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10.1371/journal.pone.0254786 http://scihub22266oqcxt.onion/10.1371/journal.pone.0254786 34310618!8312968!34310618     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2021 ; 16 (7): e0254786 Nephropedia Template TP {{tp|p=34310618|t=2021. Pricing through health apps generated data-Digital dividend as a game changer: Discrete choice experiment |pdf=|usr=}}{{34310618}} gab.com Text Pricing through health apps generated data-Digital dividend as a game changer: Discrete choice experiment JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=34310618 #FOAvip OBJECTIVES: The objective of this paper is to study under which circumstances wearable and health app users would accept a compensation payment, namely a digital dividend, to share their self-tracked health data. METHODS: We conducted a discrete choice experiment alternative, a separated adaptive dual response. We chose this approach to reduce extreme response behavior, considering the emotionally-charged topic of health data sales, and to measure willingness to accept. Previous experiments in lab settings led to demands for high monetary compensation. After a first online survey and two pre-studies, we validated four attributes for the final online study: monthly bonus payment, stakeholder handling the data (e.g., health insurer, pharmaceutical or medical device companies, universities), type of data, and data sales to third parties. We used a random utility framework to evaluate individual choice preferences. To test the expected prices of the main study for robustness, we assigned respondents randomly to one of two identical questionnaires with varying price ranges. RESULTS: Over a period of three weeks, 842 respondents participated in the main survey, and 272 respondents participated in the second survey. The participants considered transparency about data processing and no further data sales to third parties as very important to the decision to share data with different stakeholders, as well as adequate monetary compensation. Price expectations resulting from the experiment were high; pharmaceutical and medical device companies would have to pay an average digital dividend of 237.30euro/month for patient generated health data of all types. We also observed an anchor effect, which means that people formed price expectations during the process and not ex ante. We found a bimodal distribution between relatively low price expectations and relatively high price expectations, which shows that personal data selling is a divisive societal issue. However, the results indicate that a digital dividend could be an accepted economic incentive system to gather large-scale, self-tracked data for research and development purposes. After the COVID-19 crisis, price expectations might change due to public sensitization to the need for big data research on patient generated health data. CONCLUSION: A continuing success of existing data donation models is highly unlikely. The health care sector needs to develop transparency and trust in data processing. An adequate digital dividend could be an effective long-term measure to convince a diverse and large group of people to share high-quality, continuous data for research purposes. Twit Text FOAVip Pricing through health apps generated data-Digital dividend as a game changer: Discrete choice experiment ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=34310618 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34310618 #FOAvip English Wikipedia <ref>{{Cite pmid|34310618}}</ref> Pricing through health apps generated data-Digital dividend as a game changer: Discrete choice experiment #MMPMID34310618 Heidel A; Hagist C; Schlereth C PLoS One 2021[]; 16 (7): e0254786 PMID34310618show ga OBJECTIVES: The objective of this paper is to study under which circumstances wearable and health app users would accept a compensation payment, namely a digital dividend, to share their self-tracked health data. METHODS: We conducted a discrete choice experiment alternative, a separated adaptive dual response. We chose this approach to reduce extreme response behavior, considering the emotionally-charged topic of health data sales, and to measure willingness to accept. Previous experiments in lab settings led to demands for high monetary compensation. After a first online survey and two pre-studies, we validated four attributes for the final online study: monthly bonus payment, stakeholder handling the data (e.g., health insurer, pharmaceutical or medical device companies, universities), type of data, and data sales to third parties. We used a random utility framework to evaluate individual choice preferences. To test the expected prices of the main study for robustness, we assigned respondents randomly to one of two identical questionnaires with varying price ranges. RESULTS: Over a period of three weeks, 842 respondents participated in the main survey, and 272 respondents participated in the second survey. The participants considered transparency about data processing and no further data sales to third parties as very important to the decision to share data with different stakeholders, as well as adequate monetary compensation. Price expectations resulting from the experiment were high; pharmaceutical and medical device companies would have to pay an average digital dividend of 237.30euro/month for patient generated health data of all types. We also observed an anchor effect, which means that people formed price expectations during the process and not ex ante. We found a bimodal distribution between relatively low price expectations and relatively high price expectations, which shows that personal data selling is a divisive societal issue. However, the results indicate that a digital dividend could be an accepted economic incentive system to gather large-scale, self-tracked data for research and development purposes. After the COVID-19 crisis, price expectations might change due to public sensitization to the need for big data research on patient generated health data. CONCLUSION: A continuing success of existing data donation models is highly unlikely. The health care sector needs to develop transparency and trust in data processing. An adequate digital dividend could be an effective long-term measure to convince a diverse and large group of people to share high-quality, continuous data for research purposes. |*Models, Econometric[MESH] |COVID-19/economics/psychology[MESH] |Health Records, Personal/economics/*ethics/psychology[MESH] |Humans[MESH] |Information Dissemination/*ethics[MESH] |Mobile Applications/ethics[MESH] |Surveys and Questionnaires[MESH]Pricing through health apps generated data-Digital dividend as a game (epmc).pdf DeepDyve Pubget Overpricing