Category: Health

Personalized sampling experiences

personalized sampling experiences

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: Personalized sampling experiences

Why do we prefer personalized experiences?

However, there is a need for randomized studies with a face-to-face control group. These findings are confirmed in a meta-analysis which found that delivering OCS treatment via video-conferencing holds promise to be at least equally effective as face-to-face treatment but comparison studies with face-to-face control groups are necessary [ 14 ].

We therefore propose a more flexible approach, using the benefits of modern technology, in which online screen to screen communication is used to provide treatment in the real time and real world of the patient. Additionally, we will further personalize these interventions by performing experience sampling methodology ESM: keeping a digital diary, performing repeated self-measurements to identify specific temporal relationships between the symptoms of OCD and emotional states, which can be selectively targeted in therapy.

Therefore, this study aims to investigate the effect of online screen-to-screen and ESM-enhanced exposure with response prevention ERP to ERP as usual in patients with OCD. Furthermore, we will be assessing possible predictors and mediators of treatment effect. Experiential avoidance, the avoidance of unpleasant inner experiences such as fear, discomfort and bodily sensations is found to play a crucial role in the development and maintenance of obsessive—compulsive symptoms [ 15 , 16 , 17 , 18 , 19 ].

Also, meta-analysis showed that this effect differed between the different subtypes of OCD, patients with the focus on obsessions and responsibility for harm seem to benefit more from a reduction in experiential avoidance [ 17 ].

Therefore, we will examine the possible effect of experiential avoidance as a predictor for treatment outcome and investigate if this differs between subgroups of patients. Moreover, self-efficacy was found to have a mediating role in the treatment of OCS [ 22 ].

To investigate the effectiveness of personalized exposure and response prevention ERP in patients with OCD in improving OCD symptom severity. The design of the study will be 20 sessions on a weekly basis by a 2-group ERP as usual versus personalized ERP randomized controlled clinical trial with repeated measurements at baseline T0 , at 5 weeks of treatment T1 , at 10 weeks of treatment T2 , at 15 weeks of treatment T3 , posttest at 20 weeks of treatment T4 , 6-week follow-up T3 , 3-month follow-up T4 , 6-month follow-up T5 , and a year follow-up T6.

Assessment will consist of a semi-structured clinical OCD interview and a broad spectrum of questionnaires consisting of personality trait and symptom state lists. In the experimental condition, ESM data will be gathered throughout the study. After conclusion of the treatment period, the patients will have a 6-week therapy break to independently apply what has been learned in therapy.

If necessary, patients will be referred for additional help within or outside the department. After this session, patients will enter a naturalistic follow-up period in which they are allowed to seek help the way they would normally do when confronted with an increase of anxious symptoms e.

The study will be performed at multiple sites of anxiety disorders departments of PsyQ. PsyQ is part of the Parnassia group, a large urban ambulatory mental health organization in the Netherlands. Other departments in the regions of The Hague and Rotterdam will be made aware of the study and will be asked to refer eligible patients.

To be eligible to participate in this study, a participant must meet all the following criteria: 1 an OCD diagnosis according to DSM-5, 2 not having received any treatment for OCD in the past 3 months, 3 stable medication for at least three months, and 4 willing to refrain from following other treatment for OCD and keep medication stable during the experimental part of the study.

When entering the naturalistic follow-up phase, these restrictions will be released. Our exclusion criteria will only relate to our obligation to offer appropriate care and to guarantee patient safety.

Since the treatment and questionnaires will be in Dutch or English insufficient fluency in the Dutch or English language is also a criterion for exclusion. All therapists providing treatment are fully licensed to give cognitive behavioral therapy and must complete a training in exposure skills specifically designed for this study.

Furthermore, therapist providing the experimental therapy must complete a training in working with the smartphone application NiceDay. All therapists will be supervised 2-weekly by an experienced and licensed therapist.

After their intake interview, all patients with OCD will be asked to participate in the study. Patients will receive verbal information regarding the study and an information letter containing all relevant information.

One week after receiving the letter, one of the research assistants will contact the patient by phone to answer possible questions. If the patient decides to participate in the study, the research assistant will schedule a meeting and will make sure informed consent is given with a signed informed consent form.

