Initial evidence of effects of a novel digital behavioural treatment for chronic pain: A series of replicated randomized single-case experimental design studies

Abstract

Background

Chronic pain affects up to 20% of the population and often results in significant distress, disability, and reduced quality of life. Despite compelling evidence for behavioural treatments, treatment access remains limited. To improve access, a digital behavioural treatment for chronic pain was developed as part of the multi-phase ‘DAHLIA’ project. The present study evaluates the effects of this newly developed digital behavioural treatment.

Methods

Data were collected in a single-arm, iterative trial using a replicated single-case experimental design (SCED). In total, 56 participants received treatment (mean age 47, 87% female, varied pain regions). Participants completed twice daily digital diaries and questionnaires at pre-, and post-treatment, and at 3- and 6-months follow-ups. Diary items and questionnaires assessed psychological flexibility and acceptance, pain-related functioning, pain intensity, and general well-being. Daily diary data were examined using a combined p-value approach to conduct a meta-analysis of the SCED data, and questionnaire data were analysed using linear mixed models.

Results

Meta-analyses of daily diary data showed significant improvements in most pain-related items, indicating the utility of treatment in everyday life. Linear mixed model analyses of effects showed significant improvements over time in key outcomes with the largest changes observed in pain acceptance and psychological flexibility and smaller effects in pain-related functioning.

Conclusion

This study provides initial evidence for the newly developed digital behavioural treatment for chronic pain. Findings suggest that the treatment can improve resilience, daily functioning and symptoms and suggest the potential of the treatment as an effective evidence-based treatment for chronic pain.

Keywords

digital < general chronic pain single-case experimental study linear mixed models randomization test

1. Background

Chronic pain, defined as pain persisting or recurring more than three months,¹ is a prevalent and heterogeneous condition² that affects 20-30% of the population worldwide.^2,3 Chronic pain commonly results in significant distress and disability for the individual, and a financial burden for the society.⁴ Traditionally, pain management has focused on symptom alleviation, with limited effects on health-related quality of life and daily functioning.⁵

Today, there is strong empirical support for behavioural approaches aimed at enhancing resilience,^6–8 defined as the ability to overcome adversity while maintaining adaptive functioning.⁹ Such approaches are normally based on cognitive behavioural (CB) frameworks, including the fear-avoidance model¹⁰ and the psychological flexibility model.¹¹ From a CB perspective, resilience can be targeted by increasing psychological flexibility (PF) and its core processes such as acceptance.¹² PF is defined as the capacity to adapt behaviour while staying open to thoughts and feelings, using the situation constructively, and acting in line with one’s goals and values.¹¹ Improvements in PF and acceptance are in turn associated with improved functioning and reduced disability in individuals with chronic pain.^13,14

Despite the empirical evidence supporting behavioural approaches such as cognitive behavioural therapy (CBT) and Acceptance and Commitment Therapy (ACT), few individuals with chronic pain have access to such treatments.¹⁵ Digital solutions offer a shift in how healthcare is accessed and delivered,¹⁶ particularly through increasing access to evidence-based treatments.¹⁷ The DAHLIA (Digital behaviourAl HeaLth for chronIc pAin) project intends to enhance healthcare standards by bridging the gap between empirical evidence and clinical practices.¹⁸ Within the DAHLIA program, a digital behavioural treatment for individuals with chronic pain was developed and iteratively refined,¹⁹ in accordance with the Medical Research Council’s framework for developing and evaluating complex interventions.²⁰

Traditionally, clinical trials evaluating novel treatments for chronic pain have utilized standard comparisons between pre- and post-treatment assessments to determine treatment effects.^6–8 However, there are several arguments why such an approach may not precisely capture treatment effects. Single-point pre- and post-treatment assessments may obscure important day-to-day fluctuations in symptoms, level of resilience and functioning. Also, natural fluctuations^21,22 may appear during the treatment period and lead to an over- or underestimation of effects. Additionally, recall bias can affect the accuracy of retrospective questionnaires.²³ To provide more reliable and more ecologically valid results, daily diary methods offer a complementary approach by capturing day-to-day variations in key target variables.²⁴ Thus, daily diary methods allow researchers to reliably assess pain-related outcomes.²⁵ This is particularly relevant for important constructs in chronic pain such as psychological flexibility and acceptance,¹¹ which are expected to fluctuate over time²⁶ and thus may be insufficiently captured by retrospective, group-level assessments.

Accordingly, current evidence highlights the need for approaches that capture within-person changes in psychological processes over time during treatment. Thus, the DAHLIA program employs a precision health approach²⁷ with a focus on individual effects and variations and therefore utilizes a replicated randomized single case experimental design (SCED). In SCEDs, each participant is assessed frequently across different phases, including baseline and treatment, and serves as their own control, with a randomized timing of the treatment start.²⁸

As part of the DAHLIA program, the present study aims to provide initial evidence of effects of a newly developed digital behavioural treatment for chronic pain in a sample of individuals with various chronic pain conditions. Building on previously published findings where preliminary evidence of the treatment’s potential benefits and the importance of using SCED to explore effects was provided,²⁹ this study expands the analysis by including a larger cohort using data from the first three iterations of the optimization phase.¹⁸ In this study, effects are assessed by examining day-to-day and pre-post-follow-up data. In addition, data from the present study is used to evaluate if the daily diary items are sensitive to change in a digital behavioural treatment. Results from the present study will guide the design and methods used in a larger clinical trial with a randomized controlled design.

More specifically, this study aims to address the following research questions:

1. To what extent do daily assessments of psychological flexibility and acceptance, pain-related functioning, pain intensity and well-being change over the course of a digital behavioural treatment, in this heterogeneous sample consisting of individuals with distinctly different chronic pain conditions?

2. How do psychological flexibility and acceptance, pain-related functioning, pain intensity and well-being change from pre-treatment to post-treatment and follow-up among individuals with chronic pain receiving a digital behavioural treatment?

2. Methods

2.1. Study design

This replicated randomized multiple-baseline SCED study used pre- and post-treatment and follow-up assessments, along with digital diary data collected during the optimization phase of the multi-phase DAHLIA project.¹⁸ The optimization phase included several iterations with small cohorts to evaluate and improve the treatment. Iteration one preceded iteration two and three (region Kalmar and Stockholm), whereas iterations two and three were executed in parallel (iteration two: Stockholm, iteration three: region Kalmar). To evaluate the effects, a sequentially replicated SCED was used, as outlined in the previously published study protocol.¹⁸ Thus, the study examined individual-level treatment effects in a heterogenous chronic pain sample, consisting of individuals with various chronic pain conditions (see detailed description and results of iteration one²⁹). In the present study, we examined group-level effects across three iterations.

2.2. Participants and recruitment

Minor adjustments were made across iterations to improve the recruitment process and enhance inclusivity (for example age, outreach). Participants’ inclusion criteria were: (i) age 18 or older, (ii) pain for at least three months, (iii) ability to communicate fluently in Swedish, and (iv) access to a computer/tablet and smartphone with internet connection. During iteration one, we also applied an upper age limit of 65 years. Exclusion criteria were: (i) severe psychiatric comorbidity (such as psychosis, suicidality), (ii) severe injury/illness requiring immediate treatment or examination, or expected to worsen in the next six months, and (iii) ongoing or recent behavioural treatment for chronic pain (<6 months during iteration one; <3 months during iteration two and three).

Clinical coordinators (LE and SP) provided information about the study to staff and managers in healthcare units at primary- and specialist care levels. Patients were informed about the study by their healthcare provider: psychologist, medical doctor, occupational therapist, or physiotherapist. In iterations two and three, we also posted flyers with information about the study and QR-codes such as in university campuses, gyms and subway stations.

