Mental illness as a predictor of patient attendance and anthropometric changes: observations from an Australian publicly funded obesity management service.

OBJECTIVE: Obesity is associated with co-morbid mental illness. The Canberra Obesity Management Service (OMS) supports adults with severe obesity who have the psychosocial capacity to engage. This study will determine whether mental illness is a predictor of OMS attendance and anthropometric changes. METHOD: A retrospective audit was performed from July 2016 to June 2017. Baseline characteristics, attendance and anthropometrics were stratified according to the presence of mental illness. Outcomes included weight stabilisation and clinically significant weight loss. Descriptive analyses were performed. RESULTS: Mental illness was present in 60/162 patients (37%). Attendance was similar for those with and without mental illness. Patients with mental illness had twice as many co-morbidities (p = .001). Depressive disorders were most common (n = 28, 47%). Anxiety, schizophrenia spectrum and other psychotic disorders, and trauma- and stressor-related disorders also featured. Weight stabilisation was achieved by 25 patients (66%) with mental illness and 25 (35%) without. Clinically significant weight loss was observed in 10 patients (26%) with and 26 (40%) without mental illness. CONCLUSION: The presence of mental illness did not impact OMS attendance or weight stabilisation. The higher rate of co-morbidities in those with mental illness highlights the challenges faced by this vulnerable population.

Adding Telephone and Text Support to an Obesity Management Program Improves Behavioral Adherence and Clinical Outcomes. A Randomized Controlled Crossover Trial.

BACKGROUND: Behavioral treatment strategies improve adherence to lifestyle intervention for adults with obesity, but can be time and resource intensive when delivered via traditional face-to-face care. This study aimed to investigate the efficacy and optimal timing of using telephone calls and text message as adjunctive tools to support a community-based obesity management program. METHOD: This 8-month randomized controlled crossover trial recruited 61 adults with class III obesity (BMI > 40 kg/m2) enrolled in a publicly funded obesity management service (OMS). Participants were randomly assigned to receive telephone and text message support in addition to standard OMS care, or standard OMS care alone. After 4 months, participants crossed over to the alternative sequence. The technological support was based on self-determination theory. Outcome measures included diet, physical activity, anthropometry, self-efficacy, and treatment self-regulation. RESULTS: Telephone and text message support improved lifestyle intervention adherence and clinical outcomes when compared with standard care. Participants who received the intervention in the first 4-month period lost 4.87 kg, compared with no weight loss (+ 0.38 kg) in the standard care only group. There was no evidence to indicate an optimal timing of the intervention, with both groups achieving significant results by the end of the intervention. CONCLUSION: These results suggest a high degree of promise for the incorporation of telephone and text message support into community-based obesity management services. The findings have the potential to improve existing practices and reduce the burden on the health care system by demonstrating a resource-effective improvement to obesity management service delivery.

"They can rest at home": an observational study of patients' quality of sleep in an Australian hospital.

BACKGROUND: Poor sleep is known to adversely affect hospital patients' recovery and rehabilitation. The aim of the study was to investigate the perceived duration and quality of patient sleep and identify any environmental factors associated with patient-reported poor sleep in hospital. METHOD: A cross-sectional study was conducted involving 15 clinical units within a 672-bed tertiary-referral hospital in Australia. Semi-structured interviews to determine perceptions of sleep quantity and quality and factors that disturb nocturnal sleep were conducted with patients and nursing staff. Environmental noise, light and temperature were monitored overnight, with concurrent logging of noise sources by observers. RESULTS: Patients reported a mean reduction in hospital sleep duration, compared to home, of 1.8 h (5.3 vs. 7.1 h; p < 0.001). The proportions of patients reporting their sleep quality to be poor/very poor, fair and of good quality were 41.6, 34.2 and 24.2% respectively. Patients reported poorer sleep quality than nurses (p < 0.05). Patients, nurses and observers all reported the main factors associated with poor sleep as clinical care interventions (34.3%) and environmental noise (32.1%). Noise levels in all 15 clinical areas exceeded WHO recommended levels of < 30 dB [A] by 36.7 to 82.6%, with peak noise levels of 51.3 to 103.3 dB (A). CONCLUSION: Hospital in-patients are exposed to factors which reduce the duration and quality of their sleep. These extrinsic factors are potentially modifiable through behaviour change and reconfiguration of the clinical environment. The findings from this study provided the foundation for a quality improvement project currently underway to improve patients' sleep.

