Table 1.
Manuscript | Medical classification | Diabetes type | Research goals |
Najafi et al (2010) [44] | Patients with DMa used as case study | Not specified | Find association between posture and balance control among patients with different DM complications |
Grewal et al (2013) [56] | DM complication: DPNb and DPN with diabetic foot | Not specified | Find association between DPN and DFUc for gait |
Luštrek et al (2014) [36] | Focused on DM | Not specified | Activity recognition of walking, running, cycling, lying, sitting, and standing |
Luštrek et al (2015) [37] | Focused on DM | Not specified | Activity recognition of sleeping, home chores, home leisure, eating, and exercising |
Cvetković et al (2016) [47] | Focused on DM | Not specified | Activity recognition of working, eating, exercising, and home activities |
Calbimonte et al (2017) [38] | Focused on DM | T1Dd | Predict glycemic events |
Fraiwan et al (2017) [48] | DM complication: diabetic foot | Not specified | Diagnose development of DFU |
McLean et al (2017) [49] | Patients with DM used as case study | GDe | Measure physical proximity, physical activity, and magnetic field strength |
Razjouyan et al (2017) [57] | DM complication: diabetic foot | Not specified | Find association between physiological stress response and healing speed among outpatients with active DFU |
Reddy et al (2017) [39] | Focused on DM | T2Df | Diagnose individual’s diabetic status |
Turksoy et al (2017) [50] | Focused on DM | T1D | Find association between biometric variables and changes in glucose concentration |
Bartolic et al (2018) [40] | Focused on DM | Not specified | Measure GLg, insulin dosage, physical activity, daily movement, and sleep duration and quality |
Faccioli et al (2018) [45] | Focused on DM | T1D | Find association between glucose prediction models’ performance |
Groat et al (2018) [51] | Focused on DM | T1D | Find association between exercise behavior data with the rate of change in GL |
McMillan et al (2018) [46] | Focused on DM | T2D | Measure combined GL data, physical activity, and sedentary behavior |
Merickel et al (2018) [32] | Focused on DM | T1D | Find association between pattern of glucose and at-risk pattern of vehicle acceleration behavior |
Nguyen Gia et al (2019) [41] | DM in conjunction with other diseases: DM+cardiovascular disease | Not specified | Activity recognition of fall detection and remote health monitoring |
Rescio et al (2019) [33] | DM complication: diabetic foot | Not specified | Measure temperature and pressure of the plantar foot |
Sarda et al (2019) [52] | DM in conjunction with other diseases: DM+depression | Not specified | Find association between smartphone-sensing parameters and symptoms of depression |
Ramazi et al (2019) [34] | Focused on DM | T2D | Predict the progression of T2D |
Garcia et al (2019) [35] | Focused on DM | Not specified | Diagnose DM from facial images |
Sevil et al (2019) [42] | DM in conjunction with other diseases: DM+acute psychological stress | T1D | Find the association between acute psychological stress and the glucose dynamics |
Zherebtsov et al (2019) [43] | Patients with DM used as case study | T2D | Measure the changes in the microcirculatory blood flow of healthy patients and patients with T2D |
Rodriguez-Rodriguez et al (2019) [53] | Focused on DM | T1D | Predict blood GL for T1D with limited computational and storage capabilities using only CGMh data |
Sanz et al (2019) [54] | Focused on DM | T1D | Find the association between different signals provided by 3 different wearables devices and the accuracy of a CGM device during aerobic exercises |
Whelan et al (2019) [55] | Focused on DM | T2D | Measure the use, feasibility, and acceptability of behavioral and physiological self-monitoring technologies in individuals at risk of developing T2D |
aDM: diabetes mellitus.
bDPN: diabetic peripheral neuropathy.
cDFU: diabetic foot ulcer.
dT1D: type 1 diabetes.
eGD: gestational diabetes.
fT2D: type 2 diabetes.
gGL: glucose level.
hCGM: continuous glucose monitor.