Self-Directed Biological Transformation Initiative

SPECIFIC AIMS

1. To examine the effects of the Self-Directed Biological Transformation Initiative (SBTI) course participants compared to control group participants on key biochemical and physiological markers.
2. To examine the effects of the SBTI course participants compared to control group participants on short- and long-term changes in heart rate variability, level of activity, and sleep quality.
3. To examine the effects of the SBTI course participants as compared to control group participants on mood and wellbeing.

BACKGROUND AND SIGNIFICANCE It has long been thought that ancient practices can promote longevity and wellbeing but there is little empirical evidence and it is difficult to test this under controlled or experimental conditions. Recently, however, there have been several cellular-based markers that may index rate of biological aging. The rate of telomere shortening, as indexed by change in telomere length and telomerase activity, predicts both cellular and human longevity (Lin et al, 2012). It is related to malleable factors, lifestyle and psychological state (Puterman & Epel, 2012). A secondary measure that shows promise of understanding rate of cellular aging is examination of gene expression, particularly genes related to aging. Preliminary evidence suggests that mind-body practices may slow the rate of cellular aging by improving the telomere/telomerase maintenance system. This has been examined in four small studies so far.

In the first study of its kind, a four month intensive lifestyle modification program, including yoga and group support was associated with an increase in telomerase in 39 men with prostate cancer (in preparation). There was no control group in this study. However, those with the greatest decreases in distressing thoughts about having cancer showed the biggest increases in telomerase activity (Ornish et al, 2008). In a second study, healthy men and women were randomized to a 3-month intensive in-residence meditation group or wait list control group. The researchers examined telomerase only post intervention and found that the meditation group had 30% higher telomerase, and further increases in wellbeing were associated with increases in telomerase (Jacobs et al, 2011). In another study with an active control group, examined 39 elderly high stress dementia caregivers. They randomized half the group to Yoga Nidra, listening to a 15 minute tape each day of Yoga Nidra, which includes instruction on breathing and hand movements and half to a control group which listened to a relaxation tape for 15 minutes each day. Eight weeks later, they found greater telomerase increases in the Yoga Nidra group, and across the sample, decreases in depressive symptoms were associated with increases in telomerase activity (Lavretsky et al, 2013).

Similar studies have reported positive effects of such practices on inflammatory profiles and gene expression (Tang, Ma et al. 2009, Bhasin, Dusek et al. 2013, Saatcioglu 2013). Another well studied parameter for practices such as meditation and yoga is heart rate variability (HRV) as it provides a broad measure of autonomic nervous system activity. Observational studies report HRV to be associated with stress in the workplace,(Jarczok, Jarczok et al. 2013) depressive and anxiety disorders (Gorman and Sloan 2000), and in individuals with chronic somatic complaints (Tak, Riese et al. 2009). Meditation and yoga interventions to improve HRV can lead to improved physiologic and clinical outcomes (Wheat and Larkin 2010, Papp, Lindfors et al. 2013). Other parameters can also be of potential benefit in evaluating an individual's overall wellbeing. For example, sleep duration and quality, as well as general activity levels can both contribute to and reflect overall wellbeing. To date, however, lack of compelling data around objective measures of wellbeing is in large part due to challenges associated with long term monitoring of monitoring of appropriate patient populations. However, recent advances in biosensor technologies have overcome this limitation and now allow for the non-obtrusive and passive monitoring of individuals for long periods of time. These new data streams of real-time physiologic data, couple with sophisticated and individualized data analytics can potentially identify novel measures of individual wellness, which will allow for the development of personalized therapeutic interventions to improve wellbeing.