Good, Bad and Ugly Tech, Why We Need Persuasive Technology to Make EHealth More Productive
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Abstract
Background
This paper synthesizes empirical research aimed at behavior change via technology. The paper is based on systematic reviews, empirical research from my researchgroup (Center eHealth Research, 2005-2013).
Objective: Technology is not productive due to Bad and Ugly tech (ill-driven, woman oriented, text based, preacher-technology and tech that harm frail people). In eHealth research, we observe a black box phenomenon, overlooking the capacities of technology to change behaviors. This paper will unlock the black box phenomenon, demonstrating empirical based factors for Good Tech.
Methods
Several case studies and reviews are carried out to determine a model (using logistic regression and logfiles analysis) that predicts adherence to eHealth interventions (web-based) during real-time usage of eHealth interventions and that predict long-term effects. To understand how persuasive technology can influence the adherence to eHealth interventions logdata provide a starting point for employment of persuasive features into the design of technology.
The logdata of the usage of eHealth interventions contain a record of actions taken by each participant with for each action the following information: user-id; action type; action specification; time and day. The action types that were logged were: login, logout, start lesson, start exercises, download exercises, view success story, view feedback message, start video, turn on text message coach, turn off text message coach and view text message. From these log files, adherence could be extracted. To show this, I will use an example from emental health for reducing anxiety and depression. To relate process data about usage to characteristics of adherers and non-adherers, data were collected of participants at baseline using online questionnaires. Depressive symptoms were measured with the CES-D, anxiety symptoms with the HADS-A. Need for cognition was measured with the Need for Cognition Short Form. Need to belong was measured using Need to Belong Scale. Statistical analyses were done using PASW 18. Differences between adherers and non-adherers were investigated using one-way analyses of variance (ANOVA) and χ2 tests. Logistic regression was used to assess whether baseline characteristics predicted adherence. Analyses of use patterns were performed on 20 arbitrarily selected participants. Effort was made to ensure that selected participants had the same distribution of demographic characteristics and randomized group as the full sample. The logdata showed a significant difference in usage of content and system between adherers and non-adherence and critical episodes for employment of persuasive features to make interventions more productive: episodes to determine the willingness to follow a therapy, awareness of their non-coping strategies, and adoption of “new†skills for behavior change.
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Results
To overcome the low adherence the capacities of technology to motivate should be articulated in the development of interventions to change behaviors. Built in log data and user data provide information to know when, for whom persuasive components are important. Further research is needed about what kinds of components, the dose and timing persuasive components matter most for adherence and for the effects to be achieved. In current projects we employ different persuasive scenarios to improve the productivity (adherence and effects) of eHealth interventions
This paper synthesizes empirical research aimed at behavior change via technology. The paper is based on systematic reviews, empirical research from my researchgroup (Center eHealth Research, 2005-2013).
Objective: Technology is not productive due to Bad and Ugly tech (ill-driven, woman oriented, text based, preacher-technology and tech that harm frail people). In eHealth research, we observe a black box phenomenon, overlooking the capacities of technology to change behaviors. This paper will unlock the black box phenomenon, demonstrating empirical based factors for Good Tech.
Methods
Several case studies and reviews are carried out to determine a model (using logistic regression and logfiles analysis) that predicts adherence to eHealth interventions (web-based) during real-time usage of eHealth interventions and that predict long-term effects. To understand how persuasive technology can influence the adherence to eHealth interventions logdata provide a starting point for employment of persuasive features into the design of technology.
The logdata of the usage of eHealth interventions contain a record of actions taken by each participant with for each action the following information: user-id; action type; action specification; time and day. The action types that were logged were: login, logout, start lesson, start exercises, download exercises, view success story, view feedback message, start video, turn on text message coach, turn off text message coach and view text message. From these log files, adherence could be extracted. To show this, I will use an example from emental health for reducing anxiety and depression. To relate process data about usage to characteristics of adherers and non-adherers, data were collected of participants at baseline using online questionnaires. Depressive symptoms were measured with the CES-D, anxiety symptoms with the HADS-A. Need for cognition was measured with the Need for Cognition Short Form. Need to belong was measured using Need to Belong Scale. Statistical analyses were done using PASW 18. Differences between adherers and non-adherers were investigated using one-way analyses of variance (ANOVA) and χ2 tests. Logistic regression was used to assess whether baseline characteristics predicted adherence. Analyses of use patterns were performed on 20 arbitrarily selected participants. Effort was made to ensure that selected participants had the same distribution of demographic characteristics and randomized group as the full sample. The logdata showed a significant difference in usage of content and system between adherers and non-adherence and critical episodes for employment of persuasive features to make interventions more productive: episodes to determine the willingness to follow a therapy, awareness of their non-coping strategies, and adoption of “new†skills for behavior change.
.
Results
To overcome the low adherence the capacities of technology to motivate should be articulated in the development of interventions to change behaviors. Built in log data and user data provide information to know when, for whom persuasive components are important. Further research is needed about what kinds of components, the dose and timing persuasive components matter most for adherence and for the effects to be achieved. In current projects we employ different persuasive scenarios to improve the productivity (adherence and effects) of eHealth interventions
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