Bright green light treatment of depression for older adults [ISRCTN69400161]

BACKGROUND: Bright white light has been successfully used for the treatment of depression. There is interest in identifying which spectral colors of light are the most efficient in the treatment of depression. It is theorized that green light could decrease the intensity duration of exposure needed. Late Wake Treatment (LWT), sleep deprivation for the last half of one night, is associated with rapid mood improvement which has been sustained by light treatment. Because spectral responsiveness may differ by age, we examined whether green light would provide efficient antidepressant treatment in an elder age group. METHODS: We contrasted one hour of bright green light (1,200 Lux) and one hour of dim red light placebo (<10 Lux) in a randomized treatment trial with depressed elders. Participants were observed in their homes with mood scales, wrist actigraphy and light monitoring. On the day prior to beginning treatment, the participants self-administered LWT. RESULTS: The protocol was completed by 33 subjects who were 59 to 80 years old. Mood improved on average 23% for all subjects, but there were no significant statistical differences between treatment and placebo groups. There were negligible adverse reactions to the bright green light, which was well tolerated. CONCLUSION: Bright green light was not shown to have an antidepressant effect in the age group of this study, but a larger trial with brighter green light might be of value

Bright light treatment of depression for older adults [ISRCTN55452501]

BACKGROUND: The incidence of insomnia and depression in the elder population is significant. It is hoped that use of light treatment for this group could provide safe, economic, and effective rapid recovery. METHODS: In this home-based trial we treated depressed elderly subjects with bright white (8,500 Lux) and dim red (<10 Lux) light for one hour a day at three different times (morning, mid-wake and evening). A placebo response washout was used for the first week. Wake treatment was conducted prior to the initiation of treatment, to explore antidepressant response and the interaction with light treatment. Urine and saliva samples were collected during a 24-hour period both before and after treatment and assayed for aMT6s and melatonin respectively to observe any change in circadian timing. Subjects wore a wrist monitor to record light exposure and wrist activity. Daily log sheets and weekly mood (GDS) and physical symptom (SAFTEE) scales were administered. Each subject was given a SCID interview and each completed a mood questionnaire (SIGH-SAD-SR) before and after treatment. Also, Hamilton Depression Rating (SIGH-SAD version) interviews were conducted by a researcher who was blind to the treatment condition. A control group of healthy, age-matched, volunteers was studied for one day to obtain baseline data for comparison of actigraphy and hormone levels. RESULTS: Eighty-one volunteers, between 60 and 79 years old, completed the study. Both treatment and placebo groups experienced mood improvement. Average GDS scores improved 5 points, the Hamilton Depression Rating Scale (HDRS) 17 scores (extracted from the self-rated SIGH-SAD-SR) improved 6 points. There were no significant treatment effects or time-by-treatment interactions. No significant adverse reactions were observed in either treatment group. The assays of urine and saliva showed no significant differences between the treatment and placebo groups. The healthy control group was active earlier and slept earlier but received less light than the depressed group at baseline. CONCLUSION: Antidepressant response to bright light treatment in this age group was not statistically superior to placebo. Both treatment and placebo groups experienced a clinically significant overall improvement of 16%

Type III home sleep testing versus pulse oximetry: is the respiratory disturbance index better than the oxygen desaturation index to predict the apnoea-hypopnoea index measured during laboratory polysomnography?

ObjectivesIn its guidelines on the use of portable monitors to diagnose obstructive sleep apnoea, the American Academy of Sleep Medicine endorses home polygraphy with type III devices recording at a minimum airflow the respiratory effort and pulse oximetry, but advises against simple pulse oximetry. However, oximetry is widely available and simple to use in the home. This study was designed to compare the ability of the oxygen desaturation index (ODI) based on oximetry alone with a stand-alone pulse oximeter (SPO) and from the oximetry channel of the ApneaLink Plus (ALP), with the respiratory disturbance index (RDI) based on four channels from the ALP to predict the apnoea-hypopnoea index (AHI) from laboratory polysomnography.DesignCross-sectional diagnostic accuracy study.SettingSleep medicine practice of a multispecialty clinic.ParticipantsPatients referred for laboratory polysomnography with suspected sleep apnoea. We enrolled 135 participants with 123 attempting the home sleep testing and 73 having at least 4 hours of satisfactory data from SPO and ALP.InterventionsParticipants had home testing performed simultaneously with both a SPO and an ALP. The 2 oximeter probes were worn on different fingers of the same hand. The ODI for the SPO was calculated using Profox software (ODI(SOX)). For the ALP, RDI and ODI were calculated using both technician scoring (RDI(MAN) and ODI(MAN)) and the ALP computer scoring (RDI(RAW) and ODI(RAW)).ResultsThe receiver-operator characteristic areas under the curve for AHI ≥ 5 were RDI(MAN) 0.88 (95% confidence limits 0.81-0.96), RDI(RAW) 0.86 (0.76-0.94), ODI(MAN) 0.86 (0.77-0.95), ODI(RAW) 0.84 (0.75-0.93) and ODI(SOX) 0.83 (0.73-0.93).ConclusionsWe conclude that the RDI and the ODI, measured at home on the same night, give similar predictions of the laboratory AHI, measured on a different night. The differences between the two methods are small compared with the reported night-to-night variation of the AHI

Type III home sleep testing versus pulse oximetry: is the respiratory disturbance index better than the oxygen desaturation index to predict the apnoea-hypopnoea index measured during laboratory polysomnography?

