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GME Research Review

GME Research Review is a monthly newsletter where internationally recognized experts select, summarize, and provide a clinical commentary on the latest published research in psychiatry. Each summary has been derived from the relevant article’s abstract and the clinical commentary has been provided by our expert.

 

 

Issue 25, April 2014: Guest Commentators—M. Zachary Rosenthal, PhD, and Marcus Rodriguez, MA, Duke University Medical Center, Durham, NC

 

 

A Smartphone-Based Intervention with Diaries and Therapist-Feedback to Reduce Catastrophizing and Increase Functioning in Women with Chronic Widespread Pain: Randomized Controlled Trial 

Kristjánsdóttir J, Fors ÓB, Eide EA, et al 
Med Int Res. 2013;15(1).

 

Objective: This study examined the efficacy of a web-based CBT-grounded intervention delivered through smartphones.  Specifically, researchers in Norway conducted a randomized controlled trial (RCT) to investigate the efficacy of a smartphone delivered intervention based on Acceptance and Commitment Therapy (ACT) principles following an inpatient chronic pain program.

 

Methods:

  • Participants consisted of 140 women with chronic widespread pain in an inpatient musculoskeletal pain management program in Norway, and were randomized into intervention (N = 48) and control (N = 64) groups.
  • All participants engaged in a 4-week inpatient multidimensional program, which included psychoeducation in pain mechanisms and CBT-based pain management skills (approximately 20 hours), motivational-interviewing-based group sessions (4 hours), aerobic exercise, stretching and relaxation, individual myofascial pain treatment (Simons et al., 1999), and medication as needed.
  • Individuals in the intervention group also participated in a smartphone intervention involving diaries and daily situational feedback. It lasted for 4 weeks and started with a personal meeting followed by daily diaries and feedback via a smartphone.
  • This smartphone application, developed based on the framework of ACT, aims to increase a person’s sense of quality of life and decrease symptoms by targeting values-congruent living, cognitive diffusion, and acceptance.
    • Clinicians had immediate access to patients’ electronic diaries through a secure website, and they provided personalized written feedback in an empathic communication style. Feedback included positive reinforcement, educational information, ACT metaphors, mindfulness exercises, and questions aiming to encourage willingness to engage in meaningful activities despite pain or discouraging thoughts.
    • Outcome measures included: Chronic Pain Acceptance Questionnaire (PCS; Johnston et al., 2010), General Health Questionnaire (GHQ; Goldberg et al., 1997) and Chronic Pain Values Inventory (CPVI; McCracken  & Yang, 2006).

 

Results: After the follow-up period the intervention group reported significantly less catastrophizing (M=9.2, SD=5.8) compared to the control group (M=15.7, SD=9.1). Between-group effect size on catastrophizing was large (Cohen’s d = 0.87, p<.001) and remained moderate (Cohen’s d=0.74, p=.003) 6 months after discharge from the inpatient program (Table).

 

Table. Between-Group Effect Sizes (ES) After the Smartphone Intervention (T3) and at 5-Month Follow-up (T4)

Outcome Measures

ES at T3

P Valueb

ES at T4

P Valueb

Primary

 

 

 

 

PCS (ITT and LOCF)

0.40

.01

0.36

.05

PCS (per protocol)

0.87

< .001

0.74

.003

Secondary

 

 

 

 

CPAQ

0.62

.007

0.54

.02

FIQ

0.21

.35

0.75

.001

SF-8, physical

–0.15

.64

0.35

.13

SF-8, mental

0.63

.005

0.43

.06

GHQ-12

0.03

.56

0.33

.16

CPVI

0.63

.005

0.40

.08

Pain, VAS

–0.15

.49

0.28

.22

Fatigue, VAS

0.04

.87

0.41

.07

Sleep disturbance, VAS

0.19

.36

0.55

.02

a Note. PCS = pain catastrophizing scale; ITT = intention-to-treat; LOCF = last observation carried forward; CPAQ = Chronic Pain Acceptance Questionnaire; FIQ = Fibromyalgia Impact Questionnaire; SF-8 = Short-Form Health Survey; GHQ-12 = General Health Questionnaire; CPVI = Chronic Pain Values Inventory; VAS = visual analog scale.

b P values for independent t tests or nonparametric tests.

