TicTimer Web: software for measuring tic suppression remotely

Introduction

One of the defining characteristics of tics, compared to some other abnormal movements, is that they can usually be suppressed with an effort of will for at least a brief interval¹. Woods and Himle developed an experimental measure of tic suppression, the tic suppression paradigm (TSP)^2,3. In this paradigm, each participant is observed during several experimental conditions: usually baseline and differential reinforcement of zero-rate ticcing (DRO), and sometimes also verbal instruction to suppress tics and/or noncontingent reinforcement (NCR). The DRO condition replicates a behavior therapy long in clinical use, providing frequent rewards for absence of a problematic behavior, such as a reward token delivered after every 10 seconds without a tic. In the NCR condition, rewards are provided at a similar overall frequency as in the DRO condition, but at times unrelated to the timing of tics during the NCR session; Himle and colleagues used this approach to clarify the mechanism of the DRO condition’s tic reduction benefit⁴.

The TSP has been used in a number of studies, producing several interesting results^5,6. For instance, tic suppression measured early after the onset of a tic disorder predicts clinical outcome 6–12 months later⁷. Additional studies using the TSP are being conducted in various research centers. In the course of conducting a longitudinal study of children with Provisional Tic Disorder⁸, we found that the TSP required substantial investigator effort. That is, a tic expert must watch the session, “live” or on a video recording, and note the presence and timing of each tic. Furthermore, in the DRO and NCR conditions a second staff member must repeatedly signal a device to provide a reward for each predefined tic-free interval. To reduce this effort, we wrote a simple program to facilitate record keeping and reward delivery during research sessions⁹. The expert observer pressed a button to record each tic observed, and in the DRO and NCR conditions the program delivered reward tokens at the appropriate times by connecting to a relay module that signaled a token dispenser box. This software improved convenience for the investigator and accuracy of record-keeping.

Because of enforced social distancing during the COVID 19 pandemic, the need arose for sessions to be performed remotely. A video calling program allowed us to observe the subjects, but we still needed a way to deliver rewards during the DRO and NCR conditions of the TSP. Previously, we had created a web-based program called TicTrainer for behavioral therapy¹⁰, and we decided to expand the functionality of that program so it could be used for TSP research. The new program allows the same functionality as the previous, in-person TicTimer software, providing audiovisual rewards at appropriate intervals on a web browser viewed by the participant, based on the timing of tics recorded remotely by the investigator. We present the software here¹¹ to facilitate its use by others.

Methods

Use cases

The video file (Extended data, Supplementary File 1)¹² demonstrates the operation of TicTimer Web¹¹ from a researcher’s perspective. The sessions performed here were test sessions with no human subjects being observed. Extended data, Supplementary Files 2–4¹² are the session log files created in that video.

Conclusions

The TicTimer Web¹¹ program allows for remote implementation of the TSP, while maintaining the benefits of earlier versions of the software⁹: ease and accuracy of record keeping and automated reward delivery. TicTimer Web also simplifies the TSP, replacing a physical token dispenser box with any web browser, say a wireless tablet. While designed for our purposes in tic disorder research, TicTimer Web may find other uses. The most obvious of these may be for research on traditional habit disorders; for instance, hair pulling and skin picking appear in the “Obsessive-compulsive and related disorders” section of DSM-5¹³. TicTimer Web may also have clinical applications. These may include documenting suppression ability before and after treatment and investigations of the chaotic nature of tic timing¹⁴, in addition to the previously described behavior therapy¹⁰.

Future modifications may include adding machine detection of tics, e.g. by audio-visual observation, surface EMG or accelerometry. Artificial intelligence and machine learning techniques may be able to use these inputs to recognize and mark the occurrence of specific tics. Such improvements would be quite welcome, as they might speed tic research or even allow automated behavior therapy. However, these methods are thus far difficult to reduce to practice; separating tics from normal adventitious movements is not trivial, and the wide variety of observed tics defies a unitary definition in terms of elementary movement features and timing.

Data availability

Software availability

The source code for TicTimer Web is available at: https://github.com/jonkb/TicTrainer-node.

Archived source code at time of publication: https://doi.org/10.5281/zenodo.3990474¹¹.

License: MIT License.