Paper | Sample (N) | Age | Condition | Condition assessment or criteria | Outcome variable | Adopted technology | Research methodology | General aim | Specific aim |
---|---|---|---|---|---|---|---|---|---|
Alqahtani et al., 2017 [55] | N = 29 | mean 87, sd 6 | Frailty | Fried criteria | Upright balance, lower extremity muscle strength | Balance accelerometer; uni-axial load cell | Observational (Diagnostic accuracy study design) | Assessment | Validation of inexpensive measurements of strength and balance |
Ambagtsheer et al., 2020 [56] | N = 592 | median 88, (IQR 9.0) | Frailty | Electronic Frailty Index | Clegg’s 36-items: activity limitation, chronic disease, falls, social isolation, cognition, mobility, polypharmacy, sleep quality and weight loss. | Machine Learning: K-Nearest Neighbours, Decision Tree, Support Vector Machines | Observational (Diagnostic accuracy study design) | Assessment | Identifying frailty from administrative records |
Boumans et al., 2019 [62] | N = 42 | mean 77.1, sd 5.7 | Frailty | Frailty Index | Time for completion of the questionnaires/robot–patient and nurse–patient interactions; percentage of robot–patient interactions completed autonomously | Social robot | Observational (Diagnostic accuracy study design) | Assessment | Effectiveness and acceptability of robot assistant assessment |
Camicioli et al., 2015 [63] | N = 72 | mean 74.97, sd 1.44 | Frailty | Fried criteria | Handwriting parameters: velocity, pressure, pauses. | Writing tablet with an instrumented pen for quantifying three-dimensional aspects of copying | Observational (Diagnostic accuracy study design) | Assessment | Studying relation between handwriting measures and frailty |
Dupuy et al., 2017 [45] | N = 32 | mean 81.63, sd 1.57 | Frailty | Fried criteria | Everyday activities; safety; social participation; interaction support; functional status; caregiver burden | Assisted-living platform: a set of wireless sensors and two touchscreen tablets | Interventional (Randomized controlled trial) | Intervention | Enhancing ADL authonomy, safety and sociality |
Galan-Mercant & Cuesta-Vargas, 2013 [64] | N = 30 | mean 76.98, sd 4.85 | Frailty | Fried criteria | Variability of the three-axes accelerations, angular velocity, and displacement of the trunk during the Si-St and St-Si transitions | iPhone 4 accelerometer | Observational (Diagnostic accuracy study design) | Assessment | Detecting frailty from Sit-to-Stand and Stand-to-Sit transition measures |
Galan-Mercant & Cuesta-Vargas, 2014 [65] | N = 18 | mean 79.95, sd 5.37 | Frailty | Fried criteria | Magnitude of accelerometry values | Trialxial gyroscope, accelerometer and a magnetometer in the iPhone 4 smartphone | Observational (Diagnostic accuracy study design) | Assessment | Improving the traditional assessment tools |
Galan-Mercant & Cuesta-Vargas, 2015 [66] | N = 30 | mean 76.98, sd 4.85 | Frailty | Fried criteria | ETUG test: sit-to-stand, gait go, turning, gait come, turn-to-stand-to-sit | Tri-axial gyroscope, an accelerometer and a magnetometer in the iPhone 4. | Observational (Diagnostic accuracy study design) | Assessment | Using intertial sensors embedded in a smartphone to measure kinematic variables in frail elderly |
Garcia-Moreno et al., 2020 [67] | N = 79 | mean 75 | Frailty | Fried criteria | Fried criteria: “non-frail” 0 criteria, “pre-frail” 2 criteria, “frail” ≥3 criteria | Samsung Gear S3 wearable sensors; Microservices System Architecture; Frailty Status App; Cloud Server; Machine Learning algorithms | Observational (Diagnostic accuracy study design) | Assessment | To assess frailty status during the performance of IADLs |
Gianaria et al., 2016 [68] | N = 30 | mean 75.6, sd 7.5 | Frailty | Tillburg Frailty Indicator | Walking time/speed, covered distance, swing time, double support time, balance during walking, torso inclination angle | Microsoft Kinect sensors with skeleton tracking feature | Observational (Diagnostic accuracy study design) | Assessment | Detecting frailty from gait and posture features |
Golkap et al., 2018 [69] | N = 36 | mean 82, sd 10 | Frailty | Edmonton Frail scale | Arterial hemoglobin oxygen saturation; movement in a location; bed or chair occupancy | Home Monitoring Platform: sensors to acquire patient’s habits/clinical data; home gateway, a remote server to store patient data; clinician portal to view and manage patient data | Observational (Diagnostic accuracy study design) | Assessment | Studying an integrated care system to support independent living of frailty |
