Measuring the Impact of Physical Activity and Exercise on Alzheimer’s Disease


There is considerable and increasing body of published research investigating the impact of physical activity (PA) and exercise has on Alzheimer’s disease (AD) (Fig 1).   

Within the context of drug development, AD is one of the most challenging, and one where there have been a significant number of failures. A significant factor impacting the failure rate, is the lack of robust outcome measures and specific difficulties patients have in reliably completing existing tools. In addition there is a reliance on subjective rater scales which across a clinical trial with multiple sites adds to the complexity and variability due to inter and intra- rater variance (1). The advent of wearables has provided new opportunities to capture some of these outcome measures in a more objective manner, potentially generating outcome measures that are more robust and reliable and resulting in an improvement in data quality.

While it is generally accepted that there is a link between activity levels and AD, there is a significant body of work that directly contradicts these findings. A review of the research and the protocols from ClinTrials.gov was presented at CTAD (2016) and revealed the myriad of interventions and outcome assessments routinely used in AD research. The absence of standards in approach has added significant complexity. This is particularly striking when low burden, low cost validated wearables are available that could be used to objectively monitor the intervention. This technology has the ability to simplify and standardise the collection of objective, clinically relevant endpoints within the context of a clinical trial and could be used to generate new digital biomarkers that can make a real difference in AD clinical trials.

The term “wearables” is now ubiquitous and generates a growing interest in the potential of these technologies in Clinical Trials. The majority of the fitness trackers have accelerometers at their core. These accelerometers capture movement in a 3 dimensional plane and can be used to identify the amount and intensity of an individual’s motor movements. Not widely known is that wearables have been used in research to study sleep and activity patterns since the 1970’s and are an accepted diagnostic tool in clinical sleep since 2007 (2). They have also been used to generate primary and secondary endpoints in drug development studies for over 20 years (3). Accelerometers have also been used in Physical Activity Monitors (PAM) to measure and define physical activity levels. PAM’s have been used in large community based studies such as the Centre for Disease Control (CDC); National Health and Nutrition Examination Survey (NHANES) (4). Even more significant, these devices are low burden for both the patient and the care-giver.

Low levels of physical activity have been identified as a risk factor for AD (5), high levels of physical activity positively impact cognitive function (6), and influence the progression of AD (6).  A 2012 study (7) showed that when tested on the Mini Mental State Exam (MMSE)  “The exercised individuals of both sexes had significantly higher scores compared to non-exercised group”.  This improvement occurred over a relatively short period of time. The link between activity levels and improved cognitive function is not conclusively proven, with some recent review articles failing to identify a link; the most recent Cochrane Review failed to find a causal link among the articles considered (8). 

The Journal of Prevention of Alzheimer's Disease

Examines the range and complexity of interventions and outcome measures to assess the impact of exercise on patients with the alzheimer’s disease.

So while it is generally accepted that physical activity (PA) and exercise play a role in AD. Debate continues regarding the nature and impact, this is in part fuelled by the complexity of interventions and outcome measures and the reliance on subjective self-reports and questionnaires. Using data from both published research and clinical trials, this poster examines the range and complexity of interventions and outcome measures used in AD and the use of PAMs and other objective outcome measures to assess the impact of exercise on patients with the disease. Figure 2 lists the myriad of physical activity interventions that are used in AD trials.

Figure 2: physical activity interventions that were used in AD

Interventions

• Aerobic intervention
• Flexibility, strength, agility and balance
• Cycling
• Moderate intensive physical activity per week
• Physical activity
• Aerobic/endurance activities, strength training, balance, and flexibility training
• Exercise program
• Aerobic exercise for 10 to 45 minutes, three times per week
• Structured exercise
• Activities assessed by interview and identified as low, medium and high
• Minutes walking or aerobic activity, weights and reps for strength training, balance and flexibility using log
• Physical activity self reported
• 15 leisure-time physical activities (e.g. cycling, dancing, bowling, swimming)
• Physical activity was recorded during the clinical interview
• Leisure time PA slow walking or light chores, brisk walking, jogging or swimming jogging, running, bicycling or swimming handball or tennis
• Exercise sessions 
• 23 different physical exercise activities
• Active/inactive self report
• Peak oxygen (VO2) during graded treadmill test
• Daily energy expended in physical activity (accelerometers)
• Habitual physical activity (PASE) 12 items of leisure

    An earlier review of ClinTrials.gov in 2016 (1) identified 11 different questionnaires used as outcome measures to assess physical activity.

