Abstract
Background: Drop-outs within eHealth interventions often reach a high number, which impacts their added value on a population level. To improve eHealth adoption, it is necessary to know which demographics predict dropping out of an eHealth intervention in order to develop countermeasures.
Objective: The goal of this study is to identify demographics and personal motivation types that predict dropping out of eHealth interventions among older adults.
Methods: An observational cohort study was conducted with older adults in the Netherlands. Participants completed a pre-test questionnaire and got access to an eHealth service, called Stranded, for four weeks. With survival and Cox-regression analyses, demographics and types of personal motivation were identified that affected drop-out.
Results: Ninety older adults started using Stranded. 45.6% of these participants continued their use for four weeks. 32.2% of the 90 participants dropped out in the first week (N=29). The remaining 22.2% dropped out in the second or third week of this study (N=13 in week 2, N= 7 in week 3). The final multivariate Cox-regression model which predicts drop-out, consisted of the following variables: perceived computer skills (HR=0.69, BI=0.49-0.99, P=.04) and level of external regulation (HR=1.19, BI=1.03-1.37, P=.02).
Conclusions: Predicting the chance of dropping out of an eHealth intervention is possible by using their level of self-perceived computer skills and their level of external regulation (externally controlled rewards or punishments direct behavior). Anticipating to these factors can improve eHealth adoption.
Objective: The goal of this study is to identify demographics and personal motivation types that predict dropping out of eHealth interventions among older adults.
Methods: An observational cohort study was conducted with older adults in the Netherlands. Participants completed a pre-test questionnaire and got access to an eHealth service, called Stranded, for four weeks. With survival and Cox-regression analyses, demographics and types of personal motivation were identified that affected drop-out.
Results: Ninety older adults started using Stranded. 45.6% of these participants continued their use for four weeks. 32.2% of the 90 participants dropped out in the first week (N=29). The remaining 22.2% dropped out in the second or third week of this study (N=13 in week 2, N= 7 in week 3). The final multivariate Cox-regression model which predicts drop-out, consisted of the following variables: perceived computer skills (HR=0.69, BI=0.49-0.99, P=.04) and level of external regulation (HR=1.19, BI=1.03-1.37, P=.02).
Conclusions: Predicting the chance of dropping out of an eHealth intervention is possible by using their level of self-perceived computer skills and their level of external regulation (externally controlled rewards or punishments direct behavior). Anticipating to these factors can improve eHealth adoption.
| Original language | English |
|---|---|
| Publisher | Social Science Research Network (SSRN) |
| Number of pages | 33 |
| DOIs | |
| Publication status | Published - 13 Apr 2021 |