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Table 1 A summary of key characteristics of selected studies

From: Using body sensors for evaluating the impact of smart cycling technologies on cycling experiences: a systematic literature review and conceptual framework

Author

Experience and measurement

Analysis

Experience type

Sensor type

SCT evaluated

Statistical method

Causal inferences from mixed method triangulation

Only statistical correlations

Grounded theory

Unclear

Confounders statistically analysed

Confounders only discussed

von Stülpnagel [132]

Risk perception

ET

 

Linear mixed model

v

    

Doorley et al. [33]

Risk perception

ECG

 

ANOVA

v

    

Millar et al. [80]

Arousal

EDA

 

Multilevel regression

v

    

Pejhan et al. [93]

Anxiety

ECG or PPG (not specified)

 

ANOVA

v

    

Fitch et al. [43]

Stress

ECG

 

Multilevel regression

v

    

Venkatachalapathy et al. [127]

Stress

EDA

v

Linear mixed model

v

    

Teixeira et al. [125]

Stress

EDA

 

Multilevel regression

v

    

Yang et al. [141]

Stress

EDA

 

Propensity score matching and linear mixed model

v

    

Zeile et al. [145]

Stress

PPG, ST, ECG

 

Rule-based process

v

    

Nuñez et al. [88]

Stress

EDA, ST

 

Logistic regression

v

    

Caviedes and Figliozzi [21]

Stress

EDA

 

Random effect model

v

    

Vieira et al. [130]

Stress

ECG

 

k-Nearest neighbours

v

    

Rybarczyk et al. [106]

Unspecified

PPG

 

Local regression model

v

    

Berger and Dörrzapf [12]

Stress

EDA, ET (separate parts)

 

Not specified

 

v

   

Zink et al. [146]

Mental workload

EEG

 

ANOVA

 

v

   

Mantuano et al. [78]

Attention

ET

 

Eye tracking software

 

v

   

Scanlon et al. [112]

Attentiveness, task effort

EEG

 

T tests

 

v

   

Robles et al. [99]

Attention, excitement, task effort, task difficulty

EEG

 

ANOVA

 

v

   

Hale et al. [55]

Risk perception

ECG

 

T tests

 

v

   

Gadsby et al. [46]

Comfort

ET

 

Eye tracking software, ANOVA

 

v

   

Fyhri and Phillips [45]

Risk perception

ECG

 

ANOVA

 

v

   

Liu and Figliozzi [75]

Stress

EDA

 

ANOVA

 

v

   

Feizi et al. [41]

Comfort

Hall effect

 

Z tests and ordered probit model

 

v

   

De La Iglesia et al. [30]

Exercise

ECG or PPG (not specified)

v

Descriptive statistics

 

v

   

Resch et al. [96]

Stress

PPG, ST, ECG

 

Rule-based process

  

v

  

Ryerson et al. [107]

Mental workload

ET

 

Eye tracking software, ANOVA

  

v

  

Scanlon et al. [112]

Mental distraction

EEG

 

T tests

  

v

  

Dastageeri et al. [29]

Happy or Fear

PPG, ST, ECG

 

Multilayer perceptron classifier and decision tree

  

v

  

Werner et al. [137]

Stress

EDA, ST

 

Rule-based process

  

v

  

Kyriakou et al. [71]

Stress

EDA, ST

 

Rule-based process

  

v

  

Ducao et al. [35]

Unspecified

EEG, ECG

 

Unspecified

  

v

  

Kiryu and Minagawa [69]

Muscle fatigue

EMG

v

Regression model

  

v

  

Gorgul et al. [51]

Stress

ECG or PPG (not specified), EDA

 

Getis-Ord Gi statistic

  

v

  

Mussgnug et al. [84]

Unspecified

ET

 

Unspecified

  

v

  

Zeile et al. [144]

Stress

PPG, ST, ECG

 

Rule-based process

  

v

  

Hughey et al. [58]

Perceived exertion

HR

 

T tests

  

v

  

Andres et al. [4]

Integrated Exertion

Gyroscope

v

Thematic analysis

   

v

 

Walmink et al. [133]

Social Exertion

ECG

v

Thematic analysis

   

v

 

Andres et al. [5]

Peripheral vision

EEG

v

Thematic analysis

   

v

 

Bial et al. [13]

Ease, comfort

ECG

v

ANOVA

    

v

  1. ECG electrocardiogram, PPG photoplethysmogram, ECG and PPG sensors were used to measure heartbeat and heartbeat variability rates. EDA electrodermal activity, EMG electromyography, ET eye tracking, ANOVA analysis of variance