Emily E. Bernstein , Jennifer L. Greenberg , Hilary Weingarden , Ivar Snorrason , Berta Summers , Jasmine Williams , Rachel Quist , Joshua Curtiss , Oliver Harrison , Sabine Wilhelm
{"title":"在基于智能手机应用程序的身体畸形障碍认知行为疗法中使用教练技术","authors":"Emily E. Bernstein , Jennifer L. Greenberg , Hilary Weingarden , Ivar Snorrason , Berta Summers , Jasmine Williams , Rachel Quist , Joshua Curtiss , Oliver Harrison , Sabine Wilhelm","doi":"10.1016/j.invent.2024.100743","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>Body dysmorphic disorder (BDD) is severe and undertreated. Digital mental health could be key to expanding access to evidence-based treatments, such as cognitive behavioral therapy for BDD (CBT-BDD). Coach guidance is posited to be essential for effective uptake of digital interventions. However, little is known about how different patients may use coaching, what patterns correspond to meaningful outcomes, and how to match coaching to patient needs.</p></div><div><h3>Methods</h3><p>Participants were 77 adults who received a 12-week guided smartphone CBT-BDD. Bachelor's-level coaches were available via asynchronous messaging. We analyzed the 400 messages sent by users to coaches during treatment. Message content was coded using the efficiency model of support (i.e., usability, engagement, fit, knowledge, and implementation). We aimed to clarify when and for what purposes patients with BDD used coaching, and if we can meaningfully classify patients by these patterns. We then assessed potential baseline predictors of coach usage, and whether distinct patterns relate to clinical outcomes.</p></div><div><h3>Results</h3><p>Users on average sent 5.88 messages (SD = 4.51, range 1–20) and received 9.84 (SD = 5.74, range 2–30). Regarding frequency of sending messages, latent profile analysis revealed three profiles, characterized by: (1) peak mid-treatment (16.88 %), (2) bimodal/more communication early and late in treatment (10.39 %), and (3) consistent low/no communication (72.73 %). Regarding content, four profiles emerged, characterized by mostly (1) engagement (51.95 %), (2) fit (15.58 %), (3) knowledge (15.58 %), and (4) miscellaneous/no messages (16.88 %). There was a significant relationship between frequency profile and age, such that the early/late peak group was older than the low communication group, and frequency profile and adherence, driven by the mid-treatment peak group completing more modules than the low contact group. Regarding content, the engagement and knowledge groups began treatment with more severe baseline symptoms than the fit group. Content profile was associated with dropout, suggesting higher dropout rates in the miscellaneous/no contact group and reduced rates in the engagement group. There was no relationship between profile membership and other outcomes.</p></div><div><h3>Discussion</h3><p>The majority of participants initiated little contact with their coach and the most common function of communications was to increase engagement. Results suggest that older individuals may prefer or require more support than younger counterparts early in treatment. Additionally, whereas individuals using coaching primarily for engagement may be at lower risk of dropping out, those who do not engage at all may be at elevated risk. Findings can support more personalized, data-driven coaching protocols and more efficient allocation of coaching resources.</p></div>","PeriodicalId":48615,"journal":{"name":"Internet Interventions-The Application of Information Technology in Mental and Behavioural Health","volume":"36 ","pages":"Article 100743"},"PeriodicalIF":3.6000,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214782924000368/pdfft?md5=4341dac24ae6911e4ff9edf5ff8388c7&pid=1-s2.0-S2214782924000368-main.pdf","citationCount":"0","resultStr":"{\"title\":\"The use of coaching in smartphone app-based cognitive behavioral therapy for body dysmorphic disorder\",\"authors\":\"Emily E. Bernstein , Jennifer L. Greenberg , Hilary Weingarden , Ivar Snorrason , Berta Summers , Jasmine Williams , Rachel Quist , Joshua Curtiss , Oliver Harrison , Sabine Wilhelm\",\"doi\":\"10.1016/j.invent.2024.100743\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>Body dysmorphic disorder (BDD) is severe and undertreated. Digital mental health could be key to expanding access to evidence-based treatments, such as cognitive behavioral therapy for BDD (CBT-BDD). Coach guidance is posited to be essential for effective uptake of digital interventions. However, little is known about how different patients may use coaching, what patterns correspond to meaningful outcomes, and how to match coaching to patient needs.</p></div><div><h3>Methods</h3><p>Participants were 77 adults who received a 12-week guided smartphone CBT-BDD. Bachelor's-level coaches were available via asynchronous messaging. We analyzed the 400 messages sent by users to coaches during treatment. Message content was coded using the efficiency model of support (i.e., usability, engagement, fit, knowledge, and implementation). We aimed to clarify when and for what purposes patients with BDD used coaching, and if we can meaningfully classify patients by these patterns. We then assessed potential baseline predictors of coach usage, and whether distinct patterns relate to clinical outcomes.</p></div><div><h3>Results</h3><p>Users on average sent 5.88 messages (SD = 4.51, range 1–20) and received 9.84 (SD = 5.74, range 2–30). Regarding frequency of sending messages, latent profile analysis revealed three profiles, characterized by: (1) peak mid-treatment (16.88 %), (2) bimodal/more communication early and late in treatment (10.39 %), and (3) consistent low/no communication (72.73 %). Regarding content, four profiles emerged, characterized by mostly (1) engagement (51.95 %), (2) fit (15.58 %), (3) knowledge (15.58 %), and (4) miscellaneous/no messages (16.88 %). There was a significant relationship between frequency profile and age, such that the early/late peak group was older than the low communication group, and frequency profile and adherence, driven by the mid-treatment peak group completing more modules than the low contact group. Regarding content, the engagement and knowledge groups began treatment with more severe baseline symptoms than the fit group. Content profile was associated with dropout, suggesting higher dropout rates in the miscellaneous/no contact group and reduced rates in the engagement group. There was no relationship between profile membership and other outcomes.