We chose to compare personalized exposure with exposure as usual because we aim to improve standard of care. Therefore, we want to directly compare personalized exposure with the current treatment of choice, exposure as usual. Patients in both conditions will receive exposure with response prevention ERP , the first step in both national and international treatment guidelines for OCD [ 5 ].

ERP can be defined as planned and repeated systematic confrontation with internal and external fear-provoking cues exposure combined with refraining from rituals reducing fear response prevention. There is strong support for the effectiveness of ERP, this was confirmed in a fairly recent meta-analysis [ 23 ].

Although traditionally habituation was seen as the primary mechanism in ERP, this focus shifted to inhibitory learning in the past decade [ 6 , 24 ].

According to this rationale, both unlearning old catastrophic interpretations and learning a wide range of new associations form the key to therapeutic change.

Exposure is hypothesized to be effective when the new association becomes stronger than the old association. Within this explanatory model, the context in which the exposure takes place is extremely important. Several studies have shown that learning is context-dependent and that exposure in multiple contexts appears to counteract a return of fear [ 6 , 24 ].

Patients in the exposure with response prevention as usual group will be treated according to the current national guidelines for OCD [ 25 ]. This means that they will receive ERP as described above.

Exposure will take 20 sessions and will be offered on an individual basis. This number of sessions is in line with the current national guidelines [ 25 ]. ERP will take place once a week, for 60 min, supplemented with homework exercises which consist of ERP exercises.

Sessions will consist of discussion of the homework exercises, in session ERP and preparing homework for the following week.

The course and effect of therapy will be monitored by means of the questionnaires which are part of the study.

Patients in the experimental condition will receive ERP as described above but offered through the smartphone app NiceDay. Also, they will receive personalized feedback, based on ESM data gathered with NiceDay, wherein their OCD symptoms will be placed into the context of emotional, cognitive, and motivational states and daily life events.

NiceDay is a smartphone application which is developed in close collaboration with mental health care professionals and patients. NiceDay will be used both as a tool which enables video-calling with patients and a data collection tool. NiceDay collects raw data but does not interpret this data or uses it to intervene.

During treatment, patients will have min weekly sessions with their therapists. However, in the experimental condition, these sessions will be offered digitally, through video-calling. Exposure exercises will be offered in real time and real world i. Digital sessions offer the possibility that patients can practice exposure e.

This way, interventions can be offered in a much more flexible, relevant, and personalized way. When patients encounter a situation in which OCD symptoms are triggered, they can use the app to self-guide themselves through an exposure exercise right away, using the build-in exposure form in the application.

Practicing exposure exercises in real time and place is expected to increase the ecological validity of the exercises. This method fits perfectly into the inhibitory learning principles of exposure theory as mentioned previously.

Also, the smartphone application collects ESM data regarding emotional states. Based on these data, participants will receive personalized feedback weekly by their therapist. Feedback will be provided both verbally and graphically and will reveal interaction between symptoms i.

The interactions will be determined based on the clinical insight of the therapist in agreement with the vision of the patient shared-decision making. The primary aim of the feedback is to create awareness regarding interactions between symptoms and the personal context of the patient.

If indicated, exposure exercises will be tailored to the specific needs of the participant. For example, if the ESM data indicates that a participant experiences an increase in compulsions in an angry state, exposure exercises will take place in that context.

Personalized feedback based on ESM data is new to the field of psychology but several studies have tested the intervention in patients with depression with preliminary positive effects on feelings of empowerment and a decrease in symptoms of depression [ 26 , 27 , 28 , 29 ].

Participants can leave the study at any time for any reason if they wish to do so without any consequences. If the interventions are a risk for participant health or safety, modifications will be submitted for approval to the accredited METC; after a positive decision from the accredited METC, the changes will be implemented.

Furthermore, the investigator will suspend the study if there is sufficient ground that continuation of the study will jeopardize participant health or safety. The investigator will notify the accredited METC without undue delay of a temporary halt including the reason for such an action.

The study will be suspended pending a further positive decision by the accredited METC. The investigator will make sure that all subjects are kept informed.

