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Exercise as it relates to Disease/How resistance training can help with knee osteoarthritis

This is a critique of the research article: Jan MH, Lin JJ, Liau JJ, Lin YF, Lin DH. Investigation of clinical effects of high-and low-resistance training for patients with knee osteoarthritis: a randomized controlled trial. Physical therapy. 2008 Apr 1;88(4):427-36.

What is the background to this research?
Osteoarthritis (OA) is a common musculoskeletal disorder which commonly affects hands, knees, hips, the lower back and neck. According to a 2011-12 national survey, 8% of Australians had been reported with osteoarthritis with twice as many female cases as males (10.2% and 5.6% respectively). There are many factors that have been linked to causing OA: Osteoarthritis can also have a heavy impact on the lifestyle of the population. Common symptoms of osteoarthritis include pain, reduced mobility, stiffness and decreased muscle strength which may lead to decreased physical activity, and ultimately further health concerns. A decrease in physical activity has potential to lead to weight gain or obesity which then may bring upon health issues such as high cholesterol, diabetes, heart disease and high blood pressure. People living with OA are also at greater risk of falls and fall-related injuries. The National Council on Aging found that people with OA have a 30% greater chance of having a fall, and 20% greater chance of a fracture from a fall. Resistance training has been linked to reducing pain and promoting exercise in OA patients. The current literature suggests that resistance training (resulting in increased muscle strength) has been found to reduce pain and improve joint function. Another study on blood flow restriction training found a greater increase than just standard resistance training. The aim of the present study was to determine the effects of high- and low-intensity training on pain and functional scores related to knee OA. The hypothesis of the study was that the resistance training groups would present a greater functional improvement compared to the control group.
 * Age
 * Joint injury/overuse: common in both work and sporting environments
 * Obesity: extra weight and forces being placed on joints
 * Weak muscles: weak muscles causing instability
 * Genetics
 * Sex/gender

Where is the research from?
The authors of this study were The study was completed in Taiwan with participants recruited from the Department of Orthopaedics at the National Taiwan University Hospital.
 * MH Jan, PT, MS, is Associate Professor, Medical College, School and Graduate Institute of Physical Therapy, National Taiwan University, and Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital
 * JJ Lin, PT, PhD, is Associate Professor, Medical College, School and Graduate Institute of Physical Therapy, National Taiwan University
 * JJ Liau, PhD, is Associate Professor, Medical College, School and Graduate Institute of Physical Therapy, National Taiwan University
 * YF Lin, MD, PhD, is Chairman, Department of Orthopaedics, West Garden Hospital
 * DH Lin, MD, is Attending Doctor, Department of Orthopaedic Surgery, En Chu Kong Hospital, Taipei Hsien, Taiwan, Republic of China

What kind of research was this?
This study was a single-blind randomised controlled trial. The evaluation of results was performed by the same examiner, who also remained blinded to the experiment. There were three groups in this study, two exercise groups and a control group – of which participants were assigned to via a randomisation number table.

What did the research involve?
There were 102 participants split into three groups – high-resistance (HR), low-resistance (LR) and a control group. These participants needed to meet the needs of the American College of Rheumatology criteria of knee OA (hyperlink a link of needs). Participants were also excluded if they had received physical therapy in the 3 months prior to the study or had other musculoskeletal issues with the knee joint. There were 34 participants in both the resistance (high and low) groups with 30 participants featuring in the control group. All participants were required to do some pre-testing for pain and physical function. The WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) pain scale was used to compare pain levels of the participants whilst performing general housework tasks such as bathing, walking, sleeping and sitting. The physical function tests were Participants were required to complete these tasks as quick as they could, time was measured in seconds. Participants were seated in an upright position against a backrest inclined to 15 degrees. The test required participants to either extend or flex their knee as far as possible with starting angles of the knee being at 60 degrees, 120 degrees and 180 degrees. The practical part of the study was an 8-week resistance training program that was individually prescribed and monitored by an experienced therapist. The programs all had similar total volume (resistance x repetitions x sets) and was referred from a predicted 1RM. Every two weeks the participants’ 1RM was re-tested to adjust the resistance increased 5%. The sessions consisted of: For each session, there was a 10-minute stationary bike warm up and post exercise cold packs were applied to the participants knees for 10 minutes.
 * Walking time over 4 different terrains: hard/level surface, a figure-eight pattern, a staircase consisting of 13 stairs and along a spongey surface
 * Knee extensor and flexor muscular torque
 * High resistance: participants resistance was set to 60% 1RM for 3 sets of 8 repetitions. Sessions lasting 30 minutes
 * Low resistance: participants resistance was set to 10% 1RM for 10 sets of 15 repetitions. Sessions lasting 50 minutes.
 * Control group: the control groups were not permitted any exercise throughout the duration of the study

