MD)2 is evidence-based and uses a set of risk factors and community-level pandemic dynamics in the state of residence.3,4 It was compared to the list of CDC medical conditions that are considered to put an individual “at increased risk.” Both measures were calculated retrospectively on current participants to determine how many could safely attend in-person visits based on each risk assessment method.
Results
Sample characteristics of the 110 participants were: mean age: 49.5±13.7(24–76); mean BMI: 32.3±5.3(20.9–46.1); mean AHI: 24.3±21.4(5.1–110). Mortality Risk Calculator scores were: 91(82.7%) close to/lower than average Level 1; 12(10.9%) moderately elevated; 6(5.5%) substantially elevated; 1(0.9%) high; and 0(0%) very high Level 5. Using CDC guidance, 63 (57.3%) had at least one at-risk condition and 47 (42.7%) had 0. Using only Level 1 of the Risk Calculator would allow an additional 28 (25%) participants to attend in-person visits; using Levels 1 and 2 would allow an additional 40 (37%) participants.
Conclusion
Policies based on CDC at-risk conditions resulted in higher levels of participant exclusion in research during the COVID-19 pandemic than use of an evidence-based Mortality Risk Calculator. This analysis shows that researchers can use risk-adjusted scores to make informed decisions about study participation that balances both participant safety and research study progress.
Support (if any)
This project was supported in part by Department of Veteran Affairs VA HSRD IIR 16–277, VA RRD D2651-R, and VA San Diego Healthcare System Research Service.
Introduction Sleep apnea (OSA) is considered a syndrome because it results in the experience of different symptoms in different patients. Most instruments measure a single construct. However, because ...patients’ different symptom clusters, a different measurement approach is important to explore and the Sleep Apnea Quality of Life (SAQLI) Index offered this opportunity. Methods We had the opportunity to examine the SAQLI in a group of 348 diagnosed sleep apnea patients who were taking part in a larger CPAP adherence clinical trial.Patients had a mean age of 53.1±13.6, BMI of 32.6±5.7, and AHI of 30.3±18.9. All were given identical PAP instruction and follow-up at four-months. The SAQLI at baseline includes 14 multiple-choice items and a 21-item symptom checklist; at follow-up it includes an additional 26-item treatment symptom checklist. The checklists are comprised of 3 steps: (1)Check the boxes of all symptoms; (2)List up to the 5 most important symptoms; and (3)Rate each of those top symptoms. SAQLI scores are based on a range from 1(large problem) to 7(no problem). Results The SAQLI score improved from baseline to 4-month timepoint (4.1±1.4 to 5.1±1.4), but the sleep apnea symptoms score decreased (3.1±2.1 to 2.4±1.8). The treatment symptom score at follow-up was 2.1±1.7, indicating a large problem. 76% endorsed the 5 most important symptoms at baseline and 46% at follow-up. The most commonly endorsed symptoms at baseline were “Decreased energy” and “Waking up in the morning feeling unrefreshed and/or tired”. All 22 items were endorsed and write-in symptoms were included as well indicating the experience of a wide range of symptoms. Conclusion This study suggests that overall sleep apnea-related quality of life improves with CPAP therapy, but that there are number of sleep apnea symptoms and treatment symptoms still experienced by those who are using CPAP 4 months after starting therapy. The SAQLI functions as a patient-centered instrument because it can pick up individualized symptoms and issues that are still being experienced 4 months later. Support (If Any) This project was supported in part by Department of Veteran Affairs IIR 12-069.
Abstract
When viewing familiar stimuli (e.g., common words), processing is highly automatized such that it can interfere with the processing of incompatible sensory information. At least two ...mechanisms may help mitigate this interference. Early selection accounts posit that attentional processes filter out distracting sensory information to avoid conflict. Alternatively, late selection accounts hold that all sensory inputs receive full semantic analysis and that frontal executive mechanisms are recruited to resolve conflict. To test how these mechanisms operate to overcome conflict induced by highly automatized processing, we developed a novel version of the color-word Stroop task, where targets and distractors were simultaneously flickered at different frequencies. We measured the quality of early sensory processing by assessing the amplitude of steady-state visually evoked potentials (SSVEPs) elicited by targets and distractors. We also indexed frontal executive processes by assessing changes in frontal theta oscillations induced by color-word incongruency. We found that target- and distractor-related SSVEPs were not modulated by changes in the level of conflict whereas frontal theta activity increased on high compared to low conflict trials. These results suggest that frontal executive processes play a more dominant role in mitigating cognitive interference driven by the automatic tendency to process highly familiar stimuli.
Abstract
Introduction
Insufficient sleep is pervasive in U.S. Navy (USN) sailors and has been associated with increased risk of injury and mood disturbances. As such, monitoring sleep and identifying ...modifiable targets that influence sleep in this population is essential. We examined the sleep of USN sailors during 9-months in port and explored the influence of different lifestyle behaviors on sleep.
