FRIENDS GUI: A graphical user interface for data collection and visualization of vaping behavior from a passive vaping monitor

Ashley Schenkel; Brett Fassler; Edward Sazonov; Larry W. Hawk; Md Rafi Islam; Shehan Irteza Pranto

arxiv: 2511.15007 · v2 · submitted 2025-11-19 · 💻 cs.SE

FRIENDS GUI: A graphical user interface for data collection and visualization of vaping behavior from a passive vaping monitor

Shehan Irteza Pranto , Brett Fassler , Md Rafi Islam , Ashley Schenkel , Larry W. Hawk , Edward Sazonov This is my paper

Pith reviewed 2026-05-17 21:26 UTC · model grok-4.3

classification 💻 cs.SE

keywords graphical user interfacevaping monitorpuffing topographydata visualizationopen source softwareENDSevent decodingbehavioral analysis

0 comments

The pith

A Python graphical user interface extracts, decodes, and visualizes 24-hour puffing data from the FRIENDS vaping monitor device.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper introduces a Python-based graphical user interface for the FRIENDS device that records puffing and touching events on electronic nicotine delivery systems. The interface processes the collected data to make it easier to understand puffing patterns like duration and frequency. Such understanding matters because it helps assess toxicant exposure from vaping and supports regulatory decisions about these products. Tests with 24-hour data sets showed that the tool correctly converts timestamps, decodes events, and creates useful visualizations of behavior. The software is available for anyone to use freely.

Core claim

The FRIENDS GUI is an open-source Python tool designed to extract, decode, and visualize 24-hour puffing data recorded by the FRIENDS device attached to ENDS products. Through validation on experimental 24-hour data, the GUI demonstrated accurate timestamp conversion, reliable event decoding, and effective visualization of vaping behavior.

What carries the argument

The graphical user interface that converts raw device data into interpretable visual displays of puffing topography including duration, intervals, and counts.

If this is right

Puffing topography data becomes more accessible for researchers studying electronic nicotine delivery systems.
Behavioral visualizations aid in evaluating patterns of use and potential toxicant exposure.
Open availability of the tool encourages wider adoption in data collection for regulatory purposes.
The validation supports confidence in using the GUI for analyzing real vaping sessions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Extending the GUI to real-time processing could allow immediate feedback during monitoring sessions.
Combining outputs with other sensor data might reveal correlations between vaping and other behaviors.
The approach could apply to similar monitoring devices in other health behavior studies.

Load-bearing premise

The 24-hour experimental data used to test the GUI represents typical real-world vaping and the FRIENDS hardware captures every puff and touch event correctly.

What would settle it

Running the GUI on data from a session with a precisely known number and timing of puffs and touches, then verifying that the output matches the known sequence without discrepancies.

Figures

Figures reproduced from arXiv: 2511.15007 by Ashley Schenkel, Brett Fassler, Edward Sazonov, Larry W. Hawk, Md Rafi Islam, Shehan Irteza Pranto.

**Figure 2.** Figure 2: The main window of the FRIENDS GUI 3 [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Flow Diagram of FRIENDS GUI The pseudo-code of processing data, converting timestamps, and extracting information has been given in algorithm 1, algorithm 2 and algorithm 3, respectively, in Appendix. Algorithm 1 processes extended POSIX timestamps using thermistor data when “Use Thermistor” is selected, improving puff detection for vape models where EM sensing is unreliable. A temperature difference thres… view at source ↗

**Figure 4.** Figure 4: Graphical representation of puffing and touching events from the conducted experiment, recorded while the [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗

read the original abstract

Understanding puffing topography (PT), which includes puff duration, intra-puff interval, and puff count per session, is critical for evaluating Electronic Nicotine Delivery Systems (ENDS) use, toxicant exposure, and informing regulatory decisions. We developed FRIENDS (Flexible Robust Instrumentation of ENDS), an open-source device that can be attached to ENDS and records puffing and touching events. This paper introduces the FRIENDS graphical user interface (GUI) that improves accessibility and interpretability of data collected by FRIENDS. The GUI is a Python-based opensource tool that extracts, decodes, and visualizes 24-hour puffing data from the FRIENDS device. Validation using 24-hour experimental data confirmed accurate timestamp conversion, reliable event decoding, and effective behavioral visualization. The software is freely available on GitHub for public use.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

A practical open-source GUI for vaping sensor data with validation that could use more rigor.

