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A Bayesian Cost-effectiveness analysis of Holobalance, Holograms for personalized virtual coaching and motivation in an ageing population with balance disorders

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  • Abreha, Fasika Molla
  • Salmasi, Luca
  • Ianuale, Nicola
  • Pegoraro, Enrico

Abstract

Introduction: The world population is aging and it is only expected to grow in the next 30 years reaching 16% of the total world population (1). Globally, fall among older adults is a major public health problem.The burden of mortality and morbidity as a result of fall incidents is high in the elderly. There is a multitude of fall prevention programs designed based on the different risk factors associated with fall risk. Among these exercise programs have been shown to reduce the incidence of falls by 13% (6) to 40% (7).The Otago Exercise Program is an eight-week exercise that involves 17 different exercises that vary based on intensity with different repetitions and weight. It has been used as the cornerstone of most fall prevention exercise programs and has been shown to reduce fall risk in the elderly population by 35% (2,3). More recently, the use of virtual technologies has been an emerging phenomenon in the practice of elderly rehabilitation and exercise program. The Holobalance program, a novel methodology developed under the EU Horizon 2020 innovation project (8), was initiated to develop and validate a new personalized hologram coach platform for virtual coaching, motivation, and empowerment of the aging population with balance disorders. Methods: Bayesian cost-effectiveness analysis of two alternatives namely Otago Exercise Program and Holobalance technology is performed by considering the relevant probabilities of the outcomes, the associated cost, and utility loss for each relevant outcome for three countries i.e. Italy, Germany, and Greece. Relevant data was obtained using literature review of the enumerated parameters and the associated cost as well as utility. Additional data regarding Holobalance technology was obtained from the first data of an ongoing clinical pilot. Results: The ICER associated with the highest WTP under consideration, 30000€, is - 103879 for Italy, -86560 for Greece, and -107666 for Germany. The average expected utility loss for the WTP of 30,000 Euros in the case of the Otago exercise program is -819.51, -624.77, -774.41 for Italy, Greece, and Germany respectively. In the case of Holobalance, it is -478.31, -352.57, and -383.53 respectively for Italy, Greece, and Germany. All the values of the EIB are positive in all of the willingness to pay values that were considered (10,000, 25,000, 30,000) for all of the three countries. The mean cost to implement the Otago exercise program is higher compared to the average cost for Holobalance for the three countries. Conclusion: In all the three countries considered in this analysis, Holobalance is found to be more cost-effective than the Otago exercise program. In this Bayesian Cost-Effectiveness Analysis, the Holobalnce technology is the dominant alternative with a lower cost and a higher level of effectiveness compared to the current standard therapy i.e Otago Exercise program.

Suggested Citation

  • Abreha, Fasika Molla & Salmasi, Luca & Ianuale, Nicola & Pegoraro, Enrico, 2021. "A Bayesian Cost-effectiveness analysis of Holobalance, Holograms for personalized virtual coaching and motivation in an ageing population with balance disorders," MPRA Paper 109301, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:109301
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    References listed on IDEAS

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    1. Risha Gidwani & Louise B. Russell, 2020. "Correction to: Estimating Transition Probabilities from Published Evidence: A Tutorial for Decision Modelers," PharmacoEconomics, Springer, vol. 38(11), pages 1277-1277, November.
    2. Risha Gidwani & Louise B. Russell, 2020. "Estimating Transition Probabilities from Published Evidence: A Tutorial for Decision Modelers," PharmacoEconomics, Springer, vol. 38(11), pages 1153-1164, November.
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    More about this item

    Keywords

    Bayesian Cost Effectiveness Analysis; Holobalance; Fall; Elderly; Virtual Reality; Economic Evaluation;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • I00 - Health, Education, and Welfare - - General - - - General
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I11 - Health, Education, and Welfare - - Health - - - Analysis of Health Care Markets
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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