Methods
Sailors (n = 101, 31 ± 7 years old mean ± SD, 21% female) wore a commercial wearable sleep-tracking device (Oura Ring, Gen2) during a 9-month home port maintenance period (September 2021 – May 2022). During the study, the sailors slept at home and had periods working on the ship (on-hull; higher demands) and off the ship (off-hull; lower demands). Participants also completed 5 self-report surveys that assessed sleep quality (Pittsburgh Sleep Quality Index; PSQI) and multiple lifestyle behaviors including: hours of natural light exposure, hours of moderate/vigorous physical activity, and intake of caffeine, nicotine, and alcohol. Sleep outcome measures from the wearable device included total sleep time (TST), standard deviation of TST (SD TST), and sleep efficiency (SE). Linear mixed models were used to examine the effects of lifestyle behaviors on sleep. Timepoint and on/off-hull condition were also included as fixed effects, while participant was included as a random effect.
Results
Participants slept 6.5 ± 0.7 hours a night (6.2 ± 0.9 on-hull; 6.4 ± 0.8 off-hull; p > .05) and had slightly elevated PSQI scores that were higher on-hull (8.3 ± 4.6) than off-hull (6.5 ± 3.5; B = 1.83, Std. Error = 0.71, p = .01). Lifestyle behaviors were not significant predictors of PSQI, TST, SD TST, or SE (p > .05).
Conclusion
Lifestyle behaviors had weak relationships with self-reported and objective sleep outcomes in this exploratory analysis. Therefore, modifying lifestyle behaviors may not significantly influence sleep in USN sailors during home port periods. Still, additional behavioral targets (e.g., diet), specific aspects of behavioral outcomes (e.g., light exposure timing, exercise intensity), and other work-related factors (e.g., workload, schedule) that can affect sleep should be explored in future efforts.
Support (if any)
Military Operational Medicine Research Program (work unit no. N2010).
Introduction The effectiveness of continuous positive airway pressure (PAP) therapy is primarily measured by the reduction of the apnea-hypopnea index (AHI). A proxy measure of the sleep ...study-derived AHI can be obtained from the PAP device during the portion of the sleep period during which it is worn. Sleep quality and daytime functioning are considered two of the main outcomes desired by patients. Surprisingly few studies have examined the effectiveness of PAP therapy on sleep quality. Methods OSA participants (n = 695) from a combination of larger trials that examined a PAP adherence intervention were included. Participants were provided with PAP instruction and followed at 2 months. The Pittsburgh Sleep Quality Index (PSQI) was used as the primary measure of sleep quality. Results The PSQI total score was significantly correlated with PAP adherence at the 2-month time point, such that lower sleep quality was associated with lower PAP use. This finding held for the sleep disturbance subscale of the PSQI. The total PSQI score at baseline was 12.8±3.4 and at 2-month follow-up was 9.7±3.6, which is over the threshold of 5 for the PSQI total score and indicates poor sleep quality. Over 52% of those using PAP therapy at the 2-month time point reported significantly disturbed sleep, with the top three causes: 1) Wake up in the middle of the night or early morning (59%); 2) Have to get up to use the bathroom (56%); and 3) Have pain (33%). Conclusion This study shows that PAP therapy does not improve sleep quality to an acceptable degree. Over 50% patients using PAP therapy still experienced disturbed sleep. Whether the disturbed sleep is directly attributable to the PAP device itself or to disturbed sleep secondary to uncontrolled OSA when PAP is not worn requires further investigation. Clinical practice needs to focus on patient outcomes and not a single proxy measure of device effectiveness. Support (If Any) This project was supported in part by Department of Veteran Affairs and VA San Diego Healthcare System Research Service.
Obstructive sleep apnea (OSA) is a widespread condition that adversely affects physical health and cognitive functioning. The prevailing treatment for OSA is continuous positive airway pressure ...(CPAP), but therapeutic benefits are dependent on consistent use. Our goal was to investigate the relationship between CPAP adherence and measures of sustained attention in patients with OSA. Our hypothesis was that the Psychomotor Vigilance Task (PVT) would be sensitive to attention-related improvements resulting from CPAP use.
This study was a secondary analysis of a larger clinical trial. Treatment adherence was determined from CPAP use data. Validated sleep-related questionnaires and a sustained-attention and alertness test (PVT) were administered to participants at baseline and at the 6-month time point.
Over a 6-month time period, the average CPAP adherence was 3.32 h/night (standard deviation SD = 2.53), average improvement in PVT minor lapses was -4.77 (SD = 13.2), and average improvement in PVT reaction time was -73.1 milliseconds (standard deviation = 211). Multiple linear regression analysis showed that higher CPAP adherence was significantly associated with a greater reduction in minor lapses in attention after 6 months of continuous treatment with CPAP therapy (β = -0.72, standard error = 0.34,
= .037).
The results of this study showed that higher levels of CPAP adherence were associated with significant improvements in vigilance. Because the PVT is a performance-based measure that is not influenced by prior learning and is not subjective, it may be an important supplement to patient self-reported assessments.
Name: Effect of Self-Management on Improving Sleep Apnea Outcomes, URL: https://clinicaltrials.gov/ct2/show/NCT00310310, Identifier: NCT00310310.
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