read the letter

The key point is that this is a Python GUI for the FRIENDS vaping monitor that handles timestamp conversion, event decoding from puff and touch sensors, and visualization of 24-hour data, with the code released openly. It does well by providing a concrete, accessible tool for a niche but important area of public health research on electronic cigarette use patterns. Labs working with the hardware will appreciate not having to build their own data pipeline from scratch. Making it open source is the right move for this kind of applied work. The main limitation is the validation evidence. Success on a single 24-hour experimental run is reported, yet without quantitative error rates, independent ground truth like video annotation, or checks against real-world noise and edge cases such as rapid successive puffs or device movement, it's unclear how well it holds up outside controlled conditions. The stress test concern about transferability seems valid based on what's described. The rest of the work is standard software engineering for sensor data streams, with appropriate references to prior device descriptions. No red flags in the approach itself. This paper targets researchers already using or planning to use the FRIENDS device for topography studies. It offers practical value there but is unlikely to interest a wide audience in software or broader sensing. It deserves peer review to get input on the code and to strengthen the performance claims. I would recommend sending it out for review with attention to expanding the validation section.

Referee Report

1 major / 1 minor

Summary. The paper presents FRIENDS GUI, a Python-based open-source graphical user interface for extracting, decoding, and visualizing 24-hour puffing and touching event data collected by the FRIENDS passive vaping monitor device attached to ENDS. The central claim is that validation on 24-hour experimental data confirmed accurate timestamp conversion, reliable event decoding, and effective behavioral visualization, with the tool made freely available on GitHub to improve accessibility for studying puffing topography.

Significance. If the validation claims hold, the GUI would meaningfully lower the barrier to analyzing data from the open-source FRIENDS device, supporting research on ENDS use patterns, toxicant exposure, and regulatory questions. The explicit open-source release and focus on 24-hour data handling are strengths that aid reproducibility.

major comments (1)

Validation section: the statement that validation 'confirmed accurate timestamp conversion, reliable event decoding, and effective behavioral visualization' is supported only by a qualitative description with no quantitative error metrics (e.g., timestamp MAE, event detection precision/recall), no exclusion criteria for the 24-hour sessions, and no comparison to independent ground truth such as manual annotation or synchronized video. This directly weakens the central claim that the GUI performs reliably for its intended use.

minor comments (1)

The abstract and methods could clarify the exact data format output by the FRIENDS hardware and the specific decoding rules implemented in the GUI to allow readers to assess edge-case handling without running the code.

Simulated Author's Rebuttal

1 responses · 0 unresolved

Thank you for the constructive review and for recognizing the potential significance of the FRIENDS GUI in supporting research on ENDS use patterns. We have addressed the major comment on the validation section below and will incorporate the necessary revisions to strengthen the manuscript.

read point-by-point responses

Referee: Validation section: the statement that validation 'confirmed accurate timestamp conversion, reliable event decoding, and effective behavioral visualization' is supported only by a qualitative description with no quantitative error metrics (e.g., timestamp MAE, event detection precision/recall), no exclusion criteria for the 24-hour sessions, and no comparison to independent ground truth such as manual annotation or synchronized video. This directly weakens the central claim that the GUI performs reliably for its intended use.

Authors: We agree that the validation section would benefit from greater quantitative rigor and explicit details on methodology. In the revised manuscript we will expand this section to report specific error metrics, including mean absolute error for timestamp conversion derived from the 24-hour experimental sessions, as well as precision and recall figures for event decoding based on comparison against the known experimental puffing sequences. We will also document the exclusion criteria applied to the sessions and provide a clearer description of how ground truth was established through the controlled laboratory setup in which puffing and touching events were predefined and directly observed. These additions will be made without altering the original experimental data or claims. revision: yes

Circularity Check

0 steps flagged

No circularity detected in derivation or validation chain

full rationale

The manuscript describes a Python GUI tool that extracts, decodes, and visualizes 24-hour puffing data from the FRIENDS hardware device. Its central claims rest on validation performed against separate 24-hour experimental sessions rather than on any fitted parameters, self-defined quantities, or prior self-citations that would make the reported accuracy equivalent to the inputs by construction. No equations, ansatzes, uniqueness theorems, or renaming of known results appear; the validation is presented as an independent check on timestamp conversion and event decoding. The derivation chain is therefore self-contained against external experimental data.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a software tool paper. No mathematical free parameters, domain axioms, or newly postulated entities are required; the claims rest on the described implementation and empirical validation.

pith-pipeline@v0.9.0 · 5464 in / 1104 out tokens · 46762 ms · 2026-05-17T21:26:35.169508+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Validation using 24-hour experimental data confirmed accurate timestamp conversion, reliable event decoding, and effective behavioral visualization.
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Algorithm 2 converts 64-bit extended POSIX timestamps into local time.