All the therapists providing treatment are trained in motivating patients to adhere to treatment protocols. Also, the research assistants will have frequent contact with participants by telephone to help with any questions and to motivate participants to keep following the treatment protocol.

During the active treatment phase of 20 weeks, participants are asked to refrain from any other form of care. After concluding the active treatment phase, a treatment-free period of 6 weeks is followed by a booster session. After the booster session, patients will enter a naturalistic follow-up period in which they are allowed to seek help the way they would normally do when confronted with an increase of anxious symptoms e.

After conclusion of the treatment period, the patients will have a 6-week therapy break to apply independently the skills learned in therapy. Primary outcome is a decrease in symptom severity, measured with the Y-BOCS, between the measurements taken at baseline and at posttest.

The Y-BOCS is a semi-structured clinical interview which assesses the severity of obsessions and compulsions separately and is the gold standard to measure OCD severity in clinical practice and scientific research.

The Y-BOCS consists of 10 items which are rated on a 4-point Likert scale. The internal consistency and validity of the Y-BOCS have proven to be good.

Moreover, the Y-BOCS is sensitive to change. The Y — BOCS has been validated in a Dutch clinical population [ 30 ]. The Y-BOCS will be administered at each assessment moment by telephone by trained outcome assessors.

The WHOQOL-Bref 26 items was developed as an international cross-culturally comparable self-report quality of life assessment instrument [ 31 ]. The internal consistency of the four domains of the WHOQOL-Bref ranged from 0. Domain scores of the WHOQOL-Bref correlated around 0.

Relatively low correlations were found between demographic characteristics age and sex and WHOQOL-Bref domain scores. It is concluded that the content validity, construct validity, and the reliability of the WHOQOL-Bref in a population of adult Dutch psychiatric outpatients are good [ 31 ].

The WHOQOL will be administered at baseline, midtest, posttest, and all follow-up assessment moments. The QIDS is derived from the item Inventory of Depressive Symptomatology IDS and is available in both self-report QIDS-SR16 and clinician-rated QIDS-C formats [ 32 ].

Furthermore, the QIDS-SR16 is sensitive to symptom change and has highly acceptable psychometric properties [ 32 ]. The QIDS-SR16 has been validated in a Dutch clinical population [ 33 ].

This scale is included because there is a high comorbidity rate between depression and OCD. The QIDS will be administered at baseline, midtest, posttest, and all follow-up assessment moments. The STAI consists of 2 subscales state and trait anxiety , each comprising 20 items [ 34 ].

Scores range from 20 to 80, with higher scores suggesting greater levels of anxiety. A Dutch validation study of the STAI showed its reliability and sensitivity in the measurement of anxiety The STAI has been validated in a Dutch clinical population [ 35 ].

Administration takes about 10 min. This scale is included because there is a high comorbidity rate between anxiety states and OCD. This scale allows for better differential diagnostics and more detailed subgroup definition on our population. The STAI will be administered at baseline, midtest, posttest, and all follow-up assessment moments.

ESM data will be gathered via NiceDay in the experimental condition. At three random moments per day, participants will be asked to register their emotional states. Participants will be asked to register their compulsions in the app throughout the day.

Furthermore, participants can use the dairy option to plan activities, such as exposure exercises. Participants in the experimental condition will be invited to fill out experience sampling data during the active treatment phase of 20 weeks.

We will be using the following measures to assess for a possible mediating role on treatment effect. The SEQ-PS is a item psychometric scale that is designed to measure the perceived ability to cope with phobic symptoms when approaching feared stimuli [ 36 ].

Responses are recorded on a 5-point scale. We adapted the SEQ-PS to measure symptoms of OCD when being confronted with the object of intrusions and not being able to perform compulsions. The SEQ-OCD will be included in all measurement moments. The Dutch translation of the AAQ-II has been found to have good psychometric properties [ 38 ].

Experiential avoidance has been found to play some role in the development of obsessive—compulsive symptoms but it remains yet unclear to what extent [ 15 , 18 , 19 ]. We included the AAQ-II to assess the possible role on symptom severity and treatment effect. The AAQ-II will be included in all measurement moments.

