Home-Based Training With Feedback to Improve Outcomes in Adolescents and Young Adults With Cerebral Palsy..

Browse trials for

• Cerebral Palsy 1,891

Keywords

• cerebral palsy
• Home-based training
• Extrinsic feedback
• Movement behavior
• Motor learning

Cerebral Palsy

1,891 studies on the registry are indexed under Cerebral Palsy; 439 are open to participants now.

This study's planned enrollment of 70 is above the median of 34 across 1,396 interventional studies indexed under Cerebral Palsy.

Lead sponsor

University of Copenhagen is the lead sponsor of 465 studies on the registry; 63 are open to participants now.

Study arms

• Experimental
  Music Motion Group

  Behavioral: Music Motion Group

• Active comparator
  Control

  Behavioral: Control

Interventions

• BehavioralMusic Motion Group

  Participants will engage in personalized, home-based motor training programs tailored to their individual goals. Each will wear a wireless Inertial Measurement Unit (IMU) on the targeted body part, which transmits movement data via Bluetooth to a tablet app. The training emphasizes task specificity and intensity, with five virtual check-ins to review progress and adjust training parameters. The intervention studied is extrinsic feedback; the app analyzes movement data and provides feedback through music. Before each training session, the app guides participants to set personalized intensity thresholds based on current capacity. When participants meet the intensity threshold, musical elements (e.g., drumbeats, vocals) play. If they fall short, elements drop out, providing knowledge of erroneous performance.

• BehavioralControl

  Participants will follow personalized, home-based motor training programs designed like the Music motion group. They will wear a wireless Inertial Measurement Unit (IMU) on the targeted body part, transmitting movement data via Bluetooth to a tablet app. However, unlike the Music Motion Feedback group, participants in the Control group will not receive any extrinsic feedback during their training.

Primary outcomes

1. Percentage change in daytime movement of the trained extremity (right or left arm or leg) as measured by 3-axis accelerometer and gyroscope data analyzed via deep-learning neural network

   Movement data will be collected using seven Inertial Measurement Units (IMUs) worn continuously for 72 hours at three time points: baseline, 12 weeks (end of intervention), and 24 weeks (follow-up). IMUs will be attached to the sternum, wrists, thighs, and lower legs using adhesive patches and will capture 3-axis accelerometer and gyroscope data. A validated custom neural network (Novosel et al. 2023) will convert signals into images and analyze them using convolutional layers to extract features related to movement behaviors. The primary metric will be the percentage change (minutes a day) in real-world daytime movement of the trained limb, computed relative to baseline. This outcome reflects changes in functional mobility resulting from the motor training intervention.

   Time frame: Baseline, at 12 weeks (end of intervention period), and at 24 weeks (long term follow up)

Secondary outcomes

1. Number of daily logins to the tablet app

   Attendance will be measured as the number of times participants log into the tablet app during the intervention. Unit of Measure: Number of logins

   Time frame: The app stores the data during the intervention period and will be collected at 12 weeks (end of intervention period)

2. Daily session duration recorded in the tablet app

   Adherence will be assessed by the average length of each daily training session. This outcome reflects how long participants remain engaged with the motor training program once they log in. Unit of Measure: Minutes

   Time frame: The app stores the data during the intervention period and will be collectedThe app stores the data during the intervention period and will be collected at 12 weeks (end of intervention period)

3. Time spent moving the targeted extremity during training recorded in the tablet app

   This measure reflects adherence by quantifying the active movement time of the trained extremity during each training session. Unit of Measure: Minutes

   Time frame: The app stores the data during the intervention period and will be collected The app stores the data during the intervention period and will be collected at 12 weeks (end of intervention period)

4. Time spent within target movement intensity threshold recorded in the tablet app

   For participants in the Music Motion Feedback group, adherence will also be evaluated by measuring the time spent at or above a predefined movement intensity threshold. Unit of Measure: Minutes

   Time frame: The app stores the data during the intervention period and will be collected The app stores the data during the intervention period and will be collected at 12 weeks (end of intervention period)

5. Action Research Arm Test (ARAT) total score - (Upper Extremity)

   In participants engaged in upper extremity training, the ARAT will be used to evaluate upper extremity functional capacity. It consists of 19 items assessing grasp, grip, pinch, and gross movement. Scores range from 0 to 57, with higher scores indicating better arm function. Time Frame: Baseline, 12 weeks (end of intervention), and 24 weeks (follow-up) Unit of Measure: ARAT total score (0-57)

   Time frame: Baseline, at 12 weeks (end of intervention period), and at 24 weeks (long term follow up)

6. Maximum isometric arm strength - (Upper Extremity)

   Participants engaged in upper extremity training will have the isometric strength of the targeted upper extremity measured using a handheld dynamometer. They will perform maximum voluntary contractions against resistance, and the peak force will be recorded. Unit of Measure: Kilograms of force (kgf)

   Time frame: Baseline, at 12 weeks (end of intervention period), and at 24 weeks (long term follow up)

