CogniFit - A holistic approach to assess older adults’ wellness using e-health technologies

Original Name: A Holistic approach to assess older adults’ wellness using e-health technologies.

Autores: Hilaire J. Thompson1, George Demiris1,2, Tessa Rue3, Evelyn Shatil4,5, Katarzyna Wilamowska2, Oleg Zaslavsky1 y Blaine Reeder1.

• 1. Biobehavioral Nursing and Health Systems, School of Nursing, University of Washington, Seattle, Washington.
• 2. Department of Medical Education and Biomedical Informatics, School of Medicine, University, University of Washington, Seattle, Washington.
• 3. Center for Biomedical Statistics, Institute for Translational Health Sciences, University of Washington, Seattle, Washington.
• 4. Department of Psychology and the Center for Psychobiological Research, Max Stern Academic College of Emek Yezreel, Yoqneam Ilit, Israel.
• 5. CogniFit Ltd., Yoqneam Ilit, Israel.

Journal: Telemedicine and e-health (2011), vol. 17 (10): 794-800.

References to this article (APA style):

• Thompson, H., Demiris, G., Rue, T., Shatil, E., Wilamowska, K., Zaslavsky, O., Reeder, B. (2011). A Holistic approach to assess older adults’ wellness using e-health technologies. Telemedicine and e-health, vol. 17 (10), pp.794-800.

Study Conclusion

The different areas of well-being correlate with each other, especially the cognitive and physiological areas. The data shows the importance of holistically assessing the health and well-being of seniors. CogniFit can be an excellent tool for evaluating cognitive health. Negative correlation between age and divided attention (r=-0.48, p=0.029), planning (r=-0,53, p=0.013) and spatial perception (r=-0.718, p<0.0005). The negative correlation between some chronic diseases and planning (r=-0.52, p=0.016).

Study Summary

The prediction and slowing of the progression of chronic diseases in seniors help maintain functional autonomy. Therefore, we can use holistic assessments in order to get a more concrete and adapted the idea of the state of this population. Currently, there are few methodologies that holistically assess the well-being of our seniors. Fortunately, e-health (health technologies), such as CogniFit, can be a good tool for performing these assessments.

Participants were shown how to use different e-health technologies to assess their cognitive, physiological and functional variables of well-being. The correlations between the different parameters were evaluated and a hierarchical cluster analysis was used to determine the validity of the model.

There were strong associations between different wellness parameters, such as cognitive, physical. However, spirituality did not correlate with any other parameter studied. Participants also welcomed e-health technologies.

The parameters correlated strongly across the different areas of well-being. A highlighted strong link between cognitive and physiological areas was discovered, which makes the need for a holistic approach to assessing wellness relevant.

Context

To be able to measure, predict, prevent or slow down chronic diseases and seniors general health problems, it is necessary to address the problem with a holistic and interdisciplinary vision. To this end, e-health can be a good tool. If we want to assess the real degree a person's well-being, we must take into account different parameters of their everyday environment:

• Physical health and fitness.
• Mental and cognitive health.
• Social well-being.
• Spiritual well-being

This study attempted to assess well-being in a holistic way to learn how these four parameters interact. The technology helps collect, store and analyze a large amount of data in a convenient and non-invasive way. With e-health, we will be able to measure the parameters relevant to the well-being of the elderly.

Methodology

Participants

Twenty-seven people aged 78 to 94 from a retirement community in Seattle, Washington participated. Participants meeting the criteria underwent a detailed eligibility assessment and informed consent was obtained.

Procedure

Participants were taught how to use equipment, study procedures, and pre-test evaluations were conducted. For 8 weeks, participants provided cognitive, physiological and functional data three times a week. All this took about 1 hour. Participants could obtain feedback by accessing their own data. From the first week onwards, most users were able to manage the e-health tools without any help. The e-health tools used were

• Telehealth kiosk,which evaluates physiological patterns.
• WebQ, which evaluates functional, social and spiritual well-being.
• CogniFit, neuropsychological tool that evaluates cognitive parameters.

Once we have completed the data collection from the study, we will be able to download the results of each participant to our computer for analysis.

Statistical Analysis

To analyze the data, SPSS 15.0 descriptive statistics were used, correlations of the parameters and a hierarchical cluster analysis with Hoeffding's statistic D was performed.

Results and Conclusions

Overall, participants indicated that they had 0-9 health problems. 17% said their health was excellent and 67% said they were very good. As for their physical health, they tended to say that in the last 30 days it had not been good. On the other hand, social support was perceived as very good. The importance they gave to spirituality was very different from one participant to another. Age correlated negatively with the score in cognitive tasks requiring divided attention (r=-0.48, p=0.029), planning (r=-0,53, p=0.013) and spatial perception (r=-0.718, p<0.0005).

It is striking that social support and spirituality did not correlate with other well-being parameters, which clashes with some previous studies. In the cognitive, physical and functional areas, there were a number of chronic diseases that correlated negatively with the scoring in tasks requiring planning (r=-0.52, p=0.016) , while the difficulties in daily living activities correlated with inhibition (r=0).46, p=0.03) . The subjective perception of having better health correlated positively with the score in inhibition tasks (r=0.493, p=0.0027) and planning tasks (r=0.47, p=0.037). The hierarchical cluster analysis showed two groups of data: physiological and other parameters.

In this study, large correlations between the different areas of well-being are observed, especially cognitive and physiological. These data speak to the importance of holistically assessing the health and well-being of seniors. CogniFit has proven to be a tool capable of effectively and truthfully measuring cognitive health.