Sample size was primarily based on SCED methodology, where inference relies on replication across independent cases.³⁰ Methodological standards recommend a minimum of three demonstrations of effects.³¹ However, simulation studies using randomization tests (see section 2.6.1) suggest that adequate statistical power may require larger samples, with a minimum of four participants and 20 observations per participant.³² In addition, the project protocol pre-specified a target of 10 participants per to ensure a sufficient replication.¹⁸

2.3. Procedure

Therapists, consisting of licenced psychologists and psychotherapists, and master students in psychology under supervision, conducted eligibility screening via phone or video, following a semi-structured interview guide with inclusion and exclusion criteria. All participants provided written informed consent digitally via the secure web-based data collection tool Research Electronic Data Capture (REDCap³³) prior to the initiation of the study. Consent was obtained by actively ticking electronic checkboxes for participation and data handling consent, and by entering their name and contact information. The informed consent sheet provided information on data collection and handling, the voluntary nature of participation, and the right to withdraw at any point without providing a reason.

After signing informed consent, participants completed baseline assessments, including socio-demographic information, and questionnaires (see section 2.5.2), and initiated the daily digital diary (see section 2.5.1). Subsequently, therapists provided access to the digital treatment (see section 2.4). Participants were instructed to complete questionnaires after treatment completion and at 3- and 6-months follow-ups after they completed treatment. In addition to twice daily during treatment, digital diary was also completed for 5 days at post-treatment and at 3- and 6-month follow-ups. No financial compensation was provided for participation.

In addition, weekly evaluations and exit interviews (see the protocol¹⁸ for details) were collected to assess feasibility, and will be presented elsewhere (Taygar et al., in preparation).

2.4. Treatment

The DAHLIA treatment (Swedish: ‘Leva med smärta’) was provided using the Swedish digital healthcare platform “1177 stöd och behandling” and consists of six-modules, and weekly digital therapist interaction following module completion. Each model consists of four short 15-minute self-guided micro-sessions of structured treatment content including psychoeducation reading materials and reflective writing exercises, designed to facilitate the application of learned skills and strategies in the participant’s daily life. Participants were able to choose the format of the weekly digital therapist interaction: video call, phone call or asynchronous chat. However, during iteration three, as part of the feasibility testing, participants were only able to chat with their therapist.

The treatment is based on learning theory and an integration of the fear-avoidance¹⁰ and psychological flexibility models.¹¹ Treatment development utilized a user-centred approach during Phase 0 (preparation) and Phase 1 (design) of the DAHLIA project, including focus groups and iterative prototype testing. A detailed description of the process can be found in the development paper.¹⁹ Minor adjustments were made to the treatment throughout the optimization phase (between iterations), while the structure and content overview remained consistent across all iterations. Booster sessions were offered after two and four months to help maintain treatment effects. Table 1 provides an overview of the treatment content and structure (also presented in a previously published paper for iteration one²⁹).

Table 1.

Overview of treatment content.

Module	Session	Content of the micro-sessions
1 – You and your pain	1	Psychoeducation on recurring pain and psychological treatment of pain
	2	Psychoeducation on the interplay between psychological factors and pain
	3	The role of behaviour in connection to pain and vicious circles
	4	Functional analysis of behaviour in relation to long- and short-term consequences
2 – Where do you want to go?	1	Introduction to values in different areas of life
	2	Life values in relation to family and close relationships
	3	Values in relation to friends and health
	4	Values in relation to work/education and leisure activities
3 – What do you want to achieve?	1	Values in relation to goals and goals in relation to family and close relationships
	2	Goals in relation to friends and health
	3	Goals in relation to work/education and leisure activities
	4	Reflections on a life led in the direction of one’s values
4 – Moving forward	1	Taking steps towards goals based on values
	2	Taking difficult steps towards goals even when it is difficult
	3	Negative thoughts and emotions as part of change and strategies to handle catastrophic thoughts
	4	Using the support from others
5 – Increasing strength and resilience	1	Building skills to handle difficult change – i.e. decreasing avoidance behaviour through exposure
	2	Dissecting goals and steps into manageable parts
	3	Emotion regulation strategies for strong affects
	4	Acceptance and validating strategies for self-criticism
6 – The road ahead	1	Creating detailed plans of steps to take in relation to goals
	2	Repeating actions to form new habits
	3	Setting up plan for maintaining progress after the treatment
	4	Reflections on treatment progress and planning for the future

Across iterations, a total of ten therapists provided treatment (n=8 female, n=2 male; aged 24-58 years; 0-33 years of clinical experience and 0-30 years of experience working with chronic pain patients). Clinical coordinators briefed therapists on how to deliver the treatment and instructed them to follow the treatment manual. Furthermore, in Kalmar, therapists who were not affiliated with the research team participated in check-in meetings convened four times per semester and were provided access to support as required. During these check-in meetings, therapists could ask questions about the treatment delivery and discuss potential challenges encountered in practice.

2.5. Data collection

2.5.1. Digital diary

Participants completed a digital diary using the m-Path smartphone application.³⁴ Diary items were selected following established outcome recommendations in chronic pain research, the IMMPACT (Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials) recommendations.³⁵ Also, the constructs measured using questionnaires are consistent with the goals of optimization frameworks such as MOST (The Multiphase Optimization Strategy; refs. 36), and principles of process-based therapy.³⁷ Thus, the digital diary assessed (i) process outcomes: psychological flexibility and acceptance (including avoidance and engagement in meaningful activities), (ii) primary outcomes: pain-related functioning (such as self-efficacy, catastrophizing, interference), and (iii) secondary outcomes: pain intensity and general well-being (including pain intensity, sleep, mood, fatigue, stress, social support).

Diary items were piloted in a single-case observation design (SCOD) study,³⁸ and discussed with the research team, experts in the experience sampling methods, and healthcare professionals. Diary items proposed in the study protocol¹⁸ were refined accordingly. Items were rated on a 7-point Likert scale following the design of Experience Sampling trials.³⁹ Notable exceptions are pain intensity which was measured on a 0-10-point scale following the Numeric Rating Scale for Pain⁴⁰, and a multiple-choice item assessing pain interference. See Table 2 for daily diary items (also presented in a previously published paper on iteration 1²⁹).

Table 2.

Diary items used in the present study.

	Construct	Question	Answer scale
Process outcomes	Pain avoidance	During the morning/afternoon,… I stopped doing things because of my pain.	7-point numeric scale (1: not at all – 7: very much)
	Engagement in meaningful activity	… I did things that are important to me.
	General avoidance	… I avoided doing things because of how I felt.
Primary outcomes	Pain self-efficacy	… I felt confident that I could do activities despite my pain.	7-point numeric scale (1: not at all – 7: very much)
	Pain catastrophizing	… I worried about my pain getting worse or not.	7-point numeric scale (1: not at all – 7: very much)
	Pain interference*	… my pain interfered with …	Multiple-choice:
			- General activities
			- Mood
			- Walking abilities
			- Normal work (including housework)
			- Relationship with others
			- Enjoyment of life
			- Did not disturb
Secondary outcomes	Pain intensity	... my pain level was: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10	10-point numeric scale (1: no pain – 10: worse possible pain)
	Positive affect	…I felt happy, energetic, or enthusiastic.	7-point numeric scale (1: not at all – 7: very much)
	Negative affect	… I felt sad, irritated, depressed, or hopeless.
	Sleep (General)¹	Last night, I generally slept well.
	Sleep (Falling asleep)¹	Last night, I had problems falling asleep.
	Sleep (Waking up)¹	Last night, I woke up frequently or too early.
	Stress	During the morning/afternoon,… I felt stressed.
	Fatigue	… I felt tired.
	Social support	… I felt supported by others.

¹Only included in the lunch questionnaire.

*Not included in the analysis as it yielded nominal data.

Note. In the study, the items were presented in Swedish and translated into English for transparency.

Participants were prompted to complete the digital diary twice daily (at 12.00 and 18.00) prior, during, and after the treatment. Diary entries were open for two hours and a reminder was sent after one hour if not completed. Participants were requested to complete the digital diary for a randomized period of 5-10 days prior to the treatment (baseline, or A-phase). In iterations two and three, the baseline length was extended to 7-10 days to attempt to achieve higher baseline stability. The exact baseline length for each participant was determined through randomization, and the diary was scheduled to align with each participant’s treatment start date as scheduled by the treating therapist. During the treatment phase (B-phase), diaries were initially scheduled for six weeks (treatment), plus 5 days following end of treatment (post-treatment), with an option to extend the daily diary if participants engaged with the treatment for a longer period.