Graňa et al., 2020 [76] | N = 645 | mean 84.2 sd 6.76 | Frailty | Fried criteria | Readmissions rates | Machine Learning - Linear discrimination analysis, Support vector machines, Multilayer perceptrons, K nearest neighbors, Random forests | Observational (Retrospective cohort study design - Predictive model) | Prediction | Studying frailty as a predictor of hospital readmissions |
Hassler et al., 2019 [72] | N = 474 | ≥ 65 | Frailty | Fried criteria | ≥ 3 Fried criteria | Machine Learning – naïve Bayes classifier (NB), CART algorithm tree and bagging CART, C5.0 algorithm, Random Forest analysis, SVM, LDA | Observational (Retrospective cohort study design - Predictive model) | Prediction | Finding predictive factors for frailty |
Held et al., 2017 [77] | N = 1686 | ≥ 70 years | Geriatric syndromes (Frailty; Cognitive impairment; Falls; Incontinence) | Fried criteria; Clinical assessment for cognition; The International Consultation of Incontinence Questionnaire; ≥ 2 falls in 12 months | Frequency of medication combinations | Machine Learning - Association Rule, Frequent-Set analysis | Observational (Cross-sectional study design) | Prevalence | Detect patterns of medication combinations according to geriatric syndrome status |
Kubicki et al., 2014 [49] | N = 46 | mean 81.87, sd 5.9 | Frailty | Fried criteria | Postural control, rapid arm movement | 2D virtual reality-based program of motor telerehabilitation | Interventional (Randomized controlled trial) | Intervention | Enhancing postural control and balance |
Kubicki, 2014 [57] | N = 37 | mean 82.25, sd 6.01 | Frailty | Fried criteria | Gait speed; hand maximal velocity; timed up and go | Semi-immersive virtual reality with active motion-capture system based onvision technology | Observational (Diagnostic accuracy study design) | Assessment | improving detection of motor control efficiency |
Lee et al., 2019 [51] | N = 65 | ≥ 65 | Frailty | Custom questionnaire (based on Study of Osteoporotic Fractures index) | Health status, exercise, frailty, handgrip, body mass | Smart phone learning and balance/flexibility exercise | Interventional (Non-randomized trial) | Intervention | Reducing frailty |
Martin-Lesende et al., 2016 [70] | N = 83 | mean 81.3 (IQR: 77.1–85.4) | Multimorbidity | Presence of heart failure and/or chronic lung disease; ≥ 2 admission to hospital in the previous year | Mortality rate | Telemonitoring | Observational (Retrospective cohort study design) | Mortality rate | To assess mortality according to multimorbidity and telemonitoring status |
Mateo-Abad et al., 2020 [52] | N = 856 | mean 77.6, sd 7.7 | Multimorbidity | CIRS | Use of health care services, clinical control of the examined conditions, physical functional status, patient ́s satisfaction. | ICT-based platforms | Interventional (Non-randomized trial) | Intervention | Impact of an integrated care program on health resources use, clinical outcomes, and functional status |
Merchant et al., 2020 [71] | N = 2.589 | mean 73.1, sd 6.5 | Geriatric Syndromes (Frailty, Cognitive impairment, Sarcopenia, Anorexia of aging) | FRAIL questionnaire | Prevalence of frailty, sarcopenia, anorexia of aging | iPad mobile application for Rapid Geriatric Assessment | Observational (Cross-sectional study design) | Prevalence | Studying prevalence of frailty, sarcopenia and anorexia of aging |
Orlandoni et al., 2016 [46] | N = 188 | mean 85.47, sd 7.03 | Multimorbidity | CIRS | Incidence rates of complications, outpatient hospital visits, hospitalizations | Samsung Galaxy tablet for video consultation | Interventional (Randomized controlled trial) | Intervention | Enhancing home enteral nutrition management |
Ozaki et al., 2017 [54] | N = 27 | mean 73, sd 6 | Frailty | Fried criteria | Preferred and maximal gait speeds, tandem gait speeds, timed up-and-go test, functional reach test, functional base of support, postural stability, muscle strength of the lower extremities, grip strength | Balance exercise assist robot | Interventional (Cross-over randomized controlled trial) | Intervention | Enhancing balance and walking |
Paliokas et al., 2020 [58] | N = 80 | mean 78.08, sd 5.48 | Frailty | Fried criteria | Errors related to the product types/number, payment errors, overall duration, selected item types/number, payment score, overall score | Non-immersive Virtual Reality Serious Game | Observational (Diagnostic accuracy study design) | Assessment | Detecting frailty from Virtual Reality Serious Game |
