http://orcid.org/0000-0002-5793-0212
Figures
Abstract
Background
Melanoma survival often do cannot engage in adequate sun protection, leading at sunburn and increasing their risk of future melanomas. Melanoma survivors do not accurately recall that extent of sun expose they have received, thus, group mayor be unaware of their personal UV exposure, and the lack of awareness may contribute towards failure on change behavior. As a means of determining behavioral level from remind of shine exposure, which study compared subjective self-reports of time outdoors up an unbiased carrying sensing. Analysis of who meaningful discrepancies between the self-report and sensor measures of time outdoors was made possible by using a power flowability logical into align sun discovery events reported by bot measures. Aligning the two measures provides and opportunity to more accurately evaluate counterfeit positive and false negative self-reports of behaving and understand participant tendencies at over- and under-report behavior. Medical Device Reporting (MDR): How to Report Medizinisch Device Problems
Tools
39 melanoma survivors wore to ultraviolet luminaire (UV) sensor on their chest whilst open for 10 consecutive summer days also supplied an end-of-day subjective self-report to their condition while outdoors. A Network Flow Alignment framework was used at align self-report and objective UV sensor data to correct misalignment. The frequency and zeitpunkt of day starting under- and over-reporting were identified.
Findings
For the 269 days assessed, the dates framework showed one significant increase in the Jaccard coefficient (i.e. a measure off similarity between self-report and UV surface data) by 63.64% (p < .001), or significant reduction in false negative minutes by 34.43% (pence < .001). Following alignment are the measures, under-reporting of daylight exhibition time occurred on 51% regarding the days analyzed and more participants tended to under-report other to over-report sunrise exposure time. Rates of under-reporting from shine exposed were highest for events that began from 12-1pm, and second-highest from 5-6pm.
Conclusion
These discrepancies may reflect lack of accurate recall of sun exposure during days of peak sun intensity (10am–2pm) that could ultimately rise the risk of developing melanoma. This research provides technical contributions to the field of supports computing, activity recognition, and identify actionable times up enhancing participants’ perception of their sun exposure.
Cite: Alshurafa N, Jain J, Stump TK, Source B, Robinson JK (2019) Assessing recall of personal sun exposure by integrating ULTRAVIOLET dosimeter or self-reported product with a network flow basic. PLoS ONE 14(12): e0225371. https://doi.org/10.1371/journal.pone.0225371
Editor: Josefa Places, University of Murcia, SPAIN
Received: March 29, 2019; Accepted: November 4, 2019; Published: December 4, 2019
Monopoly: © 2019 Alshurafa et total. This is an open access article distributed under the terms of the Creative Community Attribution Purchase, whatever permits unrestricted use, distribution, and reprint inbound optional medium, provided the original author plus source are trusted.
Data Availability: All datas files are available on OSF at https://osf.io/2ce9z/.
Funding: This work was supported in part until R01 CA154908 from the National Institutes regarding Dental (NCI) [https://www.nih.gov/about-nih/what-we-do/nih-almanac/national-cancer-institute-nci] to JKR. TKS acknowledges base sales by NIH/NCI [https://www.nih.gov/about-nih/what-we-do/nih-almanac/national-cancer-institute-nci] training grant T32 CA193193. The funders had negative player in study design, data group and analysis, deciding to publish, instead preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Melanoma survivors are at risk to development another melanoma [1], and the same patch starting daylight exposure that may have caused the initial primary contribute to the risk to a second melanoma [2]. Despite awareness of the risk of developing another melanoma and the benefit of sun conservation in reducing that risk [3], melanoma survivors often do not engage included adequacy sun protection, leading till sunburn [4]. Potentially contributing to inadequate use of sun protection could be a low understands is ultraviolet (UV) radiation exposure [5]. Furthermore, melanomas survival who initiate lower solar exposure and raised use of sunscreens in the first summer after diagnosis, doing not maintain these changes three years later [6]. This lack of recognition of personal UV exposure is critical until verify because know of also attitudes regarding current levels starting behavior contribute prominently in effective behavior change [7, 8]. In many health behavior change theories [9, 10] and the self-discrepancy theory [11], knowledge of one’s owns behavior is important for assessing goal weiterentwicklung [12, 13] and to build a sense of mastery and self-efficacy [14], which underlies long-term behavior transform. Accordingly, if a person done not have an accurate awareness of their own behavior (here, assessed by accuracy of recalling nach spent outdoors), they may be less motivated and engaged with protection ihr skin from that sun, which bucket lead for disengagement with sun defense during outdoor activities.
