Background: Smartphone-assisted technologies potentially provide the opportunity for large-scale, long-term, repeated monitoring of cognitive functioning at home.
Objective: The aim of this proof-of-principle study was to evaluate the feasibility and validity of performing cognitive tests in people at increased risk of dementia using smartphone-based technology during a 6 months follow-up period.
Methods: We used the smartphone-based app iVitality to evaluate five cognitive tests based on conventional neuropsychological tests (Memory-Word, Trail Making, Stroop, Reaction Time, and Letter-N-Back) in healthy adults. Feasibility was tested by studying adherence of all participants to perform smartphone-based cognitive tests. Validity was studied by assessing the correlation between conventional neuropsychological tests and smartphone-based cognitive tests and by studying the effect of repeated testing.
Results: We included 151 participants (mean age in years=57.3, standard deviation=5.3). Mean adherence to assigned smartphone tests during 6 months was 60% (SD 24.7). There was moderate correlation between the firstly made smartphone-based test and the conventional test for the Stroop test and the Trail Making test with Spearman ρ=.3-.5 (P<.001). Correlation increased for both tests when comparing the conventional test with the mean score of all attempts a participant had made, with the highest correlation for Stroop panel 3 (ρ=.62, P<.001). Performance on the Stroop and the Trail Making tests improved over time suggesting a learning effect, but the scores on the Letter-N-back, the Memory-Word, and the Reaction Time tests remained stable.
Conclusions: Repeated smartphone-assisted cognitive testing is feasible with reasonable adherence and moderate relative validity for the Stroop and the Trail Making tests compared with conventional neuropsychological tests. Smartphone-based cognitive testing seems promising for large-scale data-collection in population studies.
Keywords: cognition; neuropsychological tests; telemedicine.
In epidemiology, the regression discontinuity design has received increasing attention recently and might be an alternative to randomized controlled trials (RCTs) to evaluate treatment effects. In regression discontinuity, treatment is assigned above a certain threshold of an assignment variable for which the treatment effect is adjusted in the analysis. We performed simulations and a validation study in which we used treatment effect estimates from an RCT as the reference for a prospectively performed regression discontinuity study. We estimated the treatment effect using linear regression adjusting for the assignment variable both as linear terms and restricted cubic spline and using local linear regression models. In the first validation study, the estimated treatment effect from a cardiovascular RCT was -4.0 mmHg blood pressure (95% confidence interval: -5.4, -2.6) at 2 years after inclusion. The estimated effect in regression discontinuity was -5.9 mmHg (95% confidence interval: -10.8, -1.0) with restricted cubic spline adjustment. Regression discontinuity showed different, local effects when analyzed with local linear regression. In the second RCT, regression discontinuity treatment effect estimates on total cholesterol level at 3 months after inclusion were similar to RCT estimates, but at least six times less precise. In conclusion, regression discontinuity may provide similar estimates of treatment effects to RCT estimates, but requires the assumption of a global treatment effect over the range of the assignment variable. In addition to a risk of bias due to wrong assumptions, researchers need to weigh better recruitment against the substantial loss in precision when considering a study with regression discontinuity versus RCT design.
Background: Mobile phone-assisted technologies provide the opportunity to optimize the feasibility of long-term blood pressure (BP) monitoring at home, with the potential of large-scale data collection.
Objective: In this proof-of-principle study, we evaluated the feasibility of home BP monitoring using mobile phone-assisted technology, by investigating (1) the association between study center and home BP measurements; (2) adherence to reminders on the mobile phone to perform home BP measurements; and (3) referrals, treatment consequences and BP reduction after a raised home BP was diagnosed.
Methods: We used iVitality, a research platform that comprises a Website, a mobile phone-based app, and health sensors, to measure BP and several other health characteristics during a 6-month period. BP was measured twice at baseline at the study center. Home BP was measured on 4 days during the first week, and thereafter, at semimonthly or monthly intervals, for which participants received reminders on their mobile phone. In the monthly protocol, measurements were performed during 2 consecutive days. In the semimonthly protocol, BP was measured at 1 day.
Results: We included 151 participants (mean age [standard deviation] 57.3 [5.3] years). BP measured at the study center was systematically higher when compared with home BP measurements (mean difference systolic BP [standard error] 8.72 [1.08] and diastolic BP 5.81 [0.68] mm Hg, respectively). Correlation of study center and home measurements of BP was high (R=0.72 for systolic BP and 0.72 for diastolic BP, both P<.001). Adherence was better in participants measuring semimonthly (71.4%) compared with participants performing monthly measurements (64.3%, P=.008). During the study, 41 (27.2%) participants were referred to their general practitioner because of a high BP. Referred participants had a decrease in their BP during follow-up (mean difference final and initial [standard error] −5.29 [1.92] for systolic BP and −2.93 [1.08] for diastolic BP, both P<.05).
Conclusion: Mobile phone-assisted technology is a reliable and promising method with good adherence to measure BP at home during a 6-month period. This provides a possibility for implementation in large-scale studies and can potentially contribute to BP reduction.
Background: The iVitality online research platform has been developed to gain insight into the relationship between early risk factors (ie, poorly controlled hypertension, physical or mental inactivity) and onset and possibly prevention of dementia. iVitality consists of a website, a smartphone application, and sensors that can monitor these indicators at home. Before iVitality can be implemented, it should fit the needs and preferences of users, ie, offspring of patients with dementia. This study aimed to explore users’ motivation to participate in home-based health monitoring research, to formulate requirements based on users’ preferences to optimize iVitality, and to test usability of the smartphone application of iVitality.
Methods: We recruited 13 participants (aged 42-64 years, 85% female), who were offspring of patients with dementia. A user-centered methodology consisting of four iterative phases was used. Three semistructured interviews provided insight into motivation and acceptance of using iVitality (phase 1). A focus group with six participants elaborated on expectations and preferences regarding iVitality (phase 2). Findings from phase 1 and 2 were triangulated by two semistructured interviews (phase 3). Four participants assessed the usability of the smartphone application (phase 4) using a think aloud procedure and a questionnaire measuring ease and efficiency of use (scale 1-7; higher scores indicated better usability).
Results: All participants were highly motivated to contribute to dementia research. However, the frequency of home-based health monitoring should not be too high. Participants preferred to receive feedback about their measurements and information regarding the relationship between these measurements and dementia. Despite minor technical errors, iVitality was considered easy and efficient to use (mean score 5.50, standard deviation 1.71).
Conclusion: Offspring of patients with dementia are motivated to contribute to home-based monitoring research by using iVitality and are able to use the smartphone application. The formulated requirements will be embedded to optimize iVitality.