 

Conclusions: The present findings suggest this personalized web-based smartphone delivered intervention can change psychological processes related to Acceptance and Commitment principles among chronic pain patients. However, booster sessions may be needed to enhance prolonged effects. Providing immediate, situational feedback close to the moment dysfunctional thoughts (e.g., catastrophizing) and behaviors (e.g., avoidance) occur may help to increase patients’ self-management skills and alleviate their somatic complaints (Dorn, 2012).

 

Clinical Commentary
It is important for clinical scientists to develop accessible and cost-effective psychological interventions, as it is unlikely that clinicians will be able to serve all clients in need with traditional face-to-face interventions, particularly in developing countries. The present study makes a valuable contribution to the growing body of literature exploring ways that mobile technologies can be leveraged to facilitate this work. ACT has been shown to be an efficacious treatment for a variety of mental health problems (Strosahl et al., 2004). This study was an initial evaluation of a new platform for the delivery of an ACT-based intervention (Hayes et al., 1999) for patients suffering from chronic pain. The intervention made use of technology through four principal elements: Electronic diaries (where participants registered activities, emotions and pain cognitions three times daily using the smartphone), individualized written situational feedbacks (from clinicians), access to a website with information, and audio files with mindfulness exercises installed on the smartphone and available at the website. The implications of the present findings have potential to extend beyond rehabilitation programs for chronic pain patients. The generalizability of the results is reduced by several factors, including the withdrawal rate of 30% in the intervention group and the response rates (below 70%) at both follow-ups. Replication studies are needed, including the application of this intervention to populations with different mental health problems (e.g., depression, anxiety, or disordered eating). Note: Kristjánsdóttir and colleagues also published results of an 11-month follow-up of this randomized trial.

 

 

Behavioral Activation Versus Mindfulness-Based Guided Self-Help Treatment Administered Through a Smartphone Application: A Randomised Controlled Trial 

Ly KH, Trüschel A, Jarl L, et al 
BMJ Open. 2014;4(1):e003440.

 

Objective: The aim of this study was to evaluate the effectiveness of two smartphone-delivered treatments, one based on behavioral Activation (BA) and the other based on mindfulness. Both BA and mindfulness have strong empirical bases (Cuijpers et al., 2007; Hofmann et al., 2010). However, the present study is the first to compare these treatments when delivered entirely via an application on participants’ personal smartphones.

 

Methods:

  • Participants consisted of 81 adults with major depressive disorder (MDD), who were not currently receiving psychological treatment, did not suffer from a severe comorbid psychiatric condition, and were not suicidal. 
  • Participants were randomized into a parallel controlled open trial: 40 received BA treatment and 41 received mindfulness treatment.
  • Both treatments were 8-week smartphone-based interventions with minimal therapist contact (maximum of 20 minutes per participant per week). Intervention consisted of a brief web-based psychoeducation, and a step-by-step behavior program administered via a smartphone application. Therapist could access all participants’ quantitative (e.g., behavior frequency) and qualitative (i.e., participant reflections) data, and they could send short text messages to the participants via a messaging system or email.
  • BA treatment:  Psychoeducation text was inspired by the BA treatment manuals of Martell et al. (2001) and Lejuez et al. (2001). The application suggested behaviors to start performing and tracking.
  • Mindfulness treatment: Psychoeducation text was inspired by the self-help book by Williams et al. (2007). The application consisted of a number of audio tracks with exercises to facilitate the practice of mindfulness in short (3-minute) and long (30-minute) formats.
  • Primary outcome measures included the Beck Depression Inventory-II (BDI-II; Beck et al., 1996) and the nine-item Patient Health Questionnaire Depression Scale (PHQ-9; Kroenke et al., 2001). 

 

Results: Results showed no significant interaction effects of group and time. The BA treatment was significantly more effective than the mindfulness treatment among participants with higher initial severity of depression from pretreatment to the 6-month follow-up, as measured using the PHQ-9, F(1, 362)=5.2, p<.05 (Table). However, the mindfulness treatment worked better than the BA treatment among participants with lower initial severity from pretreatment to the 6-month follow-up, as measured using the PHQ-9, F(1, 69)=7.7, p<.01, and the BDI-II, F(1, 53)=6.25, p<.05. 