2.5.2. Questionnaires

See Table 3 for the list of questionnaires (also presented in a previously published paper on iteration 1²⁹; reduced number of instruments compared to the protocol¹⁸ to reduce respondent burden). As with daily diary items, questionnaires were selected following established outcome recommendations in chronic pain research,³⁵ and followed the goals of optimization frameworks,³⁶ and principles of process-based therapy.³⁷ Thus, questionnaires were also selected to assess (i) process variables (psychological flexibility and acceptance), (ii) primary outcomes: pain-related functioning (including self-efficacy and catastrophizing), and (iii) secondary outcomes: (pain intensity and general well-being, specifically health-related quality of life, perceived stress, insomnia, anxiety, and depression,).

Table 3.

Questionnaires used in the present study.

Focus	Variable	Instrument
Process outcomes	Pain Acceptance	Chronic Pain Acceptance Questionnaire (CPAQ-8)⁴¹
	Psychological flexibility	Multidimensional Psychological Flexibility Inventory (MPFI-24)⁴²
	Avoidance/Fusion	Psychological Inflexibility in Pain Scale (PIPS)⁴³
Primary outcomes: Pain-related functioning	Catastrophizing	Symptom Catastrophizing Scale (SCS)⁴⁴
	Pain self-efficacy	Pain Self-Efficacy Questionnaire (PSEQ)⁴⁵
	Work ability	Work Ability Index (WAI)⁴⁶
	Functioning	Brief Pain Inventory – Short Form (BPI-SF)⁴⁷
Secondary outcomes: General well-being	Health-related quality of Life	EuroQol 5-Dimension Questionnaire (EQ-5D)⁴⁸
	Perceived stress	Perceived Stress Scale (PSS)⁴⁹
	Anxiety	Generalized Anxiety Disorder Assessment (GAD-7). 5-Dimension Questionnaire.⁵⁰
	Depression	Patient Health Questionnaire (PhQ-9)⁵¹
	Insomnia	Insomnia Severity Index (ISI)⁵²

2.6. Analyses

Data were downloaded from REDCap and m-Path. Python scripts were used to add NA (Not Available) markers to missing diary data, conduct statistical analyses of daily diary, and calculate individual questionnaire scores. An alpha level of .05 was used as criteria for statistical significance. As a strategy to address multiple comparisons, we applied the Benjamini–Hochberg procedure to control for false discovery rate (FDR) at 5%⁵³ when analysing both daily diary and questionnaires.

2.6.1. Aggregation of individual-level effects using daily assessments

Aggregated statistical significance tests for all iterations were conducted using the SCED data from the daily diaries. We excluded participants with very low diary compliance rates (<30%), or little data collected during either phase (<5 data points) following the design of similar studies⁵⁴ and established guidelines.³⁰ We also excluded participants who dropped out before starting module 2, as we did not expect changes to occur that early in treatment.

To assess changes in daily diary measures, we first applied an AB-phase Randomization test (RT) for each participant and item, adjusting for gradual and delayed effects.⁵⁵ The RT compared the observed series to an ideal gradual response function (ramp) remaining at zero during the A-phase and increasing monotonically (for example 1-2-3) during the B-phase. The ramp was constant during the baseline phase and then increased linearly throughout the treatment, allowing to measure gradual effects.⁵⁵ To account for delayed treatment effects, we set a delay of 1/6^th of the data after the treatment onset. This delay allows for detection of delayed gradual effects following the first contact with the therapist (occurring after completion of the first module), as we expect changes to begin to occur following the first module. For drop-out participants, we applied a delay which was proportional to the completed portion of the treatment, hence, 1/3 of the B-phase if the participant completed module 3.

To evaluate group-level treatment effects measured with diary items, we used a combined p-value approach.⁵⁶ Specifically, we summed individual directional p-values across participants for each diary item, which we then compared to a reference distribution of sums generated under the null hypothesis with the p-values following a uniform [0,1] distribution.

2.6.2. Determining pre-post-follow-up treatment effects

To evaluate changes over time, a priori specified analyses were conducted using linear mixed model analyses for each questionnaire using Jamovi.⁵⁷ Assumptions of normality for difference scores were evaluated using the Shapiro–Wilk test and Q–Q plots. Effect sizes (Cohen’s d) were calculated for each variable.

3. Results

3.1. Participant flow and characteristics

Figure 1 provides a flow chart for the study. Across all iterations, n=58 participants started the treatment. Number of completers and dropout rates varied by iteration; in iteration one, n=9 participants competed the treatment and n=2 dropped out (18%), in iteration two, n=28 participants completed the treatment and n=4 dropped out (14%), and in iteration three, n=11 participants completed the treatment and n=4 dropped out (26%).

Figure 1.

Participant flow through the study.

Participants were predominately women (87%), with a mean age of 47 years (SD = 14.28, range = 21–83). Half of the participants (50,8%) reported widespread pain. Most participants reported having experienced chronic pain for several years. Reported pain duration ranged from approximately 3 months to 40 years; however, several participants described their pain duration in non-specific terms (“several years” or “many years”), precluding precise calculation of summary statistics. Frequency of pain in specific body regions are presented in Figure 2.

Figure 2.

Percentage of participants reporting pain in specific regions.

Most participants reported having received an explanation for their pain, as reported in Figure 3 below.

Figure 3.

Explanations for pain received by participants (in percentages).

3.1. Daily diary

Table 4 shows an overview of the effects in the daily diary measures based on comparisons between the baseline phase (5–10 days) and the treatment phase (6–8 weeks). In short, significant improvements were seen in the key outcome variables with consistent improvements in pain avoidance, pain self-efficacy, and engagement in meaningful activities. In contrast, some items, such as pain intensity and pain catastrophizing, showed no consistent change.

Table 4.

Combined p-values of daily diary measures by construct (meta-analysis of randomization tests).

	Construct	Combined p-value
Process outcomes	Pain avoidance	0.021*
	Engagement in meaningful activity	0.010*
	General avoidance	0.024*
Primary outcomes	Pain self-efficacy	0.010*
Primary outcomes	Pain catastrophizing	0.080
Secondary outcomes	Pain intensity	0.141
	Positive affects	<0.001*
	Negative affects	0.002*
	Sleep (General)	0.004*
	Sleep (Falling asleep)	0.335
	Sleep (Waking up)	0.150
	Stress	0.764
	Fatigue	0.113
	Social support	0.227

Note. Diary item pain interference was not included in the analysis as the multiple-choice format does not allow for gradual comparisons.

*Significant using Benjamini-Hochberg adjustment α for multiple comparisons; raw p-values are reported.

3.2. Pre-post-follow-up questionnaires

Results of the linear mixed model analyses are presented in Table 5. Assumption checks for the models were conducted and considered acceptable. As shown in Figure 4, participants showed significant and maintained improvements in process outcomes, with the largest changes observed in activity engagement (CPAQ-8 subscale ‘Engagement in meaningful activities’: post: Δ = 2.24, p < .001, d = 0.90; 3-month follow-up: Δ = 2.63, p < .001, d = 0.86; 6-month follow-up: Δ = 3.18, p < .001, d = 0.86) and pain avoidance (PIPS subscale ‘Pain avoidance’: post: Δ = -7.73, p < .001, d = -1.50; 3-month: Δ = -9.79, p < .001, d = -1.53; 6-month: Δ = -8.94, p < .001, d = -1.42). No significant changes were observed for psychological flexibility according to the Multidimensional Psychological Flexibility Inventory (MPFI-24).

Table 5.

Estimated marginal means and standard errors in outcome measures by time point (linear mixed model analysis).