What were the basic results?
The results from the study was that resistance training does reduce pain and increase function of OA sufferers. Both resistance groups scored much better after the 8-week training program compared to the control group with similar results between the high-resistance and low-resistance groups. The WOMAC pain index scores decreased, the walking times for the figure-eight pattern and the spongey surface times decreased and the muscle torque for flexion and extension all improved. Some key results included the WOMAC pain subscale scores (8.5 to 4.8 and 7.8 to 4.8 in HR and LR), figure-eight pattern time (11.0 seconds to 6.1 seconds and 10.9 seconds to 6.8 seconds) and the spongey surface times (12.6 seconds to 6.3 seconds and 12.5 seconds to 7.3 seconds).

What conclusions can we take from this research?
The conclusion of the study found that both high- and low-resistance training groups are beneficial in reducing pain and increasing function in people with knee OA compared to the control group. Previous literature has stated that high-resistance training has greater results compared to low-resistance training but with the results of this current study we can see that low-resistance training can be just as effective. Between the high- and low-resistance training groups the differences in improvements for the WOMAC pain scores were quite little, but for knee function the high-resistance training proved to have a greater result.

Practical Advice
People who are experiencing pain or a loss of function in their knee(s) from OA should be doing some resistance training as part of their rehab and/or management. All through the literature there are lots of papers supporting the findings of this study. Although most OA studies have been looking at participants of the older population, the younger population can still benefit off the same style of training.

Further information/resources
Osteoarthritis Australia

Strength training with Arthritis


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Transportation Geography and Network Science/Road networks

Background
The road network is the system of interconnected roads designed to accommodate wheeled road-going vehicles and pedestrian traffic .

The role of the road network is to allow the movement of goods, services and people . The road network generally forms the most basic level of transport infrastructure within urban areas and will link with all other areas, both within and beyond the boundaries of the urban area . A network is made up of road corridors that perform different functions, known as the road hierarchy .

A road network can be divided into parts such as :
 * Intersections
 * Urban Roads
 * Rural Roads
 * Motorways
 * Bicycle Lanes
 * Footpaths and Pedestrian Areas
 * Pedestrian Crossings
 * Bridges and Tunnels

Road Network Hierarchy

A functional hierarchy is the most common type which ranks roads according to how the roads are expected to function with respect to local through-traffic. In doing so, it recognises that the roads form part of an interconnected network and addresses the competing road uses of mobility and access . While sources differ on the exact nomenclature, the basic hierarchy comprises freeways, arterials, collectors, and local roads . A system of interconnected roads with the same hierarchy forms a network with relative hierarchy.

Travel in Road Network
Long distance travel in a mature road network typically has the following pattern:

Origin - Local Roads - Collectors - Arterials - Freeways - Arterials - Collectors - Local Roads - Destination

An example of this is travelling from Sydney Showground in Sydney Olympic Park to the University of Sydney Quadrangle Clocktower:

Urban Road Network Patterns

Grid Street Pattern
The grid pattern, grid street pattern, or gridiron pattern is a type of city plan in which streets run at right angles to each other, forming a grid. The infrastructure cost for regular grid patterns is generally higher than for patterns with discontinuous streets. This geometry helps with orientation and wayfinding and its frequent intersections with the choice and directness of route to desired destinations. All streets in a grid are accessible to traffic (non-hierarchical).

Typical uniform grids are unresponsive to topography. In a modern context, steep grades limit accessibility by car, and more so by bicycle, on foot, or wheelchair, particularly in cold climates. The same inflexibility of the grid leads to disregarding environmentally sensitive areas such as small streams and creeks or mature woodlots in preference for the application of the immutable geometry.

The frequency of intersections in grid patterns, however, becomes also a disadvantage for pedestrians and bicycles. It disrupts the relaxed canter of walking and forces pedestrians repeatedly onto the road, a hostile, anxiety-generating territory. People with physical limitations or frailties, children and seniors, for example, can find a regular walk challenging. For bicycles, this disadvantage is accentuated as their normal speed is at least double that of pedestrians.

Organic Street Pattern
Organic streets can come from disorganized, fast-paced development, as is common in today’s slums; or it can grow slowly over time, as one plot of land is divided, and streets are built over the new boundary, like many of the old towns of England and other European powers. The Organic street pattern is typically unique. This geometry makes traffic congestion occurs a lot more often, and road users easily to get lost.

