Author, year | Study design, sample including number of participants, mean age (SD) and sex | Cognitive Status | Record of falls/ fall history | Main findings |
---|---|---|---|---|
Bautmans, 2011 [40] | Cross-sectional Community-based Total n = 121, 80 (5), 50% female; Younger adults n = 40, 22(1), 50% female | Cognitively intact according to MMSE (MMSE≥24) | Retrospective 6 months, Tinetti Assessment Tool, Timed-Up and Go HFR n = 40, LFR n = 41 | - Participants with HFR showed slower gait speed (p < 0.05) - With cut-off value 1.58 m/s gait speed discriminates between HFR and LFR with 78% sensitivity and 76% specificity |
Bizovska, 2018 [43] | Prospective study Community-based Total n = 131, 71 (6), 82% female | CI as exclusion criterion | Prospective 1 year SF = 35, MF = 15, NF = 81 | - Trunk medial-lateral acceleration in short-term Lyapunov exponent differed between MF and NF (p < 0.05) but not after Bonferroni correction; - Poor MF predictive ability of trunk medio-lateral short-term Lyapunov exponent but results improved when combining with clinical examination |
Brodie, 2017 [59] | Cross-sectional Community-based Total n = 96, 75 (8), 59% female | CI as exclusion criterion according to MiniCog | Retrospective 12 months F = 33, NF = 63 | - Fallers showed significantly reduced gait endurance and increased within-walk variability (p < 0.05) |
Brodie, 2015 [47] | Cross-sectional Community-based Total n = 96, 80 (4), 67% female | No information about CI | Retrospective 1 year, Physiological Profile Assessment Tool F = 35, NF = 61 | - 8-step mediolateral harmonic ratio identified significant differences in between F and NF based on age, walking speed and physiology (p < 0.05) |
Buckinx, 2015 [48] | Prospective study Nursing homes Total n = 100, 86 (6), 80% female | No information about CI | Prospective 2 years F = 75, NF = 25 | - Gait characteristics were not predictive of long-term falls |
Buisseret, 2020 [64] | Prospective study Nursing homes Total n = 73, 83 (8) 62% female | CI included, 16% with dementia | Prospective 6 months F = 23, NF = 50 | - When the Timed-Up and Go test results are coupled with indicators of gait variability measured during a six-minute walk test, accuracy of fall prediction improved from 68 to 76% |
Ejupi, 2017 [60] | Cross-sectional Community-based Total n = 94, 80 (7), 68% female | CI as exclusion criterion according to MiniCog and MMSE | Retrospective 12 months F = 34, NF = 64 | - F showed significantly lower maximum acceleration, velocity and power during sit-to-stand movements compared to NF (p < 0.05) |
Gietzelt, 2014 [36] | Cohort-study Nursing homes Total n = 40, 76 (8), 50% female | CI included (MMSE 9.3 ± 8.0) | Prospective for 2, 4 and 8 months F = 13, NF = 27 | - It is possible to classify gait episodes of F and NF for mid-term monitoring (4 months) during daily life using body-worn sensors (75.0% accuracy) |
Greene, 2012 [55] | Prospective study Community-based Total n = 226, 72 (7), 73% female | CI as exclusion criterion | Prospective 2 years F = 83, NF = 143 | - Sensor-derived features yielded a mean classification accuracy of 79.69% for 2-year prospective falls |
Howcroft, 2016 [56] | Cross-sectional Community-based Total n = 100, 76 (7), 56% female | CI as exclusion criterion according to self-reports | Retrospective 6 months F = 24, NF = 76 | - Best fall classification model using pressure-sensing insoles and head, pelvis and shank accelerometers (84.0% accuracy) - Best single-sensor model with parameters derived from a head sensor during single task (84.0% accuracy) |
Howcroft, 2018 [57] | Prospective study Community-based Total n = 75, 75 (7), 59% female | CI as exclusion criterion according to self-reports | Prospective 6 months F = 28, NF = 47 | - F had significantly lower dual-task head anterior-posterior Fast Fourier Transform first quartile, single-task left shank medial-lateral Fast Fourier Transform first quartile, and single-task right shank superior maximum acceleration (p < 0.05) |
Hua, 2018 [41] | Cross-sectional Community-based Total n = 67, 76 (6), 100% female | No information about CI | Retrospective 1 year, Short Physical Performance Battery HFR = 19, LFR = 48 | - Coefficient of variance, cross-correlation with anteroposterior accelerations, and mean acceleration were the top features for classification in HFR and LFR group |
Ihlen, 2018 [44] | Prospective study Community-based Total n = 303, 76 (7), 50% | Including CI (MMSE≥19) | Prospective 6 months SF = 58, MF = 46, NF = 199 | - Higher phase-dependent multiscale entropy of trunk acceleration at 60% of step cycle in F compared to NF (p < 0.05) - PGME has predictive ability of falls among SF |
Ihlen, 2016 [49] | Cross-sectional Community-based Total n = 71, 78 (5), 65% female | Cognitively intact according to MMSE score (≥24) | Retrospective 12 months F = 32, NF = 39 | - Refined composite multiscale entropy and refined multiscale permutation entropy of trunk velocity and trunk acceleration can distinguish between daily-life walking of F and NF (75.0–88.0% sensitivity, 85.0–90.0% specificity) |
