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Table 1 Study design, sample characteristics and main results

From: Sensor-based fall risk assessment in older adults with or without cognitive impairment: a systematic review

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.
  1. SD: standard deviation, n: number, MMSE: Mini-Mental State Examination, HFR: high fall risk, LFR: low fall risk, CI: cognitive impairment, SF: single faller, MF: multiple faller, NF: non-faller, F: faller, NFR: no fall risk