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Telephone coaching for the prevention of depression in farmers: Results from a pragmatic randomized controlled trial

Article

Telephone coaching for the prevention of depression in farmers: Results from a pragmatic randomized controlled trial

Abstract

Introduction Farmers have a high risk for depression (MDD). Preventive measures targeting this often remotely living population might reduce depression burden. The study aimed to evaluate the effectiveness of personalized telephone coaching in reducing depressive symptom severity and preventing MDD in farmers compared to enhanced treatment as usual (TAU + ). Methods In a two-armed, pragmatic randomized controlled trial ( N = 314) with post-treatment at 6 months, farming entrepreneurs, collaborating family members and pensioners with elevated depressive symptoms (PHQ-9 ≥ 5) were randomized to personalized telephone coaching or TAU + . The coaching was provided by psychologists and consists on average of 13 (±7) sessions a 48 min (±15) over 6 months. The primary outcome was depressive symptom severity (QIDS-SR16). Results Coaching participants showed a significantly greater reduction in depressive symptom severity compared to TAU + ( d = 0.39). Whereas reliable symptom deterioration was significantly lower in the intervention group compared to TAU + , no significant group differences were found for reliable improvement and in depression onset. Further significant effects in favor of the intervention group were found for stress ( d = 0.34), anxiety ( d = 0.30), somatic symptoms ( d = 0.39), burnout risk ( d = 0.24–0.40) and quality of life ( d = 0.28). Discussion Limiting, we did not apply an upper cutoff score for depressive symptom severity or controlled for previous MDD episodes, leaving open whether the coaching was recurrence/relapse prevention or early treatment. Nevertheless, personalized telephone coaching can effectively improve mental health in farmers. It could play an important role in intervening at an early stage of mental health problems and reducing disease burden related to MDD. Trial registration number and trial register German Clinical Trial Registration: DRKS00015655
TELEPHONE COACHING FOR PREVENTION IN FARMERS
AUTHORS MANUSCRIPT
Publication available: Thielecke, J., Buntrock, C., Titzler, I., Braun, L., Freund, J., Berking, M.,
Baumeister, H., & Ebert, D. D. (2022). Telephone coaching for the prevention of depression in
depression in farmers: Results from a pragmatic from a pragmatic randomized controlled trial.
Journal of Telemedicine and Telecare. https://doi.org/10.1177/1357633X221106027
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Telephone coaching for the prevention of depression in farmers? Results from
a pragmatic randomized controlled trial
Janika Thielecke1,3*, Claudia Buntrock1, Ingrid Titzler 1, Lina Braun2, Johanna Freund1,3,
Matthias Berking1, Harald Baumeister 2 and David D. Ebert 3
1 Department of Clinical Psychology and Psychotherapy, Institute of Psychology, Friedrich-Alexander-
University Erlangen-Nürnberg, Erlangen, Germany
2 Department of Clinical Psychology and Psychotherapy, Institute of Psychology and
Education, Ulm University, Ulm, Germany
3 Professorship of Psychology and Digital Mental Health Care, Technical University of Munich,
Munich, Germany
Word count: 3173/3000
*Corresponding author: Janika Thielecke
Friedrich-Alexander-University Erlangen-Nürnberg
Department of Clinical Psychology and Psychotherapy
Nägelsbachstr. 25a
91052 Erlangen
Telephone: +49 9131 85 67567, Fax: +49 9131 85-67576
E-Mail: janika.thielecke@fau.de
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Abstract
Introduction: Farmers have a high risk for depression (MDD). Preventive measures targeting this
often remotely living population might reduce depression burden. The study aimed to evaluate the
effectiveness of personalized telephone coaching in reducing depressive symptom severity and
preventing MDD in farmers compared to enhanced treatment as usual (TAU+).
Methods: In a two-armed, pragmatic randomized controlled trial (N=314) with post-treatment at 6-
months, farming entrepreneurs, collaborating family members and pensioners with elevated
depressive symptoms (PHQ-9≥5) were randomized to personalized telephone coaching or TAU+. The
coaching was provided by psychologists and consists on average of 13 (±7) sessions à 48 minutes
(±15) over 6 months. Primary outcome was depressive symptom severity (QIDS-SR16).