</p></div><div><h3>Discussion</h3><p>The majority of participants initiated little contact with their coach and the most common function of communications was to increase engagement. Results suggest that older individuals may prefer or require more support than younger counterparts early in treatment. Additionally, whereas individuals using coaching primarily for engagement may be at lower risk of dropping out, those who do not engage at all may be at elevated risk. Findings can support more personalized, data-driven coaching protocols and more efficient allocation of coaching resources.</p></div>\",\"PeriodicalId\":48615,\"journal\":{\"name\":\"Internet Interventions-The Application of Information Technology in Mental and Behavioural Health\",\"volume\":\"36 \",\"pages\":\"Article 100743\"},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2024-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2214782924000368/pdfft?md5=4341dac24ae6911e4ff9edf5ff8388c7&pid=1-s2.0-S2214782924000368-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Internet Interventions-The Application of Information Technology in Mental and Behavioural Health\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2214782924000368\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet Interventions-The Application of Information Technology in Mental and Behavioural Health","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214782924000368","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
The use of coaching in smartphone app-based cognitive behavioral therapy for body dysmorphic disorder
Background
Body dysmorphic disorder (BDD) is severe and undertreated. Digital mental health could be key to expanding access to evidence-based treatments, such as cognitive behavioral therapy for BDD (CBT-BDD). Coach guidance is posited to be essential for effective uptake of digital interventions. However, little is known about how different patients may use coaching, what patterns correspond to meaningful outcomes, and how to match coaching to patient needs.
Methods
Participants were 77 adults who received a 12-week guided smartphone CBT-BDD. Bachelor's-level coaches were available via asynchronous messaging. We analyzed the 400 messages sent by users to coaches during treatment. Message content was coded using the efficiency model of support (i.e., usability, engagement, fit, knowledge, and implementation). We aimed to clarify when and for what purposes patients with BDD used coaching, and if we can meaningfully classify patients by these patterns. We then assessed potential baseline predictors of coach usage, and whether distinct patterns relate to clinical outcomes.
Results
Users on average sent 5.88 messages (SD = 4.51, range 1–20) and received 9.84 (SD = 5.74, range 2–30). Regarding frequency of sending messages, latent profile analysis revealed three profiles, characterized by: (1) peak mid-treatment (16.88 %), (2) bimodal/more communication early and late in treatment (10.39 %), and (3) consistent low/no communication (72.73 %). Regarding content, four profiles emerged, characterized by mostly (1) engagement (51.95 %), (2) fit (15.58 %), (3) knowledge (15.58 %), and (4) miscellaneous/no messages (16.88 %). There was a significant relationship between frequency profile and age, such that the early/late peak group was older than the low communication group, and frequency profile and adherence, driven by the mid-treatment peak group completing more modules than the low contact group. Regarding content, the engagement and knowledge groups began treatment with more severe baseline symptoms than the fit group. Content profile was associated with dropout, suggesting higher dropout rates in the miscellaneous/no contact group and reduced rates in the engagement group. There was no relationship between profile membership and other outcomes.
Discussion
The majority of participants initiated little contact with their coach and the most common function of communications was to increase engagement. Results suggest that older individuals may prefer or require more support than younger counterparts early in treatment. Additionally, whereas individuals using coaching primarily for engagement may be at lower risk of dropping out, those who do not engage at all may be at elevated risk. Findings can support more personalized, data-driven coaching protocols and more efficient allocation of coaching resources.
期刊介绍:
Official Journal of the European Society for Research on Internet Interventions (ESRII) and the International Society for Research on Internet Interventions (ISRII).
The aim of Internet Interventions is to publish scientific, peer-reviewed, high-impact research on Internet interventions and related areas.
Internet Interventions welcomes papers on the following subjects:
• Intervention studies targeting the promotion of mental health and featuring the Internet and/or technologies using the Internet as an underlying technology, e.g. computers, smartphone devices, tablets, sensors
• Implementation and dissemination of Internet interventions
• Integration of Internet interventions into existing systems of care
• Descriptions of development and deployment infrastructures
• Internet intervention methodology and theory papers
• Internet-based epidemiology
• Descriptions of new Internet-based technologies and experiments with clinical applications
• Economics of internet interventions (cost-effectiveness)
• Health care policy and Internet interventions
• The role of culture in Internet intervention
• Internet psychometrics
• Ethical issues pertaining to Internet interventions and measurements
• Human-computer interaction and usability research with clinical implications
• Systematic reviews and meta-analysis on Internet interventions