Table 4. Summary of the included studies’ implications and reported drawbacks.

Reference	Implications concerning end-user rehabilitation compliance or adherence	Study drawbacks and limitations
Aharon et al [28]	A dropout rate could decrease, and home exercise performance average time could increase due to using an AIa-based rehabilitation device, which could be considered as measures for improved end-user compliance or adherence to rehabilitation	Small number of participants, discrepancy in the number of case and control participants, and the probability of selection bias
Chae et al [29]	To increase adherence, any intervention should be tailored to the end user’s preferences and behavioral profile, and this end user–tailored intervention approach could improve adherence with no change to the therapeutic regime	The potential bias related to the unblinded nature of this study with health care providers, the possible influence of the method of recruiting participants on the results
Thiengwittayaporn et al [30]	AI-based algorithms could be used to objectively monitor and assess the end-user rehabilitation compliance or adherence by measuring the frequency and duration an end user is engaged with exercises	The limited number of participants, no participants had symptomatic shoulder disorders, significantly younger age of the participants, and how well the proposed AI-based system would generalize to adherence monitoring in a clinical population is uncertain
Capecci et al [31]	An end user’s ability to accurately perform the exercises at the final follow-up reflects adherence to home exercise, which could be improved due to the role of AI-based rehabilitation regimen	The end users included in this study were able to efficiently use smartphones, which may not be reflective of a larger older adult population; according to the structure of the study, it was impossible to blind the participants to their intervention, which could potentially skew self-reported outcomes, and the majority of the end users were female participants
Ramkumar et al [32]	AI-based devices allow for real-time monitoring of several aspects of adherence: both daily adherence and repetition, based on exercise recognition	The small sample size and the absence of a control group
Burns et al [33]	The AI-based remote end-user monitoring system could offer the newfound ability to more completely evaluate the end-user rehabilitation compliance	The data represented a small cohort with no broadly generalizable conclusions, a small sample size, and the potential risk for selection and recall bias by end users

^aAI: artificial intelligence.