Developing a Decision Aid for Clinical Obesity Services in the Real World: the DACOS Nationwide Pilot Study.

PURPOSE: The purpose of this study is to develop a decision aid tool using "real-world" data within the Australian health system to predict weight loss after bariatric surgery and non-surgical care. MATERIALS AND METHODS: We analyzed patient record data (aged 16+years) from initial review between 2015 and 2020 with 6-month (n=219) and 9-/12-month (n=153) follow-ups at eight clinical obesity services. Primary outcome was percentage total weight loss (%TWL) at 6 months and 9/12 months. Predictors were selected by statistical evidence (p<0.20), effect size (±2%), and clinical judgment. Multiple linear regression and bariatric surgery were used to create simple predictive models. Accuracy was measured using percentage of predictions within 5% of the observed value, and sensitivity and specificity for predicting target weight loss of 5% (non-surgical care) and 15% (bariatric surgery). RESULTS: Observed %TWL with bariatric surgery vs. non-surgical care was 19% vs. 5% at 6 months and 22% vs. 5% at 9/12 months. Predictors at 6 months with intercept (non-surgical care) of 6% include bariatric surgery (+11%), BMI>60 (-3%), depression (-2%), anxiety (-2%), and eating disorder (-2%). Accuracy, sensitivity, and specificity were 58%, 69%, and 56%. Predictors at 9/12 months with intercept of 5% include bariatric surgery (+15%), type 2 diabetes (+5%), eating disorder (+4%), fatty liver (+2%), atrial fibrillation (-4%), osteoarthritis (-3%), sleep/mental disorders (-2-3%), and ≥10 alcohol drinks/week (-2%). Accuracy, sensitivity, and specificity were 55%, 86%, and 53%. CONCLUSION: Clinicians may use DACOS to discuss potential weight loss predictors with patients after surgery or non-surgical care.

Assessing service provision and outcomes at the Canberra Obesity Management Service: A retrospective chart review.

OBJECTIVE: This study aimed to evaluate the multidisciplinary care model of the Canberra Obesity Management Service (COMS) with regard to patient demographics and clinical outcomes, particularly in comparison with previous COMS outcome reviews. METHODS: A retrospective chart review was carried out on all patients attending an initial assessment at COMS between July 2018 and June 2019. Existing patients attending follow-up reviews were excluded so as to avoid repeating analyses of data from previous COMS reviews. Patient data were recorded and deidentified and underwent descriptive analyses. RESULTS: A total of 234 patients with a mean age of 45.6 (SD = 13.9) years, mean BMI of 50.1 kg/m2 (SD = 8.5), and a female majority (72.2%) were analyzed. Of the 165 patients who attended follow-up appointments, 27.9% experienced ≥10% weight loss (46/165). Sleeve gastrectomy was associated with the largest mean weight reduction (15.6% at 6 months [n = 18]). CONCLUSIONS: Compared with previous COMS studies, both the throughput and proportion of participants achieving clinically meaningful weight reduction were observed to have increased. Further studies assessing service cost-effectiveness, the development of standardized treatment pathways, and the use of a systematic data collection system would be valuable in allowing comparison between outcomes with similar obesity services in Australia and internationally.

The association of age with continuous positive airway pressure ventilation acceptance in an outpatient cohort of patients with obstructive sleep apnea.

STUDY OBJECTIVES: This study examined the association between age and continuous positive airway pressure (CPAP) acceptance in a consecutive series of patients with obstructive sleep apnea being managed in a physician-led outpatient CPAP acclimatization program in Canberra, Australia. METHODS: We performed a retrospective consecutive case series analysis of registry data collected from patients attending the Canberra Hospital PAP Acclimatization Clinic between 2011 and 2019. Data on patient demographics, diagnostic polysomnography results, CPAP device download parameters during acclimatization, and overall CPAP acceptance at the end of acclimatization were extracted from the Clinic Registry. Analysis of variance and chi-square were used to assess for associations between patient age, CPAP acceptance, and other clinical characteristics. Univariate and stepwise multiple logistic regression was used to identify predictors of CPAP acceptance. RESULTS: We found that 1,075 consecutive CPAP trials among 1,043 patients were eligible for inclusion. CPAP acceptance was lower in those aged > 75 years compared with those aged ≤ 75 years (odds ratio: 0.57; 95% confidence interval, 0.36-0.92; P = .02). Patients aged > 75 years had lower body mass index, had higher initial and final visit 95th percentile mask leak, and were less likely to be CPAP naïve. Using univariate regression, younger age, severe obstructive sleep apnea, obesity, shorter trial duration, more clinic visits, higher initial visit CPAP usage, and lower final visit mask leak were predictors of CPAP acceptance. In a multiple logistic regression model, younger age, severe obstructive sleep apnea, shorter trial duration, more clinic visits, higher first visit usage, and lower final visit leak predicted acceptance. CONCLUSIONS: Older age is associated with lower CPAP acceptance. The factors contributing to this association are unclear and require further investigation. CITATION: Han M, Wee R, Shadbolt B, Huang H-CC. The association of age with continuous positive airway pressure ventilation acceptance in an outpatient cohort of patients with obstructive sleep apnea. J Clin Sleep Med. 2022;18(1):217-224.

