## 4 Discussion
### Limitations
Our system exhibited relatively high rates of data capture (IRF: 82%; home: 80.9%). Likewise, patient compliance with wearing the devices was high in both settings (IRF: 94%; home: 94-96%). Taken together, these results demonstrate patient willingness to engage with (and _Souvenir_'s ability to conduct) at least 8 h of activity monitoring per day. Errors in the phone application contributing to missing data were easier to diagnose and document in the IPR setting compared to the home settings due to our performance of end-of-day check-ins in the IRF; upon checking data file integrity at the end of the day, study staff were able to detect and respond quickly when automatic Android software updates interrupted cue delivery and activity monitoring. However, such events highlight the need for ongoing system code maintenance to protect against forced obsolescence caused by uncontrollable events like system updates, cloud service API changes, changes in applicable regulations, etc. Enhancing the smartphone app to include error logging and diagnostics could also help identify technical issues, which would be particularly useful for device use in less supervised home settings. Participants received written instructions for using the device at home, including a section on basic troubleshooting to reduce the risk of data loss. We believe the most effective approach to addressing data loss is through technological improvements, rather than placing the burden on the user by adding to these instructions. Lastly, our calculations of compliance did not include an estimation of non-wear compliance in which the user dofts the wristbands while keeping them within proximity of the phone. While computations for this type of non-wear exist [67], they have not yet been validated in the stroke population. Excluding non-wear time from our compliance calculation is unlikely to significantly affect the overall trends observed between pre-cue activity and post-cue response rates because choosing to doff the devices equally impacts pre- and post-cue activity. Finally, our sample included a higher percentage of participants with relatively high motor and sensory function (reflected by FMA-UE scores) and normal cognition (reflected by MoCA scores), which may limit the generalizability of our results. Many participants achieved the maximum FMA-UE score of 66 despite presenting with coordination deficits and/or strength deficits (reflected by maximum grip strength). Post hoc analyses found no significant differences in user experience survey scores between those with mild versus moderate-severe motor impairments or between those with normal versus mild-moderate cognitive deficits, highlighting that these factors did not significantly impact outcomes in our study (see Appendix A). Although our post hoc analyses did not reveal any significant differences in user experience based on cognition or motor function, we acknowledge that our study was not originally designed to adequately compare these groups. Future research should include larger sample sizes and a wider range of functional stratification (such as patients with severe motor and/or sensory impairments). Doing so could provide more robust evidence that user experience is comparable across cognitive, sensory, and motor function levels.