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Do treatment effects of a web-based cognitive behavioral therapy for insomnia intervention differ for users with and without pain interference? A secondary data analysis.

ABSTRACT: Cognitive-behavioral therapy for insomnia (CBT-I) shows treatment benefits among individuals with pain interference; however, effects of Internet-delivered CBT-I for this population are unknown. This secondary analysis used randomized clinical trial data from adults assigned to Internet-delivered CBT-I to compare changes in sleep by pre-intervention pain interference. Participants (N = 151) completed the Insomnia Severity Index (ISI) and sleep diaries [sleep onset latency (SOL); wake after sleep onset (WASO)] at baseline, post-assessment, 6- and 12-month follow-ups. Linear mixed-effects models showed no differences between pain interference groups (no, some, moderate/severe) for changes from baseline to any follow-up timepoint for ISI (p = .72) or WASO (p = .88). There was a small difference in SOL between those reporting some versus no or moderate/severe pain interference (p = .04). Predominantly comparable and sustained treatment benefits for both those with and without pain interference suggest that Internet-delivered CBT-I is promising for delivering accessible care to individuals with comorbid pain and insomnia.

SUBMITTER: Shaffer KM

PROVIDER: S-EPMC6885103 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Do treatment effects of a web-based cognitive behavioral therapy for insomnia intervention differ for users with and without pain interference? A secondary data analysis.

Shaffer Kelly M KM Camacho Fabian F Lord Holly R HR Chow Philip I PI Palermo Tonya T Law Emily E Thorndike Frances P FP Ingersoll Karen S KS Ritterband Lee M LM

Journal of behavioral medicine 20190531 3

Cognitive-behavioral therapy for insomnia (CBT-I) shows treatment benefits among individuals with pain interference; however, effects of Internet-delivered CBT-I for this population are unknown. This secondary analysis used randomized clinical trial data from adults assigned to Internet-delivered CBT-I to compare changes in sleep by pre-intervention pain interference. Participants (N = 151) completed the Insomnia Severity Index (ISI) and sleep diaries [sleep onset latency (SOL); wake after sleep ...[more]

PMID: 31152334

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Project description:BackgroundDelivery of behavioral health interventions on the internet offers many benefits, including accessibility, cost-effectiveness, convenience, and anonymity. In recent years, an increased number of internet interventions have been developed, targeting a range of conditions and behaviors, including depression, pain, anxiety, sleep disturbance, and eating disorders. Human support (coaching) is a common component of internet interventions that is intended to boost engagement; however, little is known about how participants interact with coaches and how this may relate to their experience with the intervention. By examining the data that participants produce during an intervention, we can characterize their interaction patterns and refine treatments to address different needs.ObjectiveIn this study, we employed text mining and visual analytics techniques to analyze messages exchanged between coaches and participants in an internet-delivered pain management intervention for adolescents with chronic pain and their parents.MethodsWe explored the main themes in coaches' and participants' messages using an automated textual analysis method, topic modeling. We then clustered participants' messages to identify subgroups of participants with similar engagement patterns.ResultsFirst, we performed topic modeling on coaches' messages. The themes in coaches' messages fell into 3 categories: Treatment Content, Administrative and Technical, and Rapport Building. Next, we employed topic modeling to identify topics from participants' message histories. Similar to the coaches' topics, these were subsumed under 3 high-level categories: Health Management and Treatment Content, Questions and Concerns, and Activities and Interests. Finally, the cluster analysis identified 4 clusters, each with a distinguishing characteristic: Assignment-Focused, Short Message Histories, Pain-Focused, and Activity-Focused. The name of each cluster exemplifies the main engagement patterns of that cluster.ConclusionsIn this secondary data analysis, we demonstrated how automated text analysis techniques could be used to identify messages of interest, such as questions and concerns from users. In addition, we demonstrated how cluster analysis could be used to identify subgroups of individuals who share communication and engagement patterns, and in turn facilitate personalization of interventions for different subgroups of patients. This work makes 2 key methodological contributions. First, this study is innovative in its use of topic modeling to provide a rich characterization of the textual content produced by coaches and participants in an internet-delivered behavioral health intervention. Second, to our knowledge, this is the first example of the use of a visual analysis method to cluster participants and identify similar patterns of behavior based on intervention message content.