 

Table. Means, Standard Deviations (SD), and Effect Sizes (Cohen’s d) for Between-Group Comparison of Depression Scores

 

Mean (SD)

Effect size, d (95% CI)

Outcome

measure

Pre-treatment

Post-treatment

6-month

follow-up

Pre to post

Pre to 6-month follow-up

Total

 

 

 

 

 

BDI-II

 

 

 

 

 

BA

23.50 (7.85)

10.89 (5.92)

12.71 (10.56)

0.25 (−1.65 to 2.15)

0.03 (−2.63 to 2.69)

MF

24.68 (9.47)

12.94 (10.18)

13.09 (12.24)

PHQ-9

 

 

 

 

 

BA

12.53 (4.43)

5.83 (3.85)

6.77 (5.83)

0.28 (−0.85 to 1.40)

0.15 (−1.39 to 1.69)

MF

13.22 (4.81)

7.19 (5.84)

7.74 (7.33)

 

 

 

 

 

 

H-L Dep

 

 

 

 

 

BDI-II

 

 

 

 

 

BA

26.87 (7.14)

12.00 (6.31)

11.81 (10.63)

0.42 (−2.09 to 2.93)

0.39 (−2.95 to 3.73)

MF

28.00 (8.61)

15.68 (10.76)

16.28 (12.71)

PHQ-9

 

 

 

 

 

BA

15.52 (3.29)

6.64 (4.42)

6.48 (5.59)

0.36 (−1.17 to 1.90)

0.47 (−1.46 to 2.40)*

MF

15.57 (3.35)

8.60 (6.29)

9.60 (7.71)

 

 

 

 

 

 

L-L Dep

 

 

 

 

 

BDI-II

 

 

 

 

 

BA

18.94 (6.47)

9.14 (4.96)

14.07 (10.71)

−0.51 (−2.36 to 1.34)

−1.21 (−4.13 to 1.71)*

MF

17.54 (7.09)

6.73 (4.86)

4.22 (3.63)

PHQ-9

 

 

 

 

 

BA

8.47 (1.59)

4.57 (2.34)

7.21 (6.36)

−0.23 (−1.20 to 0.74)

−0.98 (−2.68 to 0.72)**

MF

8.15 (3.34)

4.00 (2.86)

2.56 (1.51)

Note. PHQ-9 = Patient Health Questionnaire Depression Scale; BDI-II = Beck Depression Inventory-II; BA = Behavioral Activation; MF = Mindfulness; H-L Dep = High-level depression; L-L Dep = Low-level depression.

* p < 0.05; **p < 0.01; NS = Not significant (i.e., p > 0.5).

Conclusions: When analyzing data from the whole sample (i.e., averaging across different levels of depression severity), the two interventions did not differ significantly from one another in terms of their efficacy. However, BA worked significantly better for participants with higher severity of depression (as measured by the PHQ-9), whereas mindfulness worked significantly better for participants with lower initial severity (as measured by both the BDI-II and the PHQ-9). 

Clinical Commentary
This study is one of the first to perform a randomized controlled trial comparing two evidence-based treatments delivered via smartphone applications. In contrast with results from Cuijpers et al.’s (2010) meta-analyses, BA was found to be comparable in efficacy to the alternative treatment for depression. These findings’ study suggest that BA might work better for a more severely depressed population, whereas mindfulness might work better for a mildly depressed population. Most importantly, the results from this study demonstrate the potential for mild-to-moderate MDD to be treated effectively among a non-treatment-seeking population by means of a smartphone application. Using mobile technology to disseminate interventions has potential to reach a broader group of people, since this platform attracts less attention and allows users to interact with a personal device without fear of judgment or stigma (Boschen & Casey, 2008). Future studies are needed to control for the different components of these treatments, because based on the present findings it is impossible to determine which parts of the treatments were effective. 

 

The Smartphone Psychology Manifesto 

Miller G.
Perspectives on Psychological Science. 2012;7(3):221-237.