Iluz, 2016 [35] | Cross-sectional Community-based Older adults total n = 71, 78 (5), 65% females; Younger adults Total n = 30, 28 (4), 57% female | Cognitively intact according to MMSE score (≥24) | Retrospective 1 year F = 33, NF = 38 | - Temporal and distribution-related features from sit-to-walk and sit-to-stand transitions during daily-life differed significantly between F and NF - Mean classification accuracy was at 88.0% and better than traditional laboratory assessment |
Mancini, 2016 [45] | Cross-sectional, prospective Community-based Total n = 35. 85 (5), 66% female | Dementia as exclusion criterion according to Clinical Dementia Rating Scale and/or MMSE | Retrospective 12 months, prospective 6 months Retrospective analysis: SF = 12, RF = 7, NF = 16 Prospective analysis: F = 7, NF = 28 | - Quality of turning (mean turn duration, mean peak speed of turning, mean number of steps to complete a turn) were significantly compromised in RF compared to NF (p < 0.05) |
Marschollek, 2009 [61] | Cross-sectional Geriatric setting Total n = 110, 80 (−), 74% female | no information about CI | Retrospective n/a F = 26, NF = 84 | - Pelvic sway while walking, step length and number of steps in TUG differed significantly between F and NF (p < 0.05) - Adding sensor-based gait parameters to geriatric assessment improves specificity in fall prediction from 97.6 to 100.0% |
Marschollek, 2011 [62] | Prospective Geriatric setting Total n = 46, 81 (−), − | No information about CI | Prospective 1 year n/a | - Sensor-derived parameters can be used to assess individual fall-risk (58% sensitivity, 78% specificity) and identified more persons at fall risk than a conventional clinical assessment tool |
Pozaic, 2016 [63] | Cross-sectional Community-based Total n = 136, 73 (6), 69% female | CI as exclusion criterion according to Screening of Somatoform Disorders (> 10) | Prospective 1 month Fn = 13, NF = 123 | - Time and frequency domain-based features derived from a wrist-worn accelerometer on the dominant and non-dominant hand can significantly distinguish between F and NF (p < 0.05) |
Qui, 2018 [50] | Cross-sectional Community-based Total n = 196, 72 (4), 100% female | No information about CI | Retrospective 5 years F = 82, NF = 114 | - Sensor-based data distinguished accurately between F and NF (89.4% accuracy) |
Rivolta, 2019 [19] | Cross-sectional Hospital setting Older adults total n = 79, 69 (17), − Younger adults total n = 11, 35 (−), − | No information about CI | Tinetti Assessment Tool HFR = 33, LFR = 46 | - Sensor-based balance and gait features assessed during Tinetti Test differed significantly between individuals with HFR and LFR (p < 0.05) - Linear model and artificial neural network had a misclassification error of 0.21 and 0.11, respectively, in predicting Tinetti outcome |
Sample, 2017 [58] | Cross-sectional Community-based Total n = 150, 76 (9), 59% female | No information about CI | Retrospective 12 months F = 59, NF = 91 | - Sensor-based data collected during Timed-Up and Go resulted in a more sensitive model (48.1% sensitivity, 82.1% specificity) than including Timed-Up and Go time duration only (18.2% sensitivity, 93.1% specificity) |
Senden, 2012 [51] | Cross-sectional Community-based Total n = 100, 77 (6), 56% female | CI as exclusion criterion | Tinetti Assessment Tool HFR = 19, LFR = 31, NFR  = 50 | - Walking speed, step length and root mean square had high discriminative power to classify the sample according to the Tinetti scale (76.0% sensitivity, 70.0% specificity). |
van Schooten, 2015 [52] | Cross-sectional, prospective Community and residential care home Total n = 169, 75 (7), 54% female | CI included (MMSE≥18) | Retrospective 6 months; prospective 6 months Retrospective analysis: F = 60, NF = 109 Prospective analysis: F = 59, NF = 110 | - Sensor-derived parameters of the amount of gait (number of strides), gait quality (complexity, intensity, smoothness) and their interactions can predict prospective falls (67.9% sensitivity, 66.3% specificity). |
Wang, 2017 [46] | Prospective Community-based Total n = 81, 84 (4), 44% female | No information about CI | Prospective 12 months MF = 11, NF = 70 | - Rate in stair descent was higher in MF than in NF (p < 0.05). |
Weiss, 2011 [53] | Cross-sectional Community-based Total n = 41, 72 (7), 66% female | Cognitively intact according to MMSE score (≥24) | Retrospective 1 year F n = 23, NF n = 18 | - Sensor-derived Timed-Up and Go duration was significantly higher in F compared to NF (p < 0.05) - Jerk Sit-to-Stand, SD and average step duration correctly classify 87.8% of F and NF (91.3% sensitivity, 83.3% specificity) |
Weiss, 2013 [54] | Prospective Community-based Total n = 71, 78 (5), 65% female | Cognitively intact according to MMSE score (≥24) | Prospective 6 months F = 39, NF = 32 | - Gait variability differed significantly between F and NF (p < 0.05); |
Zakaria, 2015 [42] | Cross-sectional Hospital setting Total n = 38, 67 (7), 47% female | No information about CI | Timed-Up and Go HFR = 21, LFR = 17 | - Sensor-derived parameters of Timed-Up and Go phases can classify into people at HFR and people at LFR. |