Radial Street Pattern
In a radial street pattern, the road is spread out from a central point, similar to a spider's web. The radial street pattern could be largely found in Paris. The radial street pattern makes the traffic flow easier, but slower as there are no shortcuts. All roads lead to a central point, which generally causes traffic jams near the centre.

Planned Irregular (Cul-de-sac) Pattern
Planned irregular pattern includes a significant amount of Cul-de-sac (dead-end road) in the network. The network has less intersection, and the road in the network curve a lot. This pattern could be largely found in modern cities and newer suburbs. The planned irregular pattern helps to improve the flow of traffic. Road in the network is quieter as there are fewer intersections and less through-traffic. The nature of topography could be nicely accommodated in this pattern. It is easy for the road users to get lost in an irregular pattern, and this pattern is not easy to subdivide to expand.

Topological Measurement

 * $$e$$: Number of edges (road segments)

 * $$v$$: Number of vertices (nodes), including road intersections, travel origins and destinations

 * $$g$$: Number of maximally connected components (a planar network may be unconnected but may consist of connected pieces, which are called 'maximally connected components' or 'con-nected components'.)

The cyclomatic number indicates the number of circuits in a network.

 * $$\alpha$$ index is the ratio between the actual number of circuits in the network and the maximum number of circuits.

 * $$\beta$$ index is the ratio between the number of links and the number of nodes.

 * $$\gamma$$ index is the ratio between the actual number of links and the maximum number of possible links in the network

Values for $$\alpha$$ index and $$\gamma$$ index varies between 0 to 1. A higher value of cyclomatic, $$\alpha$$, $$\beta$$ and $$\gamma$$ represents a more connected network.

Heterogeneity Measurement
Heterogeneity is a common feature of many complex networks. A statistically collective measure of entropy ($$H$$) can be used to evaluate the link‐based heterogeneity of road networks:

 * $$H(X) = -\sum\limits_{i=1}^m p_i {log}_2(p_i)$$

Where:
 * $$X$$ represents the road network system
 * $$m$$ is a road link subset base on different road properties such as functional type, traffic volume or LOS.
 * $$p_i$$ as the frequency of road links in the $$i$$th subset over the total number of links

The entropy value of a homogenous group is zero. A positive entropy measure indicates that there exists heterogeneity in a network with more than one group of links. A larger entropy measure indicates a greater heterogeneity of the network.

Connection Patterns
Firstly, circuit blocks need to be identified from a road network base on the processor in the figure below.

A circuit block is defined as a block that contains at least one circuit and contains neither bridges nor articulation points. If a circuit block contains only one circuit, it is defined as a ring; if it contains more than one circuit, it is defined as a web.

'Ringness' of a network could be measured by:

 * $$\Phi_{ring} = \frac{Total\; Length\; of\; Arterials}$$

'Webness' of a network could be measured by:

 * $$\Phi_{web} = \frac{Total\; Length\; of\; Arterials\; on\; Webs}{Total\; Length\; of\; Arterials}$$

The concepts of 'circuitness' and 'treeness' can be defined as:

 * $$\Phi_{circuit} = \Phi_{ring} + \Phi_{web}$$

 * $$\Phi_{tree} = 1 - \Phi_{circuit}$$

These ratios range from 0 to 1, indicating to what extent arterials are connected as circuits or trees. A high ratio of treeness indicates a branching structure while a high ratio of circuitness indicates a circuit network.

Beltway in a road network is defined as one dominant circuit block. If the dominant block is a ring, the ring is identified as the beltway; if the dominant block is a web, the beltway is defined as the envelope of the circuit block. The 'beltness' can be measured by:

 * $$\Phi_{belt} = \frac{Length\; of\; the\; beltway}{Total\; Length\; of\; Arterials}$$

An urban arterial network may have multiple concentric beltways around its CBD(s). An inner beltway can be identified by breaking its outer beltway and repeating the above procedures. These steps can be repeated until no dominant circuit block is found. The beltness of each beltway can be calculated.

Continuity
The dicontinuity of a road network can be measured by:

 * $$Y = \frac{\sum_{all(R,S)} Y(P_{RS}) *q_{RS}}{\sum_{all(R,S)} l(P_{RS}) *q_{RS}}$$

Where $$P_{RS}$$ the shortest path between a given O–D node pair $$(R, S)$$, $$q_{RS}$$ is the number of trips between the origin and the destination, and $$l_{RS}$$ is the length of the shortest path. $$Y_{RS}$$ is the discontinuity of trip from R to S, and can be calculated by:

 * $$Y(P_{RS}) = {\sum_{a \in P_{RS}}y_a}$$

Where $$a$$ is the upstream link.