Project description:ContextInsomnia is a major health problem with significant psychological, health, and economic consequences. However, availability of one of the most effective insomnia treatments, cognitive behavioral therapy, is significantly limited. The Internet may be a key conduit for delivering this intervention.ObjectiveTo evaluate the efficacy of a structured behavioral Internet intervention for adults with insomnia.Design, setting, and participantsForty-five adults were randomly assigned to an Internet intervention (n = 22) or wait-list control group (n = 23). Forty-four eligible participants (mean [SD] age, 44.86 [11.03] years; 34 women) who had a history of sleep difficulties longer than 10 years on average (mean [SD], 10.59 [8.89] years) were included in the analyses.InterventionThe Internet intervention is based on well-established face-to-face cognitive behavioral therapy incorporating the primary components of sleep restriction, stimulus control, sleep hygiene, cognitive restructuring, and relapse prevention.Main outcome measuresThe Insomnia Severity Index and daily sleep diary data were used to determine changes in insomnia severity and the main sleep variables, including wake after sleep onset and sleep efficiency.ResultsIntention-to-treat analyses showed that scores on the Insomnia Severity Index significantly improved from 15.73 (95% confidence interval [CI], 14.07 to 17.39) to 6.59 (95% CI, 4.73 to 8.45) for the Internet group but did not change for the control group (16.27 [95% CI, 14.61 to 17.94] to 15.50 [95% CI, 13.64 to 17.36]) (F(1,42) = 29.64; P < .001). The Internet group maintained their gains at the 6-month follow-up. Internet participants also achieved significant decreases in wake after sleep onset (55% [95% CI, 34% to 76%]) and increases in sleep efficiency (16% [95% CI, 9% to 22%]) compared with the nonsignificant control group changes of wake after sleep onset (8% [95% CI, -17% to 33%) and sleep efficiency (3%; 95% CI, -4% to 9%).ConclusionsParticipants who received the Internet intervention for insomnia significantly improved their sleep, whereas the control group did not have a significant change. The Internet appears to have considerable potential in delivering a structured behavioral program for insomnia.Trial registrationclinicaltrials.gov Identifier: NCT00328250.

Project description:BackgroundChronic pain is common, disabling, and costly. Few clinical trials have examined cognitive behavioral therapy (CBT) interventions embedded in primary care settings to improve chronic pain among those receiving long-term opioid therapy.ObjectiveTo determine the effectiveness of a group-based CBT intervention for chronic pain.DesignPragmatic, cluster randomized controlled trial. (ClinicalTrials.gov: NCT02113592).SettingKaiser Permanente health care systems in Georgia, Hawaii, and the Northwest.ParticipantsAdults (aged ≥18 years) with mixed chronic pain conditions receiving long-term opioid therapy.InterventionA CBT intervention teaching pain self-management skills in 12 weekly, 90-minute groups delivered by an interdisciplinary team (behaviorist, nurse, physical therapist, and pharmacist) versus usual care.MeasurementsSelf-reported pain impact (primary outcome, as measured by the PEGS scale [pain intensity and interference with enjoyment of life, general activity, and sleep]) was assessed quarterly over 12 months. Pain-related disability, satisfaction with care, and opioid and benzodiazepine use based on electronic health care data were secondary outcomes.ResultsA total of 850 patients participated, representing 106 clusters of primary care providers (mean age, 60.3 years; 67.4% women); 816 (96.0%) completed follow-up assessments. Intervention patients sustained larger reductions on all self-reported outcomes from baseline to 12-month follow-up; the change in PEGS score was -0.434 point (95% CI, -0.690 to -0.178 point) for pain impact, and the change in pain-related disability was -0.060 point (CI, -0.084 to -0.035 point). At 6 months, intervention patients reported higher satisfaction with primary care (difference, 0.230 point [CI, 0.053 to 0.406 point]) and pain services (difference, 0.336 point [CI, 0.129 to 0.543 point]). Benzodiazepine use decreased more in the intervention group (absolute risk difference, -0.055 [CI, -0.099 to -0.011]), but opioid use did not differ significantly between groups.LimitationThe inclusion of only patients with insurance in large integrated health care systems limited generalizability, and the clinical effect of change in scores is unclear.ConclusionPrimary care-based CBT, using frontline clinicians, produced modest but sustained reductions in measures of pain and pain-related disability compared with usual care but did not reduce use of opioid medication.Primary funding sourceNational Institutes of Health.

Dataset Information

Do treatment effects of a web-based cognitive behavioral therapy for insomnia intervention differ for users with and without pain interference? A secondary data analysis.

Publications

Do treatment effects of a web-based cognitive behavioral therapy for insomnia intervention differ for users with and without pain interference? A secondary data analysis.

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