OBJECTIVE: This scoping review aimed to gather current knowledge on accurately identifying and distinguishing between non-frail, pre-frail, and frail older adults using gait and daily physical activity (DPA) parameters and/or models that combine gait with DPA parameters in both controlled and daily life environments. METHODS: Following PRISMA-ScR guidelines, a systematic search was conducted across seven databases using key terms: "frail", "gait or walk", "IMU", and "age". Studies were included if they focused on gait analysis using Inertial Measurement Units (IMUs) for walking distances greater than 10 meters. Extracted data included study design, gait and DPA outcomes, walking conditions, and classification model performance. Gait parameters were grouped into four domains: spatio-temporal, frequency, amplitude, and dynamic gait. DPA parameters were synthesized into three categories: postural and transition, variability, and physical activity pattern. RESULTS: A total of 15 cross-sectional studies involving 2,366 participants met the inclusion criteria. Gait analysis showed (pre)frail individuals had slower, shorter steps with longer stride times compared to non-frail individuals. Pre-frail individuals showed distinct gait patterns in periodicity, magnitude range, and variability. In daily activities, (pre)frail individuals displayed shorter, fragmented walking periods and longer transitions between positions. Walking variation identified pre-frail status, showing progressive decreases from non-frail to frail states. Combined gait and daily physical activity models achieved over 97% accuracy, sensitivity and specificity in distinguishing between groups. DISCUSSION: This review provides an updated synthesis of the relationship between various gait and/or DPA parameters and physical frailty, highlighting gaps in pre-frailty detection and the variability in measurement protocols. It underscores the potential of long-term, sensor-based monitoring of daily physical activity for advancing pre-frailty screening and guiding future clinical trials. Structured Abstract BACKGROUND: Changes in gait and physical activity are critical indicators of frailty. With advancements in wearable sensor technology, long-term gait analysis using acceleration data has become more feasible. However, the contribution of parameters beyond gait speed, such as gait dynamics and daily physical activity (DPA), in identifying frail and pre-frail individuals remains unclear. OBJECTIVE: This scoping review aimed to gather knowledge on accurately identifying and differentiating physical pre-frail and frail individuals from non-frail individuals using gait parameters alone or models that combine gait and DPA parameters, both in controlled settings and daily life environments. METHODS: The review followed PRISMA-ScR guidelines. A search strategy incorporating key terms-"frail", "gait or walk", "IMU", and "age"-was applied across seven databases from inception to March 1, 2024. Studies were included if they focused on gait analysis in controlled or daily environments using Inertial Measurement Units (IMUs) and involved walking distances longer than 10 meters. Data on walking conditions, gait outcomes, classification methods, and results were extracted. Gait parameters were categorized into four domains: spatio-temporal, frequency, amplitude, and dynamic gait. DPA parameters were synthesized into three categories: postural and transition, variability, physical activity pattern. RESULTS: A total of 15 cross-sectional observational studies met the eligibility criteria, covering 2,366 participants, with females representing 27%-80% of the sample and ages ranging from 60 to 92 years. Regarding gait parameters, (pre)frail individuals exhibited longer stride times, slower walking speeds, shorter steps, and reduced cadence compared to non-frail individuals. In three studies, pre-frail could be distinguished from the non-frail and frail group through gait periodicity, range of magnitude, and gait variability. DPA patterns differed between groups, with (pre)frail individuals showing shorter and more fragmented walking periods, brief walking bouts and longer postural transitions. Walking bout variation (CoV) effectively identified pre-frail status, decreasing 53.73% from non-frail to pre-frail, and another 30.87% from pre-frail to frail. Models combining both gait and DPA parameters achieved the highest accuracy (97.25%), sensitivity (98.25%), and specificity (98.25%) in distinguishing between groups. DISCUSSION: This scoping review provides an updated overview of the current knowledge and gaps in understanding the relationship between gait parameters across different domains and DPA parameters along with physical frailty. Significant variability in gait measurement methods and protocols complicates direct comparisons between studies. The review emphasizes the need for further research, particularly in pre-frailty screening, and underscores the potential of inertial sensor-based long-term monitoring of daily physical activity for future clinical trials.