The discontinuity of a movement can be calculated by:

 * $$y_a = |k_1 - k_2|$$

Where $$k_1,k_2$$ is the hierarchies of the upstream and downstream links.

Travel Time Window
The travel time window is a simple statistic which considers the travel time range a standard deviation, $$\sigma$$, either side of the mean travel time, $$\mu$$

 * $$Travel\; Time\; Window = \mu_{travel\; time} \pm \sigma_{travel\; time}$$

This metric can be applied to different spatial and temporal segmentations as well as applied across both public and private transport modes. The weakness of this measure is its independence, the change in travel time window for a particular link or route may be useful but it is difficult to compare routes and links. Furthermore, the measure does not indicate whether the travel time window is acceptable or unacceptable for a user and thus may not provide a beneficial measure for practitioners. Finally, the standard deviation is not sensitive to the tail of the distribution, so this measure conveys information about the typical range rather than the likelihood of encountering an especially bad travel time.

Coefficient of Variation
The coefficient of variation, is a standardised measure of dispersion and is the ratio of the standard deviation of a sample ($$\sigma$$) to the mean ($$\mu$$). In the context of travel time variability, the Coefficient of Variation Equation is presented below:

 * $$Coefficient\; of\; Variation = \frac{\sigma_{travel\; time}}{\mu_{travel\;time}}$$

The standardised approach using the coefficient of variation is an improved measure compared to using standard deviation as it is not dependent on the trip length. Furthermore it can also be quantified for road segments, entire road corridors and for a network as a whole. The coefficient of variation can be thought of as a fractional uncertainty. It is easy-to-calculate and accounts for trip length, but it builds on the (probably incorrect) assumptions that a travel time is equally likely to be above and below the expectation value and that a traveller’s tolerance for uncertainty scales linearly with the duration of the journey. Additionally, the weakness mentioned for travel time window regarding insensitivity to the tail of the distribution holds for coefficient of variation as well.

Variability Index
The variability index compares peak and off-peak driving conditions and can be calculated as shown below:

 * $$Variability\; Index = \frac{{CI}_{95%Upper,Peak} - {CI}_{95%Lower,Peak}}{{CI}_{95%Upper,Off\;Peak} - {CI}_{95%Lower,Off\;Peak}}$$

Where:

 * $${CI}_{95%Upper,Peak}$$ = The upper bound of the 95% confidence interval of travel time during the peak period

 * $${CI}_{95%Lower,Peak}$$ = The lower bound of the 95% confidence interval of travel time during the peak period

 * $${CI}_{95%Upper,Off\;Peak}$$ = The upper bound of the 95% confidence interval of travel time during the off peak period

 * $${CI}_{95%Lower,Off\;Peak}$$ = The lower bound of the 95% confidence interval of travel time during off peak period

The index presents the ratio of the range of travel times at the 95% confidence interval between peak and off peak conditions. Values approaching 1 indicate a reliable road link or system as both off-peak and peak 95th percentile travel times are similar. Whereas, a value in excess of 1 indicates a less reliable road link or system as there is a greater difference in the travel time range. The Variability Index is useful in comparing the severity of peak period traffic conditions. The Variability Index is also more sensitive to the tails of the distribution, which are important in determining the traveller’s user experience. A disadvantage of this statistic is an ambiguity between reliability congested and reliably uncongested facilities. Consider a road with a variability index approaching 1 because the road is near capacity at all times of day and overall travel times are far greater than free flow travel times. In this case, presence of traffic incident could potentially result in incredibly unreliable conditions. The Variability Index is very sensitive to the definition of the peak period, and it combines day-to-day unpredictability and time-of-day variability into one measure. Thus, without detailed data which captures both recurrent and non-recurrent congestion at a fine temporal scale, the index may be biased.

