In this paper, we describe and evaluate the strategies used to maximize intervention fidelity in a randomized controlled trial to examine the efficacy of a sexual assault resistance intervention. The EAAA program was based on the best available theory and evidence on how women can successfully resist sexual coercion from male acquaintances. Extensive protocols for hiring, training, and supervising facilitators were established a priori. Detailed intervention manuals were developed that clearly described program goals, learning objectives, core elements, troubleshooting tips, sections that must be delivered verbatim, adaptations that could be made if necessary, and the ideal and minimum dose. Program sessions were audio-recorded, and a subsample of recordings were scored for adherence to the manuals using detailed Intervention Fidelity Checklists (IFC) developed specifically for this research. The Gearing et al.  Comprehe Sexual assault is a major public health issue, underscoring the need for preventative interventions  and 63% less likely to experience attempted rape (p = 0.001) over the next year compared to their female counterparts in the control arm. The incidence of attempted coercion and nonconsensual sexual contact were also reduced by 36% (p = 0.001) and 34% (p = 0.001), respectively  program is a small group, evidence-based, sexual assault resistance education program delivered to first year undergraduate women by pairs of well-trained peer educators. As shown in a randomized controlled trial (RCT), the EAAA program fills an important service delivery gap by reducing the incidence of rape/sexual assault among women on university campuses , to assist investigators in assessing intervention fidelity in their own research. In the current paper, we used the Gearing et al.  model asEAAA is anchored in feminist and social psychological theory and supported by empirical evidence shown to reduce women’s risk of sexual violence from male acquaintances. It was developed, pilot tested, and revised over several years. The theoretical underpinnings have been described extensively elsewhere Senn, .The goals of the program are to assist women to (1) more quickly identify potential or actual risk cues for sexual violence in social settings and/or in men’s behaviour, (2) trust their judgement and perceptions, (3) overcome their emotional barriers to risk detection and resistance, and (4) increase their confidence in their ability to choose from and successfully employ an array of effective strategies for resisting sexual assault from male acquaintances. The program emphasizes that the perpetrator is always to blame for sexual violence, does not promote any one preventative strategy, and underscores that women are always the best judge of how to respond to sexual violence.A well-articulated, manualized program was developed to guide effective implementation. The program format consists of scripted mini-lectures, games, activities, small and large group discussion topics, and role-playing scenarios. A program overview guide was developed to provide facilitators with general guidelines for program delivery, the rationale for the setting and timing of delivery, and ways to manage time constraints without compromising content. Within the manual for each unit, learning outcomes and verbatim scripts were provided, with key points listed for the few unscripted sections. Instructions on how to troubleshoot potential challenging situations were included to help optimize facilitation. Tangible materials such as PowerPoint presentations, posters, scenario cards, and audio and visual recordings were included in the EAAA Program Kit.EAAA is delivered by facilitators working in pairs with the lead facilitator taking a more dominant role within each program offering. All facilitators were required to learn both the lead and secondary roles as part of their training and were assigned as the lead facilitator for half the programs they facilitated. Newly hired facilitators completed a comprehensive reading list on the theory and evidence-base behind the intervention’s content and had weekly meetings (4 times) with their supervisor to discuss the readings and have questions answered. Only after the readings were completed were facilitators given the manuals and the rest of the EAAA Program Kit and began practicing program delivery alone and with their co-facilitators. An 8-day training program was then provided by the program developer for all facilitators together at a central site in late August prior to the beginning of data collection. At least one co-investigator from each site also attended this training to buttress their supervision of facilitators in delivering EAAA. The training consisted of didactic learning sessions (2 days), critiqued, mock delivery of the 3 non-self-defense units of the program to student audiences (3 days), and 3 days of Wen-Do self-defence instruction . Mock delivery with critique provided facilitators with the opportunity for behavioural rehearsal, a best practice for maximizing training success and intervention fidelity according to researchers in intervention science  under the supervision of co-investigator experts at each of the participating universities. Full dress rehearsals in front of a mock audience were only required in the winter semester as facilitators would have just practiced in front of a mock audience at the August training. The self-defense portion of EAAA is based on Wen-Do Women’s Self-Defence. Because it takes considerable skill and practice to become a Wen-Do instructor (requiring years of training), scale up would have been impossible if facilitators needed to be certified Wen-Do instructors. Instead, considerable emphasis was placed on the skills facilitators needed to accurately deliver the subset of self-defense techniques covered in this unit. Finally, facilitators were instructed to maintain a journal detailing their thoughts and feelings after each EAAA session. This exercise was intended to help facilitators reflect on their delivery of EAAA and develop greater self-awareness of their strengths and challenges.Prior to the second year of data collection, returning facilitators reviewed key background readings  and attended a slightly shorter (6 day), in-person, refresher training together at the central site. This training consisted of 1 day of didactic instruction, practice sessions with critiques from the developer (3 days), and a repeat of the 2-day Basic Wen-Do Course. The developer subsequently travelled to each research site to assist with the critique of facilitators’ final (self-defense unit) practice session. New facilitators attended the refresher training with returning