Cluster comparison.

IntroductionThe availability of consumer-facing health technologies for chronic disease management is skyrocketing, yet most are limited by low adoption rates. Improving adoption requires a better understanding of a target population’s previous exposure to technology. We propose a low-resource approach of capturing and clustering technology exposure, as a mean to better understand patients and target health technologies.MethodsUsing Multiple Sclerosis (MS) as a case study, we applied exploratory multivariate factorial analyses to survey data from the Swiss MS Registry. We calculated individual-level factor scorings, aiming to investigate possible technology adoption clusters with similar digital behavior patterns. The resulting clusters were transformed using radar and then compared across sociodemographic and health status characteristics.ResultsOur analysis included data from 990 respondents, resulting in three clusters, which we defined as the (1) average users, (2) health-interested users, and (3) low frequency users. The average user uses consumer-facing technology regularly, mainly for daily, regular activities and less so for health-related purposes. The health-interested user also uses technology regularly, for daily activities as well as health-related purposes. The low-frequency user uses technology infrequently.ConclusionsOnly about 10% of our sample has been regularly using (adopting) consumer-facing technology for MS and health-related purposes. That might indicate that many of the current consumer-facing technologies for MS are only attractive to a small proportion of patients. The relatively low-resource exploratory analyses proposed here may allow for a better characterization of prospective user populations and ultimately, future patient-facing technologies that will be targeted to a broader audience.</div

Study flow chart.

IntroductionThe availability of consumer-facing health technologies for chronic disease management is skyrocketing, yet most are limited by low adoption rates. Improving adoption requires a better understanding of a target population’s previous exposure to technology. We propose a low-resource approach of capturing and clustering technology exposure, as a mean to better understand patients and target health technologies.MethodsUsing Multiple Sclerosis (MS) as a case study, we applied exploratory multivariate factorial analyses to survey data from the Swiss MS Registry. We calculated individual-level factor scorings, aiming to investigate possible technology adoption clusters with similar digital behavior patterns. The resulting clusters were transformed using radar and then compared across sociodemographic and health status characteristics.ResultsOur analysis included data from 990 respondents, resulting in three clusters, which we defined as the (1) average users, (2) health-interested users, and (3) low frequency users. The average user uses consumer-facing technology regularly, mainly for daily, regular activities and less so for health-related purposes. The health-interested user also uses technology regularly, for daily activities as well as health-related purposes. The low-frequency user uses technology infrequently.ConclusionsOnly about 10% of our sample has been regularly using (adopting) consumer-facing technology for MS and health-related purposes. That might indicate that many of the current consumer-facing technologies for MS are only attractive to a small proportion of patients. The relatively low-resource exploratory analyses proposed here may allow for a better characterization of prospective user populations and ultimately, future patient-facing technologies that will be targeted to a broader audience.</div

Radar plot of the three groups derived from the final factor analysis.

Each grey plot line corresponds to a proportion, with the center marking 0% and the outermost line 100%. The colored lines indicate the proportion of respondents in each cluster that report at least weekly use of internet-connected devices and associated health and non-health-related activities.</p

Questionnaire.

IntroductionThe availability of consumer-facing health technologies for chronic disease management is skyrocketing, yet most are limited by low adoption rates. Improving adoption requires a better understanding of a target population’s previous exposure to technology. We propose a low-resource approach of capturing and clustering technology exposure, as a mean to better understand patients and target health technologies.MethodsUsing Multiple Sclerosis (MS) as a case study, we applied exploratory multivariate factorial analyses to survey data from the Swiss MS Registry. We calculated individual-level factor scorings, aiming to investigate possible technology adoption clusters with similar digital behavior patterns. The resulting clusters were transformed using radar and then compared across sociodemographic and health status characteristics.ResultsOur analysis included data from 990 respondents, resulting in three clusters, which we defined as the (1) average users, (2) health-interested users, and (3) low frequency users. The average user uses consumer-facing technology regularly, mainly for daily, regular activities and less so for health-related purposes. The health-interested user also uses technology regularly, for daily activities as well as health-related purposes. The low-frequency user uses technology infrequently.ConclusionsOnly about 10% of our sample has been regularly using (adopting) consumer-facing technology for MS and health-related purposes. That might indicate that many of the current consumer-facing technologies for MS are only attractive to a small proportion of patients. The relatively low-resource exploratory analyses proposed here may allow for a better characterization of prospective user populations and ultimately, future patient-facing technologies that will be targeted to a broader audience.</div

Cluster comparison.