Buffer Time Measures
Buffer measures are derivatives of how much extra time is required for on-time arrival at a destination. Indicators in this category include buffer time, buffer time index and planning time index. The greatest advantage of buffer time indicators is they are more intuitive and better reflect traveller behaviour compared to statistical range measures, which require a familiarity with statistics concepts like standard deviation. Users are comfortable with allocating a time period related to travel as long as on-time arrival can be guaranteed at a certain level of probability. Buffer time could be defined as :

 * $$Buffer\; Time = 95th\; percentile\; travel\; time - Average\; travel\; time$$

The buffer time is the amount of additional time which must be allocated to be 95% certain of achieving on-time arrival. This measure considers the travel time budget a user would allocate for a particular trip. As this is related to a particular trip, comparability is affected by trip length and is generally route specific. Buffer Time Index (BI) is a weighted buffer time and is described in the equation below :

 * $$BI =\frac{ 95th\; percentile\; travel\; time - Average\; travel\; time}{Average\; travel\; time}$$

Lower values of BI suggest more reliable road links or routes. Standardisation based on the mean travel time improves the ability to compare between different parts of the road network. For an example consider a route, Route A, with a mean travel time of 40 minutes and 95th percentile travel time of 50 minutes. Also consider Route B, with a mean travel time of 10 minutes and 95th percentile travel time of 20 minutes. It is clear that Route B is less reliable than Route A, however both routes have a buffer time value of 10 minutes. The buffer time index for Route A (BI = 0.25) is lower than Route B (BI = 0.5) highlighting the benefit of standardisation.

Planning Time Index (PTI) compares the 95th percentile travel time to free flow travel time:

 * $$PTI =\frac{ 95th\; percentile\; travel\; time}{Free\; flow\; travel\; time}$$

Similar to the BI, the PTI is also a standardised measure of buffer time which enhances the metrics suitability when conducting comparative assessments. Comparing to the coefficient of variation, PTI presents a normalised assessment of reliability across links or routes with the added feature of sensitivity to the tail of the distribution. With roughly 20 working days per month, measures that build on the 95th percentile travel time are often interpreted relating to the travel time experienced on the worst commute of the month. While the buffer measures are directly measured from travel time distributions, they offer more natural interpretations than those measures based on statistical width.

Florida Reliability Method
The Florida Reliability Method was developed through Florida’s Mobility Performance Measures Program completed by the Florida Department of Transportation (FDOT). The method measures reliability as the percent of trips on a corridor that are less than or equal to the expected travel time including a buffer, as shown in the equation below :

 * $$Florida\; Reliability\; Statistic = 1 - CDF(E(TT))$$

Where:

 * $$CDF(E(TT))$$ = The proportion of trips that have a travel time $$(TT)$$ which is less than the expected travel time $$(E(TT))$$.

 * $$E(TT)$$ = The expected travel time is sum of the mean travel time and a buffer time that is deemed acceptable by either the transport authority or the road user.

Complexity of the approach arises from the ambiguity of setting a benchmark expected travel time. Similar to the buffer metrics, this method accounts for distance and geometric variances of links and routes as it is based on occurrences of failure/success in achieving a targeted travel time and not the quantification of travel time values.

Skewness Metrics
The skewness statistic, $$\lambda^{skew}$$, indicates the asymmetry of the travel time dataset. It is calculated as the ratio of difference of the 90th percentile and 50th percentile travel time and the difference between the 50th percentile and 10th percentile travel time.

 * $$\lambda^{skew} = \frac{T_{90} - T_{50}}{T_{50}-T_{10}}$$

The skewness statistic quantifies the comparison between the width of the right (slow) and left (fast) sides of the travel time distribution. It is a valuable counterpart to the statistical width measures because it explicitly addresses the asymmetry of the distribution and the importance of the tail of slow travel times to travellers.

The width of travel time, $$\lambda^{var}$$, is defined as the ratio of the difference of the 90th and 50th percentile travel time to the 50th percentile travel time. This ratio provides a standardised range of travel time that is defined by percentiles rather than mean and standard deviation.

 * $$\lambda^{var} = \frac{T_{90} - T_{10}}{T_{50}}$$

Unlike the skewness statistic, this metric does not address asymmetry, but it is sensitive to the tails and it accurately describes a confidence interval (80% of observations fall within the width) normalised by the expected value (the median or 50th percentile travel time).

Road Networks Performance Measurement
'A Guidebook for Performance-Based Transportation Planning' from the US National Cooperative Highway Research Program provided a structured inventory of performance measures used in eight categories representing typical agency goals :