Results: Coaching participants showed a significantly greater reduction in depressive symptom
severity compared to TAU+ (d=0.39). Whereas reliable symptom deterioration was significantly lower
in the intervention group compared to TAU+, no significant group differences were found for reliable
improvement and in depression onset. Further significant effects in favor of the intervention group
were found for stress (d=0.34), anxiety (d=0.30), somatic symptoms (d=0.39), burnout risk (d=0.24-
0.40), and quality of life (d=0.28).
Discussion: Limiting, we did not apply an upper cut-off score for depressive symptom severity or
controlled for previous MDD episodes, leaving open whether the coaching was recurrence/relapse
prevention or early treatment. Nevertheless, personalized telephone coaching can effectively
improved mental health in farmers. It could play an important role in intervening at an early stage of
mental health problems and reducing disease burden related to MDD.
German Clinical Trial Registration: DRKS00015655.
Words: 250/250
Keywords: depression, prevention, RCT, farmers, telephone coaching
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Introduction
Lifetime prevalence rates for major depressive disorder (MDD) are estimated between 2% and 21.0%
worldwide (1). MDD is associated with high individual (2,3) and societal burden (4,5). Yet, primary
care providers fail to recognize MDD in nearly half of patients (6) and only 20% receive specialized
mental health care (7). But even in a hypothetical scenario of full coverage and compliance to
evidence-based treatments only one-third of MDD-related disease burden could be averted (8,9).
Preventive approaches are promising in reducing disease burden. According to recent meta-analyses,
psychological interventions could effectively prevent MDD onset when targeting people in risk
groups or with subthreshold symptoms (10,11).
A recent systematic review including primarily studies from English-speaking countries showed an
elevated risk in farmers for mental disorders compared to people in non-farming occupations (12).
Farming-related risk factors include poor physical health, high workload, exposure to pesticides,
financial hardship, and uncertain weather conditions. Farmers also face barriers accessing mental
health services including few providers in rural areas (12,13), preferring help from oneself, family or
friends (14), and anticipated stigma (15,16).
Therefore, low-threshold preventive interventions for this vulnerable group are warranted.
Telephone coaching could aid by reducing travel time and being easily accessible. One-on-one
telephone interventions have been shown to be effective in supporting people with physical long-
term conditions (17) and in delivering psychological interventions for depression (e.g., cognitive
behavioral therapy, interpersonal psychotherapy) (18). In a recent meta-analysis, cognitive
behavioral therapy delivered over phone has shown comparable outcome improvements compared
to face-tot-face therapy for MDD (19,20). However, for coaching conducted over telephone, only two
studies could be identified that evaluated the effectiveness in reducing depressive symptom severity
and revealed moderate effects in mostly white collar workers (21,22). Yet the overall effectiveness of
telephone coaching in farmers as well as its effect on preventing MDD remains unclear.
The present study evaluated the effectiveness of telephone coaching personalized to the individual
farmer’s needs compared to enhanced treatment as usual in reducing depressive symptom severity
and preventing MDD onset at 6-month follow-up. This study is part of a nationwide prevention
project by the German social insurance for farmers, foresters, and gardeners (SVLFG) which aims to
implement internet- and tele-based interventions into routine care.
Methods
TELEPHONE COACHING FOR PREVENTION IN FARMERS
The study was a two-armed pragmatic randomized controlled trial comparing the effectiveness of
personalized telephone coaching delivered by a third party independent healthcare provider
“IVPNetworks” to a control group receiving information material in addition to usual care (TAU+) at
post-treatment (6 months after randomization).
The trial was approved by the Medical Ethics Committee of the Friedrich-Alexander University
Erlangen-Nürnberg (No. 345_18 B) and registered in the German clinical trial register
(DRKS00015655). The study protocol can be found elsewhere (23).
Study population
Individuals were included if they were (a) insured by the SVLFG, (b) entrepreneur, assisting family
member or pensioned farmer, (c) 18 years or older, (d) showed elevated depressive symptoms (PHQ-
9≥5), (e) had internet/telephone access for online assessments/coaching, and (f) provided written
informed consent.
Applicants were excluded if they were (a) currently receiving psychotherapy, (b) unwilling to sign a
non-suicide contract in case of suicidal ideation or (c) living in the German Federal States Bavaria or
Schleswig-Holstein, as roll-out of the intervention into routine care was ongoing there (24). Due to
the parallel recruitment for two RCTs evaluating online-trainings in the same target group (25,26), we
(d) excluded participants of these studies. To mimic routine care, no diagnostic interviews were
conducted.