Determining patient attendance, access to interventions and clinical outcomes in a publicly funded obesity programme: Results from the Canberra Obesity Management Service.

Multi-disciplinary specialist services have a crucial role in the management of patients with obesity. As demand for these services increases, so too does the need to monitor individual service performance and compare outcomes across multiple sites. This paper reports on results from the publicly funded Canberra Obesity Management Service. A descriptive observational study was conducted on new patients who attended an initial medical review from July 2016 to June 2017. Baseline characteristics, comorbidities, attendance, service utilization and outcomes were collated until June 2018. Of the 162 patients identified, 64% continued to attend beyond initial medical review. Dietetics was the most commonly accessed allied health service, followed by exercise physiology and psychology. Very low-energy diet was the most commonly trialled intensive intervention, followed by pharmacotherapy and bariatric surgery. Mean baseline weight for those who continued beyond initial medical review was 142.0 kg (SD 26.6 kg), with a mean weight change of -6.2 kg (SD 10.2 kg) and a mean change in percentage body weight of -5% (SD 7%). Clinically significant weight loss was achieved in 36% of these patients, with a further 47% achieving weight stabilization. Mean Depression, Anxiety and Stress Scale scores reduced from 8-6-8 to 7-5-5, and mean Epworth Sleepiness Scale scores decreased from 8/24 to 6/24. Polysomnography referrals were made for 37% of all new patients, 87% of whom were diagnosed with varying degrees of obstructive sleep apnoea. We present these findings in the hope that they may serve as an example for data collection, individual service monitoring and comparison across multiple obesity services.

Investigating the application of motion accelerometers as a sleep monitoring technique and the clinical burden of the intensive care environment on sleep quality: study protocol for a prospective observational study in Australia.

INTRODUCTION: Sleep is a state of quiescence that facilitates the significant restorative processes that enhance individuals' physiological and psychological well-being. Patients admitted to the intensive care unit (ICU) experience substantial sleep disturbance. Despite the biological importance of sleep, sleep monitoring does not form part of standard clinical care for critically ill patients. There exists an unmet need to assess the feasibility and accuracy of a range of sleep assessment techniques that have the potential to allow widespread implementation of sleep monitoring in the ICU. KEY MEASURES: The coprimary outcome measures of this study are to: determine the accuracy and feasibility of motion accelerometer monitoring (ie, actigraphy) and subjective assessments of sleep (nursing-based observations and patient self-reports) to the gold standard of sleep monitoring (ie, polysomnography) in evaluating sleep continuity and disturbance. The secondary outcome measures of the study will include: (1) the association between sleep disturbance and environmental factors (eg, noise, light and clinical interactions) and (2) to describe the sleep architecture of intensive care patients. METHODS AND ANALYSIS: A prospective, single centre observational design with a within subjects' assessment of sleep monitoring techniques. The sample will comprise 80 adults (aged 18 years or more) inclusive of ventilated and non-ventilated patients, admitted to a tertiary ICU with a Richmond Agitation-Sedation Scale score between +2 (agitated) and -3 (moderate sedation) and an anticipated length of stay >24 hours. Patients' sleep quality, total sleep time and sleep fragmentations will be continuously monitored for 24 hours using polysomnography and actigraphy. Behavioural assessments (nursing observations) and patients' self-reports of sleep quality will be assessed during the 24-hour period using the Richards-Campbell Sleep Questionnaire, subjective sleepiness evaluated via the Karolinska Sleepiness Scale, along with a prehospital discharge survey regarding patients' perception of sleep quality and disturbing factors using the Little Sleep Questionnaire will be undertaken. Associations between sleep disturbance, noise and light levels, and the frequency of clinical interactions will also be investigated. Sound and luminance levels will be recorded at 1 s epochs via Extech SDL600 and SDL400 monitoring devices. Clinical interactions will be logged via the electronic patient record system Metavision which documents patient monitoring and clinical care. ETHICS AND DISSEMINATION: The relevant institutions have approved the study protocol and consent procedures. The findings of the study will contribute to the understanding of sleep disturbance, and the ability to implement sleep monitoring methods within ICUs. Understanding the contribution of a clinical environment on sleep disturbance may provide insight into the need to address clinical environmental issues that may positively influence patient outcomes, and could dispel notions that the environment is a primary factor in sleep disturbance. The research findings will be disseminated via presentations at national and international conferences, proceedings and published articles in peer-reviewed journals. TRIAL REGISTRATION NUMBER: ACTRN12615000945527; Pre-results.

