measures of movement Flashcards
(82 cards)
1
Q
what are the four measures of movement?
A
- gait (walking)
- biomechanics
- posture
- balance
2
Q
describe objective measures for movement analysis
A
- lab- based movement biomechanics
- standardised, repeatable and precise
3
Q
what are the examples of objective movements?
A
- 3D motion data
- trajectories
- joint angles
4
Q
how are objective measures precise?
A
- exact joint angles
5
Q
what are the disadvantages of scaling up?
A
- limited to small scale studies
- complex analyses of raw data
6
Q
do we always need details of how they move?
A
- no, also need to know how much they move
7
Q
what are the three ways of measuring physical activity?
A
- self- report
- pedometers
- research grade activity monitors
8
Q
what are self report measure examples?
A
- Global PAQ
- GP- PAQ
9
Q
what are the two main limitations of self report measures?
A
- open to bias and over estimation
- vague time periods
10
Q
what are pedometers?
A
- small portable device that counts the number of steps a person takes
11
Q
are pedometers simple?
A
- very simple operation
- requires a battery
12
Q
what are the advantages of pedometers?
A
+ low cost
+ can monitor large samples at one time
13
Q
what are the limitations of pedometers?
A
- only 1 dimension of data
14
Q
what are research grade activity monitors?
A
- range from low- cost e.g., axivity to high cost e.g., actigraphy APDM
15
Q
what is a axivity?
A
- data logger that includes MEMS 3- axis accelerometer
- categorises activity levels
16
Q
what are the advantages of axivity?
A
+ simple functions
+ large sample
17
Q
what is a actigraphy?
A
- monitors human- rest activity cycles using wrist- worn device
18
Q
what is the advantage of actigraphy?
A
+ multiple functions
19
Q
what units are acceleration and orientation?
A
- inertial measurement unit
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)
21
Q
describe the prevalence of smartphones and wearables
A
- increasingly ubiquitous within population
22
Q
what do smartphones and wearables allow us to understand?
A
- health
- lifestyle
- behaviour
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
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
25
how can we measure frequency?
- step count
- number of bouts
26
how can we measure intensity?
- cadence
- metabolic equivalent of task = 02/ energy expenditure during quiet setting
27
how do you measure time?
- periods of moderate- vigorous PA
- sedentary periods
28
how do you measure type?
- inclines/ stair climbing
- walking/ running
- sitting/ standing
29
what can the data cause?
- causes data overload
- alot of raw data collected
30
what is required to help us understand values?
- context is needed
31
what is the trial you can do with the data?
- randomised controlled trial
- compare placebo or healthy group with health condition and intervention
32
what test can be used with the data?
- pre- post test
- compare baseline prior to intervention
33
what other data can you combine data with?
- questionnaire data
- qualitative data
34
what does questionnaire data allow?
- allows grouping/ predictors of change
35
what does qualitative data give us?
- details on individual insights
36
what can we identify from data? and how?
- identify patterns using data- driven analyses e.g., machine learning
37
what ethical considerations are there when working with wearables/ phone data?
- privacy
- health inequality
- biases
38
what happens to data in relation to privacy
- data use by tech company behind device
- transfer of data for research
39
what questions can be asked regarding privacy when using phones/ wearables?
- where will the data be stored?
- will the data remain anonymous?
40
what is anonymised data ?
- all identifiable data is removed e.g., name, address, mobile number
- no way of tracking data back to an individual
41
what is pseudonymised data?
- same as anonymised but data has participant ID
- separate file contains ID and identifiable
42
can individual be tracked in pseudonymised data?
- people with access to file can track back to an individual
43
why may you think data is anonymous but it's not?
- large datasets interlinked with many variables
- does removing the identifiable information make it fully anonymous?
44
what is the test for anonymity?
- motivated intruder test
45
what is the motivated intruder test?
- starts without any previous knowledge
- aims to identify an individual from an anonymised dataset by accessing resources
46
what resources are accessed in the motivated intruder test?
- internet
- libraries
- all public documents
47
what is motivated intruder test reasonably?
- reasonably competent
48
what does the motivated intruder test employ?
- employs investigative techniques
- including questioning people who may have additional knowledge of the individual
49
what is the person doing the test assumed to have?
- assumed to have no specialist knowledge such as computer hacking skills or any access to specialist equipment
50
what does the motivated intruder test not resort to?
- criminality such as burglary to gain access to data that is kept securely
51
how do you add extra layers of privacy?
- keeping detailed variables
- always a chance an adversary will be able to de- anonymise data
52
what should you remove to add an extra layer of privacy?
- remove all signals in a dataset to anonymise
- can make some analyses pointless
53
what is the solution of anonymity?
- randomisation
- differential privacy
54
what can you insert to help anonymity?
- insert random noise into the information made available
- done correctly, meaningful answers can still be retrieved
55
what is an example of randomisation?
- 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
what is the health inequality relating to smartphones/ wearables?
- exclusion if can't afford the device
57
what are the other health inequalities?
- impact on access to healthcare for those with and without data
- accessibility/ usability of the technology (age exclusion)
58
what are the biases in terms of expenses of devices?
- more expensive devices = better data quality?
59
what is the biases with step count ?
- might only work with healthy gait
60
what are the biases with Ai models?
- trained on representative data
61
what are the potential issues with the activity trackers?
- quality/ validation
- how accurate are trackers?
62
what are activity trackers designed for?
- designed for healthy population
63
how can slow gait or asymmetrical gait cause issues?
- slow gait causes issues e.g., heel strikes and toe offs less pronounced
- asymmetrical gait, other pattern changes > affects algorithms
64
describe incentives for physical activity case study
- 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
what are the three factors of data cleansing and formatting?
- outliers
- data variability
- missing variable
66
what are outliers in data cleansing and formatting?
- are there anomalous data points/ users?
- excessive step counts?
- remove data point/ user
67
what is an example of an outlier?
- 3 SD above the mean
68
what is data variability in data cleansing and formatting?
- does the mean step- count of an individual vary widely each month/ week
- remove user
69
what is an example of data variability?
- top- percentile of variance
70
what is a missing variable in data cleansing and formatting?
- does an individual have many days of missing values?
- remove user
71
what is an example of missing variables?
- > xx%
- n days missing
72
what is used as reference data? what does it define?
- registration date
- defines monthly interval
73
what does objective data variable include? describe variable
- daily step count
Dependent variable = change in step
74
what does subjective/ questionnaire variable include? describe variable
- questionnaire
Independent variable = age, gender
75
how do you work out simple regression? - describe different letters
y = B0 + B1x
y= DV; x= IV ; B0= intercept
- relationship between x and y described by B
76
how do you work out multiple regression? what relationship is shown?
y = B0 + B1x1 + B2X2 + BnXn
- what is the relationship between y and x1, x2, xn
- defined by B
77
what regression types are used for continuous/ dichotomous class?
- continuous= regression
- dichotomous= logistic regression
78
what is B in logistic regression?
- increase in 'log odds' of the outcome (DV) per unit increase in IV
79
what do we take from B in regression and why?
- complicated to understand so we take the exp (B) to get the odds- ratio (OR) relative to reference category
80
what does OR= 1 mean?
- IV doesn't affect outcome
81
what does OR> 1 mean?
- category associated with higher odds of outcome relative to reference
82
what does OR < 1 mean?
- category associated with lower odds of outcome relative to reference
