Online-Befragung zur Bekanntheit von Angeboten zur Aufklärung, Prävention, Beratung und Nachsorge bei Essstörungen

Mühleck, Julia; Borse, Sigrid; Wunderer, Eva; Strauß, Bernhard; Berger, Uwe

doi:10.1007/s11553-019-00728-1

Cited by 9 publications

(4 citation statements)

References 16 publications

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“…Recovery accounts can thus be described as a low-threshold service that is available regardless of time and place. According to the online survey by Mühleck et al [ 69 ], low-threshold care services can generally positively influence the development of an ED. Similarly, Williams [ 4 ] reports that flexible and fast access to self-help materials can bring some advantages for patients.…”

Section: Discussionmentioning

confidence: 99%

“That You Just Know You’re Not Alone and Other People Have Gone through It Too.” Eating Disorder Recovery Accounts on Instagram as a Chance for Self-Help? A Qualitative Interview Study among People Affected and Self-Help Experts

Wenig

Janetzke

2022

IJERPH

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In addition to the professional treatment of eating disorders, the use of self-help groups has become increasingly important. Social media offers new possibilities for self-help, not only as online groups but also in increased access to recovery stories of people with similar diseases. People with eating disorders use the internet and social media depending on their motivation in different ways. Eating disorder recovery stories on social media have not yet been systematically used in treatment as appropriate guidelines are still lacking. This study provides an initial insight into the possibilities of using social media for self-help for eating disorders. Due to the exploratory nature, a qualitative design was used, combining interviews with people who have a recovery account on Instagram (n = 6) and self-help experts (n = 2). The results show that recovery stories on Instagram could serve as door openers for further treatment, motivation for therapy, a first step towards behaviour change, and support for existing therapies. If affected people can cope with the self-protection strategies, they can use Instagram positively for themselves and their disease. Nevertheless, there is a risk of negative influence as well as a risk of content and time overload. Therapeutic personnel can use these results to improve existing support services.

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Section: Discussionmentioning

confidence: 99%

“That You Just Know You’re Not Alone and Other People Have Gone through It Too.” Eating Disorder Recovery Accounts on Instagram as a Chance for Self-Help? A Qualitative Interview Study among People Affected and Self-Help Experts

Wenig

Janetzke

2022

IJERPH

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“…Currently, there are a variety of websites and other media platforms where people can communicate with each other and talk conidentially about their diiculties. People around the world can share their thoughts and experiences [24]. Online monitoring has the potential to be a proactive and promising method for identifying high-risk individuals.…”

Section: Introductionmentioning

confidence: 99%

Deep Hierarchical Attention Active Learning for Mental Disorder Unlabeled Data in AIoMT

Ahmed

Lin

Srivastava

2023

ACM Trans. Sen. Netw.

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In the Artificial Intelligence of Medical Things (AIoMT), Internet-Delivered Psychological Treatment (IDPT) effectively improves the quality of mental health treatments. With the advent of COVID-19, psychological tasks have become overloaded and complicated for medical professionals due to the overlap of sentimental values. The development of an AIoMT tool requires labeling of data to achieve clinical-level performance. Text data requires an appropriate set of linguistic features for vector latent representation and segmentation. Emotional biases could lead to incorrect segmentation of patient-authorized texts, and labeling emotional data is time-consuming. In this paper, we propose an assistant tool for psychologists to assist them in mental health treatment and note-taking. We first extend the word and emotion lexicon, and then apply a hierarchical attention method to support data labeling. The learned latent representation uses word position prediction and sentence-level attention to create a semantic framework. The augmented vector representation helps in highlighting words and classifying nine different symptoms from the text written by the patient. Our experimental results show that the emotion lexicon helps to increase the accuracy by 5% without affecting the overall results, and that the hierarchical attention method achieves an F1 score of 0.89.

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“…Many Internet fora and social media sites now allow individuals to interact anonymously and to talk about their misery, bereavement, and future treatment options [19]. People from all around the world can freely express their opinions and feelings [26]. Online surveillance can be a proactive and promising way to identify those issues at high risk.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Contrast Set Based Deep Attention Network for Lexical Analysis and Mental Health Treatment

Ahmed

Lin

Srivastava

2022

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Internet-delivered psychological treatments (IDPT) consider mental problems based on Internet interaction. With such increased interaction because of the COVID-19 pandemic, more online tools have been widely used to provide evidence-based mental health services. This increase helps cover more population by using fewer resources for mental health treatments. Adaptivity and customization for the remedy routine can help solve mental health issues quickly. In this research, we propose a fuzzy contrast-based model that uses an attention network for positional weighted words and classifies mental patient authored text into distinct symptoms. After that, the trained embedding is used to label mental data. Then the attention network expands its lexicons to adapt to the usage of transfer learning techniques. The proposed model uses similarity and contrast sets to classify the weighted attention words. The fuzzy model then uses the sets to classify the mental health data into distinct classes. Our method is compared with non-embedding and traditional techniques to demonstrate the proposed model. From the experiments, the feature vector can achieve a high ROC curve of 0.82 with problems associated with nine symptoms.

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Online-Befragung zur Bekanntheit von Angeboten zur Aufklärung, Prävention, Beratung und Nachsorge bei Essstörungen

Cited by 9 publications

References 16 publications

“That You Just Know You’re Not Alone and Other People Have Gone through It Too.” Eating Disorder Recovery Accounts on Instagram as a Chance for Self-Help? A Qualitative Interview Study among People Affected and Self-Help Experts

“That You Just Know You’re Not Alone and Other People Have Gone through It Too.” Eating Disorder Recovery Accounts on Instagram as a Chance for Self-Help? A Qualitative Interview Study among People Affected and Self-Help Experts

Deep Hierarchical Attention Active Learning for Mental Disorder Unlabeled Data in AIoMT

Fuzzy Contrast Set Based Deep Attention Network for Lexical Analysis and Mental Health Treatment

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