facilitators but were required to complete the full list of readings and three days of self-defense instruction. They were assigned secondary facilitator roles in their first facilitations.Facilitators were instructed to complete a Fidelity Issues Form (FIF) after any program session where their delivery deviated from the corresponding manuals. They were expected to bring any completed FIFs along with their journal notes to their supervisory meetings with the program developer. The notes were not examined but rather acted as prompts to memory for facilitators for discussions of adherence.The monitoring of the facilitators’ adherence to the program content was accomplished through regular supervisory meetings led by the program developer and attended by a co-investigator at one site and a site coordinator at the second site. All three facilitators at each site were required to attend these meetings following delivery of each weekly or weekend program session thereby providing an opportunity for the facilitators who had just delivered EAAA to debrief together and address any issues that arose, but also allowing the third facilitator to benefit from any lessons learned by her colleagues. During these meetings, after inquiring as to facilitators’ impressions of the most recent session, the program developer and facilitators would examine the FIFs for issues emerging from the session backed up by facilitators’ sharing of pertinent issues raised in their self-reflection journals. Finally, they would page through the corresponding manual together as a means of jogging facilitators’ memory for further deviations from intervention protocols.Program sessions were recorded (with permission of participants) and a subsample of these recordings were scored to assess facilitators’ adherence to the corresponding program manual.Participant attendance was taken at each session as a measure of intervention receipt.Twelve female graduate and senior undergraduate students from Psychology, Sociology and Social Work under the age of 30 years (with the exception of two) were hired and trained as facilitators across the three university sites, with priority given to those who could commit to the initiative for 2 years. Because the program is delivered in pairs, three facilitators were hired at each site and pairs rotated so that there was always a third facilitator available as a backup if needed. A behavioural interview protocol was developed for screening potential candidates for the facilitator role with requisite experience and preferred skills established from the outset. Of the 9 facilitators initially hired, 6 stayed on for the full 2 years of data collection (67%). Three replacement facilitators were hired and trained in the summer before the second year of data collection.The facilitators were supervised directly (in person or remotely) by the program developer, and one or more co-investigator at each of the two sites where the developer was not based, all of whom were experts in violence against women. The clinical trial manager, who had expertise in managing multi-site research, was responsible for ensuring facilitators were aware of their duties regarding the research and intervention fidelity .A Fidelity Issues Form (FIF) was developed to capture facilitators’ immediate, post-session, self-reports of deviations from the manualized program. This form had a yes/no response format, with additional open-ended questions to solicit general feedback about the quality of the session delivery . Facilitators were directed to complete an FIF whenever delivery of a program session diverged in any way from its description in the manuals. In fact, they were encouraged to complete one after every session regardless of whether they thought it was needed to ensure that all deviations were reported. Facilitators were also instructed to bring completed FIFs to their supervisory meetings with the program developer for review.Providing an objective way to monitor intervention delivery is essential for assessing intervention fidelity. To this end, detailed Intervention Fidelity Checklists (IFCs) and instructions for scoring were developed prior to the beginning of data collection that aligned with the manualized program content. The IFCs were used to score a randomly selected subsample of audio recordings of program sessions. Facilitators were trained to audio record each EAAA session they delivered. A randomly selected subsample (25% stratified by site and lead facilitator) of these audio recordings were scored for intervention fidelity using the IFCs described above.Measurement of participant attendance at each of the four program sessions was essential for monitoring intervention delivery and receipt. EAAA attendance was recorded by the facilitators who were instructed to note participants who arrived late, left early, or missed sessions. Full attendance at any one session was defined as missing no more than 30 min of the 3-h session .The Gearing et al.  22-item Audio recordings of program sessions were scored by two independent raters using the corresponding IFCs. In total, 192 sessions were delivered at the three sites across the 2 years of participant enrolment in the trial. Fifty-two session recordings were randomly selected for fidelity scoring stratified by site and lead facilitator. Due to technical issues, one session was not recorded at all and one was only partially recorded, neither of which could be included in our selection. Because of the stratification, the number of recordings selected is larger than would be expected if we simply selected 25% of all sessions delivered. Most of the recordings were scored after data collection was complete. However, at the request of the facilitators, a sample of session recordings  were scored after the first year of data collection in order to provide facilitators with feedback on their delivery of EAAA.Selected session recordings were initially scored by the clinical trial manager. After data collection was complete, a second rater  scored a subsample of 28  of those already scored. IFC scores were converted to percentages for ease of comparison across units. The two raters compared scores on the IFCs and resolved any disagreements until their interrater reliability attained 90%.Each of the first two authors independently reviewed the strategies used to maximize intervention fidelity in the EAAA trial and scored our efforts using the Gearing et al.  