We will use a technology acceptance questionnaire TAQ-NL to assess whether acceptance of the NiceDay application influences treatment outcome in the intervention group. The TAQ is adapted from the Technology Acceptance Model and extended with constructs from the Unified theory of Acceptance and Use of Technology [ 39 ].

Constructs of the questionnaire include intention of use, perceived ease of use, perceived usefulness, and several organization context factors i. The TAQ-NL was pilot tested and is currently being validated in a Dutch sample [ 40 ].

The TAQ-NL will be administered to participants at posttest. To determine whether eHealth Literacy influences treatment effect in the intervention group, we will use the eHealth Literacy Questionnaire. The eHealth Literacy Questionnaire eHLQ aims to measure eHealth literacy based on the 7-dimensional eHealth Literacy Framework [ 41 ].

The eHLQ consist of 35 items that are scores on a 7-scale answers scale. It consists of 7 scales, namely; using technology to process health information, understanding of health concepts and language, ability to actively engage with digital services, feel safe and in control, motivated to engage with digital services, access to digital services that work and digital services that suit individual needs.

The eHLQ was found to have good psychometric abilities [ 41 ]. Figure 1 shows the participant timeline, and Fig. Flowchart of participant timeline. In the context of parallel research goals outside the scope of this paper multiple other questionnaires not mentioned in the flowchart will be administered.

A statistical power analysis to estimate sample size for longitudinal, correlated data was performed [ 42 , 43 ]. This analysis revealed that with an alpha of 0. We expect a small to medium effect since a large effect is unlikely due to the small differences between the conditions.

Also, we are not interested in finding a small effect; therefore, we choose to calculate a sample size large enough to detect a clinically relevant effect with a medium effect size.

Therefore, we propose a total sample size of patients. Participants will be recruited within the different departments of PsyQ, other mental health institutions within the region, and directly through general practitioners.

General practitioners, mental health institutions, and possible participants will be informed about the study through social media, patient associations, and word to mouth communication.

Recruitment started in the first half of and will continue until the sample size is reached, which will likely be around July Patients will be assigned to either 1 ERP as usual or 2 personalized ERP.

Scientific literature shows that the prevalence of OCD is not determined by gender a ratio of [ 44 ]. However, there are differences in symptomatology between the genders, with women presenting more contamination obsessions and men reporting more obsessions of a sexual nature [ 45 , 46 ].

Furthermore, males with OCD have an earlier age of onset and being of male gender is a prognostic factor for a poor treatment outcome [ 10 , 45 , 47 ]. Since gender is a prognostic factor of treatment response, randomization will be stratified according to gender.

There is no gender difference in the prevalence of OCD so the randomization ratio will be The allocation sequence is generated by the software package Research Manager. Research Manager is an online research platform which provides multiple tools to manage clinical trials e.

After generation of the sequence, it is not possible to open the randomization table. Only the research assistants have authorisation to randomize participants.

There is a strict protocol which assures that the randomization outcome stays concealed. Only one research assistant is aware of the outcome while the other performs the measurement blinded.

The allocation sequence was generated by the principal investigator before start of the study. Two research assistants will assign participants to interventions.

Since we are investigating two active treatment conditions, both participants and therapist will not be blinded to assignment. All outcome assessors and data analysts will be blinded for intervention assignment. In our study, we will administer a combination of structured clinical interviews and self-report questionnaires.

Clinical interviews will be administered by trained outcome assessors. Outcomes of the questionnaires will be collected through the software package Research Manager and stored in encrypted form on a secured server.

Measurement moments will be at baseline, 5 weeks of treatment, 10 weeks of treatment midtest , 15 weeks of treatment, 20 weeks of treatment posttest , 6 weeks follow-up, 3 months follow-up, 6 months follow-up, and 12 months follow-up. Primary and mediation outcomes will be measured at all measurement moments.

Secondary outcomes will be measured at baseline, midtest, posttest, and in all follow-up moments. Control variables will be measured at baseline.

For more information on outcomes, see {12}. Participants will be approached by telephone to motivate them to fill out the questionnaires and to see if any help is needed. When participants decide to drop out of the intervention protocol, the reason for dropping out will be noted and participants will be asked to continue to fill out all the measurements.