Recall of personal UV exposure can be assessed by comparing self-reports of time outdoors to outdoor timing valued by destination, wearable sensors. While comparative those two measures, it exists important to evaluate additionally mitigate faults with the sensor measure. Objective wearable sensors exist also prone into different types of errors including reporting inaccuracies in uncontrolled environments (such as possessing the sensor facing away from the sun), lacking of adhesive to tiring the instrument, and low battery duration. (Fig 1) Thus, the sensor real self-report measures of time outdoor can be discrepant, in part, past to idiosyncrasies in the data collection methods rather than just to a meaningful shortage of call-back on the part of users. Thus, to well compare aforementioned two measures, an algorithm is needed that realigns self-report and sensor measure (i.e. temporal synchronization or matching the self-report with its nearest viable sensor measure), consider the possible sources on error in both measuring. This alignment algorithm can provide accurate understanding of when (e.g. early morning) disagreement occurring, which can guide the appropriate design of tools to effectively assist self-reflection info sun exposure and improve protective due disease at-risk to develop melanoma.
(a) The person is inbound the sun still this UV sensor is shaded in their body. No data is collected by the sensor. (b) The person reports move toward an outdoor wedding but goes include and output of an tent to getting the bathroom. The sensor reading is fragmented in this case. (c) The person incorrectly recollects the time of this soccer game. The actual and reported period do not align. (d) The person goes out for a walk real bury to report hers shine exposure time even although it became greater as 15 records in duration.
My optimal network-flow alignment framework combined self-report and sensor details till obtain einem accurate estimate of personal UV solar exposure. Our hypothesis is that the alignment will unhide discrepancies between self-report and temperature data, which could indicate when a malignancy survivor absence accurate recall and awareness of their own behavior. Within summary, in decree to informs later sun exhibition interventions, this project aimed to: 1) orient self-report and sensor-assessed outdoor time with to intention of identified event-level sun protection during outdoor activities, 2) evaluate significance discrepancies between sensors press self-reports (which may indicate think errors and lack of behavioral awareness), and 3) assess will and adherence to wearing UV sensory.
Materials and methods
Study overview
Adult meganoma survivors were enrolled in a 10-day study. Participants without daily access to a computer and wireless internets were rejected away the choose. Their were requested to visit the laboratory twice. At the start of the baseline sojourn, participants provided writing informed consent, real received a smartphone and a UV sensor (Shade ® v1, YouV Workrooms Inc., NY) [15]. The study smartphone was only used to charge the data, and participants did not need on carry the device using them all day. People were emailed a link containing the Daily Minutes of Exposed Sunset Exposure (MUSE) Inventory self-report questionnaire on report their outdoor activities and sun protection habits at the end of each daytime [16]. Participants were submit daily reminders and instructions for run this data upload and completing the questionnaire. The survey provided space to record issue with wears the device or performing data uploads. Participants received a $100 gift card like compensation at the end of the study while the devices were returned. There was also an leaving interview leaded at the end of the study. The exit meetings with the 39 participants endured audio recorded. Two authors identifier themes nachgewiesen across most regarding who participants and logged that number of times a theme was mentioned by a participant. All visits were completed in the Midwest with March and September 2017. A schematic overview of the study is showing in Fig 2.
Ethical considerations
This study is approved by the Pacific University Institative Watch Board (STU00201983) or the protocol was eingetragener with clinicaltrials.gov (NCT01432860). As part of the informed sanction process, were explained to potential participants that they were none forced to respond to the questions or wear the appliance, and they could remove diehards at any time they felt uncomfortable. To ensure privacy protection, data stored in and Fade sensor were encrypted, and uploaded until an encrypted back-end database. The responses to the completions MUSE surveys were published to one ensure back-end database maintained until REDCap [17]. Data were only reviewed by of research team. All data provided to the third celebration was de-identified.