CIFG cheWe expected that facilitators would complete an FIF after most  of the 192 program sessions delivered. However, it became apparent during the early stage of data collection that facilitators were reluctant to complete an FIF thinking that doing so would reflect badly on them. Compliance increased slightly once it was explained to them that because at least some deviation from protocol is unavoidable, they were expected to complete an FIF most if not all of the time and that these reports should include any deviation from standard delivery no matter how slight even if it had a positive effect . Nevertheless, only 117 FIFs were received by the Coordinating Centre (67% of the 174 expected). In addition, during the regularly scheduled supervisory meetings early in the second year, it was discovered that one activity in the fourth unit  was carried out incorrectly by a facilitator (and her two co-facilitators) at one site throughout most of the first full year of program delivery, but she/they had not noted the deviation on a FIF. Finally, in scoring the audio recordings for intervention fidelity, the clinical trial manager discovered five additional instances where delivery of a program session had obviously deviated from intended delivery, but the facilitators had neglected to complete an FIF.Maximum scores on the IFCs ranged from 120 to 293 depending on the unit, with higher scores indicating greater adherence to the delivery instructions and content of the manuals. These scores were converted to a percentage of unit that was delivered correctly. The mean IFC score across the four units was 94.2% with a range of 80.9% and 100% for the first rater, and 93.8% with a range of 88.0% and 100% for the second rater.A majority of program participants (76%) attended all four program sessions in their entirety, with 91% attending at least three session, 94% attending at least two, and 97% attending at least one complete session. Two participants who were assigned to the treatment arm left immediately after randomization without attending any of the program. Three participants attended only part of the first session but missed the remaining three. All five of these participants remained in the study.The Comprehensive Intervention Fidelity Guide (CIFG) was used to assess our efforts in maximizing intervention fidelity across all four phases of intervention research identified by Gearing et al. . The metThe findings from this study revealed that the SARE Trial led by Senn et al. , 2017 shFacilitator training was supplemented with session by session supervision with the developer. These meetings resulted in prompts to carry out protocol adherence or correct protocol deviations. They also provided the opportunity for facilitators to create and participate in a community of practice of sorts that prompted peer to peer support and sharing of lessons learned, all of which helped to enhance their competencies in the facilitation process. There were other benefits to supplementing self-report adherence checks with live supervisory meetings including the discovery that one activity was carried out incorrectly by a facilitator at one site throughout the first year of the trial. Perfection in intervention delivery is an elusive outcome. If a facilitator does not see their mistake, they will not record or discuss it. Multiple checks on intervention fidelity are, therefore, recommended. The highly scripted nature of this intervention, the inclusion of troubleshooting tips, and the extensive training and ongoing supervision likely contributed to the high intervention fidelity scores obtained overall.It is worth noting that in the SARE Trial, all four components of intervention fidelity recommended by Gearing et al.  were assDespite having multiple outcome measures, participants were not asked to complete assessments of their understanding or recollection of what they learned in a way that was specifically designed to assess this learning. They were asked, at each follow-up time point, what if any strategies from EAAA they subsequently put into practice to resist sexual coercion (the results from this research will be published in a forthcoming paper), but these were intended as outcome measures rather than measures of implementation receipt. We recognize that including measures of implementation receipt would have been beneficial from the standpoint of assessing intervention fidelity; however, any additional assessment steps would have increased the already high research burden for participants. Balancing the benefits of implementation measurement with the risk of participant attrition due to burden are key features of consideration when designing longitudinal intervention research. Our ability to retain research participants over time , our reassurances did little to improve facilitators’ compliance. Further, although facilitators were directed to maintain journal notes after each program session they facilitated and to bring these notes to their supervisory meetings, in an effort to protect their privacy, these journals were never collected. As a result, the extent to which facilitators complied with this direction cannot be determined. Based on our observations, facilitators appeared to be compliant with and attentive to the procedures around recording of program sessions and the transfer of these recordings to the Co-ordinating Centre. They also seemed eager for and receptive to feedback on their delivery of EAAA from the developer, although there were occasions when facilitators expressed frustration with the level of critique they received during dress rehearsals. In fact, some veteran facilitators  expressed reluctance and dismay at having to continue to perform critiqued dress rehearsals in front of the program developer or their site coordinator/investigator, arguing that because of their level of expertise these requirements were unnecessary and overly onerous. Their overconfidence was always made apparent during these rehearsals when at least one, if not several, activities these facilitators were certain they had mastered completely were delivered incorrectly after the 6-month hiatus between the end of program delivery in the previous academic year and the fall semester of the subsequent year. Perceptions aside, it is likely that facilitators varied in their willingness to comply with procedures put in place to maximize fidelity, but without an objective indicator of compliance we cannot say for certain.The findings stemming from assessment of intervention fidelity can aid in more nuanced interpretation of the results of outcome or efficacy research (Greenberg & Barnow, Future research could examine implementation in a more naturally occurring setting than an RCT where there may be greater variance in levels of intervention fidelity, allowing for the identification of a threshold of fidelity monitoring that yields positive outcomes in delivery of the intervention. This is worthwhile to explore given the resource intense nature of fidelity monitoring. Of note, innovative methods to capture and rate fidelity are beginning to be studied. For example, Caperton et al.  compared


