| As population continues to age and more people are living longer,the prevalence of chronic diseases and the emergence of new illnesses are inevitable.Especially for illnesses that have been associated with ageing such as dementia.Globally,dementia affected about 50 million in 2018 with numbers predicted to continue to increase astronomically over the year.In Sub-Saharan Africa,the percentage of the population affected by dementia keeps increasing.As of 2015,there were about 2.13 million people living with dementia in the region and this number is expected to double every 20 years,increasing to 3.48 million by 2030 and 7.62 million by 2050.Data available for dementia cases in Ghana as of 2014 revealed that there were about17,000 people leaving with the condition,with figures projected to increase year-on-year at a percentage of 5%over the next 20 years.According to the latest WHO data published in 2017,Alzheimer’s and Dementia related deaths in Ghana reached 1,701(0.81%)of total deaths,with an age adjusted death rate of 16.47 per 100,000 of population.This percentage puts Ghana at number102 in the world in terms of dementia death cases.Though the economic cost of dementia to the Ghanaian economy and caregivers is largely unknown,it is estimated to be over several millions of Ghana Cedis(GH￠),most of which are made up of cost of informal care,predominantly provided by family members.Dementia is overwhelming not only for caregivers;impacting their careers,physical,psychological,and economic well-being,it also affect the desire of independent living of patients with dementia,which consequently derail their quality of life(QoL).Although,institutionalization may seem to be the best option for managing dementia and relief the burdens on caregivers,the majority of these older adults with dementia prefer to live in their respective homes for as long as they possibly can,while receiving the best healthcare.This desire of patients to live independently mean that caregivers will have to look for alternatives to augment the care type they currently offer to these patients.These factors have necessitated the need to increase the variety and quality of healthcare services delivered to these patients.Owing to the advancement of technological innovations in the recent decades and increases in the number of personalized healthcare wearable devices such as sensor-based networks for activity monitoring,fall and wandering detection,smart socks,clevercare smartwatch,and various e-health applications for patients with dementia,these patients are able to stay in their homes and live their lives independently.Notwithstanding the enormous benefits healthcare wearable devices has on patients as well as on society,there are limited or no research focused on determining the relationship,if any,that exists between the actual use of healthcare wearable devices and the quality of life(QoL)of dementia patients,or a furtherance of the impact of these devices on the quality of life of dementia patients especially in Ghana.The very few studies in other jurisdictions that have made contributions to the understanding of health-related quality of life,have concentrated primarily,and in many cases,exclusively on objective indicators,such as sickness(mostly general diseases),income levels and social status.Thus,this research sought to examine the relationship,if any,that exists between the actual use of healthcare wearable devices and the quality of life of dementia patients(in reference to Ghana)by using constructs from the extended Unified Theory of Acceptance and Use of Technology(UTAUT2)model and the SF-36 quality of life instrument.Using mixed sampling techniques,data was gathered from patients with dementia and in some cases from caregivers(serving as proxies)through a method of administering structured questionnaire and semi-structured interviews(mainly follow-up questions)from towns and villages located in thirteen administrative regions of Ghana.Out of the total 356 responses received,36 responses were excluded from the study due to either substantially incomplete responses or missing values,leaving a total of 320 responses.These responses were used for further analysis employing the Spearman’s Correlation and Partial Least Squares(PLS)based on Structural Equation Modeling(SEM).Using Holm corrections to adjust the correlations between actual use behavior and the various constructs of the extended UTAUT model for multiple comparisons based on an alpha value of 0.05 for both patients and caregivers,this study found a significant positive correlation between actual use behavior(AUB)and performance expectancy(PE),actual use behavior and facilitating conditions(FC),and between actual use behavior and effort expectancy(EE).Also,a significant positive correlation was observed between actual use behavior and behavioral intention(BI),actual use behavior and resistance to change(RC),and between actual use and behavior technology anxiety(TA).The correlation coefficient between actual use behavior and these constructs revealed a moderate to large effect sizes,indicating that as actual use behavior increases,the effects of these constructs also increase.In examining the relationship between actual use behavior and quality of life,results showed a significant positive correlation between actual use behavior and quality of life with a correlation coefficient of 0.55,indicating a large effect size.This correlation indicates that as actual use behavior of healthcare wearable devices increases,quality of life tends to increase.In order to examine the relationship between the latent variables and their measures,the internal consistency reliability,convergent and discriminant validity were tested.Results for constructs reliability and validity showed that,most of the constructs;behavioral intention,effort expectancy,facilitating conditions,performance expectancy,resistance to change,technology anxiety,and actual use behavior all had Cronbach’s alpha values higher than 0.7,except for social influence and quality of life which had Cronbach’s alpha values of 0.614 and 0.451 respectively.Additionally,the composite reliability values for all the constructs were greater than 0.7,suggesting a strong internal consistency reliability.Convergent validity evaluated for the model by using an average variance extracted(AVE)and the factor loadings of the constructs,revealed AVE