ObjectivesIn its guidelines on the use of portable monitors to diagnose obstructive sleep apnoea, the American Academy of Sleep Medicine endorses home polygraphy with type III devices recording at a minimum airflow the respiratory effort and pulse oximetry, but advises against simple pulse oximetry. However, oximetry is widely available and simple to use in the home. This study was designed to compare the ability of the oxygen desaturation index (ODI) based on oximetry alone with a stand-alone pulse oximeter (SPO) and from the oximetry channel of the ApneaLink Plus (ALP), with the respiratory disturbance index (RDI) based on four channels from the ALP to predict the apnoea-hypopnoea index (AHI) from laboratory polysomnography.DesignCross-sectional diagnostic accuracy study.SettingSleep medicine practice of a multispecialty clinic.ParticipantsPatients referred for laboratory polysomnography with suspected sleep apnoea. We enrolled 135 participants with 123 attempting the home sleep testing and 73 having at least 4 hours of satisfactory data from SPO and ALP.InterventionsParticipants had home testing performed simultaneously with both a SPO and an ALP. The 2 oximeter probes were worn on different fingers of the same hand. The ODI for the SPO was calculated using Profox software (ODI(SOX)). For the ALP, RDI and ODI were calculated using both technician scoring (RDI(MAN) and ODI(MAN)) and the ALP computer scoring (RDI(RAW) and ODI(RAW)).ResultsThe receiver-operator characteristic areas under the curve for AHI ≥ 5 were RDI(MAN) 0.88 (95% confidence limits 0.81-0.96), RDI(RAW) 0.86 (0.76-0.94), ODI(MAN) 0.86 (0.77-0.95), ODI(RAW) 0.84 (0.75-0.93) and ODI(SOX) 0.83 (0.73-0.93).ConclusionsWe conclude that the RDI and the ODI, measured at home on the same night, give similar predictions of the laboratory AHI, measured on a different night. The differences between the two methods are small compared with the reported night-to-night variation of the AHI

Genetic variants associated with sleep disorders.

ObjectiveThe diagnostic boundaries of sleep disorders are under considerable debate. The main sleep disorders are partly heritable; therefore, defining heritable pathophysiologic mechanisms could delineate diagnoses and suggest treatment. We collected clinical data and DNA from consenting patients scheduled to undergo clinical polysomnograms, to expand our understanding of the polymorphisms associated with the phenotypes of particular sleep disorders.MethodsPatients at least 21 years of age were recruited to contribute research questionnaires, and to provide access to their medical records, saliva for deoxyribonucleic acid (DNA), and polysomnographic data. From these complex data, 38 partly overlapping phenotypes were derived indicating complaints, subjective and objective sleep timing, and polysomnographic disturbances. A custom chip was used to genotype 768 single-nucleotide polymorphisms (SNPs). Additional assays derived ancestry-informative markers (eg, 751 participants of European ancestry). Linear regressions controlling for age, gender, and ancestry were used to assess the associations of each phenotype with each of the SNPs, highlighting those with Bonferroni-corrected significance.ResultsIn peroxisome proliferator-activated receptor gamma, coactivator 1 beta (PPARGC1B), rs6888451 was associated with several markers of obstructive sleep apnea. In aryl hydrocarbon receptor nuclear translocator-like (ARNTL), rs10766071 was associated with decreased polysomnographic sleep duration. The association of rs3923809 in BTBD9 with periodic limb movements in sleep was confirmed. SNPs in casein kinase 1 delta (CSNK1D rs11552085), cryptochrome 1 (CRY1 rs4964515), and retinoic acid receptor-related orphan receptor A (RORA rs11071547) were less persuasively associated with sleep latency and time of falling asleep.ConclusionsSNPs associated with several sleep phenotypes were suggested, but due to risks of false discovery, independent replications are needed before the importance of these associations can be assessed, followed by investigation of molecular mechanisms

Genetic variants associated with sleep disorders.

ObjectiveThe diagnostic boundaries of sleep disorders are under considerable debate. The main sleep disorders are partly heritable; therefore, defining heritable pathophysiologic mechanisms could delineate diagnoses and suggest treatment. We collected clinical data and DNA from consenting patients scheduled to undergo clinical polysomnograms, to expand our understanding of the polymorphisms associated with the phenotypes of particular sleep disorders.MethodsPatients at least 21 years of age were recruited to contribute research questionnaires, and to provide access to their medical records, saliva for deoxyribonucleic acid (DNA), and polysomnographic data. From these complex data, 38 partly overlapping phenotypes were derived indicating complaints, subjective and objective sleep timing, and polysomnographic disturbances. A custom chip was used to genotype 768 single-nucleotide polymorphisms (SNPs). Additional assays derived ancestry-informative markers (eg, 751 participants of European ancestry). Linear regressions controlling for age, gender, and ancestry were used to assess the associations of each phenotype with each of the SNPs, highlighting those with Bonferroni-corrected significance.ResultsIn peroxisome proliferator-activated receptor gamma, coactivator 1 beta (PPARGC1B), rs6888451 was associated with several markers of obstructive sleep apnea. In aryl hydrocarbon receptor nuclear translocator-like (ARNTL), rs10766071 was associated with decreased polysomnographic sleep duration. The association of rs3923809 in BTBD9 with periodic limb movements in sleep was confirmed. SNPs in casein kinase 1 delta (CSNK1D rs11552085), cryptochrome 1 (CRY1 rs4964515), and retinoic acid receptor-related orphan receptor A (RORA rs11071547) were less persuasively associated with sleep latency and time of falling asleep.ConclusionsSNPs associated with several sleep phenotypes were suggested, but due to risks of false discovery, independent replications are needed before the importance of these associations can be assessed, followed by investigation of molecular mechanisms