	Outcome	Baseline	ΔPost (SE)	P-value	Cohen’s d	Δ3FU	P-value	Cohen’s d	Δ6FU	P-value	Cohen’s d
Process outcomes	CPAQ-8 (total)	25,84	3,78 (1,04)	<,001*	0,87^a	4,94 (1,27)	<,001*	0,93^a	5,28 (1,46)	<,001*	0,87^a
	Engagement in Activity	14,19	2,24 (0,60)	<,001*	0,90^a	2,63 (0,74)	<,001*	0,86^a	3,18 (0,85)	<,001*	0,86^a
	Willingness to Have Pain	11,70	1,61 (0,61)	0,01*	0,62^b	2,37 (0,75)	0,00*	0,74^b	2,17 (0,87)	0,01*	0,74^b
	MPFI
	Flexibility	4,2	0,278 (0,11)	0,016*	0,56^b	0,41 (0,14)	0,00*	0,67^b	0,68 (0,16)	<,001*	0,98^a
	Inflexibility	2,9757	-0,082 (0,08)	0,306	-0,24	-0,09 (0,10)	0,34	-0,23	-0,16 (0,11)	0,154	-0,23
	PIPS (total)	45,46	-9,34 (1,45)	<,001*	-1,44^a	-12,86 (1,80)	<,001*	-1,60^a	-11,45 (1,77)	<,001*	-1,60^a
	Avoidance	27,64	-7,73 (1,16)	<,001*	-1,50^a	-9,79 (1,45)	<,001*	-1,53^a	-8,94 (1,42)	<,001*	-1,42^a
	Fusion	17,81	-1,62 (0,55)	0,004*	-0,65^b	-3,07 (0,68)	<,001*	-0,99^a	-2,53 (0,67)	<,001*	-0,83^a
Primary outcomes	SCS	6,806	-0,99 (0,35)	0,006*	-0,64^b	-1,93 (0,43)	<,001*	-1,00^a	-1,79 (0,42)	<,001*	-0,94^a
	PSEQ	8,15	1,29 (0,42)	0,003*	0,67^b	1,43 (0,51)	0,01*	0,61^b	1,56 (0,51)	0,003*	0,67^b
	WAI	25,4	-1,18 (0,90)	0,194	-0,31	-1,17 (1,15)	0,31	-0,24	-2,91 (1,32)	0,031*	-0,53^b
	BPI-SF
	Pain severity	4,71	-1,32 (0,35)	<,001*	-0,81^a	-1,57 (0,44)	<,001*	-0,78^b	-0,96 (0,43)	0,03*	-0,49^c
	Pain interference	4,86	-0,46 (0,23)	0,05	-0,44	0,00 (0,29)	0,99	0,00	-0,34 (0,28)	0,23	-0,26
Secondary outcomes	EQ5D	0,8365	0,038 (0,01)	0,006*	0,67^b	0,04 (0,02)	0,01*	0,63^b	0,02 (0,02)	0,196	0,31
	PSS10	15,59	-2,60 (0,98)	0,01*	-0,61^b	-4,35 (1,20)	<,001*	-0,83^a	-3,80 (1,39)	0,01*	-0,64^b
	GAD7	6,01	-1,33 (0,679)	0,054	-0,47	-3,05 (0,83)	<,001*	-0,87^a	-2,27 (0,96)	0,021*	-0,57^b
	ISI	14,181	-0,734 (0,89)	0,411	-0,19	-1,86 (1,09)	0,09	-0,40	-0,69 (1,25)	0,586	-0,13
	PHQ9	10,22	-1,5 (0,79)	0,062	-0,45	-2,09 (0,97)	0,03*	-0,51^b	-1,32 (1,11)	0,239	-0,28

Note. All participants who completed baseline assessments (n=63) were included in the analysis.

*Significant using Benjamini-Hochberg adjustment α for multiple comparisons; raw p-values are reported.

ΔPost = estimated change from baseline to post-treatment; Δ3FU = estimated change from baseline to 3-month follow-up; Δ6FU = estimated change from baseline to 6-month follow-up.

^a = large (|d| ≥ 0.8),

^b = moderate (|d| = 0.5–0.79),

^c = small (|d| = 0.2–0.49).

PIPS: Psychological Inflexibility in Pain Scale. CPAQ-8: Chronic Pain Acceptance Questionnaire. MPFI-24: Multidimensional Psychological Flexibility Inventory-24. PSEQ: Pain Self-Efficacy Questionnaire. SCS: Symptom Catastrophizing Scale. BSI-SF: Brief Pain Inventory – Short Form. WAI: Work Ability Index. PHQ-9: Patient Health Questionnaire-9. GAD-7: Generalized Anxiety Disorder Assessment. PSS: Perceived Stress Scale. ISI: Insomnia Severity Index. EQ5D: EuroQol 5-Dimension Questionnaire.

Figure 4.

Plots depicting predicted means across time points in process outcome measures psychological inflexibility in pain (PIPS) and pain acceptance (CPAQ-8).

Several improvements were observed for the primary outcomes. As seen in Figure 5, pain severity (BPI-SF subscale ‘Pain Severity’) showed large improvements at pre to post (Δ = -1.32, p < .001, d = -0.81), and small to medium changes at follow-ups (3-month follow-up: Δ = -1.57, p < .001, d = -0.78; 6-month follow-up: Δ = -0.96, p = .03, d = -0.49) whereas pain interference (BPI-SF subscale ‘Pain Interference’) showed no significant changes. Secondary outcomes improved mainly at follow-ups in stress (PSS-10), anxiety (GAD-7), and quality of life (EQ5D).

Figure 5.

Plots depicting predicted means across time points in the primary outcome measure pain severity and pain interference.

4. Discussion

This study evaluated the effects of a digital behavioural treatment designed to enhance resilience and daily functioning in individuals with chronic pain. We analysed data from three iterations completed during the optimisation phase of the multi-phase DAHLIA project.¹⁸ The primary objective was to examine initial evidence of treatment effects on psychological flexibility and acceptance, indicators of pain-related functioning, and secondary outcomes including pain intensity and general well-being. These findings are intended to inform the design of a larger randomized controlled trial enhanced with single-case experimental methodology.

4.1. Benefits of digital behavioural treatments for chronic pain

Findings suggest improvements in psychological flexibility, acceptance, and pain-related functioning, as demonstrated in both daily diary data and pre– to follow-up assessments of questionnaire data. Overall, daily diary data and questionnaires generated similar results (with minor inconsistencies, see Section 4.2), providing complementary support for the treatment’s ability to improve resilience and functioning in people with chronic pain. Participants showed increased pain acceptance, greater psychological flexibility, and higher engagement in meaningful activities, alongside reductions in pain avoidance. Notably, findings were mixed for pain intensity: while questionnaire data (pre– to follow-up) showed significant improvements, daily diary data did not illustrate corresponding effects. Furthermore, general well-being outcomes suggested improved mood, while results on sleep measures were inconsistent.

These findings align with previous research showing strong empirical evidence for behavioural treatments for chronic pain, where improvements are more consistently observed in pain-related functioning and psychological processes (i.e. pain catastrophizing, pain acceptance, psychological inflexibility) rather than in pain intensity itself.^6–8 Additionally, the results correspond with the growing body of research supporting digital delivery formats of behavioural interventions targeting chronic pain.^58,59 The observed improvements also support the treatment’s proposed theoretical mechanisms, aligning with both the fear-avoidance model¹⁰ and the psychological flexibility model.¹¹ As such, empowering individuals with chronic pain to respond to daily stressors using high psychological flexibility instead of an avoidance and catastrophizing mindset can improve functioning and well-being in this population.^13,60

Moreover, the use of daily diary data contributes to the growing literature emphasizing high-frequency, ecologically valid measurement to capture dynamic treatment effects.^24,25 By showing both consistencies and inconsistencies between questionnaires and daily diary data (see section 4.2), this study emphasizes how different types of measures may capture distinct aspects of change. As seen in previous research, information about how the pain manifests in daily life may influence how patients perceive their situation and how it can be managed.⁶¹

The lower attrition rate in the third iteration suggests a need for flexibility and person-centred support in treatment delivery. Tentatively, a slightly higher rate of dropouts in iteration three compared to iterations one and two may relate to restricting the patient-therapist communication to asynchronous chat-based contact. This observation corresponds to participant reports during the development phase, highlighting the value of flexibility in the communication with therapists.¹⁹ The feasibility and features of the treatment will further be explored in a feasibility trial.¹⁸

4.2. Methodological considerations

Of methodological relevance, inconsistent results were seen within the same measurement domains. Psychological inflexibility measured by the subscale of the Multidimensional Psychological Flexibility Inventory-24 (MPFI-24) did not show any significant changes despite medium to strong effects observed in psychological inflexibility in Pain (Psychological Inflexibility in Pain Scale (PIPS)) and pain acceptance (Chronic Pain Acceptance Questionnaire (CPAQ-8)). This difference may be due to PIPS and CPAQ-8 assessing pain-specific psychological flexibility and acceptance,^43,62 whereas MPFI-24 measures broader psychological flexibility and inflexibility⁶³ not affected by the treatment.