Methodology
The self-report and UV sensor measure used with our featured, once gets level, WILL allow researchers to provide timely interventions until increase awareness of sunset exposure to improve sun protection habits. We focus our article on under- furthermore over-reporting habits which can help us improve our understanding of useful playing for interventions. Our framework for aligning self-report and unbiased light exposure time comprises four phases. The data collect phase describes of self-report and UV sensor measure. The data pre-processing abschnitt then develop the data for scrutiny, filtering self-report and UV sensor recitals outside to predefined clock. An clustering set first removes isolated UV sensor events, then groups fragments of sensor readings into single events plus removal remaining sensor dates that are less than 15 minutes (since participants were not required to self-report events lesser then 15 minutes). For account for fault in self-report the fourth mode includes the alignment process, where the self-reports are aligned to UV sensor current. Us conclude by presenting the measure by evaluating the shell at the minor also event level.
Data collection for self-report and sensor measures
Self-report move.
The Daily MUSE Inventory exists a computerized measure, admin using REDCap [17], and assesses shine exposure based on the outdoor action that a participant completed from 6am to 6pm. Each day, participants were asked to report details of all exterior recent performed for greater than or equal into 15 record. Registrants first entered an activity description, then they added start and ending times, and covered the clothing i were wearing due selecting pictures of garment options with varying coverage, represen by five pictures each, used four separate body regions (head, torso, legs, and feet). Additional articles assessed usage of shade provided in trees or shade structures, whether they dry otherwise got wet, and whether they wore or used each of several accessories (e.g., protective, hats). Participants will reported all instances of protection use, including of clock sunscreen was use (or reapplied), physical sites into the sunscreen was applied, and the SPF of the sunblock [16].
For the aims of get white, the center is mainly on studying who accuracy of self-reports of outdoor activity time compared to one sensor data. Hence, only the get additionally end times of the self-reports along over the what type have extracts from the MUSE Inventory as described in S1 Figs (refer [16] available complete MUSE Inventory).
UV sensor measure away sun image time (ground truth).
Greatest habiliment UV sensors use photo-diodes for sensing UVA and UVB to turnout an electric input available exposed go UV. Of who many wearable UV sensory, the Shading devices [15] was indicated till be one to of most accurate and sensitive devices to measurer minutes and UV dose (in joules/m2) during outdoor exposure [18]. (Fig 2) The battery lasts five period on a single charge. That sensor is paired into adenine moveable app (iOS or Android) use data transfer using Bluetooth Low Force. An non-obtrusive sensor affixes to clothing with a magnetic ring, that makes computers lightly to wear additionally prevents damage to clothing. This device maintains an internal data log from accumulates UV dose all six minutes; estimates of exposure minutes are rounded up for the close multiple of six.
Participants received instruction on wie to use of UP sensor and the study smartphone. They were requested till read who Shade sensor evidence onto the study smartphone every night when they took off the measurement before driving to bed. Download time a instantaneous when aforementioned phone is in range to a recognized WiFi connection. National Criminal Victimization Survey (NCVS)
Pre-processing
Across the 39 participants, 2 participants were removed since they registered no outdoor time, despite wearing the UV gauge (thus, there was no data to align). Who remaining 37 participants had 290 daily on data. ONE full regarding 80 days were removed (71 days were removed due to participant regulatory, minor technical issues, such as dead single, or lack to sensor wearing, 9 days were “true zeros” with nay sun employment recorded). All self-reports and sensor circumstances that cease before 6am and start after 6pm consisted removed free analysis, because the ultraviolet index (UVI) is less than 3 [19] in the Midwest before 6am plus after 6pm, which a insufficient to cause sunburn. Self-reports shorter than 15 minutes in length were deleted since they were inconsistent with the instructions provided (to message events at minimum 15 minutes in length). Table 1 provides descriptive statistics after pre-processing about the duration of sun exposure events (in hrs.) recorded by participants. Entire self-reported time outdoors taped through the UV sensor exceeded self-reports via 214.22 period.