All involved researchers have a thorough understanding of the AVG Dutch and GDPR European privacy regulations, are certified regarding Good Clinical Practice GCP , and will ensure that the research adheres to these standards. Data generated with the NiceDay application will be stored in encrypted form on a secured server owned by Sense Health.

Sense Health conforms to the Dutch NEN and international ISO standards for information security management. The NiceDay app used in the study also conforms to these standards, and any data processed or stored during this research will be handled in a GDPR compliant fashion.

This includes measures for tracking explicit consent, anonymizing or pseudonymising data, and limiting access only to approved research personnel. A full overview of the collected variables and how they will be managed can be provided on request.

Outcomes of the questionnaires will be collected through Research Manager and stored in encrypted form on a secured server. A subject identification code list will be used to link the data to subjects.

This code will be secured, and the key to the code will be safeguarded by the investigator and senior researchers. The Health Care Inspection Inspectie Gezondheidszorg en Jeugd and the internal science committee of the Parnassia.

Groep can request access to the data to ensure the quality of the study. All data will be stored for the legal retention period of 15 years. Multilevel mixed modelling analysis will be used to investigate differences in treatment effect, measured through symptom reduction on the Y-BOCS.

Multilevel analysis is especially suitable to analyze repeated measurement data because it takes into account the dependencies among observations nested within individuals.

Another advantage of this methodology is its ability to handle missing data. We will start with a three-level structure with treatment effect Y-BOCS score as dependent variable, repeated measures at the first, individuals at the second, and treatment condition at the third level.

We will then add age, gender, Ehealth literacy scale scores, and medication use to the model as covariates. We will determine the covariance structure empirically. Effect sizes and clinical significant change according to the Jacobson and Truax criteria will be calculated to estimate respectively the magnitude of the treatment effect and the significance of the results for clinical practice [ 49 ].

All analyses will be done both according to the intention-to-treat principle and per protocol in order to gain as much insight as possible into the efficacy of the intervention, as recommended by CONSORT.

Multilevel mixed model analysis will be used to assess differences in quality of life, anxiety states, and depressive symptoms between the conditions. We will start with a three-level structure, with respectively quality of life, anxiety states, and depressive symptoms as dependent variable, repeated measures at the first, individuals at the second, and treatment condition at the third level.

Age, gender, Ehealth literacy scale scores, and medication use will be added as covariates. In order to assess differences between the groups in self-efficacy and experiential avoidance, we will apply multilevel analysis with the scores on the SEQ-OCD and AAQ-II scores at baseline, midtest, and posttest will serve as dependent variables, treatment condition ERP as usual versus personalized ERP as a fixed dichotomous factor.

Furthermore, we will use the macro of Preacher and Hayes and apply bootstrapping methods to assess the possible mediating effect of changes in self-efficacy and experiential avoidance on subsequent treatment effects.

All data will be analyzed according both to the intention-to-treat and per protocol principle in order to gain as much insight as possible into the efficacy of the intervention, as recommended by CONSORT.

We will provide access to participant-level data without any kind of demographic information, to ensure anonymity of the participants. Data and statistical code will be provided by the corresponding author at request. This trial is being conducted within PsyQ, center The Hague.

PsyQ is part of the Parnassia Groep, a large urban ambulatory mental health organization in the Netherlands. The study will be monitored once a year according to the Standard Operating Procedure monitoring within the Parnassia Groep.

PsyQ has appointed a Research Committee CWO to oversee all scientific studies in the organization. The CWO consists of multiple researchers, at least one professor, a methodologist, and a manager responsible for the quality of care.

The committee gathers every 3 months. All studies conducted within PsyQ are monitored once a year to ensure the quality of medical-ethical concerns, the storage of data, and progression of recruitment. In addition, the Parnassia Groep has appointed a Central Research Committee CCOI who is responsible for quality assessment of all studies within the Parnassia Groep.

Once a year, this committee will consult the local CWO in regard to quality assessment and progress regarding the ongoing studies. Not applicable. Since our treatment is not expected to be harmful and all our participants are likely able to express themselves, no data safety monitoring board is established.

No serious adverse events are to be expected, but when a serious adverse event occurs the same procedures will be followed as in routine treatment.