Moreover, while significant improvements were seen in measures of psychological inflexibility in pain (PIPS) and pain acceptance (CPAQ-8), as well as in daily diary measures of pain avoidance and engagement in meaningful in activities, changes in pain interference measured by the BPI-SF interference subscale were non-significant. Importantly, prior research shows that changes in pain interference are closely associated with changes in psychological inflexibility in pain and pain acceptance,⁶⁴ suggesting present null findings in overall pain interference may not necessarily indicate the absence of treatment-related change. This discrepancy may however reflect the broad range of domains covered by the BPI-SF interference subscale, with one item per domain (general activity, mood, walking, relationships, sleep, enjoyment of life).⁶⁵ Possibly, improvements in pain interference occurred in specific domains, with a lower overall responsivity of the total interference score, as noted in prior research.⁶⁶ Future studies could consider other items measuring pain interference such as the Pain Interference Index⁶⁷ and Pain Disability Index.⁶⁸

Furthermore, no changes were observed for the daily diary item pain catastrophizing despite clear improvements in pain-related functioning captured both by questionnaires and daily diary items, and improvements in symptom catastrophizing captured by the Symptom Catastrophizing Scale (SCS). This difference may reflect limitations with using a single-item measurement, as pain catastrophizing is increasingly understood as a multifaceted construct.⁶⁹ Although single-item measures can have good validity, they might not always be adequate when measuring multifaceted constructs,⁷⁰ suggesting that a multiple-item diary approach might be more appropriate for such constructs.

In the pain severity outcomes, significant improvements were seen in the pre-to follow-up questionnaire data but not in the daily diary ratings. This finding could be due to the high variability in reporting of pain intensity⁷¹ and the natural fluctuations of symptoms.^21,22 As a reliable measure of pain severity requires multiple measurement points across several days,⁷²single-point questionnaires might be prone to random error and therefore be less reliable as measures of pain levels in the chronic pain population. Accordingly, retrospective questionnaires may capture global, evaluative judgments of pain. As pain experience is typically influenced by psychological and social factors,⁷³ retrospective questionnaires might be more affected by improvements in overall well-being. Daily diary measures, however, reflect momentary pain experiences and are less influenced by susceptible to recall bias and retrospective reconstruction^23,74 and may therefore be less influenced by overall improvements.

Additionally, no significant improvements were observed regarding sleep according to the Insomnia Severity Index (ISI), although daily diary items showed improvements in general sleep quality. ISI measures several constructs, including falling asleep, staying asleep, waking up early, satisfaction, interference, notice ability, and distress,⁷⁵ while single daily diary items, on the other hand, specifically assess single constructs (such as sleep satisfaction). Similar discrepancies have previously been reported in other studies, as ISI shows moderate to poor convergent validity with sleep diary parameters.⁷⁶

Thus, inconsistent effects across measures of the same or similar domains illustrate the importance of utilizing adequate instruments, and points at the relevance of evaluating the psychometric properties of daily diary items for chronic pain trials as well as further developing and updating guidelines.

4.3. Strengths, limitations and recommendations for future research

The strengths of this study include the use of a replicated SCED, as it allows for detailed, within-person evaluations⁷⁷ across three treatment iterations, providing a strong basis for examining initial evidence of treatment effects. Also, the combination of pre-post-follow-up questionnaires with daily diary assessments offers a comprehensive view of general clinical improvements and more specific day-to-day changes in functioning, as each approach offers distinct advantages.⁷⁸ Assessing multiple change processes, namely psychological flexibility, acceptance, pain-related functioning, pain intensity, and well-being, further allowed for a detailed discussion of key change mechanisms as well as methodological challenges.³⁵ Additionally, testing the treatment continuously during the iterative development of the digital intervention allowed for data-driven decisions on how to improve both the treatment and the assessment, prior to evaluating it in a larger randomized controlled trial.^36,79

Several limitations should be considered when interpreting the findings from the present study. The single-arm design and modest sample size limit causal inference and generalizability.⁸⁰ Although control groups are not typically required in single-case experimental designs, as inference is based on repeated within-person comparisons across baseline and intervention phases comparing the individual to their own baseline as a control period,^28,30 the absence of a concurrent control condition limits causal interpretation especially of the linear mixed model estimates. Observed pre–post-follow-up changes cannot fully exclude alternative explanations such as natural symptom fluctuation, regression to the mean, or expectancy effects.⁸⁰ However, an RCT is currently being conducted within the DAHLIA project,¹⁸ allowing for within- and between-individual comparisons across allocations.

Moreover, the sample was predominantly female (87%) and the intervention was delivered exclusively in a digital format, which may limit generalizability to male patients and to populations with limited access to or familiarity with digital interventions. Also, in iteration one, an upper age limit of 65 years was applied as part of the inclusion criteria, primarily due to the initial focus on the working population. Although this criterion was only applied in the first iteration, only a small proportion of the sample were older adults (6%), limiting the representativeness of the sample for older adults.

In addition, the exclusion of participants with low diary compliance or early dropout may have introduced attrition bias,⁸⁰ as individuals who remained in the analyses may differ systematically from those excluded, potentially leading to an overestimation of treatment effects. However, these criteria were applied to ensure sufficient data quality and valid estimation in the SCED analyses.^30,54 Also, as mentioned, some assessments (such as pain catastrophizing) used single items, which may not fully capture their multidimensional nature,⁷⁰ which may be reflected in discrepancies between questionnaire and diary outcomes. Moreover, the patient-therapist communication differed across iterations, with asynchronous text messaging in iteration three. Additionally, follow-up periods were not long enough to reliably evaluate long-term effects,⁸¹ which was considered outside the scope of the study. Furthermore, the low response rate at follow-up limits the interpretability of long-term effects. Also, treatment fidelity was not formally assessed, and it is therefore unclear to what extent the intervention was delivered consistently across therapists and iterations. However, the micro-session content was fully standardized and delivered in a fixed order across all participants, limiting variability in the core digital intervention component. Potential variation in fidelity is therefore primarily related to the adjunct therapist support rather than the intervention content itself. Also, most therapists were affiliated with the research group and involved in the development and evaluation of the intervention, which may have supported consistency in delivery. In addition, clinicians not affiliated with the research group were offered regular supervision and access to support when needed. Future studies should consider adding measures of treatment fidelity.

Hence, the study provides initial evidence of the treatment’s potential utility to increase resilience and daily functioning in patients with chronic pain, and future research should be conducted to validate these treatment effects. Based on the DAHLIA protocol for development and evaluation,¹⁸ a SCED-enhanced RCT may be conducted following treatment optimization, informed by findings that may suggest adjustments in, for instance, daily diary items and questionnaires. Furthermore, future studies may include analyses of cost-effectiveness and post-market surveillance as well as mediators and moderators of change, to provide more detailed insight regarding the benefits of the treatment. Also, future research should further investigate whether treatment effects differ across demographic or clinical subgroups. Additionally, future research may consider tailoring the treatment to subgroups with specific profiles or needs, such as pain after cancer, endometriosis, or comorbid insomnia.

4.4. Conclusion

The present study, with a replicated SCED approach to evaluate treatment effects in a chronic pain sample, provides initial evidence for the potential benefits of this novel digital behavioural treatment. The findings suggest the DAHLIA treatment generates meaningful improvements in key outcomes, including psychological flexibility, pain acceptance, and several aspects of pain-related functioning. These findings warrant a randomized controlled trial and add to the growing support for digital health treatments for individuals living with chronic pain.

Footnotes

ORCID iDs

Haya Al Sharaa

Patrick Onghena

Ethical considerations

The study plan has been reviewed and approved by Swedish Ethical Review Authorities (Etikprövningsmyndigheten) [2021-06-28, Dnr 2021-02437].