Ongoing rehabilitation after stroke is limited. Using video-guided exercises, which are implemented with a self-management approach, may be a way to facilitate ongoing exercise in the home environment.  To investigate the feasibility of a video-guided exercise program, implemented with a self-management approach for people with stroke.  A phase I, single-group, clinical trial. The study comprised two phases: in phase one, four weeks of the program was supported by weekly supervised sessions and in phase two, four weeks of the program was completed without direct supervision. Demographic information was recorded at baseline. Adherence and adverse events were self-reported via a logbook. Acceptability was measured through a purpose-built scale. Physical performance, physical activity, and exercise self-efficacy were measured at baseline and 4 and 8 weeks. p = 0.02). Self-efficacy and physical activity did not change over the duration of the program.  Sixteen people with stroke were recruited; however, 14 commenced and completed the study. Adherence during the supervised phase was 3.3 hours per week and 2.3 hours per week during the self-directed phase. There were no adverse events. Most participants indicated that the program was easy to use (92%) and would recommend the program to others (86%). Walking speed improved over the duration of the program (mean difference -0.12 m/s, 95% CI -0.22 to -0.02,  The findings support the feasibility of a video-guided exercise program for people with stroke. Further research to confirm the effectiveness of this intervention to improve physical function is warranted. In Australia, people with stroke do not commonly have the opportunity to undertake long-term rehabilitation. After discharge from hospital, they attend an average of 11 sessions of physiotherapy and occupational therapy . People Self-management is a process in which an individual is supported in managing their own health condition. Self-management has been validated as an effective tool in many chronic health conditions , particuA limited number of self-management programs for people with stroke living in the community have been developed. These include remotely delivered advice, education about physical activity, and activities to enhance the skills required for self-management of physical activity . The invIt is possible that, with complex conditions such as stroke, a more structured approach is required to maximise the chances of self-management being effective. This structured approach includes resources (TASK), training, ongoing support, and enhancing self-efficacy for sustained self-management of exercise.to determine if it is feasible to deliver TASK embedded in a self-management approach in terms of safety, adherence, acceptability, and costto determine if TASK, delivered in two stages—TASK supervised practice and TASK self-directed practice—improves outcomes The specific research aims wereOur hypothesis was that it would be feasible to deliver TASK with a high adherence rate and acceptability. The intervention would provide a low-cost solution when compared to ongoing institution-based sessions, with no significant safety issues or adverse events.A phase I, single-group, clinical trial was conducted. People with stroke were recruited from a private community-based rehabilitation clinic in Sydney, Australia. Participants undertook the TASK program. First, they undertook 4 weeks of physiotherapist-supervised practice at the rehabilitation clinic. Second, participants undertook 4 weeks of self-directed practice at home. Outcomes were feasibility and clinical outcomes , collected at baseline, week 4, and week 8, by a researcher blinded to the research questions. The study was approved by the Macquarie University Human Research Ethics Committee . This study was prospectively registered on 6/8/2018 (registration number ACTRN12618001316291p).People with stroke were included if they were an adult, have been discharged from rehabilitation, and could walk at >0.4 m/s with/without an aid. They were excluded if they could not understand verbal/written English or did not have Internet access at home. Information such as age, gender, side of hemiparesis, cognition (measured via the MMSE), and time poststroke was collected to describe the sample.From week 1 to 4, participants undertook TASK physiotherapist-supervised practice, for 30 minutes once a week at the rehabilitation centre. The TASK program involves the practice of five everyday tasks: sitting, standing up, standing, stepping, and stepping up. Each task is practised for approximately 5 minutes. The program is set to music. Each task has a video that demonstrates the exercise, highlights the correct technique, and notes common errors. Participants received a resource package including an electronic copy of the program (via an app or website), exercise mat (for the floor and table), and exercise step. The exercise floor mat is designed to be a visual cue to remind people to exercise within the home and to guide equipment placement. It also provides stepping targets. During the four weeks of supervised training, the physiotherapist supervising the program implemented a self-management approach, in which they taught the participant how to monitor their own technique, how to progress or regress each exercise, and how to alter the dose. The main aim of these sessions was to equip the participant with the skills needed to continue the program safely with increasing independence in the home. As soon as the participant was familiar with the program and safe to complete aspects, they were asked to commence self-directed practice of TASK at home.From week 5 to 8, participants undertook TASK self-directed practice, for 30 minutes three times a week at home. The physiotherapist contacted each participant weekly via the participant's preferred contact method . This contact was designed to provide coaching, motivation, troubleshoot issues, etc.Feasibility of delivering the intervention was measured in terms of safety, adherence, acceptability, and cost. For safety, data on near misses and adverse events was collected. Adverse events consisted of minor events including muscle soreness, and major events will include falls and injuries requiring medical care. Participants completed a logbook documenting their adherence to the program and minor or major adverse events. For acceptability, participants were asked four questions: (i) Were you satisfied with the TASK program? (ii) Was TASK program