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

23 extracted references · 23 canonical work pages

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Ashley N Dowd, Lovina John, Jennifer M Betts, Prajakta Belsare, Edward Sazonov, and Stephen T Tiffany. An examination of objective and self-report measures of ad libitum electronic cigarette use: identifying patterns of puffing behavior and evaluating self-report items.Nicotine & Tobacco Research, 25(7):1391–1399, 2023

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Puffpacket: A platform for unobtrusively tracking the fine-grained consumption patterns of e-cigarette users

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Max Spencer, Shohreh Sheiati, and Xiao Chen. Aquada gui: A graphical user interface for automated quantification of damages in composite structures under fatigue loading using computer vision and thermography.SoftwareX, 22:101392, 2023

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Use Thermistor

Junjun Guo, Aijun Ye, Xiaowei Wang, and Zhongguo Guan. Openseespyview: Python programming-based visualization and post-processing tool for openseespy.SoftwareX, 21:101278, 2023. 8 APPENDIX Algorithm 1Pseudo-code for processing raw data Input:A text file with extended POSIX timestamps and temperature readings Output:A dataframe with processed data and exte...

work page 2023

[1] [1]

Melis Selamoglu, Bircan Erbas, Karthika Kasiviswanathan, and Chris Barton. General practitioners’ knowledge, attitudes, beliefs and practices surrounding the prescription of e-cigarettes for smoking cessation: a mixed-methods systematic review.BMC Public Health, 22(1):2415, 2022

work page 2022

[2] [2]

Estimation of the global number of e-cigarette users in 2020.Harm reduction journal, 18(1):109, 2021

Tomasz Jerzy´nski, Gerry V Stimson, Harry Shapiro, and Grzegorz Król. Estimation of the global number of e-cigarette users in 2020.Harm reduction journal, 18(1):109, 2021

work page 2020

[3] [3]

General and device-specific reasons for ends use: a qualitative study with adult ends users.International journal of environmental research and public health, 19(11):6822, 2022

Mohammed M Alqahtani, Zachary B Massey, Robert T Fairman, Victoria Churchill, David L Ashley, and Lucy Popova. General and device-specific reasons for ends use: a qualitative study with adult ends users.International journal of environmental research and public health, 19(11):6822, 2022

work page 2022

[4] [4]

Adverse effects of electronic cigarettes on human health.Journal of Dental Sciences, 19(4):1919–1923, 2024

Yu-Hsueh Wu and Chun-Pin Chiang. Adverse effects of electronic cigarettes on human health.Journal of Dental Sciences, 19(4):1919–1923, 2024

work page 1919

[5] [5]

Aldehyde detection in electronic cigarette aerosols.ACS omega, 2(3):1207–1214, 2017

Mumiye A Ogunwale, Mingxiao Li, Mandapati V Ramakrishnam Raju, Yizheng Chen, Michael H Nantz, Daniel J Conklin, and Xiao-An Fu. Aldehyde detection in electronic cigarette aerosols.ACS omega, 2(3):1207–1214, 2017

work page 2017

[6] [6]

Pasquale Caponnetto. Well-being and harm reduction, the consolidated reality of electronic cigarettes ten years later from this emerging phenomenon: a narrative review.Health Psychology Research, 8(3):9463, 2021. 7

work page 2021

[7] [7]

Alyssa F Harlow, Andrew C Stokes, Daniel R Brooks, Emelia J Benjamin, Adam M Leventhal, Rob S McConnell, Jessica L Barrington-Trimis, and Craig S Ross. Prospective association between e-cigarette use frequency patterns and cigarette smoking abstinence among adult cigarette smokers in the united states.Addiction, 117(12):3129– 3139, 2022

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[8] [8]

David T Levy, Zhe Yuan, Yuying Luo, and David B Abrams. The relationship of e-cigarette use to cigarette quit attempts and cessation: insights from a large, nationally representative us survey.Nicotine and Tobacco Research, 20(8):931–939, 2018

work page 2018

[9] [9]

Trends and patterns of dual use of combustible tobacco and e-cigarettes among adults in england: A population study, 2016–2024.Addiction, 120(4):608–619, 2025

Sarah E Jackson, Sharon Cox, Lion Shahab, and Jamie Brown. Trends and patterns of dual use of combustible tobacco and e-cigarettes among adults in england: A population study, 2016–2024.Addiction, 120(4):608–619, 2025

work page 2016

[10] [10]

Protocol for the develop- ment of a vaping cessation intervention for young adult veterans.Contemporary Clinical Trials Communications, 39:101309, 2024

Neal Doran, Samantha Hurst, Jie Liu, Omar El-Shahawy, Mark Myers, and Paul Krebs. Protocol for the develop- ment of a vaping cessation intervention for young adult veterans.Contemporary Clinical Trials Communications, 39:101309, 2024

work page 2024

[11] [11]

Interventions for tobacco smoking.Annual Review of Clinical Psychology, 9(1):675–702, 2013

Tanya R Schlam and Timothy B Baker. Interventions for tobacco smoking.Annual Review of Clinical Psychology, 9(1):675–702, 2013

work page 2013

[12] [12]