Parvaneh et al., 2017 [59] | N = 120 | mean 78, sd 8 | Frailty | Fried criteria | Daily postural transition | Unobtrusive shirt-embedded sensor with a three-axis accelerometer | Observational (Diagnostic accuracy study design) | Assessment | Identifying frailty from daily postural transitions |
Peng et al., 2020 [73] | N = 86.133 | mean 82.5 | Frailty | Multimorbidity frailty index | All-cause mortality; unplanned hospitalizations; intensive care unit admissions. | Machine Learning - random forest method, Kaplan-Meier survival curve/log-rank test, Cox proportional hazard models | Observational (Retrospective cohort study design - Predictive model) | Predicition | Developing a machine learning–based multimorbidity frailty index |
Persson et al., 2020 [53] | N = 94 | mean 80, sd 8 | Comorbidity | CCI | Health-related quality of life; influence of healthcare dependency measures on HRQoL or vice versa | Telemonitoring system: digital pen technology supported by hospital-based home care | Interventional (Pre-post study design) | Intervention | Enhancing quality of life |
Ritt et al., 2017 [60] | N = 123 | mean 82.4, sd 6.25 | Frailty | Fried criteria; Frailty Index; Clinical Frailty Scale; Frailty index based on a comprehensive geriatric assessment assessment | Spatio-temporal gait parameters | Electronic walkway; shoe-mounted inertial sensor-based mobile gait analysis system. | Observational (Diagnostic accuracy study design) | Assessment | Detecting frailty satus from gait analysis |
Sargent et al., 2020 [74] | N = 1453 | mean 79, sd 0.54 | Frailty | Fried criteria; MMSE score ≤ 23, TMT-A score ≥ 78, TMT-B score ≥ 106 | Cognitive frailty: MMSE score ≤ 23, TMT-A score ≥ 78, TMT-B score ≥ 106; Physical frailty: ≥ 3 Fried criteria | Machine Learning - tree boosting approach model | Observational (Retrospective cohort study design - Predictive model) | Predicition | Studying biological mechanisms that relate physical frailty and cognitive impairment. |
Schiltz et al., 2020 [75] | N = 6.617 | ≥ 65 | Multimorbidity | Self-reported multimorbidity | 30 day hospital readmission | Machine Learning - Random forest analysis, Classification and regression tree, Modified Poisson regression analysis, generalized estimating equation approach | Observational (Retrospective cohort study design - Predictive model) | Predicition | Studying IADL dependency as a predictor of hospital readmissions |
Takahashi et al., 2012 [47] | N = 205 | mean 80.3, sd 8.2 | Multimorbidity | CIRS | Hospitalization and emergency department visits | Telemonitoring device | Interventional (Randomized controlled trial) | Intervention | Reducing hospitalizations and emergency department visits |
Tomita et al., 2007 [48] | N = 78 | mean 73.8, sd 4.7 | Multimorbidity | CIRS | Functional status (ADL, IADL, MMSE, CHART) | Ambient assistive living: computer with internet access, X10-based smart home technology | Interventional (Randomized controlled trial) | Intervention | Studying feasibility and effectivenes of smart home technologies |
Tsipouras et al., 2018 [61] | N = 73 | mean 78.15, sd 5.5 | Frailty | Fried criteria | Number and durantion of transitions | Bluetooth localization system: sensorobluetooth beacons, smartphone andMaschine Learning for frailty level assessment: Naïve Bayes classifier, k-Nearest Neighbour, Neural Networks, Decision Trees algorithm, Random Forests | Observational (Diagnostic accuracy study design) | Assessment | Correlation between indoor activities and frailty status |
Violán et al., 2019 [78] | N = 916.619 | mean 75.4, sd 7.4 | Multimorbidity | > 1 of 60 chronic diseases | > 1of selected 60 chronic diseases; sociodemographics; number of invoiced drugs; use of health services | Machine Learning - fuzzy c-means clustering algorithm | Observational (Cross-sectional study design - Predictive model) | Predicition | Identifying multimorbidity patterns in the electronic health records |
Volders et al., 2020 [50] | N = 585 | mean 74.5, sd 6.4 | Multimorbidity | Self-reported multimorbidity | Physical activity | ActiGraph GT3X-BT accelerometer | Interventional (Randomized controlled trial) | Intervention | Studying the effect of a computer-tailored phisical activity intervention |