7. Maximum grip strength - (Upper Extremity)

   Participants who are engaged in upper extremity training will have their grip strength of the targeted upper limb measured using a hand dynamometer. The highest of three attempts will be recorded. Unit of Measure: Kilograms of force (kgf)

   Time frame: Baseline, at 12 weeks (end of intervention period), and at 24 weeks (long term follow up)

8. Gross Motor Function Measure-66 (GMFM-66) total score - (Lower Extremity)

   In participants who are engaged in lower extremity training the GMFM-66 will be used to assess gross motor function. It evaluates activities such as standing, walking, and running. Scores range from 0 to 100, with higher scores indicating better function. Unit of Measure: GMFM-66 total score (0-100)

   Time frame: Baseline, at 12 weeks (end of intervention period), and at 24 weeks (long term follow up)

9. Maximum isometric leg strength - (Lower Extremity)

   The isometric strength of the trained leg will be assessed using a handheld dynamometer. Participants will perform maximum voluntary contractions, and peak force will be recorded. Unit of Measure: Kilograms of force (kgf)

   Time frame: Baseline, at 12 weeks (end of intervention period), and at 24 weeks (long term follow up)

10. Distance walked during the Six-Minute Walk Test (6MWT) - (Lower Extremity)

    The 6MWT will be used to evaluate walking endurance and aerobic capacity. Participants engaged in lower extremity training will walk as far as possible in six minutes along a flat, indoor course. Time Frame: Baseline, 12 weeks, and 24 weeks Unit of Measure: Meters walked

    Time frame: Baseline, at 12 weeks (end of intervention period), and at 24 weeks (long term follow up)

Other outcomes

1. The Canadian Occupational Performance Measure (Self-Rated Performance)

   During a semi-structured interview, participants identify an activity they want, need, or are expected to perform and set one long-term goal. They then rate their performance of that activity on a 0-10 scale, where 0 = not able to perform the activity at all and 10 = able to perform it extremely well. Unit of Measure: Performance score (0-10 scale)

   Time frame: Before baseline (at the motor training program design), at 12 weeks (end of intervention), and at 24 weeks (long-term follow-up)

2. The Canadian Occupational Performance Measure (Self-Rated Satisfaction)

   In the same semi-structured interview, participants rate their satisfaction with their performance of the selected activity on a 0-10 scale, where 0 = not satisfied at all and 10 = extremely satisfied. Unit of Measure: Satisfaction score (0-10 scale)

   Time frame: Before baseline (at the motor training program design), at 12 weeks (end of intervention), and at 24 weeks (long-term follow-up)

3. Change in time spent walking per day (as measured by IMUs)

   Time spent walking will be measured using 3-axis accelerometer and gyroscope data collected from seven IMUs worn continuously for 72 hours. Data will be analyzed using a validated deep-learning neural network (Novosel et al., 2023) to classify walking behavior. Unit of Measure: Minutes per day

   Time frame: Baseline, at 12 weeks (end of intervention period), and at 24 weeks (long term follow up)

4. Change in time spent standing per day (as measured by IMUs)

   Standing time will be extracted from IMU data collected during 72 hours of wear. Signals will be analyzed via a validated deep-learning neural network (Novosel et al., 2023) Time Frame: Baseline, 12 weeks, and 24 weeks Unit of Measure: Minutes per day

   Time frame: Baseline, at 12 weeks (end of intervention period), and at 24 weeks (long term follow up)

5. Change in time spent sitting per day (as measured by IMUs)

   Sitting time will be recorded using IMUs worn on the sternum, wrists, thighs, and lower legs after 72 hours of wear and analysed with a validated deep-learning neural network (Novosel et al., 2023) Unit of Measure: Minutes per day

   Time frame: Baseline, at 12 weeks (end of intervention period), and at 24 weeks (long term follow up)

6. Change in time spent lying down per day (as measured by IMUs)

   Time spent lying will be measured using 3-axis accelerometer and gyroscope data collected from seven IMUs worn continuously for 72 hours. Data will be analyzed using a validated deep-learning neural network (Novosel et al., 2023) to classify walking behavior. Unit of Measure: Minutes per day

   Time frame: Baseline, at 12 weeks (end of intervention period), and at 24 weeks (long term follow up)

7. Change in number of posture transitions per day (as measured by IMUs)

   Posture transitions (e.g., sit-to-stand) will be identified from 72-hour IMU data using a validated deep-learning neural network (Novosel et al., 2023) Unit of Measure: Number of transitions per day

   Time frame: Baseline, at 12 weeks (end of intervention period), and at 24 weeks (long term follow up)

8. Change in relative extremity usage ratio

   The movement ratio between the trained and non-trained extremity will be calculated from IMU signals using using a validated deep-learning neural network (Novosel et al., 2023) Unit of Measure: Ratio (trained / non-trained limb)

   Time frame: Baseline, at 12 weeks (end of intervention period), and at 24 weeks (long term follow up)

9. Adverse events

   Adverse events include both serious (e.g. death, hospitalization) and non-serious adverse events (e.g. pain, fatigue). Unit of Measure: Number of events

   Time frame: Through week 12 (baseline to end of intervention)

Individual participant data

Plan to share: Undecided