Dataset Information

Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithm.

ABSTRACT: The cartoon Fidgety Philip, the banner of Western-ADHD diagnosis, depicts a 'restless' child exhibiting hyperactive-behaviors with hyper-arousability and/or hypermotor-restlessness (H-behaviors) during sitting. To overcome the gaps between differential diagnostic considerations and modern computing methodologies, we have developed a non-interpretative, neutral pictogram-guided phenotyping language (PG-PL) for describing body-segment movements during sitting (Journal of Psychiatric Research). To develop the PG-PL, seven research assistants annotated three original Fidgety Philip cartoons. Their annotations were analyzed with descriptive statistics. To review the PG-PL's performance, the same seven research assistants annotated 12 snapshots with free hand annotations, followed by using the PG-PL, each time in randomized sequence and on two separate occasions. After achieving satisfactory inter-observer agreements, the PG-PL annotation software was used for reviewing videos where the same seven research assistants annotated 12 one-minute long video clips. The video clip annotations were finally used to develop a machine learning algorithm for automated movement detection (Journal of Psychiatric Research). These data together demonstrate the value of the PG-PL for manually annotating human movement patterns. Researchers are able to reuse the data and the first version of the machine learning algorithm to further develop and refine the algorithm for differentiating movement patterns.

SUBMITTER: Chan M

PROVIDER: S-EPMC7851356 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithm.

Chan Melvin M Tse Emmanuel K EK Bao Seraph S Berger Mai M Beyzaei Nadia N Campbell Mackenzie M Garn Heinrich H Hussaina Hebah H Kloesch Gerhard G Kohn Bernhard B Kuzeljevic Boris B Lee Yi Jui YJ Maher Khaola Safia KS Carson Natasha N Jeyaratnam Jecika J McWilliams Scout S Spruyt Karen K Van der Loos Hendrik F Machiel HFM Kuo Calvin C Ipsiroglu Osman O

Data in brief 20210117

The cartoon Fidgety Philip, the banner of Western-ADHD diagnosis, depicts a 'restless' child exhibiting hyperactive-behaviors with hyper-arousability and/or hypermotor-restlessness (H-behaviors) during sitting. To overcome the gaps between differential diagnostic considerations and modern computing methodologies, we have developed a non-interpretative, neutral pictogram-guided phenotyping language (PG-PL) for describing body-segment movements during sitting (<i>Journal of Psychiatric Research</i ...[more]

PMID: 33553523

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Project description:Study objectivesTo validate the Multiple Suggested Immobilization Test (m-SIT), a symptom-provocation test measuring restless legs syndrome (RLS) severity multiple times a day while the patient is awake and resting under controlled conditions. The m-SIT was designed to overcome some limitations in measuring RLS severity with rating scales.DesignPatients completed two m-SITs on 2 consecutive days while on 24-h dopaminergic medication. After treatment discontinuation, they completed one more m-SIT 3 days later. Controls performed only one m-SIT.SettingSleep laboratory.ParticipantsNineteen patients with RLS and 10 healthy controls.InterventionsThe original m-SIT consisted of seven modified 60-min SITs performed every 2 h between noon and midnight. During each SIT, the subject reclined quietly but could move his or her legs without restriction to alleviate symptoms. Every 10 min, periodic leg movements during wakefulness (PLMW) were evaluated and the m-SIT Disturbance Scale (m-SIT-DS; range 0-10) was completed.Measurements and resultsThe m-SIT, composed of 6:00pm, 8:00pm, 10:00pm, and 12:00pm SITs, discriminated patients from controls (mean m-SIT-DS: 2.68 ± 2.35 versus 0.08 ± 0.26; mean PLMW/h, P = 0.0001) and between treatment groups (on medication versus taken off medication; mean m-SIT-DS, P = 0.0001; mean PLMW/h, P < 0.01). It proved reliable on retest and covariated well with the International Restless Legs Scale (IRLS) and scales measuring daytime symptoms (Spearman ? > 0.4).ConclusionsThe m-SIT is a valid and reliable test to evaluate RLS severity and treatment response, and could be useful in the future to confirm diagnosis and identify daytime symptoms. Although it was primarily designed for clinical trials, it might be useful in clinical settings because it provides a standardized testing condition to measure RLS symptoms.CitationGarcia-Borreguero D; Kohnen R; Boothby L; Tzonova D; Larrosa O; Dunkl E. Validation of the Multiple Suggested Immobilization Test: a test for the assessment of severity of restless legs syndrome (Willis-Ekbom disease). SLEEP 2013;36(7):1101-1109.

Project description:BackgroundPatient engagement is a critical but challenging public health priority in behavioral health care. During telehealth sessions, health care providers need to rely predominantly on verbal strategies rather than typical nonverbal cues to effectively engage patients. Hence, the typical patient engagement behaviors are now different, and health care provider training on telehealth patient engagement is unavailable or quite limited. Therefore, we explore the application of machine learning for estimating patient engagement. This can assist psychotherapists in the development of a therapeutic relationship with the patient and enhance patient engagement in the treatment of mental health conditions during tele-mental health sessions.ObjectiveThis study aimed to examine the ability of machine learning models to estimate patient engagement levels during a tele-mental health session and understand whether the machine learning approach could support therapeutic engagement between the client and psychotherapist.MethodsWe proposed a multimodal learning-based approach. We uniquely leveraged latent vectors corresponding to affective and cognitive features frequently used in psychology literature to understand a person's level of engagement. Given the labeled data constraints that exist in health care, we explored a semisupervised learning solution. To support the development of similar technologies for telehealth, we also plan to release a dataset called Multimodal Engagement Detection in Clinical Analysis (MEDICA). This dataset includes 1229 video clips, each lasting 3 seconds. In addition, we present experiments conducted on this dataset, along with real-world tests that demonstrate the effectiveness of our method.ResultsOur algorithm reports a 40% improvement in root mean square error over state-of-the-art methods for engagement estimation. In our real-world tests on 438 video clips from psychotherapy sessions with 20 patients, in comparison to prior methods, positive correlations were observed between psychotherapists' Working Alliance Inventory scores and our mean and median engagement level estimates. This indicates the potential of the proposed model to present patient engagement estimations that align well with the engagement measures used by psychotherapists.ConclusionsPatient engagement has been identified as being important to improve therapeutic alliance. However, limited research has been conducted to measure this in a telehealth setting, where the therapist lacks conventional cues to make a confident assessment. The algorithm developed is an attempt to model person-oriented engagement modeling theories within machine learning frameworks to estimate the level of engagement of the patient accurately and reliably in telehealth. The results are encouraging and emphasize the value of combining psychology and machine learning to understand patient engagement. Further testing in the real-world setting is necessary to fully assess its usefulness in helping therapists gauge patient engagement during online sessions. However, the proposed approach and the creation of the new dataset, MEDICA, open avenues for future research and the development of impactful tools for telehealth.

Dataset Information

Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithm.

Publications

Fidgety Philip and the Suggested Clinical Immobilization Test: Annotation data for developing a machine learning algorithm.

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