Consent to participate

All participants provided informed consent via the secure we-based data collection tool Research Electronic Data Capture (REDCap³³).

Author contributions

H.A.S. conducted data analysis and was led the writing process and editing of the manuscript. S.L.B. contributed to study planning, provided supervision, and acted as project manager. P.O. provided advised and guided data analysis. A.S.T. assisted with planning and handled data collection. L.E. and S.P. contributed to planning, coordinated with therapists, and were responsible for participant recruitment and providing treatment. I.F. supervised the study and contributed to planning. K.B., L.M.M., L.S. and J.W.S.V. served as advisors. R.K.W. is the principal investigator, supervised the study, led the research group, secured funding and contributed to planning. All authors have read and approved the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by AFA försäkring [grant number 190252].

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. HAS provided treatment and conducted analysis LE and SP provided treatment but were not involved in data analysis.

Data Availability Statement

The data supporting the findings of this study is available upon reasonable request.*

References

Treede

Rief

Barke

, et al. Chronic pain as a symptom or a disease: the IASP Classification of Chronic Pain for the International Classification of Diseases (ICD-11). PAIN 2019; 160(1): 19–27. https://doi.org/10.1097/j.pain.0000000000001384

Breivik

Collett

Ventafridda

, et al. Survey of chronic pain in Europe: prevalence, impact on daily life, and treatment. Eur J Pain Lond Engl 2006; 10(4): 287–333, PubMed PMID: 16095934. https://doi.org/10.1016/j.ejpain.2005.06.009

Zimmer

Fraser

Grol-Prokopczyk

, et al. A global study of pain prevalence across 52 countries: examining the role of country-level contextual factors. Pain 2022; 163(9): 1740–1750, PubMed PMID: 35027516; PubMed Central PMCID: PMC9198107. https://doi.org/10.1097/j.pain.0000000000002557

Cohen

Vase

Hooten

. Chronic pain: an update on burden, best practices, and new advances. Lancet Lond Engl 2021; 397(10289): 2082–2097, PubMed PMID: 34062143. https://doi.org/10.1016/S0140-6736(21)00393-7

Wicksell

Vowles

. The Role and Function of Acceptance and Commitment Therapy and Behavioral Flexibility in Pain Management. Pain Manag 2015; 5(5): 319–322, PubMed PMID: 26238018. https://doi.org/10.2217/pmt.15.32

Lai

Liu

McCracken

, et al. The efficacy of acceptance and commitment therapy for chronic pain: A three-level meta-analysis and a trial sequential analysis of randomized controlled trials. Behav Res Ther 2023; 165: 104308, PubMed PMID: 37043967. https://doi.org/10.1016/j.brat.2023.104308

Martinez-Calderon

García-Muñoz

Rufo-Barbero

, et al. Acceptance and Commitment Therapy for Chronic Pain: An Overview of Systematic Reviews with Meta-Analysis of Randomized Clinical Trials. J Pain 2024; 25(3): 595–617. https://doi.org/10.1016/j.jpain.2023.09.013

Veehof

Trompetter

Bohlmeijer

, et al. Acceptance- and mindfulness-based interventions for the treatment of chronic pain: a meta-analytic review. Cogn Behav Ther 2016; 45(1): 5–31, PubMed PMID: 26818413. https://doi.org/10.1080/16506073.2015.1098724

Sturgeon

Zautra

. Psychological resilience, pain catastrophizing, and positive emotions: perspectives on comprehensive modeling of individual pain adaptation. Curr Pain Headache Rep 2013; 17(3): 317, PubMed PMID: 23338769; PubMed Central PMCID: PMC9473682. https://doi.org/10.1007/s11916-012-0317-4

10.

Vlaeyen

JWS

Crombez

Linton

. The fear-avoidance model of pain. Pain 2016; 157(8): 1588–1589, PubMed PMID: 27428892. https://doi.org/10.1097/j.pain.0000000000000574

11.

McCracken

Morley

. The psychological flexibility model: a basis for integration and progress in psychological approaches to chronic pain management. J Pain 2014; 15(3): 221–234, PubMed PMID: 24581630. https://doi.org/10.1016/j.jpain.2013.10.014

12.

Goubert

Trompetter

. Towards a science and practice of resilience in the face of pain. Eur J Pain Lond Engl 2017; 21(8): 1301–1315. https://doi.org/10.1002/ejp.1062PubMedPMID:28573783

13.

Scott

Hann

KEJ

McCracken

. A Comprehensive Examination of Changes in Psychological Flexibility Following Acceptance and Commitment Therapy for Chronic Pain. J Contemp Psychother 2016; 46: 139–148, PubMed PMID: 27397934; PubMed Central PMCID: PMC4917588. https://doi.org/10.1007/s10879-016-9328-5

14.

McCracken

Vowles

. Psychological flexibility and traditional pain management strategies in relation to patient functioning with chronic pain: an examination of a revised instrument. J Pain 2007; 8(9): 700–707, PubMed PMID: 17611162. https://doi.org/10.1016/j.jpain.2007.04.008

15.

Breivik

Eisenberg

O’Brien

. The individual and societal burden of chronic pain in Europe: the case for strategic prioritisation and action to improve knowledge and availability of appropriate care. BMC Public Health 2013; 13(1): 1229. https://doi.org/10.1186/1471-2458-13-1229

16.

Bartels

Pelika

Taygar

, et al. Digital approaches to chronic pain: A brief meta-review of eHealth interventions - Current evidence and future directions. Curr Opin Psychol 2024; 62: 101976, PubMed PMID: 39721214. https://doi.org/10.1016/j.copsyc.2024.101976

17.

Marcu

Ondersma

Spiller

, et al. The Perceived Benefits of Digital Interventions for Behavioral Health: Qualitative Interview Study. J Med Internet Res 2022; 24(3): e34300, PubMed PMID: 35353045; PubMed Central PMCID: PMC9008533. https://doi.org/10.2196/34300

18.

Bartels

Johnsson

Boersma

, et al. Development, evaluation and implementation of a digital behavioural health treatment for chronic pain: study protocol of the multiphase DAHLIA project. BMJ Open 2022; 12(4): e059152, PubMed PMID: 35428645. https://doi.org/10.1136/bmjopen-2021-059152

19.

Taygar

Bartels

Vega

, et al. User-Driven Development of a Digital Behavioral Intervention for Chronic Pain: Multimethod Multiphase Study. JMIR Form Res 2025; 9(1): e74064. https://doi.org/10.2196/74064

20.

Skivington

Matthews

Simpson

, et al. A new framework for developing and evaluating complex interventions: update of Medical Research Council guidance. BMJ 2021; 374: n2061, PubMed PMID: 34593508. https://doi.org/10.1136/bmj.n2061

21.

Kratz

Murphy

Braley

. Ecological Momentary Assessment of Pain, Fatigue, Depressive, and Cognitive Symptoms Reveals Significant Daily Variability in Multiple Sclerosis. Arch Phys Med Rehabil 2017; 98(11): 2142–2150. https://doi.org/10.1016/j.apmr.2017.07.002

22.

Schneider

Junghaenel

Keefe

, et al. Individual differences in the day-to-day variability of pain, fatigue, and well-being in patients with rheumatic disease: Associations with psychological variables. Pain 2012; 153(4): 813–822, PubMed PMID: 22349917; PubMed Central PMCID: PMC3307888. https://doi.org/10.1016/j.pain.2012.01.001

23.

Stone

Broderick

Shiffman

, et al. Understanding recall of weekly pain from a momentary assessment perspective: absolute agreement, between- and within-person consistency, and judged change in weekly pain. PAIN 2004; 107(1): 61–69. https://doi.org/10.1016/j.pain.2003.09.020

24.

Bolger

Davis

Rafaeli

. Diary methods: capturing life as it is lived. Annu Rev Psychol 2003; 54: 579–616, PubMed PMID: 12499517. https://doi.org/10.1146/annurev.psych.54.101601.145030

25.

Darnall

Sturgeon

Cook

, et al. Development and Validation of a Daily Pain Catastrophizing Scale. J Pain 2017; 18(9): 1139–1149. https://doi.org/10.1016/j.jpain.2017.05.003

26.