easy to use? (iii) Did you enjoy using the TASK program? (iv) Would you recommend this program to others?Clinical outcomes were physical activity, walking, and self-efficacy. Physical activity was measured using the Incidental and Planned Exercise Questionnaire (IPEQ), a self-reported measure of physical activity . Walkingt-tests were used to analyse within-group change over time for the clinical outcome measures at time points week 0 to week 4, week 4 to week 8, and week 0 to week 8. Differences are reported as a mean and 95% confidence interval.Data was analysed using SPSS version 25. Demographic data were reported via descriptive statistics. Adherence data was calculated from week 0 to week 4 and then week 4 to week 8 and reported as mean values for the supervised and self-directed phases. Results from the four acceptability questions were reported as frequencies comparing time points week 4 and week 8. Paired Sixteen participants were recruited to the study; however, two individuals withdrew before baseline data was collected with one individual reporting underlying health concerns and one lacking time to complete the intervention as the reasons for their withdrawal. Therefore, 14 participants completed the intervention and were assessed at baseline and week four, respectively. At 8 weeks, 13 of the participants completed all the reassessments.n = 9). The majority of the participants were male (64%). Mean initial walking speed was 0.87 m/s (SD 0.5), and 71% of participants walked without a walking aid. There were 9 individuals with right-sided hemiparesis (64%), 4 with left (29%), and one participant with no hemiparesis reported (7%).The mean age of the participants was 57 years (SD 14.2). There were large variations in timeframes poststroke . Participants' mean cognition score on the MMSE was 28/30  and between week 4 and week 8 (self-directed phase) averaged 2.3 hours per week . This indicates that adherence was higher during the phase that was supervised in comparison to the self-directed phase.Adherence of participants to the TASK program between week 0 and week 4 (supervised phase) averaged 3.3 hours per week  by the TASK program at the conclusion of the study. Almost all the participants reported the program to be easy or very easy to use (92%); however, one participant did report that it was difficult to use. All but one participant indicated that the TASK program was enjoyable. The majority of the participants indicated that they would either recommend or strongly recommend (86%) the TASK program to others. The 8-week TASK program as conducted in this study cost a total of $737 Australian Dollars per participant. This cost includes provision of equipment and therapist costs for the supervised and remote sessions.No adverse events, minor or major, were reported by the participants in this study.(1) Walking Speed. The average walking speed of participants at baseline was 0.87 m/s . At 4 weeks, the average walking speed was 0.92 m/s  and at 8 weeks 1.02 m/s . There was a significant difference between initial and final walking speed . See (2) Self-Efficacy. The mean scores at week 0, week 4, and week 8 were 28.6 , 30.6 , and 30.8 , respectively, out of a total score of 40. See (1) Physical Activity. At baseline, the mean physical activity score was 23.7 . This only varied slightly at 4 weeks  and 8 weeks . See This study investigated the feasibility of a video-guided exercise program, delivered with a self-management approach for community-dwelling people with stroke. Participants found the program acceptable, reporting that it was enjoyable and they would recommend it to others. Adherence to the program was good; however, it did decrease in the self-directed phase (3.3 hours compared to 2.3 hours per week). This was anticipated as during the supervised phase, participants could be reminded in person in comparison to the remote support. There were no adverse events reported in this study, suggesting that a self-directed video-based program can be implemented safely.Offering exercise programs facilitated by technology and videos has been previously investigated for people with stroke , 15 and In previous studies that have implemented technology for people with stroke, many did not include a supervised phase consisting of familiarisation sessions. This perhaps explains why reported adherence levels have been low . It coulThe current study did have limitations which need to be considered when interpreting the findings. Firstly, as a phase 1, feasibility study, the sample size for this study was small and diverse in characteristics. Secondly, the study utilised a survey which was purpose-built and allowed for only answers to be selected with no further input of the participant. This restricted the ability to collect in-depth information regarding each participant's experience and perspective of the TASK program. Having more information about the participants' experience with the program could have been valuable in informing future research and clinical practice.In conclusion, the findings of this study suggest that a video-guided exercise program, implemented with a self-management approach, is a feasible and accepted intervention for people with stroke. Future research should be conducted in a larger trial with a control group to determine if this type of exercise program can impact physical outcomes and self-efficacy.


Purpose: To compare listening ability (speech reception thresholds) and real-life listening experience in users with a percutaneous bone conduction device (BCD) with two listening programs differing only in high-frequency gain. In situ real-life experiences were recorded with ecological momentary assessment (EMA) techniques combined with real-time acoustical data logging and standard retrospective questionnaires. Methods: Nineteen experienced BCD users participated in this study. They all used a Ponto 4 BCD from Oticon Medical during a 4-week trial period. Environmental data and device parameters  were logged in real-time on an iPhone via a custom iOS research app. At the end of the trial period, subjects filled in APHAB, SSQ, and preference questionnaires. Listening abilities with the two programs were evaluated with speech reception threshold tests. Results: The APHAB and SSQ questionnaires did not reveal any differences between the two listening programs. The EMAs revealed group-level effects, indicating that in speech and noisy listening environments, subjects preferred the default listening program, and found the program with additional high-frequency gain too loud. This finding was corroborated by the volume log—subjects avoided the higher volume control setting and reacted more to changes