 * 1) Accessibility:
 * 2) * Average travel time from facility to destination (by mode)
 * 3) * Average travel time from facility to major highway network
 * 4) * Average trip length
 * 5) * Overall mode split
 * 6) * Mode split by facility or route
 * 7) * Number of structures with vertical (or horizontal) clearance less than X ft.
 * 8) * Bridge weight limits
 * 9) Mobility
 * 10) * Origin-destination travel times
 * 11) * Total travel time
 * 12) * Average travel time from facility to destination
 * 13) * VMT by congestion level
 * 14) * Lost time due to congestion
 * 15) * Delay per VMT
 * 16) * Level of service
 * 17) * Intersection level or service
 * 18) * Volume/capacity ratio
 * 19) Economic Development
 * 20) * Direct jobs supported or created
 * 21) * Economic costs of accidents
 * 22) * Economic costs of lost time
 * 23) * Indirect jobs supported or created
 * 24) Quality of Life
 * 25) * Lost time due to congestion
 * 26) * Accidents (or injuries or fatalities) per VMT
 * 27) * Customer perception of safety in system
 * 28) * Tons of pollution (or vehicle emissions) generated
 * 29) Environmental and Resource Conservation
 * 30) * Overall mode split
 * 31) * Tons of pollution (or vehicle emissions) generated
 * 32) * Fuel usage
 * 33) * Number of accidents involving hazardous waste
 * 34) Safety
 * 35) * Number of accidents per VMT
 * 36) * Number of accidents per year
 * 37) * Number of accidents per trip
 * 38) * Number of accidents per capita
 * 39) * Number of accidents per ton-mile traveled
 * 40) * Response time to incidents
 * 41) * Customer perception of safety while in system
 * 42) * Accidents (or injuries or fatalities) per VMT
 * 43) * Percentage of highway mainline pavement (or bridges) rated good or better
 * 44) * Average response time for emergency services
 * 45) * Railroad/highway-at-grade crossings
 * 46) * Number of accidents involving hazardous waste
 * 47) Operational Efficiency
 * 48) * Origin-destination travel times
 * 49) * Total travel times
 * 50) * Average travel time from facility to destination
 * 51) * Average travel time from facility to major highway network
 * 52) * Volume/capacity ratio
 * 53) * Overall mode split
 * 54) * Cost per ton-mile
 * 55) * Average vehicle occupancy
 * 56) System Condition and Performance
 * 57) * Percent of roadway/bridge system below standard condition
 * 58) * Age distribution
 * 59) * System Preservation
 * 60) * Percentage of highway mainline pavement (or bridges) rated good or better

Extra Readings

 * The Geography of Transport Systems
 * Road Network
 * Road Types and Road Networks
 * Assessing the Feasibility of a National Road Classification
 * Measuring the Structure of Road Networks
 * PerformanceBased Planning and Programming Guidebook
 * A Guidebook for Performance-Based Transportation Planning
 * Street Patterns

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/


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Exercise as it relates to Disease/Endometrial cancer survivors and its association with exercise, body weight and quality of life

This report is critique of the article "Associations among exercise, body weight, and quality of life in a population-based sample of endometrial cancer survivors"

What is the background to this research?
Endometrial cancer is a cancer of the uterus that begins in the layer of cells that form endometrium or the lining of the uterus. Often, the cancer is found early and can be overcome with treatments such as removal of the uterus, radiation therapy, chemotherapy and hormone therapy. Physical activity or exercise recommendations conclude that it is important to up keep regular physical activity, avoid inactivity and aim for 150 minutes of exercise per week A lack of exercise and excess body weight can have negative effects on quality of life (QoL). For cancer survivors, it can be an important part of their own person treatment in recovery from the disease. Research on endometrial cancer survivors has indicated the independent effect of exercise and decreased BMI on QoL.

Where is the research from?
The study was conducted in Alberta, Canada. The research that was conducted is important to share because the findings could prove valuable for other individuals that have survived different cancers as well as those who have suffered from endometrial cancer across all locations. The primary authors of the research include Kerry Courneya, a professor and research chair in physical activity and cancer, Kristina Karvinen and Kristin Campbell, an expert in the field of physical activity in health and chronic disease. There are numerous factors that could be a conflict of interest and bias approach, in which can affect the results substantially. Firstly, obtaining the full and correct details of each endometrial cancer survivor within the Alberta cancer registry will prove to be difficult and may have led to false information. Furthermore, seeking permission from each survivor’s oncologist or physician could impact the results by false or misleading information given about the individuals.

What kind of research was this?
The research conducted was an observational study that examined different lifestyle factors across the population sample, including exercise and body weight. By using mail delivered questionaries the researchers were able to grasp an understanding of how exercise, BMI and QoL impacted the endometrial cancer survivors. Similar studies that have been completed for other cancers survivors often use randomised control trials. This will mean there is a more likely chance the results will be valid and true as participants are not as subject to over or underestimating how much exercise they are doing