values were greater than 0.50 for almost all the constructs,except for social influence and technology anxiety,which had AVE lower than the recommended levels of 0.5.In terms of the factors loadings,all constructs but three measuring items under technology anxiety had values above 0.50.Results of the discriminant validity of the model were evaluated using the Fornell&Larcker criterion,Heterotrait-Monotrait Ratio of correlation(HTMT)and Cross Loadings.The square root of the AVE of the constructs were greater than their correlation with other constructs and the diagonal elements were also larger than the entries in the corresponding columns and rows thus,satisfying the criterion discriminant validity.Also,HTMT values for the model were less than one(1),except for the relationship between facilitating conditions and effort expectancy,quality of life and behavioral intention,quality of life and performance expectancy and between social influence and quality of life which had values of 1.073,1.117,1.184 and 1.059respectively.Cross loading report also showed indicators’outer loadings were greater than their associated constructs in exception of BI1(behavioral intention1)and SI2(social influence 2).The various hypothesis posited were tested using the structural model to identify the relationships between the constructs in the research model.The collinearity assessment,path coefficient(?),coefficients of determination(R~2 values),and predictive relevance(Q~2 values)were used to test strength of the relationship between the dependent and independent variables.Results of the collinearity assessment among latent variables assessed through the variance inflated factor(VIF)revealed that,all the latent variables;AUB→QoL,BI→AUB,EE→BI,FC→AUB,FC→BI,PE→BI,RC→BI,SI→BI and TA→BI had values less than 5,therefore the model had no collinearity problem.Path coefficient(?)analysis results also revealed that,all the constructs had strong positive relationship in exception of RC→BI which had-0.149indicating a weaker relationship between those two constructs.The results of the model found that social influence(SI),effort expectancy(EE),facilitating conditions(FC),behavioral intention(BI),and actual use behavior all had p<0.05,thus were statistically significant in explaining the actual use behavior and quality of life regarding the use healthcare wearable devices among patients with dementia from both caregivers and patients’perspective.In determining the mediating role of behavioral intention on facilitating conditions and actual use behavior of healthcare wearable devices,this study found that,there was a significant partial mediating effect of facilitating conditions(FC)on actual use behavior(AUB)through behavioral intention(BI)with an indirect effect of0.198 and a p-value of 0.000.Result for the moderating effect of resistance to change and social influence on behavioral intention showed p-value<0.05 thus,was significant,suggesting that resistance to change and social influence play a major role in whether or not dementia patients use healthcare wearable devices.A further analysis of the sign of the moderating effects revealed that,age and gender negatively moderated the effect of effort expectancy and performance expectancy on behavioral intention to use healthcare wearable devices.While education was also found to positively moderate the effect of effort expectancy and facilitating conditions on behavioral intention to use healthcare wearable devices and negatively moderate the effect of performance expectancy and social influence on behavioral intention to use healthcare wearable devices.Finally,to test the overall performance of the model,coefficients of determination(R~2 values),and predictive relevance(Q~2 values)were examined.R~2values of 0.896,0.725 and 0.985 for actual use behavior(AUB),behavioral intention(BI)and quality of Life(QoL)and Q~2 values of 0.356,0.388 and 0.264 for AUB,BI and QoL respectively indicated that,the model has substantial predictive ability and relevance,hence,could accurately predict the actual use behavior of healthcare wearable device and its impact on the quality of life of dementia patients.This study established a linking relationship between the actual use of healthcare wearable devices and quality of life and how important the adoption of healthcare wearable devices are in managing the dementia conditions,improving the quality of life,reducing the burden of the disease on caregivers and giving power to dementia patients by enabling their independent living.The study went further to uncover the various factors that influence the adoption of healthcare wearable devices in Ghana and the various mediating and moderating effects of these factors on the actual use behavior,which consequently impacted the quality of life of dementia patients.This study offers important insights into analyzing acceptance behavior and the impact the adoption of wearable devices has on the quality of life of dementia patients in Ghana.In order to reduce the burden of dementia,improve the quality of life and ultimately promote independent living amongst patients with dementia,this study suggests the need to invest more into the adoption of these devices for patients with dementia.Stakeholders must also make sure they create the necessary conditions that will facilitate the use of these devices like providing subsidies for these devices,making less inconspicuous devices,creating more awareness of measures being implemented to safeguard patients’data and in reducing risks associated with the use of these devices.As this study has found a moderating effect of resistance to change and social influence on behavioral intention,it is suggested that attention be given to the factors that could cause these patients to resist the use of these devices,like the fear that the use of healthcare wearable devices will interfere with the way they(patients)deal with relevant health problems and the fear that the use of healthcare wearable devices will change the way they(patients)interact with other people.Finally,it is suggested that caregivers(family carers and medical practitioners)be involved in the decision making process whenever policy makers plan to roll out these interventions as their perception(social influence)plays a key role in the actual use behavior of healthcare wearable devices. |
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