Depending on the seriousness of the adverse events, measures will be taken. In the current project, 1 psychiatrist per department is connected to the study who can be consulted in case of emergencies.

The investigator will report all SAEs to the sponsor without undue delay after obtaining knowledge of the events. The sponsor will report the SAEs through the web portal ToetsingOnline to the accredited METC that approved the protocol, within 7 days of first knowledge for SAEs that result in death or are life threatening followed by a period of maximum of 8 days to complete the initial preliminary report.

All other SAEs will be reported within a period of maximum 15 days after the sponsor has first knowledge of the serious adverse events. Amendments are changes made to the research after a favorable opinion by the accredited METC has been given. All amendments will be notified to the METC and updated in the trial register.

The results of this trial will be published in peer-reviewed journals and presented on relevant conventions. Also, a summary of the results will be sent to all participants. This paper described the study protocol of an RCT comparing traditional exposure to personalized exposure.

Personalized exposure will be offered through video-calling in the environment of the participant, with the intention to increase the ecological validity of the ERP.

ESM data will be collected and used as an intervention to personalize the exposure further. We will compare personalized exposure with exposure as usual, provided in the treatment room of the therapist.

Since our study is combining two interventions in the experimental condition, both the use of video-calling and an ESM intervention, a possible effect cannot be attributed to either one of the interventions.

Participants are not blinded for treatment allocation when filling out baseline measures, making them susceptible for attrition bias. There is no waitlist condition in our design.

Although there are many arguments against using a non-active condition, the lack of it may influence the internal validity of this study. If we find a possible effect, it cannot be determined to what extent the factor time has influenced the symptoms of participants. We do however believe that the risk of this is minimal since OCD is such a persistent disease with relatively stable symptom strength over time.

Although one of our inclusion criteria is that possible medication use needs to be stable for 12 weeks, we did not gather information regarding the dosage being used. Since patients with OCD need higher doses of medication compared to other anxiety disorders, this lack of information may compromise the internal validity of this study [ 50 ].

The high relapse rates in patients with OCD make it a necessary challenge to develop and examine alternative treatment methods for this population.

To our knowledge, this is the first study to assess the effect of using video-calling and ESM data as an intervention in participants with OCD. Since the global pandemic of COVID, the use of video-calling to deliver psychological treatments has become much more common, making our study even more relevant.

The sample size of patients and our lenient inclusion criteria increases the ecological validity of the trial. With this study, we aim to contribute to improving the quality of life of patients with OCD. This trial is registered in the Netherlands trial register on December 19, Initial recruitment started in January ; however, it had to be halted due to COVID—19 measures in the Netherlands.

Due the fact that all participants in the face-to-face control condition received treatment via video-calling, almost all data collected between January and May was not usable; therefore, inclusion was restarted with a new sample in June To date, participants have been included, we anticipate to reach the full sample size around July American Psychiatric Association.

Diagnostic and statistical manual of mental disorders. American Psychiatric Association; Ruscio AM, Stein DJ, Chiu WT, Kessler RC. The epidemiology of obsessive-compulsive disorder in the National Comorbidity Survey Replication. Mol Psychiatry. Article CAS PubMed Google Scholar. Steketee G, Eisen J, Dyck I, Warshaw M, Rasmussen S.

Predictors of course in obsessive-compulsive disorder. Psychiatry Res. Veale D, Roberts A. Obsessive-compulsive disorder. Article Google Scholar. Obsessive-compulsive disorder and body dysmorphic disorder: treatment Guidance and guidelines NICE.

Craske MG, Kircanski K, Zelikowsky M, Mystkowski J, Chowdhury N, Baker A. Optimizing inhibitory learning during exposure therapy.

Behav Res Ther. Article PubMed Google Scholar. Vervliet B, Depreeuw B, Craske MG. Google Scholar. Hofmann SG, Smits JAJ. Cognitive-behavioral therapy for adult anxiety disorders: a meta-analysis of randomized placebo-controlled trials.

J Clin Psychiatry. Article PubMed PubMed Central Google Scholar. Olatunji BO, Kauffman BY, Meltzer S, Davis ML, Smits JAJ, Powers MB. Sharma E, Thennarasu K, Reddy YCJ. Long-term outcome of obsessive-compulsive disorder in adults.

