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research methods and data analysis > measures of movement > Flashcards

measures of movement Flashcards

1
Q

what are the four measures of movement?

A
  • gait (walking)
  • biomechanics
  • posture
  • balance
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2
Q

describe objective measures for movement analysis

A
  • lab- based movement biomechanics
  • standardised, repeatable and precise
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3
Q

what are the examples of objective movements?

A
  • 3D motion data
  • trajectories
  • joint angles
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4
Q

how are objective measures precise?

A
  • exact joint angles
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5
Q

what are the disadvantages of scaling up?

A
  • limited to small scale studies
  • complex analyses of raw data
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6
Q

do we always need details of how they move?

A
  • no, also need to know how much they move
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7
Q

what are the three ways of measuring physical activity?

A
  • self- report
  • pedometers
  • research grade activity monitors
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8
Q

what are self report measure examples?

A
  • Global PAQ
  • GP- PAQ
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9
Q

what are the two main limitations of self report measures?

A
  • open to bias and over estimation
  • vague time periods
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10
Q

what are pedometers?

A
  • small portable device that counts the number of steps a person takes
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11
Q

are pedometers simple?

A
  • very simple operation
  • requires a battery
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12
Q

what are the advantages of pedometers?

A

+ low cost
+ can monitor large samples at one time

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13
Q

what are the limitations of pedometers?

A
  • only 1 dimension of data
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14
Q

what are research grade activity monitors?

A
  • range from low- cost e.g., axivity to high cost e.g., actigraphy APDM
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15
Q

what is a axivity?

A
  • data logger that includes MEMS 3- axis accelerometer
  • categorises activity levels
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16
Q

what are the advantages of axivity?

A

+ simple functions
+ large sample

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17
Q

what is a actigraphy?

A
  • monitors human- rest activity cycles using wrist- worn device
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18
Q

what is the advantage of actigraphy?

A

+ multiple functions

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19
Q

what units are acceleration and orientation?

A
  • inertial measurement unit
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20
Q

how can we now track data? how does this work?

A
  • can use smartphones and wearables to capture data on how much we move and when (24/7)
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21
Q

describe the prevalence of smartphones and wearables

A
  • increasingly ubiquitous within population
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22
Q

what do smartphones and wearables allow us to understand?

A
  • health
  • lifestyle
  • behaviour
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23
Q

what are some examples of major fitness trackers? what can you download?

A
  • apple health kit
  • google fit
  • fitbit
  • can download raw data
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24
Q

what are fitness apps designed to do and track?

A
  • can automate the data capture
  • long- term tracking of daily step count and other measures
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25
Q

how can we measure frequency?

A
  • step count
  • number of bouts
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26
Q

how can we measure intensity?

A
  • cadence
  • metabolic equivalent of task = 02/ energy expenditure during quiet setting
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27
Q

how do you measure time?

A
  • periods of moderate- vigorous PA
  • sedentary periods
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28
Q

how do you measure type?

A
  • inclines/ stair climbing
  • walking/ running
  • sitting/ standing
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29
Q

what can the data cause?

A
  • causes data overload
  • alot of raw data collected
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30
Q

what is required to help us understand values?

A
  • context is needed
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31
Q

what is the trial you can do with the data?

A
  • randomised controlled trial
  • compare placebo or healthy group with health condition and intervention
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32
Q

what test can be used with the data?

A
  • pre- post test
  • compare baseline prior to intervention
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33
Q

what other data can you combine data with?

A
  • questionnaire data
  • qualitative data
34
Q

what does questionnaire data allow?

A
  • allows grouping/ predictors of change
35
Q

what does qualitative data give us?

A
  • details on individual insights
36
Q

what can we identify from data? and how?

A
  • identify patterns using data- driven analyses e.g., machine learning
37
Q

what ethical considerations are there when working with wearables/ phone data?

A
  • privacy
  • health inequality
  • biases
38
Q

what happens to data in relation to privacy

A
  • data use by tech company behind device
  • transfer of data for research
39
Q

what questions can be asked regarding privacy when using phones/ wearables?

A
  • where will the data be stored?
  • will the data remain anonymous?
40
Q

what is anonymised data ?

A
  • all identifiable data is removed e.g., name, address, mobile number
  • no way of tracking data back to an individual
41
Q

what is pseudonymised data?

A
  • same as anonymised but data has participant ID
  • separate file contains ID and identifiable
42
Q

can individual be tracked in pseudonymised data?

A
  • people with access to file can track back to an individual
43
Q

why may you think data is anonymous but it’s not?

A
  • large datasets interlinked with many variables
  • does removing the identifiable information make it fully anonymous?
44
Q

what is the test for anonymity?

