Kim Mathiasen
Biographic Data
| ID | 7522014 |
|---|---|
| NAME | Kim Mathiasen |
| GIVEN NAMES | Kim |
| FAMILY NAME | Mathiasen |
| SIGNATURE | MATHIASEN K |
| AFFILIATIONS | Region of Southern Denmark |
| ORCID | 0000-0001-6067-8866 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Choosing the right treatment - combining clinicians’ expert knowledge with data-driven predictions
In 90.1% of cases, the hybrid model ranked the actual disorder treated as either the highest (67.3%) or second-highest (22.8%) on the test data. This emphasizes that instead of suggesting a single disorder to be treated, the model can offer the probabilities for multiple disorders. This allows individuals seeking treatment or their therapists to incorporate this information as an additional data-driven factor when collectively deciding on which t…
Therapist perceptions of the implementation of a new screening procedure using the ItFits-toolkit in an iCBT routine care clinic
The ItFits-toolkit appears to have been an effective mediator of the implementation process. The therapists were aided in the process of change, resulting in an enhanced ability to target the patients who can benefit from the treatment program, less expenditure of time on the wrong population, and more satisfied therapists
Early development of treatment motivation predicts adherence and symptom reduction in an internet-based guided self-help program for binge eating disorder
The results indicated that patients entering online treatment for BED feel highly motivated. However, baseline treatment motivation could not significantly predict treatment completion, which contradicts previous research. The significant predictive ability of early measures of treatment motivation supports the clinical relevance of monitoring the development of early changes to tailor and optimize individual patient care. Further research is nee…
Brain+ AlcoRecover
Background: Patients with alcohol use disorder (AUD) exhibit deficits in various cognitive domains, including executive functioning, working memory, and learning and memory, which impede the effectiveness of conventional AUD treatment and enhance relapse. Mobile health (mHealth) services are promising in terms of delivering cognitive training in gamified versions. So far, studies examining the effects of mHealth-based cognitive training in AUD pa…
A Randomized Controlled Trial of Attentional Control Training for Treating Alcohol Use Disorder
Background: There is consistent evidence that community and clinical samples of individuals with an alcohol use disorder (AUD) have attentional biases toward alcohol cues. The alcohol attentional control training program (AACTP) has shown promise for retraining these biases and decreasing alcohol consumption in community samples of excessive drinkers. However, there is a lack of evidence regarding the effectiveness of ACTP in clinical AUD samples…
Internet‐based cognitive behavioral therapy for anxiety in an outpatient specialized care setting
Knowledge about user experiences of internet‐based cognitive behavioral therapy (iCBT) has mostly been drawn from non‐clinical groups or with iCBT offered via self‐referral. The present study therefore focused on patients who had undergone iCBT with minimal support while actively awaiting outpatient psychological treatment in the form of face‐to‐face CBT. To seek out barriers to adherence the study also included patients who had withdrawn from th…
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Internet‐based cognitive behavioral therapy for anxiety in an outpatient specialized care setting
Knowledge about user experiences of internet‐based cognitive behavioral therapy (iCBT) has mostly been drawn from non‐clinical groups or with iCBT offered via self‐referral. The present study therefore focused on patients who had undergone iCBT with minimal support while actively awaiting outpatient psychological treatment in the form of face‐to‐face CBT. To seek out barriers to adherence the study also included patients who had withdrawn from th…
Brain+ AlcoRecover
Background: Patients with alcohol use disorder (AUD) exhibit deficits in various cognitive domains, including executive functioning, working memory, and learning and memory, which impede the effectiveness of conventional AUD treatment and enhance relapse. Mobile health (mHealth) services are promising in terms of delivering cognitive training in gamified versions. So far, studies examining the effects of mHealth-based cognitive training in AUD pa…
A Randomized Controlled Trial of Attentional Control Training for Treating Alcohol Use Disorder
Background: There is consistent evidence that community and clinical samples of individuals with an alcohol use disorder (AUD) have attentional biases toward alcohol cues. The alcohol attentional control training program (AACTP) has shown promise for retraining these biases and decreasing alcohol consumption in community samples of excessive drinkers. However, there is a lack of evidence regarding the effectiveness of ACTP in clinical AUD samples…
Early development of treatment motivation predicts adherence and symptom reduction in an internet-based guided self-help program for binge eating disorder
The results indicated that patients entering online treatment for BED feel highly motivated. However, baseline treatment motivation could not significantly predict treatment completion, which contradicts previous research. The significant predictive ability of early measures of treatment motivation supports the clinical relevance of monitoring the development of early changes to tailor and optimize individual patient care. Further research is nee…
Therapist perceptions of the implementation of a new screening procedure using the ItFits-toolkit in an iCBT routine care clinic
The ItFits-toolkit appears to have been an effective mediator of the implementation process. The therapists were aided in the process of change, resulting in an enhanced ability to target the patients who can benefit from the treatment program, less expenditure of time on the wrong population, and more satisfied therapists
Choosing the right treatment - combining clinicians’ expert knowledge with data-driven predictions
In 90.1% of cases, the hybrid model ranked the actual disorder treated as either the highest (67.3%) or second-highest (22.8%) on the test data. This emphasizes that instead of suggesting a single disorder to be treated, the model can offer the probabilities for multiple disorders. This allows individuals seeking treatment or their therapists to incorporate this information as an additional data-driven factor when collectively deciding on which t…
Psychology (6 works) · Medicine (5 works) · Clinical Psychology (4 works) · Psychiatry (4 works) · Clinical Psychology (3 works) · Cognition (3 works) · Computer Science (3 works) · Digital Mental Health Interventions (3 works) · Psychotherapist (3 works) · Alcohol (2 works)