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

Brightman, Louise; Dunne, Alexandra; Hsu, Chia‐Chi

doi:10.1177/1039856220953713

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…Individuals with SMI express a preference for mobile access to behavioral treatments [ 20 , 21 ] but report unique challenges to engagement in lifestyle changes that are specific to the type and severity of their behavioral health symptoms [ 22 , 23 ]. Behavioral interventions to reverse preexisting obesity in chronic SMI demonstrate modest effectiveness during active intervention [ 22 , 24 , 25 ], but longer term benefits are attenuated or reduced, in part due to limited engagement [ 16 , 26 - 28 ]. As in the general population, more frequent contacts and longer intervention periods are associated with better adherence and long-term effectiveness [ 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

Use of an Interactive Obesity Treatment Approach in Individuals With Severe Mental Illness: Feasibility, Acceptability, and Proposed Engagement Criteria

Nicol¹,

Jansen²,

Haddad³

et al. 2022

JMIR Form Res

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Background Digital and mobile health interventions are increasingly being used to support healthy lifestyle change, including in certain high-risk populations such as those with severe mental illnesses (SMIs). Life expectancy in this population lags 15 years behind counterparts in the general population, primarily due to obesity-related health conditions. Objective We tested the feasibility and usability of a 12-week interactive obesity treatment approach (iOTA) to adults with chronic SMIs (depression, bipolar disorder and schizophrenia spectrum disorder) receiving treatment in community settings. The iOTA incorporates short message service (SMS) text messages to supplement monthly in-person health coaching. Methods Factors hypothesized to be associated with weight change were illness severity and treatment engagement. Severe psychiatric symptoms were defined as baseline Clinical Global Impression severity score of >5. Criterion engagement was defined as a text messaging response rate >80% during the first 4 weeks of treatment. Disordered eating, assessed with the Loss of Control Over Eating Scores, was also evaluated. Participants provided qualitative data, further informing assessment of intervention feasibility, usability, and acceptability. Results A total of 26 participants were enrolled. The mean age was 48.5 (SD 15.67) years; 40% (10/26) were Black and 60% (15/26) female. Participants with lower symptom severity and adequate engagement demonstrated significantly decreased weight (F1,16=22.54, P<.001). Conversely, high symptom severity and lower text message response rates were associated with trend-level increases in weight (F1,7=4.33, P=.08). Loss-of-control eating was not observed to impact treatment outcome. Participants voiced preference for combination of live health coaching and text messaging, expressing desire for personalized message content. Conclusions These results demonstrate the feasibility of delivering an adapted iOTA to SMI patients receiving care in community settings and suggest testable criteria for defining sufficient treatment engagement and psychiatric symptom severity, two factors known to impact weight loss outcomes. These important findings suggest specific adaptations may be needed for optimal treatment outcomes in individuals with SMI.

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Section: Introductionmentioning

confidence: 99%

Use of an Interactive Obesity Treatment Approach in Individuals With Severe Mental Illness: Feasibility, Acceptability, and Proposed Engagement Criteria

Nicol¹,

Jansen²,

Haddad³

et al. 2022

JMIR Form Res

View full text Add to dashboard Cite

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Predictors of ongoing attendance at an Australian publicly funded specialist obesity service

Brightman,

Huang,

Georgousopoulou

2024

Obesity Science & Practice

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IntroductionThere is a demand for publicly funded specialist obesity services in Australia. A range of factors can impact on patient attendance which can result in poorer health outcomes.ObjectiveTo identify patient factors that predict ongoing in‐person attendance following initial medical assessment at the Canberra Obesity Management Service.MethodsData were collated from two retrospective reviews (July 2016–June 2017 and July 2018–June 2019). Predictive modeling was used to determine the likelihood of ongoing attendance.ResultsA total of 396 patients were identified. Mean age was 45.4 years (SD 13.1), mean weight was 139.5 kg (SD 27.8) and mean Body Mass Index was 49.87 kg/m2 (SD 8.7). Demographics and anthropometrics were not predictive of ongoing attendance. Patients with a higher comorbidity burden were more likely to continue attending (p < 0.001). Patients with obstructive sleep apnea (OSA) were 4.9 times more likely to continue attending (p < 0.001). Hypertension was more common among patients who continued attending (p = 0.005); however, this relationship was no longer significant when using a multi‐adjusted model. Comorbid depression and/or anxiety diagnoses were not predictive of ongoing attendance although the p‐value for anxiety severity classification approached significance.ConclusionsFindings are consistent with existing evidence linking OSA and attendance at specialist obesity services. Hypertension was predictive of ongoing attendance and warrants further research. Determining if anxiety is a true barrier to attendance at specialist obesity services may have implications in terms of optimizing diagnosis and treatment prior to referral or in the early stages of obesity management.

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A Mental Illness Early Warning Analysis Model Using an Improved SVM Algorithm and Eeg Signals

DONG,

SHEN,

YAN

2024

J. Mech. Med. Biol.

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The fast-paced lifestyle, intense competition, and constantly changing environment of modern society are important factors contributing to the emergence of psychological disorders. Factors such as work pressure, family conflicts, and interpersonal relationship difficulties pose challenges to individual mental health. Prolonged use of electronic devices and the widespread use of social media further exacerbate mental health issues, as the constant influx of information makes it difficult for people to relax and focus. The harm of psychological disorders to individuals and society cannot be ignored. It not only diminishes the quality of life for patients but also affects work efficiency and social participation. In extreme cases, psychological disorders can lead to serious consequences like suicide, causing immense pain to families and society. Therefore, prioritizing mental health, raising awareness of mental well-being, and timely intervention and treatment of psychological disorders are crucial for individual happiness and social stability. Based on this, this study employs an improved Support Vector Machine (SVM) and Electroencephalogram (EEG) signals for the diagnosis of psychological disorders, aiming for more effective treatment of mental health conditions. Specifically, the EEG signals of the experimental subjects are first collected as training data for the model. The dataset undergoes preprocessing operations such as cleaning and normalization. Due to the limited availability of published information about individuals with psychological disorders, an ensemble SVM algorithm is used as a warning model to improve the accuracy of psychological disorder diagnosis. This algorithm utilizes a differential measurement method to adjust the selection of SVM parameters. The training sample size remains constant, with several iterations of training performed. By combining the differential measurement method, a specific parameter combination is obtained, maximizing the differentiation between base classifiers. This ultimately addresses issues such as small sample size and insufficient sample information in the dataset. Experimental results demonstrate that the model used in this study exhibits good performance in diagnosing psychological disorders.

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

Cited by 4 publications

References 11 publications

Use of an Interactive Obesity Treatment Approach in Individuals With Severe Mental Illness: Feasibility, Acceptability, and Proposed Engagement Criteria

Use of an Interactive Obesity Treatment Approach in Individuals With Severe Mental Illness: Feasibility, Acceptability, and Proposed Engagement Criteria

Predictors of ongoing attendance at an Australian publicly funded specialist obesity service

A Mental Illness Early Warning Analysis Model Using an Improved SVM Algorithm and Eeg Signals

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