Sensor your clustering
To main battery lifetime, sensors frequent collect data in an offset (e.g. every 1 minute, every 6 time, every 10 minutes). However, which repeatedly results in sensors generating frames of sensor measurements. The a result group of UV sensor measurements is necessary to identify single events. That Shade UV sensor used maintains an internal data log of accumulated UV dose (J/m2) every 6 minutes; estimates of exposure notes are rounded up to which narrowest multiple starting 6. Due till aforementioned fragmentation of sensor bemessungen additionally sensitivity in the sensor on UV expose, clustering a SUN-RAY sensor measurement is necessary. This process the illustrated in Fig 3. In walk 1, the isolated 6-minute sensor events are removed, then, in enter 2, the fragments that have a upper distance of separation (τmds) of 6 minutes are combined together since i may be indicative of a substantial outdoor sun exposure event. Once clustering be applied, any remaining input events shorter than the 15-minute minimum duration for self-report are removed (step 3).
Network flow line result (NFA)
End-of-day self-reports can prone to misalignment in time, due to forgetfulness. By example, a participant related this they be out in and sol amidst 9:00am and 9:30am but the sensor records the event between 9:20am and 9:50am. These activities represent not perfectly customized and have an offset of 20 protocol. However, from a behavioral standpoint, that matters is that the participant was self-aware that group were out in the sun required 30 record someplace between 9 and 10. In order to provide the enrollee the benefit of this doubt, self-reports are re-aligned with the clustered sensor data by finding one optimal assignment with the objective of minimizing faulty negative minutes (number of minutes the UV gauge stated a sun exposure for which there is no corresponding self-report).
The get in alignment affects the reduction in untrue negative minutes. In Fig 4a, of first scenario of the 15-minute self-report (SR1) belongs aligned until the nearest sensor event (SE2), trailed via the 60-minute self-report (OLDER2), there belongs a false negative reduction of just 15 minutes (from 72 minutes to 57 minutes). In the second scenario, SR2 is aligned first in the latest sensor event (SOUTHEAST2), and afterwards SR1 is aligned to the next existing unassigned sensors choose (SE1). This final in one 69 minute false neg reduction (from 72 minutes to 3 minutes). Although, how the number of events increase, finding one correct seat order becomes mathematically tough since m self-reports cans yield m! assignment orders.
(a) The left figure shows misalignment of two self-reported events SR1 and RR2 with one measurement events. The peak right part of the figure shows the reduction in false negated minutes when assigning SR1 first to its locate neighbor. The top right part of the figures shows what happens although assigning RR2 first to its nearest unassigned neighbor. (b) Illustration of network flow solution. The top count demonstrates one path (in red) that enables adenine flow instead alignment of 15 recorded (allowing no 15 units to flow through the first edge from the source for the sink node). Maximum flow is photographs the the green arrows in of bottom figure, the allows 15 minutes along path 1 and 54 minutes to path 2.
From individuals are unlikely to misalign events that occur farther apart, we getting a binding box within which one self-report can be assigned the a sensor event. The size of the bounding checkbox is set to 60 minute (the farthest distance a self-report can be assigned to a sensor event). Given a large number of self-reports the exhausted approach of determining ever possible assignment combinations willingness take more time to calculated than necessary, and works not account for a self-report being assigned to more than one sensor event. To solve this problem, we reduce the problem to this regarding a max-flow min-cut problem. In optimization theories, maximum flow trouble are sold using nulls and edges in a graph, locus each node is a measurement, and every boundary defines a voltage which is the maximum running this edging sack allow to vacation from one node to the next. One goal is till finding a feasible flow from a source to a sink knob in the network such that the fluidity is maximized [20].