IntroductionThe availability of consumer-facing health technologies for chronic disease management is skyrocketing, yet most are limited by low adoption rates. Improving adoption requires a better understanding of a target population’s previous exposure to technology. We propose a low-resource approach of capturing and clustering technology exposure, as a mean to better understand patients and target health technologies.MethodsUsing Multiple Sclerosis (MS) as a case study, we applied exploratory multivariate factorial analyses to survey data from the Swiss MS Registry. We calculated individual-level factor scorings, aiming to investigate possible technology adoption clusters with similar digital behavior patterns. The resulting clusters were transformed using radar and then compared across sociodemographic and health status characteristics.ResultsOur analysis included data from 990 respondents, resulting in three clusters, which we defined as the (1) average users, (2) health-interested users, and (3) low frequency users. The average user uses consumer-facing technology regularly, mainly for daily, regular activities and less so for health-related purposes. The health-interested user also uses technology regularly, for daily activities as well as health-related purposes. The low-frequency user uses technology infrequently.ConclusionsOnly about 10% of our sample has been regularly using (adopting) consumer-facing technology for MS and health-related purposes. That might indicate that many of the current consumer-facing technologies for MS are only attractive to a small proportion of patients. The relatively low-resource exploratory analyses proposed here may allow for a better characterization of prospective user populations and ultimately, future patient-facing technologies that will be targeted to a broader audience.</div

Cost effectiveness analyses.

<p>Footnotes:</p><p>All costs are in US$.</p>a<p>TDF dominant over ZDV because of lower costs and higher QALYs.</p

Uncertainty bounds of incremental cost effectiveness (ICER) estimates, % of ICER estimates suggesting dominance of the tenofovir (TDF) treatment strategy and % of ICER estimates below the WHO threshold for high cost-effectiveness.

<p>Results were obtained by repeatedly drawing one simulation with TDF as the initial strategy and one simulation with zidovudine (ZDV) in the initial treatment. From this pair of simulations the incremental cost effectiveness ratio was calculated. Dominance was defined by lower costs and higher quality adjusted life year estimates for a specific treatment. By repeating this process 1000 times we obtained an estimate for how frequently the TDF strategy was dominant. Analogous calculations were performed to check how often the ICER estimates were below the WHO threshold for high cost effectiveness (annual per capita gross domestic product of US$ 2154). Uncertainty bounds reflect ranges that include 95% of all ICER estimates.</p><p>Abbreviations: ICER, incremental cost effectiveness ratio; TDF, tenofovir; WHO, World Health Organization.</p

This plots hypothetical pathways of resistance emergence against zidovudine (1A) or tenofovir (1B & 1C) used in this simulation.

<p>The transition probabilities given next to arrows are per 3 months spent on a failing treatment with an (unmeasured) HIV RNA >500 copies/mL. Due to scarcity of resistance data of failing tenofovir regimens from developing settings two separate pathways were tested in the simulation. The base scenario (1B) was derived from a limited set of sequences from tenofovir failures and does not include the multidrug resistance pattern Q151M. The pessimistic scenario (1C) is based on estimations from sequences obtained after virological failure with stavudine and allows for extensive multidrug (i.e. Q151M) resistance emergence. Also note that the multidrug resistance patterns in the zidovudine pathway were not observed in the data (enframed by dashed lines), but were assumed to occur at low frequency. Abbreviations: ZDV, Zidovudine; TDF, tenofovir; S, susceptible; I, intermediate resistant; R, fully resistant.</p

Selected model input parameters.

<p>Selected model input parameters.</p

Shows different outcomes of first-line therapy by type of initial combination antiretroviral therapy (either including zidovudine [ZDV] or tenofovir [TDF]).

<p>For individuals starting with TDF, resistance emergence was modelled by two different scenarios (also see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042834#pone-0042834-g001" target="_blank">Figures 1B and 1C</a>): a base scenario (red symbols) and a pessimistic scenario (blue symbols). Abbreviations: cART, combination antiretroviral therapy; WHO, World Health Organization.</p