in environmental sound pressure levels when using the high-frequency gain program. Finally, day-to-day changes in EMAs revealed acclimatization effects in the listening experience for ratings of “sound quality” and “program suitability” of the BCD, but not for ratings of “loudness perception” and “speech understanding”. The acclimatization effect did not differ among the listening programs. Conclusion: Adding custom high-frequency amplification to the BCD target-gain prescription improves speech reception in laboratory tests under quiet conditions, but results in poorer real-life listening experiences due to loudness. A percutaneous bone conduction device (BCD) is a viable solution for patients with a conductive or mixed hearing loss (HL) ,2,3, or By default, the effective gain of a BCD, defined as the bone conduction threshold minus the aided threshold , rarely The benefits of percutaneous BCDs have been clearly demonstrated using speech intelligibility measurements ,11, and Methods of evaluating listening benefits from BCDs are currently limited to laboratory settings or retrospective reports, which hinders investigations into real-life and context-specific benefits. Well-controlled listening tests do provide reliable data, but often lack validity, as actual real-life listening conditions can be vastly different from those inside the laboratory. Typically, laboratory testing of hearing abilities reaches its ceiling at negative or close to zero SNRs ,18,19, wAn alternative to using traditional self-reports is the sampling of real-world experiences in situ, referred to as experience sampling or ecological momentary assessment (EMA) . With thThe aim of this study is to evaluate whether a group of unilaterally fitted BCD users will benefit from custom high-frequency amplification, in addition to a standard prescription, under both real-life and laboratory conditions. Specifically, two listening programs differing only in high-frequency gain above 1.1 kHz are contrasted, and context-specific real-life outcomes are investigated with statistical modeling of in situ subjective listening experiences (EMAs), with real-time data logging of the ambient acoustic environment. The real-life outcomes are compared to traditional speech reception tests and retrospective questionnaires about listening experiences.In supporting analyses, data logs of BCD volume control and day-to-day changes in EMAs are assessed. The latter is important for understanding acclimatization effects , by estaThis study was designed to include twenty experienced BCD users, all with an air-bone gap of at least 20 dB and BC thresholds on the fitted side better than 45 dB HL. While we originally aimed to include 20 subjects, due to COVID-19 ‘lockdown’ measures we closed this study after including 19 subjects. At their first visit, all subjects gave their informed consent to participate in the study. Subject characteristics are shown in Ten subjects  had a bilateral conductive or mixed HL due to chronic suppurative otitis media (CSOM), and two subjects  had a bilateral conductive loss due to congenital atresia. Five subjects had a unilateral HL due to CSOM . On the contralateral side, two subjects had normal hearing , two subjects had a sensorineural HL , and one subject had no contralateral hearing (17). Subjects 16 and 19 had a unilateral HL due to congenital atresia, with normal hearing on the contralateral side.d) was created with the Genie Medical fitting software . This default program was copied to a custom program (Pc), and its gain settings were modified using an Excel worksheet implementing the following rule:0 = default gain for a 0-dB HL BC threshold.All subjects were fitted with a new Ponto 4 BCD with two listening programs. At the first visit, pure-tone thresholds were measured with a clinical audiometer  using TDH-39 headphones and a B-71 bone conductor, and BC in situ thresholds were measured with the BCD. From the BCD threshold, a default listening program  gain prescription (Equation (1)) was used with a modified third-gain rationale. The programs Pc and Pd were randomly assigned as listening program 1 or 2 in the BCD.Thus, both programs had identical gain settings for frequencies below 1.1 kHz, corresponding to the prescription of the Genie Medical fitting program. In Pc resulted in at least 5 dB more gain for frequencies above 1.1 kHz than with Pd, across all subjects. The average 1–4 kHz gain for input levels of 50, 65, and 80 dB SPL was 3.0, 7.2, and 11.7 dB higher for Pc than for Pd, respectively.The rationale in PDuring a 4-week trial period  How do you rate loudness? (2) How do you rate sound quality? (3) How suitable is this program for this environment? (4) Did you listen to speech? If yes: (5) How do you rate speech intelligibility? Henceforward, the four EMA questions are referred to as “Sound quality”, “Program suitability”, “Loudness”, and “Speech understanding”, respectively.For the trial period, PAt the second visit , speech perception in quiet and noisy environments was measured for both listening programs. Speech-in-quiet was measured with NVA-monosyllabic word lists, comprising 11 consonant-vocal-consonant syllables per sublist . The subIn addition, overall experiences with both programs were probed with “The Abbreviated Profile of Hearing Aid Benefit” (APHAB)  and “TheAll users decided to keep their BCD. The BCD was programmed according to the subject’s preferences, the research app was removed from the iPhone, and the standard Oticon ON app for controlling the device and Bluetooth sound streaming was installed on the subject’s smartphone.A time-stamped sound pressure level (SPL) and signal-to-noise (SNR) estimate, together with an acoustic analysis classifying the listening environment as ‘Quiet’, ‘Noise’, Speech’, and ‘Speech in Noise’ (soundscape), were logged and stored on the iPhone every 80 s of BCD wear time as long as the iPhone had a Bluetooth connection with the BCD. All measures are available in standard commercial Ponto 4 BCDs, and the acoustic data are similar to those logged by standard Oticon Op S behind-the-ear hearing aids  were associated with the average of the three prior logged acoustic data samples. Thus, short-term sound exposure was associated to changes in both volume and ratings of the two listening programs. Note that since the BCD automatically sets the volume to 0 on reset and program change, all logged volume changes to level 0 were excluded from further analysis.Observations were predominantly classified as belonging to either Quiet or Speech soundscapes , with observations in Speech in Noise and Noise being sparse . To increase statistical power, and given the high degree of overlap between Speech