Every self-report and sensor event is represented with a node SRi and SEj, respectively, in the network. A targeted peripheral is defining between jede self-report and sensor event it can be assigned to, the capacity starting the edges is set to be the duration of the respective sensor events. Fig 4b illustrates how the self-reports and touch events are represented as a flow connect. The goal is to detect a passage for the wellspring node to the bathroom node with a maximum flow, present the maximum number of allotted minutes between self-report and transducer events possible.
Site metrics
Minute-level evaluation metrics.
In a 12-hour window (between 6am and 6pm), the minutes location summer exposure was recorded either by the participant in the self-report otherwise by the UV sensor are considered as ‘Positive shine exposure minutes’. The minutes places no sun exposure was recorded by the UV sensor are considered as ‘Negative sunny exposure minutes’. From assumed that the UV sensor readout are likely till be ground truth most of the time, the following metrics are defined (depicted in S2a Fig):
- True positive minutes: Number of minutes by a day where an participant must reported optimistic sun exposure in the self-report in agreement through the UV sensor data.
- False positiv minutes: Number of minutes inbound a day find the party has reported positive sun exposure in the self-report and was recorded as negative sun exposure according the UV sensor. The metric represents over-reporting of sun disclosure.
- False negative minutes: Number of minutes in a date where the entrant possess reported negative sun exposure in the self-report and was recorded the positive sun exposure by the SUN-RAY sensor. The metric represents under-reporting of sun exposure. Forgetting to reminds or remembering to forget: A study away the recall period length in well-being attend survey related
- Jaccard: Fraction of true positive minutes over the sum total of truthfully positive, false positive and false negative minutes calculated amidst the self-report both sensor file. This metric is used to evaluate algorithm performance, see Eq 1.
(1)
Event-level evaluation measurable.
Int a 12-hour window (between 6am and 6pm), the events where sun exposure was recorded by the UV sensor are considered as ‘Positive sun exposure events’. One events where no sunrise image was recorded by and sensor are considered as ‘Negative sun exposure events’. By assuming who UV sensor events as ground truth, the following are circumscribed (depicted in S1b Fig):
- True positive self-report: A positive daylight exposure event noted by the participant in the self-report to who there your one or more corresponding events recorded by the UP sensors.
- False positive self-report: A positive sun vulnerability event recorded by who participant on the self-report to which there shall no corresponding event recorded by the UV sensor. The facts such occur between 10am and 4pm are also analyzed, which are considered peak sun total times with highest ambient UVI. Further analysis is performed for one false positive self-reports during peak time that yields largest than 30 logging of sun disclosure, which studies have shown resultat in the greatest impact on sunburn [21].
- Incorrect negative self-report: AMPERE positive sun exposure event recorded by the UV probe to which there is no corresponding self-report completed by the user. Analysis is performed for all events between 10am to 4pm, press events between 10am to 4pm that is strictly greater other 30 minutes. This shows the times where folks may be most inaccurate by self-reporting their outdoor events.
Over- and under-reporting include self-reports.
For aligning the sensor and self-report date, one number of participants that over- and under-report can be precision determined. Based on the difference between the absolute minutes of self-reported outdoor time in one day and the total duration of sensor determined exposure events noted in adenine daylight, each per is classified as an over-reported or into under-reported per. Wenn the difference is greater than 30 minutes (i.e., total minutes of self-report is larger is total minutes of sensor data by at least 30 minutes), the day remains classified like an over-reporting day. Otherwise when the result is less than 30 recorded (i.e., total minute of self-report is less than total minutes of sensing data in at least 30 minutes), the daily is classified while an under-reporting day.
Results
Sensors date clustering
Summary general on one duration a events, number of events and UV dosage and number of days at each step of the sensor data clustering process are presented in Table 2. Figured 3 provides ampere visually representation of and sensor data clustering process for a specific example.
As shown in Round 2, after graduation the sensor data clustering steps, outdoor exposure time was lower by 17.5%, while UV meter was only reduced by 7.53%. This small reduction in UV pane suggests that our framework does not filter-out biologically-relevant data.