What did the research involve?
The research involved contacting, via questionnaire, survivors of endometrial cancer residing in Alberta. To be eligible, the participants needed to be over eighteen years or older, be able to give written consent in English, approved for contact by their primary oncologist and had historically confirmed endometrial cancer. All of the people that were requested to complete the study were diagnosed from November 1994 and June 2003. Their contact information was obtained from the Alberta Cancer Registry. A slightly modified Total Design Method was used. This consisted of a mail out of initial questionnaire with a reminder sent out two weeks later. After another four weeks, a second questionnaire was mailed out. The participants role was to complete the questionnaires as accurately as possible about their exercise, including intensity, duration and frequency, body weight and QoL. Using this method to collect data has many benefits and limitations. It is an easy, cost-effective way to gather information, however, results are likely to not be accurate due to participants over and underestimating their level of exercise and exercise intensity, time duration of exercise and accurate height and weight measurements. These limitations could have serious impacts of how precise results are.

What were the basic results?
The results of the investigation found that descriptive data indicated that 30% of survivors met exercise guidelines, however it should be noted that this result is likely to be an overestimate due to self-reports of weight and exercise. The hypothesis of the research found a consistent positive association between exercise guidelines and QoL. An important result is that 72% of the population-sample were overweight or obese and approximately 70% did not reach exercise guidelines. When interpreting the results, the researchers have noted that exercise and body weight were significant for both independent and clinically meaningful associations in QoL, however these observations would need further study and randomised controlled trials to be validated.

What conclusions can we take from this research?
This research was a great starting point to identify the associations between exercise, body weight and QoL in those who have survived endometrial cancer. Although this research has multiple strengths, such as using a large population sample and using established measures, there are also limitations that need to be factored in. These limitations include, the transparency of the study, overestimating, self-reporting, not identifying if there was any disease reoccurrence within the population sample and because it was an observational study, exercise, body weight and QoL cannot be determined. In other studies, exercise is seen as an effective intervention to improve QoL in other cancer patients, however, it is unknown if exercise has any impact on the chances of reoccurrence in cancer survivors.

Practical advice
This article lacks further research in recommendations for exercise, monitoring the progress of individuals exercise, encouraging exercise and a criterion for not exercising. However, although it is indirect, the study does prove that exercise and body weight have an impact on someone’s QoL, therefore indicating the advice to meet exercise guidelines in order to have a better QoL.

Further information/research
For any further information and resources on associations of exercise, weight and QoL in people that have survived endometrial cancer, there are multiple websites, articles and organisations. Recommendations for further information:

Exercise and Behaviour In Population Sample of Endometrial Cancer Survivors

Physical activity and obesity in endometrial cancer survivors

Endometrial Cancer – The Women’s Royal Hospital

Centres for Disease Control

American College of Sports Medicine


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Exercise as it relates to Disease/Does increase in intensity and frequency of physical activity reduce fear of falls in older adults?

The paper is a critique on the article: Ryuichi Sawa, PT, PhD, Tsuyoshi Asai, PT, PhD, Takehiko Doi, PT, PhD, Shogo Misu, PT, PhD, Shunsuke Murata, PT, MS, Rei Ono, PT, MPH, PhD, The Association Between Physical Activity, Including Physical Activity Intensity, and Fear of Falling Differs by Fear Severity in Older Adults Living in the Community, The Journals of Gerontology: Series B, Volume 75, Issue 5, June 2020, Pages 953–960

This is an assignment created for the unit 'Health Disease and Exercise' at the University of Canberra. Semester 2, 2020

What is the background of this research?
Fear of falling (FoF) is a serious issue in elderly. It is often recognized as a lasting concern about falling that can lead an individual to avoid activities that he or she remains capable of performing. Prevalence of FoF is associated with increasing age and particularly in woman. It has been a critical issue as it affects the quality of life in elderly while decrease in physical functioning. In which it may result in more severe health and life issues such as falls, avoidance of physical activity, increased risks of chronic disease and more.

FoF needs to be addressed properly. However, studies regarding this matter are usually measured in a direct, single-item, closed-ended question approach which may possibly underestimate the severity of FoF. Therefore, this cross-sectional study is created to penetrate deeply in this area and provide a more accurate view on this matter. It is performed on a sample of community-dwelling people and aimed to measure whether FoF varies with exercise levels by step count, LPA, MVPA. It is said to be the first study to take account of both FoF severity and physical activity intensity in an older sample that includes both men and women.

Where is the research from?
The research is conducted by Ryuichi Sawa, PT, PhD, Tsuyoshi Asai, PT, PhD, Takehiko Doi, PT, PhD, Shogo Misu, PT, PhD,Shunsuke Murata, PT, MS and Rei Ono, PT, MPH, PhD. They are all experts in the field of global health, physical therapy and rehabilitation in Japan. This study was conducted with behalf of Japan Center for International Exchange, Japan, Department of Community Health Sciences, Kobe University Graduate School of Health Sciences, Japan, Department of Physical Therapy, Faculty of Rehabilitation, Kobe Gakuin University, Japan Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Japan, and Japan Society for the Promotion of Science, Japan.

