CLINICAL EFFECTIVENESS OF MHEALTH TECHNOLOGIES FOR ENHANCING POST STROKE FUNCTIONAL RECOVERY: A SYSTEMATIC LITERATURE REVIEW

Abstract

The aim of this study was to understand the extent to which mobile health (mHealth) technologies can support stroke survivors in improving their functional abilities. We were also interested in how different intervention designs and technological features might influence rehabilitation outcomes, and in evaluating the strength of the existing research evidence. Following PRISMA guidelines, we reviewed 42 studies that met our inclusion criteria. These studies employed a variety of mobile apps and wearable systems for stroke rehabilitation during the subacute and chronic phases. Overall, the findings are promising. Many studies demonstrated improvements in key functional areas such as upper limb motor skills (FMA-UE, BBT), walking performance (6MWD, STS 60) and language abilities in individuals with aphasia. However, not all measures were consistent, particularly the Action Research Arm Test (ARAT) and Motor Activity Log (MAL), which varied notably across studies. A notable pattern was that interventions incorporating active technological features, such as IMU sensors, VR-assisted exercises, electrical stimulation, real-time feedback and digital coaching, tended to yield stronger rehabilitation outcomes than those focusing primarily on educational content delivery. Of course, there are limitations to bear in mind. Many of the studies involved small participant groups, used non randomised designs or incorporated outcome measures that differed widely from one another. These issues make it harder to draw firm conclusions from the evidence as a whole. Nevertheless, the review clearly shows that mHealth has substantial potential. It can expand access to therapy, boost training intensity and facilitate long-term monitoring beyond traditional in-person sessions. Moving forward, more rigorous randomised clinical trials with consistent reporting standards are essential to confirm these findings and inform the development of digital rehabilitation models that meet patients' needs.