    Wearables are readily available and could be used for the quantification of physical activity outcome measures, including moderate and vigorous physical activity (MVPA), steps, energy expenditure and sedentary bouts. Substituting the subjective elements with objective measurements would help bring clarity to this area, allow a standard approach to be adopted and allow the consolidation and direct comparison of future research. This technology can be used in patients' homes outside of the clinical setting. The utilisation of these devices could simplify and standardise the collection of objective, clinically relevant endpoints in clinical trials.

    Conclusions
    The volume of published articles reflects the important relationship between physical activity and AD progression. This impact can occur over a relatively short period of time. However, the objective measurement of physical activity does not appear to be seen as a clinically relevant endpoint in the industry sponsored AD trials assessed to date. Measurement of physical activity has been restricted to assessment by questionnaires and to which individual activity such as time spent at shopping, food preparation etc. However, the amount of time spent exercising, the intensity of the activity or the changes in the activity level over the course of the trial is not currently being assessed and given the clinical impact of activity in this patient populations controlling and measuring this variable should be considered.

    Despite the volume of research, no clear consensus on the optimum type, intensity, length and frequency of the PA sessions has emerged. This makes it difficult to compare studies. Meta-analysis has proven to be very difficult, and review articles often end up discarding 90% of the pool of papers originally considered (3). The adoption of a standard approach when conducting these types of studies would greatly enrich the scientific record and allow more conclusive decisions to be drawn.

    The importance of physical activity as a risk factor for AD and the link between exercise and improved cognitive function should mean that the measurement of physical activity in this patient population would be a key variable that should be controlled in every trial.

    References:

    1. Mc Carthy. M, Muehlhausen,W. Schüler P The Case for Using Actigraphy Generated Sleep and Activity Endpoints in Alzheimer’s Disease Clinical Trials. J Prev Alz Dis 2016; Published online April 22, 2016, http://dx.doi.org/10.14283/jpad.2016.98. 

    2. Morgenthaler T,  Alessi C, Friedman L,.  Practice Paramters for the use of Actigraphy in the Assessment of Sleep and Sleep Disorder: An update 2007. Sleep 30, (4):521-529.

    3. Mc Carthy M,  Muehlhausen W, (2015). "Can Actigraphy Outcome Measures from existing clinical Trials provide a framework for sleep and activity endpoints standards in the clincal trials of the Future.ISPOR, (p. Poster PRM239). Milan.

    4. 2008 Physical Activity Guidelines for Americans (2008). Retrieved November 9th, 2015. 

    5. Alzheimers and  Dement. Alzheimers’s Association Report; 2013 Alzheimers’s disease facts and figures. 2013 (9)208-245.

    6. Graff-Radford N. R, (2011, April 28). "Can Aerobic Exercise Protect against Dementia?" . Alzheimer's Research and Therapy

    7. Nemati Karimooy N, Hosseini M, Nemati M, Esmaily HO. Lifelong physical activity affects mini-mental state exam scores in individuals over 55 years of age.  J Body Mov Ther. 2012 Apr;16(2):230-235.

    8. Young J, Angevaren M, Rusted L et al.  Aerobic exercise to improve cognitive function in older people without know cognitive impairment.  Cochrane Database of Systematic Reviews 2015, Issue 4. Art. No.: CD005381. DOI: 10.1002/14651858.CD005381.pub4