 

Objective:  This article includes five parts: (1) A review of previous behavioral research using mobile electronic devices, (2) an outline of what smartphones can do now and may be able to do in the near future, (3) an illustration of how a smartphone study could work given current technology, (4) a discussion of limitations and challenges of smartphone research, and (5) a comparison of smartphones to other more traditional research methods. 

Discussion:

  • Previous Research Using Mobile Electronic Devices: Miller (2012) outlines 4 types of studies that give a context for future developments in smartphone research.

i. Investigators persuade telecom service providers to share aggregated call records, including each call’s time, length, number dialed, and location (e.g., Calabrese et al., 2011; Gonzalez et al., 2008; Song et al., 2010);

ii. Investigators buy, program, and distribute limited-capability devices, such as PDAs to local samples, to collect a few types of behavioral data (e.g., Mehl et al., 2001; Bolger et al., 2003; Hekter et al., 2007; Mehl et al., 2006);

iii. Instead of a limited-capability PDA, researchers distribute a smartphone preprogrammed with a psych app, to collect behavioral data from a local sample (e.g., Shepard et al., 2011; Rachuri et al., 2010; Lu et al., 2009; Ter Hofte, 2007);

iv. Investigators distribute the software, that is, psych apps that participants can download remotely, and the app is used to manage the whole study autonomously, including consent, data gathering, data upload, debriefing, and payment (e.g., Oliver, 2010; Killingsworth & Gilbert, 2010; Dufau et al., 2011).

  • Miller (2012) predicts continual improvements to smartphones, including their size, processors, memory, connectivity, sensors, GPS, and visual, audio and motor output, input and recording.
  • Miller (2012) describes a series of thoughtfully conceived hypothetical studies on ovulatory cycle effects on women’s sexuality to illustrate how smartphone technology could work to help researchers develop new hypotheses and data analysis methods for further studies.

    - For example, researchers could examine cycle effects on (a) calls and texts with male versus female kin; (b) husbands’ mate guarding through calls and texts; (c) rates of visiting new places; (d) walking gait; and (g) voice pitch and attractiveness.

  • However, there clearly exist challenges (e.g., participant recruitment, app programming, data analysis), as well as potential ethical and legal limitations to this type of research, and Miller (2012) outlines some of these considerations.

Conclusion: Smartphone assessment methods have significant advantages over traditional research methods. However, for some research questions field study and laboratory settings will remain more useful than smartphones (e.g., perception and cognition experiments that demand high contextual control or bio sampling). 

Clinical Commentary
Smartphones are powerful tools, and they are carried around by billions of people all around the world. Although they were not designed for psychological research, but they can be used for that purpose, and Miller (2012) argues they will ultimately revolutionize the way we conduct and think about research in our field. Adapting to this new era in psychological research (e.g., learning app development skills and advanced statistical analytic methods) will not be easy, especially considering the dizzying rates of technological progress. However, Miller (2012) argues it is well worth it, considering the potential payoff. Perhaps the most exciting aspect of smartphone-based research is the ability to collect data from vast amounts of people, across the planet, relatively easily. As smartphones become ever more versatile and powerful they will increase our capacity to run perceptually and behaviorally rich experiments. Moreover, smartphones will also allow us to gather precise, objective, sustained, ecologically valid field observations of real-world behavior in real time. Miller (2012) challenges readers to think about how this new technology is going to change our research questions, theories, and models of behavior change, funding sources, and even career tracks.

 

 

The Feasibility and Validity of Ambulatory Self-report of Psychotic Symptoms Using a Smartphone Software Application 

Palmier-Claus JE, Ainsworth J, Machin M, et al 
BMC Psychiatry. 2012;12(1):172.

 
Objective:  Ambulatory, real-time, smartphone-based self-report assessment devices may have advantages over semi-structured interviews for assessing psychotic symptoms. The present study has two primary aims: (1) To assess the validity of using a smartphone-based monitoring system for the assessment of psychosis, compared to a gold-standard structured interview assessment method, and (2) to examine compliance to the procedure among individuals suffering with psychotic symptoms of varying severity.