Psychosocial interventions for supporting women to stop smoking in pregnancy.Cochrane database of systematic reviews, (2), 2017

Catherine Chamberlain, Alison O’Mara-Eves, Jessie Porter, Tim Coleman, Susan M Perlen, James Thomas, and Joanne E McKenzie. Psychosocial interventions for supporting women to stop smoking in pregnancy.Cochrane database of systematic reviews, (2), 2017

work page 2017

[13] [13]

Health benefits and cost-effectiveness of brief clinician tobacco counseling for youth and adults

Michael V Maciosek, Amy B LaFrance, Steven P Dehmer, Dana A McGree, Zack Xu, Thomas J Flottemesch, and Leif I Solberg. Health benefits and cost-effectiveness of brief clinician tobacco counseling for youth and adults. The Annals of Family Medicine, 15(1):37–47, 2017

work page 2017

[14] [14]

Effectiveness of proactive telephone counselling for smoking cessation in parents: study protocol of a randomized controlled trial

Kathrin Schuck, Roy Otten, Marloes Kleinjan, Jonathan B Bricker, and Rutger CME Engels. Effectiveness of proactive telephone counselling for smoking cessation in parents: study protocol of a randomized controlled trial. BMC Public Health, 11(1):732, 2011

work page 2011

[15] [15]

Immediate antecedents of cigarette smoking: an analysis from ecological momentary assessment

Saul Shiffman, Chad J Gwaltney, Mark H Balabanis, Kenneth S Liu, Jean A Paty, Jon D Kassel, Mary Hickcox, and Maryann Gnys. Immediate antecedents of cigarette smoking: an analysis from ecological momentary assessment. 2009

work page 2009

[16] [16]

Ashley N Dowd, Lovina John, Jennifer M Betts, Prajakta Belsare, Edward Sazonov, and Stephen T Tiffany. An examination of objective and self-report measures of ad libitum electronic cigarette use: identifying patterns of puffing behavior and evaluating self-report items.Nicotine & Tobacco Research, 25(7):1391–1399, 2023

work page 2023

[17] [17]

Monitoring of cigarette smoking using wearable sensors and support vector machines.IEEE Transactions on Biomedical Engineering, 60(7):1867–1872, 2013

Paulo Lopez-Meyer, Stephen Tiffany, Yogendra Patil, and Edward Sazonov. Monitoring of cigarette smoking using wearable sensors and support vector machines.IEEE Transactions on Biomedical Engineering, 60(7):1867–1872, 2013

work page 2013

[18] [18]

Rf hand gesture sensor for monitoring of cigarette smoking

Edward Sazonov, Kristopher Metcalfe, Paulo Lopez-Meyer, and Stephen Tiffany. Rf hand gesture sensor for monitoring of cigarette smoking. In2011 Fifth International Conference on Sensing Technology, pages 426–430. IEEE, 2011

work page 2011

[19] [19]

Puffpacket: A platform for unobtrusively tracking the fine-grained consumption patterns of e-cigarette users

Alexander T Adams, Ilan Mandel, Anna Shats, Alina Robin, and Tanzeem Choudhury. Puffpacket: A platform for unobtrusively tracking the fine-grained consumption patterns of e-cigarette users. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pages 1–12, 2020

work page 2020

[20] [20]

Optigui datacollector: A graphical user interface for automating the data collecting process in optical and photonics labs.SoftwareX, 24:101521, 2023

Juan Soto-Perdomo, Juan Morales-Guerra, Juan David Arango, Sebastian Montoya Villada, Pedro Torres, and Erick Reyes-Vera. Optigui datacollector: A graphical user interface for automating the data collecting process in optical and photonics labs.SoftwareX, 24:101521, 2023

work page 2023

[21] [21]

Hypat: A gui for high-throughput gas-driven hydrogen permeation data analysis.SoftwareX, 21:101284, 2023

George S Evans, Joseph M Watkins, Thomas F Fuerst, Chase N Taylor, and Masashi Shimada. Hypat: A gui for high-throughput gas-driven hydrogen permeation data analysis.SoftwareX, 21:101284, 2023

work page 2023

[22] [22]

Max Spencer, Shohreh Sheiati, and Xiao Chen. Aquada gui: A graphical user interface for automated quantification of damages in composite structures under fatigue loading using computer vision and thermography.SoftwareX, 22:101392, 2023

work page 2023

[23] [23]

Use Thermistor

Junjun Guo, Aijun Ye, Xiaowei Wang, and Zhongguo Guan. Openseespyview: Python programming-based visualization and post-processing tool for openseespy.SoftwareX, 21:101278, 2023. 8 APPENDIX Algorithm 1Pseudo-code for processing raw data Input:A text file with extended POSIX timestamps and temperature readings Output:A dataframe with processed data and exte...

work page 2023