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  1. Wang Y, Sani NM, Hua Y, Jiang Q, Zhao L
    PLoS One, 2024;19(12):e0311280.
    PMID: 39671431 DOI: 10.1371/journal.pone.0311280
    As China's population continues to age rapidly, smart elderly care has become a crucial strategy for addressing this societal challenge. Smart home systems have the potential to significantly enhance the quality of life, safety, and autonomy of the elderly. However, the adoption rate of these systems in this demographic remains relatively low, requiring an exploration of the key factors influencing the behavioral intention to use such systems. This study developed a comprehensive model based on the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology. Through empirical analysis using partial least squares structural equation modeling, this study systematically examined the pathways through which various factors affected the behavioral intentions of the elderly. The findings revealed that perceived usefulness, facilitating conditions, compatibility, and perceived cost had significant direct effects on behavioral intention. Additionally, social influence, perceived trust, facilitating conditions, and compatibility indirectly influenced behavioral intention through perceived usefulness and perceived ease of use. Although perceived trust did not directly affect behavioral intention, it exerted an indirect influence through perceived usefulness. This comprehensive model provides theoretical insights into the behavioral intentions of the elderly toward smart home systems and offers practical guidance for developers, designers, and policymakers in the development, design, and promotion of these systems.
  2. Sani NM, McAuslane N, Kasbon SH, Ahmad R, Yusof FAM, Patel P
    Ther Innov Regul Sci, 2020 Sep;54(5):1225-1226.
    PMID: 32865806 DOI: 10.1007/s43441-020-00150-2
    The authors have revised Figure 1. The revised figure is presented below.
  3. Sani NM, McAuslane N, Kasbon SH, Ahmad R, Yusof FAM, Patel P
    Ther Innov Regul Sci, 2020 Sep;54(5):1215-1224.
    PMID: 32865804 DOI: 10.1007/s43441-020-00140-4
    INTRODUCTION: The National Pharmaceutical Regulatory Agency (NPRA) embarked on a regulatory-strengthening program and is evaluating its processes. Optimising Efficiencies in Regulatory Agencies (OpERA) is a regulatory-strengthening program that provides benchmarking data that can define performance targets and focus performance improvement. The objective of this study was to use OpERA methodology to determine where time is spent in the NPRA approval process and to form a baseline to measure the performance improvements.

    METHODS: The OpERA tool was used to collect specific milestone data that identify time periods, review stages, and data points for new active substances and biosimilars approved by NPRA in 2017.

    RESULTS: In 2017, 25 new active substances and 1 biosimilar were approved by NPRA in a median of 515 days, representing both agency and applicant time. The median time between dossier receipt and the initiation of NPRA scientific assessment was 135 days, but there was a wide variation in queuing time. The median total assessment time was 279 days (agency and applicant timing). NPRA took a median of 166 days; applicants took a median of 131 days to respond to deficiency questions, with up to 6 cycles of review required for approval and 65% of applications requiring 4-5 cycles to provide satisfactory responses.

    CONCLUSIONS: As a result of these data, NPRA proposes three improvements: target start for scientific assessment 100 days after file acceptance, a maximum of 5 review cycles, and applicant response time limited to 6 months. These results will serve as a baseline for further assessment.

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