Kashdan

Rottenberg

. Psychological Flexibility as a Fundamental Aspect of Health. Clin Psychol Rev 2010; 30(7): 865–878, PubMed PMID: 21151705; PubMed Central PMCID: PMC2998793. https://doi.org/10.1016/j.cpr.2010.03.001

27.

Mauch

Edney

Viana

JNM

, et al. Precision health in behaviour change interventions: A scoping review. Prev Med 2022; 163: 107192. https://doi.org/10.1016/j.ypmed.2022.107192

28.

Morley

. Single Case Methods in Clinical Psychology: A practical Guide. Routledge, 2018.

29.

Al Sharaa

Bartels

Taygar

, et al. Individual-Level Effects of a Digital Behavioural Treatment for Chronic Pain: Proof-of-Concept of a Single-Case Experimental Design Study. Eur J Pain 2025; 29(10): e70128. https://doi.org/10.1002/ejp.70128

30.

Kratochwill

Hitchcock

Horner

, et al. Single-case designs technical documentation [Internet], 2010. [cited 2024 Jan 22]. Available from. https://ies.ed.gov/ncee/wwc/Document/229

31.

Tate

Perdices

Rosenkoetter

, et al. Revision of a method quality rating scale for single-case experimental designs and n-of-1 trials: The 15-item Risk of Bias in N-of-1 Trials (RoBiNT) Scale. Neuropsychol Rehabil 2013; 23(5): 619–638, PubMed PMID: 24050810. https://doi.org/10.1080/09602011.2013.824383

32.

Heyvaert

Onghena

. Analysis of single-case data: randomisation tests for measures of effect size. Neuropsychol Rehabil 2014; 24(3–4): 507–527, PubMed PMID: 23865938. https://doi.org/10.1080/09602011.2013.818564

33.

Harris

Taylor

Minor

, et al. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform 2019; 95: 103208. https://doi.org/10.1016/j.jbi.2019.103208

34.

Mestdagh

Verdonck

Piot

, et al. m-Path: an easy-to-use and highly tailorable platform for ecological momentary assessment and intervention in behavioral research and clinical practice. Front Digit Health 2023; 5: 1182175. https://doi.org/10.3389/fdgth.2023.1182175

35.

Dworkin

Turk

Wyrwich

, et al. Interpreting the Clinical Importance of Treatment Outcomes in Chronic Pain Clinical Trials: IMMPACT Recommendations. J Pain 2008; 9(2): 105–121, PubMed PMID: 18055266. https://doi.org/10.1016/j.jpain.2007.09.005

36.

Collins

Murphy

Strecher

. The Multiphase Optimization Strategy (MOST) and the Sequential Multiple Assignment Randomized Trial (SMART): New Methods for More Potent eHealth Interventions. Am J Prev Med 2007; 32(5): S112–S118. https://doi.org/10.1016/j.amepre.2007.01.022

37.

Hayes

Hofmann

. The third wave of cognitive behavioral therapy and the rise of process-based care. World Psychiatry 2017; 16(3): 245–246. https://doi.org/10.1002/wps.20442

38.

Bartels

Taygar

Onghena

, et al. The dynamics of psychological inflexibility in everyday life: A series of N-of-1 observational studies in fibromyalgia. in manuscript.

39.

Verhagen

SJW

Hasmi

Drukker

, et al. Use of the experience sampling method in the context of clinical trials. Evid Based Ment Health 2016; 19(3): 86–89. https://doi.org/10.1136/ebmental-2016-102418

40.

Hawker

Mian

Kendzerska

, et al. Measures of adult pain: Visual Analog Scale for Pain (VAS Pain), Numeric Rating Scale for Pain (NRS Pain), McGill Pain Questionnaire (MPQ), Short-Form McGill Pain Questionnaire (SF-MPQ), Chronic Pain Grade Scale (CPGS), Short Form-36 Bodily Pain Scale (SF-36 BPS), and Measure of Intermittent and Constant Osteoarthritis Pain (ICOAP). Arthritis Care Res 2011; 63(Suppl 11): S240–S252, PubMed PMID: 22588748. https://doi.org/10.1002/acr.20543

41.

Rovner

Årestedt

Gerdle

BÃ¶rsbo

McCracken

. Psychometric properties of the 8-item Chronic Pain Acceptance Questionnaire (CPAQ-8) in a Swedish Chronic pain cohort. Journal of Rehabilitation Medicine 2014; 46(1): 73-80. 10.2340/16501977-1227.

42.

Sundström

Felicia TA

Lavefjord

Amani

Buhrman

Monica

McCracken

Lance M

. Assessing Psychological Flexibility and Inflexibility in Chronic Pain Using the Multidimensional Psychological Flexibility Inventory (MPFI). The Journal of Pain 2023; 24(5): 770-781. 10.1016/j.jpain.2022.11.010.

43.

Wicksell

Renöfält

Olsson

, et al. Avoidance and cognitive fusion--central components in pain related disability? Development and preliminary validation of the Psychological Inflexibility in Pain Scale (PIPS). Eur J Pain Lond Engl 2008; 12(4): 491–500. PubMed PMID: 17884643.

44.

Moore

Emily

Adams

Heather

Ellis

Tamra

Thibault

Pascal

Sullivan

Michael J. L.

Assessing catastrophic thinking associated with debilitating mental health conditions. Disability and Rehabilitation 2016; 40(3): 317-322. 10.1080/09638288.2016.1254283.

45.

Nicholas

Michael K

McGuire

Brian E

Asghari

Ali

. A 2-Item Short Form of the Pain Self-Efficacy Questionnaire: Development and Psychometric Evaluation of PSEQ-2. The Journal of Pain 2015; 16(2): 153-163. 10.1016/j.jpain.2014.11.002.

46.

Zetterberg

Hedvig

Wagner

Sofia

Ekselius

Lisa

Karlsten

Rolf

Flink

Ida

, et al. Psychometric Assessment of the Swedish version of the Work Ability Index in Patients with Chronic Pain in Specialized Care. Journal of Rehabilitation Medicine 2023; 55: jrm7146. 10.2340/jrm.v55.7146.

47.

Mendoza

Tito

Mayne

Tracy

Rublee

Dale

Cleeland

Charles

. Reliability and validity of a modified Brief Pain Inventory short form in patients with osteoarthritis. European Journal of Pain 2006; 10(4): 353-353. 10.1016/j.ejpain.2005.06.002.

48.

Burström

Kristina

Johannesson

Magnus

Diderichsen

Finn

. Swedish population health-related quality of life results using the EQ-5D. Quality of Life Research 2001; 10(7): 621-635. 10.1023/a:1013171831202.

49.

Eklund

Mona

Bäckström

Martin

Tuvesson

Hanna

. Psychometric properties and factor structure of the Swedish version of the Perceived Stress Scale. Nordic Journal of Psychiatry 2014; 68(7): 494-499. 10.3109/08039488.2013.877072.

50.

Spitzer

Robert L

Kroenke

Kurt

Williams

Janet BW

Löwe

Bernd

. A Brief Measure for Assessing Generalized Anxiety Disorder. Archives of Internal Medicine 2006; 166(10): 1092. 10.1001/archinte.166.10.1092.

51.

Hansson

Maja

Chotai

Jayanti

Nordstöm

Annika

Bodlund

Owe

. Comparison of two self-rating scales to detect depression: HADS and PHQ-9. British Journal of General Practice 2009; 59(566): e283-e288. 10.3399/bjgp09x454070.

52.

Dragioti

Wiklund

Alföldi

Gerdle

. The Swedish version of the Insomnia Severity Index: Factor structure analysis and psychometric properties in chronic pain patients. Scand J Pain 2015; 9(1): 22-27.

53.

Efron

(ed). False Discovery Rate Control. Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction [Internet]. Cambridge University Press, 2010, pp. 46–69. [cited 2025 Nov 5] (Institute of Mathematical Statistics Monographs). Available from. https://doi.org/10.1017/CBO9780511761362.005.

54.