in Noise and Noise in terms of other acoustical characteristics , we collWe applied linear mixed-effect (LME) modelling to associate EMA ratings with days of BCD usage, to associate ambient sound levels (SNR and SPL) with volume adjustments, and to predict EMA ratings based on soundscape data and listening program. In addition, random effects were included to adjust the models for contextual confounds. For the first case, inter-individual variability in offsets and slopes were adjusted for by including a random term for subject ID. For the second case, the random effect structure was expanded to also include offsets due to time . Lastly, for the third case, the random effects structure additionally included days of usage offsets , and offsets due to volume setting.d and Quiet, respectively, and LME models were compared using likelihood-ratio tests to ensure optimal model fit.The listening program and soundscape categorical predictors were contrasted against baseline conditions Pt-tests or Kruskal–Wallis and Wilcoxon signed-rank tests  for additional paired or un-paired comparisons, and all statistical analysis were performed in R , with packages “nlme” for LME modelling, “stargazer” for regression coefficient statistical evaluation, and “coin” for Wilcoxon signed-rank tests.We used t-test; t = 2.86, df = 18; p = 0.01). The signal-to-noise ratios for sentences [t-test; t = 1.32, df = 18; p > 0.05).At the second visit, speech-in-quiet was measured with frontal speech for both listening programs with NVA monosyllables . For theentences  with frot-test; APHAB EC: t = 0.59, BN: t = 0.70, RV: t = 0.16, AV t = 2.11; SSQ Speech t = 1.53, Spatial t = 0.30, Quality t = 1.29; df = 16; p > 0.05).At the second visit, subjects filled in APHAB and SSQ questionnaires for the aided conditions. Mean scores of the APHAB questionnaire for the domains ease of communication (EC), background noise (BN), reverberation (RV), and aversiveness of loud sounds (AV) were 21.2 ± 2.8, 39.4 ± 3.6, 32.0 ± 3.6, and 39.1 ± 5.3 for the default program and 23.7 ± 4.3, 42.1 ± 4.6, 32.5 ± 4.2, and 44.9 ± 6.0 for the custom program, respectively. For the SSQ questionnaire, mean scores for speech, spatial, and sound quality were 6.4 ± 0.3, 5.1 ± 0.6, and 7.1 ± 0.3 for the default program and 6.2 ± 0,4, 5.2 ± 0.5, and 7.0 ± 0.3 for the custom program, respectively. The APHAB and SSQ scores were not significantly different for the two listening programs  preferred the default program, 6 preferred the custom program, and 3 did not have a preference.p = 0.10, and z = −1.75, p = 0.08, respectively).The total amount of logged data  per subject is summarized in t(55) = 7.45, r = 0.71, p < 0.001). Thus, the listening environments associated with EMAs were comparable to the listening environments during periods without EMAs.We also investigated when and in which contexts EMAs were submitted. d: β = 0.037, 95% CI = (0.008–0.067), p = 0.013; Pc: β = 0.036, 95% CI = (0.013–0.060), p = 0.002), and “Program suitability” , p = 0.004; Pc: β = 0.046, 95% CI = (0.023–0.069), p < 0.001) with days of use. This effect did not differ significantly between the listening programs.Lastly, since all subjects were new to the Ponto 4 BCD, we expect a certain acclimatization to the BCD, as has been reported for new users of BTE hearing aids . The graIn situ EMAs for each subject and listening program were averaged across time and EMA questions to represent “overall in situ preferences” . In this respect, the overall preference refers to the average rating of “Sound quality”, “Speech understanding”, and “Program suitability” when conversation was indicated, and otherwise to the average rating of only “Sound quality” and “Program suitability” when subjects indicated that no conversation was taking place.d minus mean preference for Pc) on the x-axis against the retrospective preference on the y-axis. The in situ preference was slightly higher for Pd , albeit not significantly different . Moreover, “Loudness” ratings were higher for Pc than for Pd , but this difference was also not significant .d while preferring Pc at the retrospective follow-up questionnaire.Notably, the in situ overall preference aligned well with the retrospective preference reports. However, four subjects  did not exhibit a clear in situ preference compared to in the retrospective reports, and one subject (9) exhibited a slightly higher overall in situ preference for PWe hypothesized that volume control logging reveals behavioral insights in support of the EMA evaluation of listening experiences with the BCD listening programs. Thus, we investigated whether volume control differed among the two listening programs in terms of overall level usage and level changes.d  model was applied to predict volume level changes from listening program and acoustic data. This tested whether subjects actively used the volume control  and whether this behavior could potentially differentiate between the two listening programs. The applied LME model is described by:c compared to Pd when evaluated at the mean observed levels of SNR and SPL. In addition, subjects preferred a lower volume when SPL increased, but a higher volume when SNR increased. A significant interaction between SPL and listening program indicates that this volume sensitivity was more pronounced with Pc than with Pd. This pattern was not driven by SPL and SNR being inversely correlated, since variance inflation factors post modeling were low (2.2 for SPL and 2.9 for SNR). Instead, the pattern indicates that ambient SPL  and ambient SNR  modulated changes in volume preference, and that subjects were more sensitive towards change in SPL when using the high-frequency gain program.The regression coefficients in The LME model explained 48.8% of the variance in all volume changes. Out of this, 3.7% of the variance was contributed by the fixed effects  alone, whereas individual differences  captured 43.8% of the total explained variance. The remaining explained variance was captured by hourly fluctuations in volume changes.Average in situ ratings  and retrc was rated higher than Pd on “Loudness” , but lower on the “Sound quality”  and “Program suitability”  questions.In c was again rated higher than Pd but only for Quiet and Noisy Speech or Noise soundscapes. For the Speech soundscape, “Speech understanding” was rated slightly higher with Pc than Pd .For the “Speech understanding” question, PTo allow for statistical testing and targeted context-dependent evaluation of in situ listening preferences , individd vs. Pc), soundscape, and the interaction between the two. The associated regression coefficients are presented in The fixed-effects independent variables for LME modelling were