Andersson E, Enander J, Andrén P, Hedman E, Ljótsson B, Hursti T, et al. Internet-based cognitive behaviour therapy for obsessive-compulsive disorder: a randomized controlled trial.

Psychol Med. Article CAS PubMed PubMed Central Google Scholar. Feusner JD, Farrell NR, Kreyling J, McGrath PB, Rhode A, Faneuff T, et al. Online video teletherapy treatment of obsessive-compulsive disorder using exposure and response prevention: clinical outcomes from a retrospective longitudinal observational study.

J Med Internet Res. Fletcher TL, Boykin DM, Helm A, Dawson DB, Ecker AH, Freshour J, et al. A pilot open trial of video telehealth-delivered exposure and response prevention for obsessive-compulsive disorder in rural Veterans.

Ferreri F, Bourla A, Peretti CS, Segawa T, Jaafari N, Mouchabac S. How new technologies can improve prediction, assessment, and intervention in obsessive-compulsive disorder e-ocd : Review. JMIR Ment Heal. Abramowitz JS, Lackey GR, Wheaton MG.

Obsessive—compulsive symptoms: the contribution of obsessional beliefs and experiential avoidance. J Anxiety Disord. Angelakis I, Gooding P. Obsessive—compulsive disorder and suicidal experiences: the role of experiential avoidance. Suicide Life-Threatening Behav. Angelakis I, Pseftogianni F.

Association between obsessive-compulsive and related disorders and experiential avoidance: a systematic review and meta-analysis. J Psychiatr Res. Manos RC, Cahill SP, Wetterneck CT, Conelea CA, Ross AR, Riemann BC.

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Image Credit: MAC Cosmetics. Virtual Try-on - Using AR and AI to create a personalized product sampling experience for cosmetics and other industries.

Automatic Shade Matching - Developing technology that uses AI to match consumers with the perfect color shade of cosmetics or fashion items through virtual try-on experiences. Ar-assisted Purchasing - Using augmented reality technology to help consumers make more confident purchasing decisions online by providing a more immersive and realistic shopping experience.

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How it works NiceDay is a perssonalized personalized sampling experiences which is persona,ized in close collaboration with mental health care professionals and personalozed. The allocation sequence personzlized generated by Skin care sample sets personalized sampling experiences package Research Manager. Anyone you share pegsonalized following link with will be able to read this content:. Limitations There are some limitations to consider. There is strong support for the effectiveness of ERP, this was confirmed in a fairly recent meta-analysis [ 23 ]. Cognitive-behavioral therapy for adult anxiety disorders: a meta-analysis of randomized placebo-controlled trials. George then filled out his ESM questionnaire five times per day, for 8 weeks.
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How Sampler Brings a Personalized Experience Right to Your Doorstep

This will be especially important if you're using AI-powered software and need to feed it information to guide its algorithm. Personalized experiences are the way of the now and the future.

The earlier you jump on, the easier it will be to keep up with consumer behavior. Editor's Note: This post was originally published in Nov.

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Company About us. Partner Brands. Help Contact us. We use cookies to ensure that we give you the best experience on our website. In fact, a recent study reveals that an impressive To effectively implement personalization marketing strategies and deliver a personalized customer experience , it is important to follow best practices that optimize customer experiences and maximize ROI.

These practices help businesses collect and analyze data to gain insights into individual preferences, segment the existing customer base, and deliver personalized content and offers to each segment, enhancing the overall experience for the existing customers.

Gain a deep understanding of individual preferences by collecting and analyzing a diverse range of customer data through engagement software. This includes behavioral data , such as browsing habits and purchase patterns, as well as demographic information, customer feedback, social media interactions, and preferences gathered from surveys and questionnaires.

By unifying these various data points into a single customer profile, businesses can create a cohesive database that provides a comprehensive view of each individual.

This knowledge enables you to tailor your marketing efforts effectively, optimize your lead generation process , and boost sales , ultimately improving conversion rates and driving overall business success. Segment your customer base based on shared characteristics or preferences. This allows you to deliver relevant content and offers to each segment, enhancing their overall experience and catering to their specific customer journeys in both offline and online stores.