Wang

Yang

Brown

, et al. Participant Compliance With Ecological Momentary Assessment in Movement Behavior Research Among Adolescents and Emerging Adults: Systematic Review. JMIR MHealth UHealth 2025; 13(1): e52887. https://doi.org/10.2196/52887

55.

Manolov

Onghena

. Testing delayed, gradual, and temporary treatment effects in randomized single-case experiments: A general response function framework. Behav Res Methods 2024; 56(4): 3915–3936, PubMed PMID: 37749426; PubMed Central PMCID: PMC11133040. https://doi.org/10.3758/s13428-023-02230-1

56.

Onghena

Edgington

. Customization of pain treatments: single-case design and analysis. Clin J Pain 2005; 21(1): 56–68, discussion 69-72; PubMed PMID: 15599132. https://doi.org/10.1097/00002508-200501000-00007

57.

The jamovi project. jamovi. 2024.

58.

Darnall

Edwards

Courtney

, et al. Innovative treatment formats, technologies, and clinician trainings that improve access to behavioral pain treatment for youth and adults. Front Pain Res 2023; 4: 1223172, PubMed PMID: 37547824; PubMed Central PMCID: PMC10397413. https://doi.org/10.3389/fpain.2023.1223172

59.

Terpstra

van der Vaart

van Beugen

, et al. Guided internet-based cognitive-behavioral therapy for patients with chronic pain: A meta-analytic review. Internet Interv 2022; 30: 100587. https://doi.org/10.1016/j.invent.2022.100587

60.

Gentili

Rickardsson

Zetterqvist

, et al. Psychological Flexibility as a Resilience Factor in Individuals With Chronic Pain. Front Psychol 2019; 10: 2016, PubMed PMID: 31551871; PubMed Central PMCID: PMC6734029. https://doi.org/10.3389/fpsyg.2019.02016

61.

Marceau

Link

Jamison

, et al.

Electronic diaries as a tool to improve pain management: is there any evidence?

Pain Med Malden Mass 2007; 8(Suppl 3): S101–S109, PubMed PMID: 17877520. https://doi.org/10.1111/j.1526-4637.2007.00374.x

62.

McCracken

Vowles

Eccleston

. Acceptance of chronic pain: component analysis and a revised assessment method. Pain 2004; 107(1): 159–166. https://doi.org/10.1016/j.pain.2003.10.012

63.

Rolffs

Rogge

Wilson

. Disentangling Components of Flexibility via the Hexaflex Model: Development and Validation of the Multidimensional Psychological Flexibility Inventory (MPFI). Assessment 2018; 25(4): 458–482. https://doi.org/10.1177/1073191116645905

64.

Karayannis

Sturgeon

Kemani

, et al. Pain acceptance and psychological inflexibility predict pain interference outcomes for persons with chronic pain receiving pain psychology. Scand J Pain 2023; 23(3): 464–475, PubMed PMID: 36745187; PubMed Central PMCID: PMC10522049. https://doi.org/10.1515/sjpain-2022-0107

65.

Mendoza

Mayne

Rublee

, et al. Reliability and validity of a modified Brief Pain Inventory short form in patients with osteoarthritis. Eur J Pain Lond Engl 2006; 10(4): 353–361, PubMed PMID: 16051509. https://doi.org/10.1016/j.ejpain.2005.06.002

66.

Koren

Leveille

You

. Brief Pain Inventory Pain Interference Subscale: Assessing Interference With Daily Living Activities in Older Adults With Multisite Musculoskeletal Pain. Front Pain Res 2022; 3: 897725. https://doi.org/10.3389/fpain.2022.897725

67.

Kemani

Zetterqvist

Kanstrup

, et al. A validation of the pain interference index in adults with long-standing pain. Acta Anaesthesiol Scand 2016; 60(2): 250–258, PubMed PMID: 26310686. https://doi.org/10.1111/aas.12599

68.

Tait

Pollard

Margolis

, et al. The Pain Disability Index: psychometric and validity data. Arch Phys Med Rehabil 1987; 68(7): 438–441, PubMed PMID: 3606368.

69.

Schumann

Craner

Kacel

, et al. The Helplessness Dimension of Pain Catastrophizing Mediates the Relation between PTSD Symptoms and Pain Rehabilitation Measures. Pain Res Manag 2022; 2022: 2112698–2112710, PubMed PMID: 36504758; PubMed Central PMCID: PMC9729044. https://doi.org/10.1155/2022/2112698

70.

Song

Howe

Oltmanns

, et al. Examining the Concurrent and Predictive Validity of Single Items in Ecological Momentary Assessments. Assessment 2023; 30(5): 1662–1671, PubMed PMID: 36004406; PubMed Central PMCID: PMC10248304. https://doi.org/10.1177/10731911221113563

71.

Pagé

Gauvin

Sylvestre

, et al. An Ecological Momentary Assessment Study of Pain Intensity Variability: Ascertaining Extent, Predictors, and Associations With Quality of Life, Interference and Health Care Utilization Among Individuals Living With Chronic Low Back Pain. J Pain 2022; 23(7): 1151–1166, PubMed PMID: 35074499. https://doi.org/10.1016/j.jpain.2022.01.001

72.

Carlozzi

Schilling

Freedman

, et al. The reliability of end of day and ecological momentary assessments of pain and pain interference in individuals with spinal cord injury. Qual Life Res Int J Qual Life Asp Treat Care Rehabil 2018; 27(11): 3003–3012, PubMed PMID: 30073468; PubMed Central PMCID: PMC6196118. https://doi.org/10.1007/s11136-018-1952-y

73.

Turk

Wilson

Swanson

. The biopsychosocial model of pain and pain management. In: Ebert

Kerns

(eds). Behavioral and Psychopharmacologic Pain Management [Internet]. Cambridge University Press, 2010, pp. 16–43. [cited 2026 Apr 23] Available from. https://doi.org/10.1017/CBO9780511781445.003. https://www.cambridge.org/core/books/behavioral-and-psychopharmacologic-pain-management/biopsychosocial-model-of-pain-and-pain-management/2CC3BB51B68A337072F00E3F7C3C896C

74.

Shiffman

Stone

Hufford

. Ecological momentary assessment. Annu Rev Clin Psychol 2008; 4: 1–32, 091415 PubMed PMID: 18509902. https://doi.org/10.1146/annurev.clinpsy.3.022806

75.

Dragioti

Wiklund

Alföldi

, et al. The Swedish version of the Insomnia Severity Index: Factor structure analysis and psychometric properties in chronic pain patients. Scand J Pain 2015; 9(1): 22–27, PubMed PMID: 29911642. https://doi.org/10.1016/j.sjpain.2015.06.001

76.

Nagy

Emert

Leete

, et al. Psychometric Evaluation of the Insomnia Severity Index in Nurses. Behav Sleep Med 2024; 22(5): 779–789, PubMed PMID: 38872302; PubMed Central PMCID: PMC11365753. https://doi.org/10.1080/15402002.2024.2362370

77.

Epstein

Dallery

. The Family of Single-Case Experimental Designs. Harv Data Sci Rev 2022; 4(SI3), PubMed PMID: 36926648; PubMed Central PMCID: PMC10016625. https://doi.org/10.1162/99608f92.ff9300a8

78.

Lewandowski

Palermo

Kirchner

, et al. Comparing Diary and Retrospective Reports of Pain and Activity Restriction in Children and Adolescents With Chronic Pain Conditions. Clin J Pain 2009; 25(4): 299–306. https://doi.org/10.1097/AJP.0b013e3181965578

79.

Nolan

Wolfenden

Benn

, et al. Experimental designs used for optimising the effects of health interventions and implementation strategies: a scoping review. BMC Health Serv Res 2025; 25(1): 1129. https://doi.org/10.1186/s12913-025-13184-9

80.

Svartdal

. Psykologins forskningsmetoder: en introduktion. Liber, 2001.

81.

Dorfman

Heapy

de C Williams

, et al. Maintenance of treatment gains from Psychological Treatments for Chronic Pain: What (little) we know, and the urgent need to extend it. Pain 2025; 166(9): 1969–1973, PubMed PMID: 40085755; PubMed Central PMCID: PMC12225740. https://doi.org/10.1097/j.pain.0000000000003552