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BACKGROUND Upper Limb (UL) recovery after stroke is strongly dependent upon rehabilitation dose; rehabilitation technologies present pragmatic solutions to dose enhancement. To date, technologies promoting self-directed rehabilitation have been poorly adopted. Understanding the barriers to adoption may shape strategies to enhance technology use, and therefore increase rehabilitation dose. OBJECTIVE We investigated how stroke survivor characteristics, technology usability, and attitudes towards technology (acceptability) influence engagement with rehabilitation technology. METHODS We conducted a feasibility study of a novel exercise-gaming technology for self-directed UL rehabilitation in subacute stroke survivors (within 30 days of stroke) (n=30) in an inpatient, acute hospital setting. Demographic and clinical characteristics were recorded, participants’ performance in using the system (usability) was assessed using a 4-point performance rating scale (adapted from the Barthel Index) and adherence with the system was logged throughout the trial. The Technology Acceptance Model (TAM) (Davis, 1989) was used to formulate a survey examining the acceptability of the system. Spearman’s rank correlations were used to examine associations between participant characteristics, user performance (usability), end-point technology acceptance and intervention adherence (adoption). Hierarchical cluster analysis characterised the structural organisation of the predictor-outcome similarity matrix. RESULTS The technology was usable for 87% of participants and the overall technology acceptance rating was 68% (95% CI: 56-79%). Participants trained with the device for a median of 26 minutes daily (IQR: 16-31), over an enrolment period of 8 days (IQR: 5-14). Technology adoption positively correlated with user performance (usability)(ρ: 0.55, 95% CI: [0.23, 0.75], P=.007) and acceptability: domains of perceived usefulness (ρ 0.42, 95%CI:[ 0.09, 0.68] P=0.03) and perceived ease of use (ρ: 0.46, 95% CI: [ 0.10, 0.74] P= 0.02). Technology acceptance decreased with increased global stroke severity (ρ: -0.56, 95% CI: [-0.79,-0.22] P= 0.007). A cluster analysis found that adoption was most closely linked with (in order of strength): usability; female sex; functional independence and UL impairment severity; prior technology exposure and cognition; perceptions of usefulness and ease of use. CONCLUSIONS Technology usability and acceptability were determinants of adoption and appear to be mediated by stroke survivor characteristics. The results demonstrate the importance of selecting technologies for stroke survivors on basis of individual needs and abilities and optimising the accessibility of technologies for the target user group. Facilitating changes in stroke survivors’ beliefs and attitudes towards rehabilitation technologies may enhance adoption.