Consistency in personalization across various touchpoints, such as custom landing pages , personalized emails, and interactive mobile apps, strengthens the customer journey and fosters engagement, encouraging customers to interact with your brand at different stages throughout their journey, and boosting engagement.

Now that you understand the best practices and approaches to personalization, check out this informative guide on How to Implement Omnichannel Personalization at Scale in While demographic and location data provide some insights, it is crucial to get an in-depth understanding of the customers and study their individual behaviors, preferences, purchase history, browsing patterns, likes, and dislikes to truly tailor each detail in your messaging according to their needs.

Supplement your marketing campaigns with valuable insights gathered from multiple sources and platforms. It is crucial to obtain appropriate consent and adhere to ethical practices throughout the process.

Strive for a balanced approach and avoid crossing the line into being intrusive or invasive. Overly personalized experiences may make customers feel uncomfortable or creeped out, negatively impacting their experience and eroding trust. Finding the right balance is essential for successful content marketing.

These examples showcase the power of personalization in various industries and highlight the effectiveness of tailored marketing strategies. By implementing personalized approaches, you can drive engagement, boost conversions, and ultimately achieve a higher return on investment.

Japanese lifestyle brand Muji implemented real-time personalized marketing strategies by integrating data from their mobile app, website, and stores. Based on this data, Itaú dynamically customized the content of the first fold of their homepage to present personalized product offers to each user.

Furthermore, Itaú was able to scale its operations significantly, offering 58 times more variations of product offers within just one week. The implementation of personalization not only enhanced customer experiences by delivering relevant content but also contributed to improved ROI for Itaú Bank.

This figure highlights the untapped potential for businesses to leverage this technology and enhance customer experiences significantly. AI-driven personalization employs advanced algorithms to analyze all the data and present it in a cohesive manner, enabling businesses to deliver tailored recommendations, messages, and experiences to individual customers.

This approach automates and optimizes personalization on a large scale, offering a glimpse into the transformative power of Artificial Intelligence in the realm of marketing. Here are some examples of personalization that demonstrate the power of AI-driven personalization in action.

It delves into the importance of marketing automation in enhancing personalization strategies and driving successful sales outcomes. Recommendation-based personalization, powered by advanced algorithms and data analysis, takes center stage in enhancing the customer experience. It aims to do so by suggesting relevant options based on preferences, browsing history, and purchase behavior.

TastryAI, a company that partners with wineries, utilizes personalization to provide customers with individualized wine recommendations. Amazon, one of the prime E-commerce stores , has revolutionized our perception of online retail with its hyper-personalized marketing approach.

This impressive figure not only underscores the significance of tailored recommendations but also highlights how they propel sales and foster meaningful customer engagement.

Hyper-personalization is a crucial step in increasing customer retention, engaging customers throughout their customer journey, and driving growth for E-commerce. By harnessing the power of segmentation-based personalization, businesses can intelligently divide their customer base into specific segments based on shared characteristics or preferences.

This strategic approach empowers them to deliver tailored content and offers that deeply resonate with what their customers expect and the unique needs and preferences of each segment. By asking customers specific questions about their skin type, problems, and age group, Kimi gathers valuable information to offer personalized recommendations.

Customers can select their specific skin issues, such as pimples, oiliness, or dryness, and Kimi immediately provides useful tips and solutions to address those concerns. The chatbot goes beyond skincare advice and employs personalization in entertainment as well.

personalized sampling experiences Personalized sampling experiences deliver data-driven product persoanlized that create personalized relationships between people personalized sampling experiences brands. Founded inSampler has powered digital pesonalized programs globally across channels, categories and industries. Consumers have joined Sampler to discover their new favorite products. Sampler is the trusted partner for leading consumer brands. Over brands have used Sampler to reach high value consumers at all stages of the buying journey.

Author: Arashimi

4 thoughts on “Personalized sampling experiences

  1. Es ist schade, dass ich mich jetzt nicht aussprechen kann - es gibt keine freie Zeit. Aber ich werde befreit werden - unbedingt werde ich schreiben dass ich in dieser Frage denke.

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