title: Examining Cigarette and Craving Rates Using ACT-Based (2Morrow Quit™) and Mindfulness-Based (Craving to Quit®) Mobile Health Applications

authors: Tan, Rachel

abstract: There has been a growing number of mobile health (mHealth) smartphone applications (apps) that target smoking cessation. There is concern that many smartphone apps for smoking cessation lack evidence-based practices, which harms the apps’ efficacy and effectiveness. Two evidence-based mHealth apps that target smoking cessation are Acceptance and Commitment Therapy (ACT)-based 2Morrow Quit™ (2Q) and mindfulness-based Craving to Quit® (CTQ). 2Q is based on the ACT framework that teaches strategies to increase acceptance and reduce experiential avoidance, while making values-guided committed behaviour changes. CTQ is based on mindfulness training (MT) that teaches techniques to decouple the link between craving and smoking, established by associative and reinforcing processes. This study’s purpose was to examine the effectiveness of 2Q and CTQ using a single-subject A1-B-A2-A3 design with a broad recruitment strategy that included anyone worldwide. There were two main areas explored: (1) to examine if ACT-based 2Q would reduce cigarette intake and cravings to smoke when compared to CTQ; and (2) to examine if having greater acceptance and commitment during post-intervention would predict better smoking outcomes (i.e., reduced cigarette consumption, reduced cravings, or quit smoking) when compared to CTQ. There was evidence of a small reduction in cigarette consumption for both intervention groups but was not statistically significant. There was limited evidence that both interventions were effective in reducing cravings to smoke because of observed differences, and no statistical significance found. There was limited evidence that higher acceptance and commitment levels predicted better smoking outcomes. It is speculated that 2Q is more engaging than CTQ; however, in terms of effectiveness, it is necessary to replicate this study with a larger sample size to produce adequate statistical power.

text: Examining Cigarette and Craving Rates Using ACT-Based (2Morrow Quit™) and Mindfulness-Based (Craving to Quit®) Mobile Health Applications There has been a growing number of mobile health (mHealth) smartphone applications (apps) that target smoking cessation. There is concern that many smartphone apps for smoking cessation lack evidence-based practices, which harms the apps’ efficacy and effectiveness. Two evidence-based mHealth apps that target smoking cessation are Acceptance and Commitment Therapy (ACT)-based 2Morrow Quit™ (2Q) and mindfulness-based Craving to Quit® (CTQ). 2Q is based on the ACT framework that teaches strategies to increase acceptance and reduce experiential avoidance, while making values-guided committed behaviour changes. CTQ is based on mindfulness training (MT) that teaches techniques to decouple the link between craving and smoking, established by associative and reinforcing processes. This study’s purpose was to examine the effectiveness of 2Q and CTQ using a single-subject A1-B-A2-A3 design with a broad recruitment strategy that included anyone worldwide. There were two main areas explored: (1) to examine if ACT-based 2Q would reduce cigarette intake and cravings to smoke when compared to CTQ; and (2) to examine if having greater acceptance and commitment during post-intervention would predict better smoking outcomes (i.e., reduced cigarette consumption, reduced cravings, or quit smoking) when compared to CTQ. There was evidence of a small reduction in cigarette consumption for both intervention groups but was not statistically significant. There was limited evidence that both interventions were effective in reducing cravings to smoke because of observed differences, and no statistical significance found. There was limited evidence that higher acceptance and commitment levels predicted better smoking outcomes. It is speculated that 2Q is more engaging than CTQ; however, in terms of effectiveness, it is necessary to replicate this study with a larger sample size to produce adequate statistical power.

year: 2019

title: Reconstructing Data Provenance from Log Files

authors: Tan, Yu Shyang

subjects: Data Provenance

abstract: Data provenance describes the derivation history of data, capturing details such as the entities involved and the relationships between entities. Knowledge of data provenance can be used to address issues, such as data quality assurance, data audit and system security. However, current computer systems are usually not equipped with means to acquire data provenance. Modifying underlying systems or introducing new monitoring software for provenance logging may be too invasive for production systems. As a result, data provenance may not always be available.

This thesis investigates the completeness and correctness of data provenance reconstructed from log files with respect to the actual derivation history. To accomplish this, we designed and tested a solution that first extracts and models information from log files into provenance relations then reconstructs the data provenance from those relations. The reconstructed output is then evaluated against the ground truth provenance. The thesis also details the methodology used for constructing a dataset for provenance reconstruction research.

Experimental results revealed data provenance that completely captures the ground truth can be reconstructed from system-layer log files. However, the outputs are susceptible to errors generated during event logging and errors induced by program dependencies. Results also show that usage of log files of different granularities collected from the system can help resolve logging errors described. Experiments with removing suspected program dependencies using approaches such as blacklisting and clustering have shown that the number of errors can be reduced by a factor of one hundred. Conclusions drawn from this research contribute towards the work on using reconstruction as an alternative approach for acquiring data provenance from computer systems.

text: Reconstructing Data Provenance from Log Files Data provenance describes the derivation history of data, capturing details such as the entities involved and the relationships between entities. Knowledge of data provenance can be used to address issues, such as data quality assurance, data audit and system security. However, current computer systems are usually not equipped with means to acquire data provenance. Modifying underlying systems or introducing new monitoring software for provenance logging may be too invasive for production systems. As a result, data provenance may not always be available.

This thesis investigates the completeness and correctness of data provenance reconstructed from log files with respect to the actual derivation history. To accomplish this, we designed and tested a solution that first extracts and models information from log files into provenance relations then reconstructs the data provenance from those relations. The reconstructed output is then evaluated against the ground truth provenance. The thesis also details the methodology used for constructing a dataset for provenance reconstruction research.

Experimental results revealed data provenance that completely captures the ground truth can be reconstructed from system-layer log files. However, the outputs are susceptible to errors generated during event logging and errors induced by program dependencies. Results also show that usage of log files of different granularities collected from the system can help resolve logging errors described. Experiments with removing suspected program dependencies using approaches such as blacklisting and clustering have shown that the number of errors can be reduced by a factor of one hundred. Conclusions drawn from this research contribute towards the work on using reconstruction as an alternative approach for acquiring data provenance from computer systems.

year: 2017

title: Sharing, listening, learning and developing understandings of Kaupapa Māori research by engaging with two Māori communities involved in education.

authors: Kana, Fred

subjects: Kaupapa Maori research methodology

abstract: This paper is a culmination of common understandings that were elicited from two pieces of research: ‘The Impact of the BHP New Zealand Steel Mining on the Tangata Whenua and the Environment’ and ‘The Impact of Maori Medium Education within a Mainstream Secondary School on the Lives of its Participants, in particular the Teachers, Caregivers and Students’. It was at the conclusion of each research project and as a consequence of informal conversations and discussions that this paper evolved. The paper discusses shared understandings in the context of Kaupapa Māori research methodology and key findings from the two research projects.

text: Sharing, listening, learning and developing understandings of Kaupapa Māori research by engaging with two Māori communities involved in education. This paper is a culmination of common understandings that were elicited from two pieces of research: ‘The Impact of the BHP New Zealand Steel Mining on the Tangata Whenua and the Environment’ and ‘The Impact of Maori Medium Education within a Mainstream Secondary School on the Lives of its Participants, in particular the Teachers, Caregivers and Students’. It was at the conclusion of each research project and as a consequence of informal conversations and discussions that this paper evolved. The paper discusses shared understandings in the context of Kaupapa Māori research methodology and key findings from the two research projects.

year: 2006