A
  • motivated intruder test
45
Q

what is the motivated intruder test?

A
  • starts without any previous knowledge
  • aims to identify an individual from an anonymised dataset by accessing resources
46
Q

what resources are accessed in the motivated intruder test?

A
  • internet
  • libraries
  • all public documents
47
Q

what is motivated intruder test reasonably?

A
  • reasonably competent
48
Q

what does the motivated intruder test employ?

A
  • employs investigative techniques
  • including questioning people who may have additional knowledge of the individual
49
Q

what is the person doing the test assumed to have?

A
  • assumed to have no specialist knowledge such as computer hacking skills or any access to specialist equipment
50
Q

what does the motivated intruder test not resort to?

A
  • criminality such as burglary to gain access to data that is kept securely
51
Q

how do you add extra layers of privacy?

A
  • keeping detailed variables
  • always a chance an adversary will be able to de- anonymise data
52
Q

what should you remove to add an extra layer of privacy?

A
  • remove all signals in a dataset to anonymise
  • can make some analyses pointless
53
Q

what is the solution of anonymity?

A
  • randomisation
  • differential privacy
54
Q

what can you insert to help anonymity?

A
  • insert random noise into the information made available
  • done correctly, meaningful answers can still be retrieved
55
Q

what is an example of randomisation?

A
  • participants told to flip a coin before answering the question
    Heads= give real answer, the truth
    Tails= answer randomly e.g., flip another coin to determine yes/ no
56
Q

what is the health inequality relating to smartphones/ wearables?

A
  • exclusion if can’t afford the device
57
Q

what are the other health inequalities?

A
  • impact on access to healthcare for those with and without data
  • accessibility/ usability of the technology (age exclusion)
58
Q

what are the biases in terms of expenses of devices?

A
  • more expensive devices = better data quality?
59
Q

what is the biases with step count ?

A
  • might only work with healthy gait
60
Q

what are the biases with Ai models?

A
  • trained on representative data
61
Q

what are the potential issues with the activity trackers?

A
  • quality/ validation
  • how accurate are trackers?
62
Q

what are activity trackers designed for?

A
  • designed for healthy population
63
Q

how can slow gait or asymmetrical gait cause issues?

A
  • slow gait causes issues e.g., heel strikes and toe offs less pronounced
  • asymmetrical gait, other pattern changes > affects algorithms
64
Q

describe incentives for physical activity case study

A
  • access to the app’s 6000 users, anonymised data
  • daily step count; 6 months since registration but also 3 months prior to registration baseline measure
65
Q

what are the three factors of data cleansing and formatting?

A
  • outliers
  • data variability
  • missing variable
66
Q

what are outliers in data cleansing and formatting?

A
  • are there anomalous data points/ users?
  • excessive step counts?
  • remove data point/ user
67
Q

what is an example of an outlier?

A
  • 3 SD above the mean
68
Q

what is data variability in data cleansing and formatting?

A
  • does the mean step- count of an individual vary widely each month/ week
  • remove user
69
Q

what is an example of data variability?

A
  • top- percentile of variance
70
Q

what is a missing variable in data cleansing and formatting?

A
  • does an individual have many days of missing values?
  • remove user
71
Q

what is an example of missing variables?

A
  • > xx%
  • n days missing
72
Q

what is used as reference data? what does it define?

A
  • registration date
  • defines monthly interval
73
Q

what does objective data variable include? describe variable

A
  • daily step count
    Dependent variable = change in step
74
Q

what does subjective/ questionnaire variable include? describe variable

A
  • questionnaire
    Independent variable = age, gender
75
Q

how do you work out simple regression? - describe different letters

A

y = B0 + B1x
y= DV; x= IV ; B0= intercept
- relationship between x and y described by B

76
Q

how do you work out multiple regression? what relationship is shown?

A

y = B0 + B1x1 + B2X2 + BnXn
- what is the relationship between y and x1, x2, xn
- defined by B

77
Q

what regression types are used for continuous/ dichotomous class?

A
  • continuous= regression
  • dichotomous= logistic regression
78
Q

what is B in logistic regression?

A
  • increase in ‘log odds’ of the outcome (DV) per unit increase in IV
79
Q

what do we take from B in regression and why?

A
  • complicated to understand so we take the exp (B) to get the odds- ratio (OR) relative to reference category
80
Q

what does OR= 1 mean?

A
  • IV doesn’t affect outcome
81
Q

what does OR> 1 mean?

A
  • category associated with higher odds of outcome relative to reference
82
Q

what does OR < 1 mean?

A
  • category associated with lower odds of outcome relative to reference

research methods and data analysis (12 decks)

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