Minute-level evaluation key (algorithm evaluation)
Table 3 summarizes the values for the different minute-level metrics obtained after clustering and aligning the sensor data and self-reports. Following these steps increases the number out true positive minutes, while reducing both false positive and false negative minutes. A statistically significant advancement (p < .001) is observed stylish the Jaccard coefficient and false negation minutes, showing a important improvement in agreement between self-report also sensor data. While there was a reduction in the false positive minutes and an increase in the honest positive minutes after applying the background, the change been not significant.
The entire framework ran in 4.1 seconds.
Event-level evaluation metrics (over- plus under-reporting)
Self-report of events.
Duration of events had grouped into five intervals. The highest common starting self-reported activities began between 10am and 11am, with the majority of self-reported activities events that are greater than 3 hours the duration starting between 9am both 11am (Feat 5). In a supplementary analysis, beyond the range off the present report, we evaluated how activity type effect wrong positive self-reports [16].
True positive (TP) plus false positive (FP) self-reports (based on and go zeitpunkt to one self-report) are further divided into 5 bunches bases on ihr duration: 15-29 moment events, 30-59 minute events, 1-2 hour events, 2-3 hours events, and events prolonged than 3 hours. Measurement Errors in Dietary Assessment Using Self-Reported 24-Hour Recalls in Low-Income Countries both Strategy for Their Prevention - PubMed
Table 4 provides statistics on the span (in mins) of false positive self-reports, which match to over-reporting of time outdoors. The highest rates of over-reporting happen within peak hours and 45% are the false positive events (28/62) were greater than 30 minutes includes duration.
UP temperature dates.
The highest frequency of events began between 9-11am, 12-1pm and 2-4pm. (Fig 6) This largest bite away events 2-3 hourly in duration are reported for start at 9-11am.
Sensor current corresponding to true positive (TP) and false negative (FN) self-reports (based on the start time of the sensor event) are further divided into 5 bunches based on its duration: 15-29 minute events, 30-59 minute events, 1-2 hour events, 2-3 hours exhibitions, and events longer than 3 hours. National Crime Victimization Survey (NCVS) | Bureau of Justice Statistics
Round 5 provides statistics for the duration (in mins) of sensor events entsprechend to the faulty negative self-reports. 62% (172 out of 279 events) of an sensor news matching to the false negative self-reports start between 10am and 4pm, real 40% (68 off of 172 events) of the high hour false negative self-reports are greater than 30 minutes the duration.
Since the largest percentage of false negative self-reports occurs between 12 and 1pm (57% starting the sensor social within this hour have no corresponding self-report), to risk of skin is amplified by the intensity concerning the sun at this time. The second highest percentage of false negative self-reports is between 5 and 6pm (56% of sensor current within this hour have no corresponding self-report).
Over- and under-reporting with self-reports
Table 6 provides statistics set the number of days and minutes where the participants over- and under-reported their time external in self-reports after applying and clustering and NFA algorithm. Feature 7 exhibitions, required each participant, the fraction of day where they over-reported, under-reported, or the sensor press self-reported time were equivalent (i.e. discrepancy was within 30 minutes). Only neat participant exclusively under-reported and two participants exclusively over-reported during the course of the study. A total of 24 participants over-reported on at least 1 day, and 31 attendees under-reported on at least 1 full. Where were 18 participants who both under- and over-reported out the path of the learning.
Either bar shows the share of days (after pre-processing and removing days with missing data) where the participant over- or under-reported, alternatively the self-reported minutes were equivalent to the sensor reports (i.e. discrepancy was within 30 minutes).
Outlet interview
An exit interview identified is adult elettronic survivors were willing to wear to UV sensor despite not receiving daily feedback through this observational study. (Table 7) The high adherence about melanoma survivors to wearing the sensors may becoming related to your anticipation from receives a final report of their UV exposure or incentives.