 

Methods:

  • Participants (Total N=36): Adults with acute (n=12) or remitted (n=12) schizophrenia and related disorders, as well as participants who met criteria for being at “ultra-high risk of developing psychosis” at some point during the past year (i.e., prodromal; n=12; Yung et al., 1995).
  • Semi-structured, face to face interviews, including the Calgary Depression Scale (CDS; Addington et al., 1990) and Positive and Negative Syndrome Scale (PANSS; Kay et al., 1987), were conducted before and after the assessment period.
  • Smatphone application and data collection: Participants completed 14 branching self-report items about psychotic symptoms on a touch-screen mobile phone. The 14 mobile phone assessment items were designed to be equivalent to 12 items of the PANSS and 2 items of the CDS. Participants received prompts by an alarm at six pseudo-random times, each day, for one week. 


Results:

  • Compliance, as defined by completion of at least 33% of all possible data-points over seven days, was 82%. Only 36 of 44 participants were “compliant.”
  • High within-subject mean squared successive difference and standard deviation scores suggest this ambulatory assessment measure was sensitive to subtle shifts in symptomatology. The Table shows test-retest reliability and sensitivity to change across time.
  • Five items (delusions, hallucinations, suspiciousness, anxiety, and hopelessness) showed moderate to strong (rho 0.6-0.8) associations with corresponding items from interview rating scales, and four items (conceptual disorganization, excitement, hostility, and passive apathetic social withdrawal) showed no significant correlation with rating scales (Table). 

 

Table. Summary Statistics for Interview and Diary Subscales, and the Results to Spearman's Correlations (in order of strength)

 

Questions

Chronbach's alpha

Correlation Between Interview and Diary Scores

rho

P-value

Hopelessness

0.87

0.80

p<.001

Delusions

0.93

0.74

p<.001

Anxiety

0.96

0.69

p<.001

Hallucinations

0.96

0.68

p<.001

Suspiciousness

0.95

0.63

p<.001

Grandiosity

0.76

0.53

p<.001

Depression

0.83

0.45*

0.006*

Guilt

0.95

0.44

0.006

Somatic concern

0.96

0.39

0.019

Passive apathetic social withdrawal

0.93

0.26

0.131

Hostility

0.86

0.25

0.145

Excitement

0.89

0.06

0.712

Conceptual disorganization

0.95

-0.04

0.832

 

*Represents correlation with item G6 on the PANSS. Correlation with item 1 on the CDS (mean = 2, SD = .7, Min = 1, Max = 4) was rho = .50, p = .002.

Conclusions: Results from the present study suggest that ambulatory monitoring of symptoms several times daily using smartphone applications represents a feasible and valid way of assessing psychotic symptoms for clinical and research purposes. This smartphone-based monitoring system shows promise and should be studied further. 

Clinical Commentary
The present study aims to assess the validity, acceptability, and feasibility of using a smartphone-based monitoring system for assessing psychosis Future studies concerning the implementation of new technologies among potential users are needed. Kilbourne et al. [43] described a useful framework called Replicating Effective Programs (REP) for implementing health care interventions. Insights from the Technology Acceptance Model (TAM; Venkatesh & Davis, 2000) should also be considered throughout the implementation process. Palmier-Claus et al.’s (2012) research is important, as traditional face-to-face assessments have significant limitations, including recall bias, averaging (e.g., overlooking potential variability in the number, type and intensity of psychotic symptoms in different contexts), variable interrater reliability, and insensitivity to change. This study lays the groundwork to be able to develop future invterventions to help individuals with psychotic symptoms. Further evaluation is required over longer assessment periods, in clinical trials and service settings.

 

A Pilot Study of the DBT Coach: An Interactive Mobile Phone Application for Individuals with Borderline Personality Disorder and Substance Use Disorder 

Rizvi SL, Dimeff LA, Skutch J, et al 
Behavior Therapy, 2011;42(4):589-600.
 

Objective: The aim of this study was to develop and test the feasibility of the DBT coach, a smartphone application designed to enhance generalization of a specific dialectical behavioral therapy (DBT) skill (opposite action) among individuals with borderline personality disorder (BPD) and comorbid substance use disorders (SUD). 