listening program  soundscape. The significant interactions between soundscape and listening program suggest that “Sound quality”, “Speech understanding”, and “Program suitability” were all rated lower with Pc than with Pd in Noisy Speech or Noise soundscapes. In addition, the higher rating of “Loudness” with Pc was also present in Speech soundscapes but not in Noisy Speech or Noise soundscapes.At baseline levels, the marginal effects indicate that “Program suitability” ratings were higher in the Speech compared to the Quiet soundscape with PIn summary, LME modeling revealed that the additional high-frequency gain in the custom listening program yielded higher ratings of “Loudness” for Quiet and Speech soundscapes and lower ratings of “Sound quality”, “Speech understanding”, and “Program suitability” for the more challenging Speech in Noise and Noise soundscapes.p = 0.001; Sound quality: dAIC = 74, Chi2(2) = 79.88, p < 0.001; Speech understanding dAIC = 60, Chi2(2) = 66.37, p < 0.001; Program suitability dAIC = 49, Chi2(2) = 55.10 p < 0.001).We also checked whether adjusting for volume, time, and day in the LME models was warranted. That is, we assessed whether these contextual factors affected how ratings were performed in the trial period. Each fitted model was compared to more simple models that only adjusted for inter-individual differences in EMAs. In all cases, adding the additional contextual parameters as co-regressors significantly improved model fits when testing with likelihood ratio tests  = 16.10, Differences in listening abilities and experiences with two BCD listening programs were investigated in a heterogeneous sample of 19 unilaterally fitted subjects. The listening programs differed only in high-frequency gain. Laboratory measures of speech reception thresholds revealed a significant benefit of the additional high-frequency gain in the speech-in-quiet condition, but not for the speech-in-noise condition. Moreover, retrospective preference ratings with the SSQ and APHAB questionnaires did not reveal significant differences in listening experiences with the two listening programs. However, Ecological Momentary Assessments (EMAs) combined with data logging of the auditory ecology of subjects identified distinct patterns in listening experiences. The additional high-frequency gain resulted in higher “Loudness” ratings in quiet and pure speech listening environments. On the other hand, the default prescribed listening program was rated higher on “Sound quality”, “Speech understanding”, and “Program suitability” when in noisy speech or noise conditions. These findings suggest that additional high-frequency gain can be beneficial for speech understanding, but only in quiet and ideal listening conditions driven by an increased audibility . In fact, EMA data indicated that additional high-frequency gain resulted in real-world listening experiences that were judged as too loud, even for the pure speech soundscape. Thus, the data do not provide evidence for the hypothesized benefit in speech recognition from the availability of more high-frequency speech cues ,45. InstSubjects tended to use the volume control differently with the two programs and used overall lower levels of volume with the custom gain program, most likely to compensate for the additional high-frequency gain. By analyzing volume control logs, we were able to identify global patterns of volume control. Specifically, we found that subjects preferred to change to higher levels of volume when in optimal listening environments , whereas lower levels of volume were activated when the sound pressure levels increased and vice versa. In addition, volume preferences depended on the listening program, with higher volumes being preferred for the default compared to the custom high-frequency gain program. This corroborates well with the documented increase in loudness perception when using the high-frequency gain program. However, strong inter-individual differences in volume control were evident. That is, some subjects consistently changed to higher or lower volume levels than the default level see . Insteadc and 36.9 dB SPL for Pd. These levels are much lower than the ones faced in real life [A possible explanation for the discrepancy between benefits of custom high-frequency gain in the laboratory versus in real-world could be because of the difference in testing hearing at threshold levels  and relating listening experiences to supra-threshold listening conditions . The documented SRTs, which were statistically different between the two listening programs, were 39.1 dB SPL for Peal life ,44, and A possible solution for increasing real-world speech perception by increasing high-frequency gain would be to implement fast acting compression on the acoustic sources and take the ambient SNR into account . CompresThe relatively high real-world SNRs documented in our study are comparable to those found in previous studies ,22,23. TBesides providing evidence of everyday listening experiences, the current study shows that EMA data can help track day-to-day acclimatization to hearing device features. None of the subjects in the current study had prior experience with the Ponto 4 BCD, and a certain acclimatization would therefore be expected . Figure In summary, our current findings highlight the potential of supporting traditional outcome measures of assistive hearing technology with ecologically valid EMA and real-world data logging. Outcome measures from laboratory tests typically target threshold hearing abilities under artificial conditions, whereas real-world EMA and data logging assess both on-the-spot subjective listening experiences and objective dimensions of hearing health behavior , which are important for interpretation of EMA ratings.Ideally, and to ensure representativeness, EMAs should be completed in varying listening conditions throughout the day. However, assessments are likely biased, since the smartphone used to complete them is not carried along in all possible listening environments . In the p > 0.1). This was also true after stratifying the EMA data by soundscape. The limited sample size  restricted further investigation into the effect of HL classification on listening experiences.The subjects were heterogeneous with respect to the aetiology of HL (e.g., PTA, laterality, BCD experience, see This novel approach to evaluating BCD performance opens up interesting possibilities for optimizing device settings by combining real-life in situ subjective experiences with listening environments and device parameters. Additionally, experiences and data logs from ecologically relevant situations can be used as a basis for in-depth counselling . We find