What kind of research is it?
It is a cross-sectional, questionnaire-based observational study held at a local community association center with 242 elderlies living independently in the community (mean age: 75.1 ± 5.4 years). FoF was defined using the Falls Efficacy Scale International and categorized into three levels (low, moderate, and high). The study is aimed to explore the relationship between Fear of falls, step count and physical activity level in the population.

The sample size is relatively small to previous questionnaire-based studies. It may affect its credibility as it increases the margin of error and variability. Which makes the result may not be significant through the population.

What did the research involved?
Sociodemographic characteristics (such as age, sex, height, weight, fall history, pain site, etc.) were collected using a self-reported questionnaire and compared in regards to the Guideline for the Prevention of Falls in Older Persons. Participants will then be categorized into three groups (low, moderate, high) based on their falling history and in accordance to FES-I scale.

Then they were instructed to attach a uniaxial accelerometer to their hip for seven consecutive days, following their initial examination. The device must be worn anytime during the trial, except for bathing and swimming. A valid wearing day should consist of at least 8 hours of valid wear time, which a valid hour defined as no more than 10 consecutive minutes of zero-activity intensity. Data with a minimum of 5 valid days will be included. They will be collected after 7 days to calculate step count and metabolic equivalent values between groups.

Strength of the study
 * The trend between FoF and physical activity levels with intensity is well described and shown in the study.
 * Different variables were categorized and set to minimize biased distribution.

Weakness of the study
 * Participants were reported well-functioning elderly, while this limit the comprehensiveness of the findings.
 * All the data in this study is through multiple self-report questionnaires and scales. Accuracy of the data is in doubt, whereas previously large population-based studies have reported self-reported physical activity levels were higher compared to accelerometer-base controlled data. While whether fall-related self-efficacy may access FoF or not is still controversial.

What were the basic results?
Results:

 * LPA= Light-intensity physical activity
 * MVPA= moderate to vigorous-intensity physical activity

Results shown significant linear associations with FoF groups (step count 95%CI: −4,370 to −1,858; LPA, 95% CI: −34.7 to −11.8; MVPA,95% CI: −24.0 to −7.6). High FoF was significantly associated with a decrease in all measures of physical activity in crude models(step count, 95% CI: −4,370 to −1,858; LPA, 95% CI: −34.7 to−11.8; MVPA, 95% CI: −24.0 to −7.6)

Step count and physical activity intensity showed significant linear trends across a fear of falling severity. High fear of falls decreased with around 2000 steps/day while associated with lower physical activity (both LPA and MVPA) duration. Higher fear of falling level was associated with a decrease in physical activity.

What conclusion can we take from this research?
From the current study, it is found that even in elderly without motor dysfunction, decreased physical activity is expected with an increase in fear of falls severity. Likely, more physically active you are and the more effort you pay, the less fear of fall you encounter. The study did bring out the association between FoF severity and physical activity deviate between groups with different physical intensity levels in the community-dwelling elderlies. However, just like many studies in this field, they are limited to cross-sectional design, it only addresses the association between FoF and physical activity while unable to raise other mental and health factors which may affect FoF. Further studies are needed to investigate other factors affecting FoF and causal relationship between FoF and objective measures of physical activity.

Partial advice
The best cure to solve the fear of falling is to tackle the problem from the inside out. Keeping yourself physically active is a good way to maintain your motor function, while adding some moderate to vigorous exercise such as resistance exercise, balance exercise can help to build up your muscles strength and sense of balance which will greatly reduce the fear of falling and the risk of falls. Mentally, when you are physically active and keeping your body mechanism functioning, your sense of fear will reduce as you have the confidence as you exercise enough. While it is advised to progressively increase your exercise intensity and frequency in order to avoid unnecessary injuries, especially for the elderly whose muscular function kept deceasing as they get older and have a relatively lower training volume. Living within your capabilities and improve over time. It is good to check out some guidelines for physical activities as your reference.

Further information/resources
Related articles

Fear of Falling in Elderly Persons: Association With Falls, Functional Ability, and Quality of Life

Fear of Falling in the Elderly: What Do We Need to Consider?

Related web resources

How to Help a Senior Overcome a Fear of Falling

Queensland Government Health page - Fear of falling ,Stay On Your Feet


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