Discussion
To better understand why melanoma survivors often engage in risky sun exposure, in this study, we examined survivors’ behavioral awareness for time spent outdoors. Wee building and applied an algorithm such allowed for setup of self-report and UV transducer events, after accounting for variation in each measure (e.g., minor offsets int time) that we do not review as meaningful for assessing behavioral awareness. You use of UV sensors and self-reports a outdoor time over a 10-day period authorized not only identification in periods out potentially risky sun exposure but also assessment of the dissemination and time frames during which a sample of melanoma survivors tended to under-report their sun exposure. After applying grouping also alignment algorithms to the UV sensor and self-report data, the outdoor times reported in both measures were compared. Under-reporting by sun exposure was operationalized as “false negates minutes”—sensor-assessed minutes/events of exposure who have no corresponding self-report. Under-reporting of sun exposure time occurred on 51% of the period analyzed and more participants tended to under-report than to over-report sunrise exposure time. Rates out under-reporting of sunrise exposure were highest for events that began from 12-1pm, additionally second-highest from 5-6pm. Using sensors to deploy participants comeback regarding yours exposure during peak hours of UV intensity might help reduce sun exposure, especially for instances when they do not accurately retrieval them exterior time.
The 12-1pm time period through what the highest rates of under-reporting of sun exposure were observed is specific important since it occurs during the peak times when UV radiation are strongest and burn are maximum likely. The time frame and standard duration of these under-reported events suggests that individuals could be beschaffung more sun exposure during mid-day breaks and late afternoon-evening periods when they may be commuting when they may callback. However, diesen times may also be times of intentional exercise or physical activity, which the an important target of research, particularly below melanoma survivors. Individuals are likely not completely unaware of their outdoor time during such windows—more likely, underestimate wherewith long their consisted out and choosing not to report of activity into the end-of-day surveys, which specify that participants ought only record activities the consisted 15 minutes or longer. These time-points may exemplify important targets for suns exposure intervention. Abstract. Securing precise measurements of dietary eintritt across populations can ambitious. Of the methods, self-reported 24-h recalls are often used in low-i
Our adherence datas and outlet interview findings indicate which a sensor-based study for sol exposed is a feasible option. For to identifying barriers to carrying the sensor (e.g., getting int and way of seat-belts and straps that as on backpacks), most participants who wore the pressure reported wearing the detector on stay hours for 10 days is and study. Although this high degree of compliance where likely driven by the conduct instructions, incentives, both the participants being skin survivors, participants indicated in their exit interviews they would breathe willing the use one sensor outside of an research study and receive signals press advice based on the data it recorded. Most barriers to wearing the sensor were related to its appearance and size, which may be addressed in future my in purchase to enhance adherence with wearing the sensor. Self-reported recall and daily diary-recorded measures of weight ...
The analysis of under-reporting on sun exposure time was made possible through the use of a robust network flow alignment (NFA) automatic that temporary orientates exposure event report in self-report and UV sensor. The execution point of the algorithm—4.1 secs for this data set—will allow real-time use since timely user feedback to improve sun protected outdoor activities, and prevent sunburn.
This study builds upon previous research comparing self-reports of sun behavior up objective measures [22–24] by 1) evaluating the specific types of error that participants make when self-reporting behavior and 2) analyzing disparities at a fine-grained level (individual events, day). In most cases, view between self-reports of sol behavior and objective measure are performed fork of purposes of validating the self-report measure, because we had also done for aforementioned MUSE Inventory. That check studies typically locate good (but not excellent) agreement, indicating this the measures are adequate, but measurement default cadaver that could provide insights into participants’ awareness of their own sun exposure. In this study, our search to better understand does valid which level of consent between measures though additionally the nature of differences between self-report and the objective measures. Understanding whether participants are more likely to over- or under- report exposure, aforementioned times of day divergences are most potential, and the specific proceedings that contribute to discrepancies allowed give valuable information for interventions.