Methods:

  • Participants consisted of 22 individuals who (1) met criteria for BPD and SUD, including nicotine dependence; (2) were receiving standard DBT in outpatient treatment programs, and (3) had learned the DBT skill opposite action (OA). (Note: OA is a behavioral technique used to change unwanted and distressful emotions by doing the opposite of your emotion’s action urges in the moment.)
  • Participants received a smartphone with the DBT coach application and the instructions to use it as often as they wanted during a 10–14-day period.
  • When participants used the DBT coach, they first recorded the intensity of their emotions and urges to use substances (0–10 scale). Next, they indicated what emotion they were currently experiencing, and whether they would be willing to work on changing the emotion. Participants who indicated willingness were then instructed to choose and practice a specific OA behavior from a scrollable list of behaviors. Then participants again recorded the intensity of their emotions and urges.
  • An optional follow-up DBT coach feature either provided a positive statement if the emotional intensity decreased (e.g., “Great job being effective”) or an invitation to review the list of emotion-specific opposite action behaviors if the emotional intensity was equal or higher than their ratings at initial use. Those who chose not to practice OA were encouraged to come back later and reminded that they can call their individual therapist for help.
  • Measures: Each day, participants were expected to complete a brief questionnaire on the phone, reporting their highest urges to use substances that day and how helpful DBT Coach and opposite action were that day. Participants also completed the Behavioral Confidence Questionnaire, Beck Depression Inventory (DBI), Brief Symptom Inventory (BSI), Therapist Questionnaire at pre- and port-trial The Satisfaction and Usability Survey was administered at post-trial only. 

 

Results: On average, participants used the DBT coach nearly 15 times. Participants gave positive ratings of helpfulness (e.g., “helpfulness in assisting learning and practicing skills”) and usability (e.g., “To what extent was the tool easy to use?”), that is, higher than 3 out of 5, on average. All 22 participants (100%) indicated they would make use of this tool in treatment on their own initiative. HLM analyses indicated that emotional intensity significantly decreased (from M=6.83 to M = 5.69) within each coaching session, B=-1.26, SE=0.20, t(21)=-6.17, P<.001. Urges to use substances also significantly decreased (from M=4.84 to M=3.95) within coaching sessions, B=-0.92, SE=0.22, t(21)=-4.22, P<.001. Over the 10–14-day trial period, participants reported a decrease in depression (i.e., BDI; t(21)=2.69, P=.014, d=.55) and general distress (i.e., BSI; t(21)=2.49, P=.021, d=.43). Therapist data regarding client skills use and phone calls are reported in the Table. 

Table. Therapist Ratings of Clients’ Skills Use and Phone Calls (N=22)

 

Pre-trial

Post-trial

t

p

d

Mean (SD)

Mean (SD)

Rating of client's skills use

3.83 (1.04)

4.12 (.63)

-1.06

.30

.28

Rating of client's OA use

3.45 (1.09)

4.26 (.70)

–2.79

.01

.84

# of client calls received

2.48 (6.55)

1.24 (1.95)

1.15

.26

.34

 

Note. OA=opposite action. Pretrial questions referenced the previous 2 weeks, post-trial questions referenced the time that the client had the DBT Coach. Ratings were made using the following scale: 1 (poor), 3 (average), 5 (effective).

Conclusions: The present findings suggest that DBT coach, a smartphone application which offers in vivo (OA) skills coaching, may be a useful tool for increasing DBT skills use and adaptive coping behaviors, while reducing urges to use substances and levels of symptomatology.

Clinical Commentary
There is a significant body of literature that suggests DBT is an effective approach to the treatment of BPD and BPD with comorbid SUD. This study builds on this line of research to examine how mobile technology can be used to help DBT clients with BPD-SUD generalize newly acquired skills to the natural settings where they most often experience urges to engage in maladaptive behavior (e.g., substance use or abuse). Although there were numerous limitations to this small feasibility pilot trial which used a quasi-experimental design, the present findings are encouraging. Results suggest that, when used as an adjunct to DBT, the smartphone application DBT coach has the potential to decrease emotional intensity, urges to use substances, and overall distress. More generally, the present research supports the use of mobile-phone based psychological interventions for facilitating skills generalization, behavior change, and symptom-reduction. This study also contributes to this field by developing the Satisfaction and Usability Scale, which can be adopted in future research to evaluate the acceptability of smartphone application-based interventions.


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