The nocturnal acoustical intensity of the intensive care environment: an observational study.

BACKGROUND: The intensive care unit (ICU) environment exposes patients to noise levels that may result in substantial sleep disruption. There is a need to accurately describe the intensity pattern and source of noise in the ICU in order to develop effective sound abatement strategies. The objectives of this study were to determine nocturnal noise levels and their variability and the related sources of noise within an Australian tertiary ICU. METHODS: An observational cross-sectional study was conducted in a 24-bed open-plan ICU. Sound levels were recorded overnight during three nights at 5-s epochs using Extech (SDL 600) sound monitors. Noise sources were concurrently logged by two research assistants. RESULTS: The mean recorded ambient noise level in the ICU was 52.85 decibels (dB) (standard deviation (SD) 5.89), with a maximum noise recording at 98.3 dB (A). All recorded measurements exceeded the WHO recommendations. Noise variability per minute ranged from 9.9 to 44 dB (A), with peak noise levels >70 dB (A) occurring 10 times/hour (SD 11.4). Staff were identified as the most common source accounting for 35% of all noise. Mean noise levels in single-patient rooms compared with open-bed areas were 53.5 vs 53 dB (p = 0.37), respectively. CONCLUSION: Mean noise levels exceeded those recommended by the WHO resulting in an acoustical intensity of 193 times greater than the recommended and demonstrated a high degree of unpredictable variability, with the primary noise sources coming from staff conversations. The lack of protective effects of single rooms and the contributing effects that staffs have on noise levels are important factors when considering sound abatement strategies.

Control of OSA during automatic positive airway pressure titration in a clinical case series: predictors and accuracy of device download data.

STUDY OBJECTIVES: To investigate the factors associated with physiologic control of obstructive sleep apnea (OSA) during automatic positive airway pressure (APAP) titration in a clinical series. To also assess the usefulness of apnea-hypopnea index (AHI) data downloaded from the APAP device (Dev AHI). DESIGN: Retrospective review of a consecutive series of patients with OSA who underwent APAP titration (Autoset Spirit, ResMed, Bella Vista, New South Wales, Australia ) with simultaneous polysomnographic (PSG) monitoring in the sleep laboratory. SETTING: Tertiary sleep clinic. PARTICIPANTS: There were 190 consecutive patients with OSA referred for APAP titration. MEASUREMENTS AND RESULTS: There were 58% of patients who achieved optimal or good control of OSA (titration PSG AHI < 10, or at least 50% reduction in AHI if diagnostic AHI < 15/hr) during APAP titration. The independent predictors of titration PSG AHI were a history of cardiac disease and elevated central apnea and arousal indices during the diagnostic study. Although the median and interquartile range (IQR) AHI from the device (7.0, 3.9-11.6 events/hr) was only slightly less than the PSG AHI (7.8, 3.9-14.4 events/hr, P = 0.04) during titration, case-by-case agreement between the two measures was poor (chi-square < 0.001). CONCLUSION: In a clinical sample control of OSA during APAP titration is often poor, and close clinical follow-up is particularly needed in patients with a history of cardiac disease or with high arousal or central apnea indices on the diagnostic study. Device AHI does not reliably assess control during APAP titration, and PSG assessment may be required if clinical response to treatment is poor. The findings relate to the ResMed AutoSet device and may not apply to other devices.