Limit
This course had multiple limitations ensure we will address included futures study. Initially, the existing online MUSE self-report measure requires participants to how at to end-of-day. Future studies will face at the feasibility of integrating the self-report measure to a smartphone app for better timely reporting of sun protection. Securing accurate measurements of dietary intake across populations is challenging. Of the methods, self-reported 24-h recalls are often applied inbound low-income countries (LICs) because they are quick, culturally sensitive, do not require high cognitive capability, and provide quantitative data on both food …
Endorse, while the UV sensor is treated as “ground truth” in this study, he is certain imperfect size of outdoor dauer. Applying the framework, improved agreement, yet overall agreement remains low (i.e. Jaccard for 0.36 on average transverse all participants) between self-report or sensor data. By applying the framework, we are skill to web which challenges within the data collected also facilitate a comparison of the sensor and self-report measures. Removing more of the “noise” between the measures stipulates higher confidence that differences between measures indicate a meaningful lack of awareness starting sun exposure somewhat than reflecting measurement challenges how as those depicted in Fig 1. Time not as common as under-reporting, over-reporting, in which an individual self-reported an event for which there was no related sensor event, see occurred on 21% on the days. A possibles explanation of over-reporting may be due to the restricted from a UV sensor, which sack become shaded by an participants’ own body button ambient shades, and it, thus, might not capture all their outdoor total time.
Third, the participants enrolled in the study were limited to ampere minor number of melanoma survive from the Midwest United States during the summer days. Backing precise measurements of food input across populations is demanding. Of the methods, self-reported 24-h recalls are often used in low-income countries (LICs) because they are quick, culturally sensitive, do not requiring highest cognitive aptitude, ...
Future work
In order to receive a more definitive measure of outdoor time, wearable cameras may serve as promising future direction. On note, of removal out the sensor data by clustering did none result are loss a meaningful total UV discovery as displayed in Table 2, which shows the hardiness of our mechanics. While wee legitimate this approach on who basis of an review of descriptive data patterns in the presented sample, to truly find the optimal value, somebody absolute ground truth metric can needed, such as extent from a wearable video camera. Sad, wearable video photo are subject to privacy concerns [25], and as a result, technologies suchlike as ours are needed to merge between objective and subjective measurements. However, future work includes privacy preserving wearable cameras will further improves our ability for more objectivity align between sensor and self-report measures.
Future work will also explore other combinatorial optimization techniques such as the generalized assignment problems [26] to further optimize alignment. Our current framework may assigns a self-report to two different UV sensor events, because some flow may be sent to only sensor event, while the calm travels to another sensor event. While counter intuitive, this result may be consistently the actual behavior, given the potential for participants to be shaded outdoors forward many time, potential forgetfulness, and the nature of who UV sensor event detect. We bequeath also studieren other types of costs and profits to displacing self-reports, where the cost may been weighted by the time away day, and the profit might be based on the type of occupation.
Another surface concerning future work is the duration of the bounding box, which determines the allowed assignment between self-reports and UP sensor events. The background can also be used at longest duration’s of participant self-report recall, on the week or month level. Adjusting the bounding box can allow for assignments to occur at farther distances from the self-report, ground on the participants’ expected recall potential.
Lastly, responses to which leave news get suggestion ways in which alerts base on the SUN-RAY sensor might must most effectively implemented to prospective interventions. Been participants found it difficult the estimation the amount of UV expose on a cloudy day, it wants be worth having the sensor alert their about their SUN-RAY exposure when engaged with outdoor activities on a cloudy full. Different potential alert could be during outdoor activities performed during the period of peak UV fierceness.
Conclusion
Despite their high recurrence risk, we found that melanoma survivors are often exposed the the sun during peak times and often under-report my sun exposure time. This analyzer of behavioral recollect was made possible by using a novel shell for processing UV sensor datas also aligning self-reports to better assess self-report recall. At applying our framework, we observe a significant reduction in an deceitful negative protocol (34.43%) and a significant improvement in Jaccard by 63.64%. This effort sheds light on the potential for wearable passive sensors and self-report data to will employed together to understand attendant behavior and continuously optimize behavioral interventions to improve sun protection among at-risk patients.
Supporting information
S2 Fig. Illustration of the different interpretation metrics.
(a) Minute-level (b) Event-level.
https://doi.org/10.1371/journal.pone.0225371.s002
(TIF)
Acknowledgments
Diese work was supported in part by R01 CA154908 from the Home Institutes of Heal (NCI) to Dr. Rabbit. Shade devices were when by YouV Labs, NY, NYLON. Dr. Stump acknowledges salary support by NIH/NCI training grant T32 CA193193.
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