Procedure
Participants were recruited and enrolled in the study from December 2018 to April 2019. Main
recruitment channel were personal invitation letters to randomly selected insured persons. The
study was also advertised in the SVLFG members’ journal and newsletter and associated websites.
Inclusion criteria were assessed online and eligible individuals who provided written informed
consent and completed the baseline assessment were randomly allocated to the intervention (IG) or
control group (CG) (see Figure 1).
Randomization was centrally done at an individual level by an independent researcher not involved
in the study. Permuted block randomization with randomly arranged block sizes (4, 6, 8, 12) and an
allocation ratio of 1:1 was used based on a web-based program (Sealed Envelope). Study participants
and coaches could not be blinded but data collectors were blind to group allocation.
Post-assessment was assessed six month after randomization, regardless of the coaching duration,
between June 2019-December 2019. Completion was rewarded with €15 in both groups. The primary
TELEPHONE COACHING FOR PREVENTION IN FARMERS
outcome was assessed via telephone if participants did not complete the assessment within two
months.
Study arms
All participants had unrestricted access to routine care. Actual health service use was monitored with
items of the TiC-P (see secondary outcomes) (27,28).
Intervention group
The study team registered participants on the management platform (IVPnet). Case managers at
IVPNetworks assigned coaches to participants. Coaches were psychologists with a diploma or
master’s degree in psychology and trained in different psychological methods (e.g., cognitive
behavioral, systemic, hypnotherapeutic) while licensed psychotherapists were available for
supervision. The coaching is an individual-centered process, which is problem-based and solution-
oriented. It aims to reactivate and build individuals’ resources by using psychoeducational and
psychological methods to enable participants to cope with stress, acute problems or general worries.
No fixed procedures or standardized manuals were applied in the coaching. Coaching methods varied
depending on the coach’s background, while timing and content were permanently adapted to the
participants’ needs. As a guideline, a coaching volume of 850 minutes or six months was set with the
possibility of prolonging for additional 150 minutes or three months, respectively. If coaches
identified a clinically relevant symptomatology, they could support participants in finding adequate
help (e.g. contact general practitioner or psychiatric clinic). Additionally, participants were supported
to find on-site support services (e.g. socioeconomic or agricultural family counseling) or switch to an
onsite-coaching if indicated.
Control group
Participants in the control group received brief psychoeducation material on stress and depression
by email, combined with information about access to regular care.
Outcomes
Primary and secondary outcomes were assessed via online-questionnaires (Unipark) at baseline and
post-treatment (T1).
Primary Outcome
Depressive symptom severity was assessed with the German Version of the Quick Inventory
Depressive Symptomology (QIDS-SR16) with scores ranging from 0 to 27. Scores between 0-5,
TELEPHONE COACHING FOR PREVENTION IN FARMERS
indicate normal health status, while those between 6-10, 11-15, 16-20 and greater than 20, indicate
mild, moderate, severe or very severe depressive symptom severity, respectively (29). Reliability in
this study was acceptable (30) with αT0=.72 and αT1=.78.
Secondary Outcomes
Lifetime history and major depressive episodes (MDE) onset within the past year, bipolar disorder
(BPD), and general anxiety disorder (GAD) were self-reported with adapted items from the web
version of the Composite International Diagnosis Interview version 3.0 (CIDI, 31). For depression
onset at post-treatment, both CIDI rating and a score of 13 or greater on the QIDS-SR16 as cut-off for
possible acute cases of clinical depression (32) were applied.
Secondary outcomes further included perceived stress (Perceived Stress Scale, PSS, range 0-40,
αT0=.85 - αT1=.90) (33), insomnia severity (Insomnia Severity Index, ISI, range 0-28, αT0=.81 - αT1=.88)
(34), somatic symptom burden (Somatic Symptom Scale, SSS-8, range 0-32, αT0=.71 - αT1=.76) (35),
severity of GAD (GAD-7, range 0-21, αT0=.78 - αT1 0.83) (36,37), severity of panic and agoraphobic
symptoms (Panic and Agoraphobia Scale, PAS, , range 0-48, αT0=.89 - αT0=.92) (38), alcohol
consumption (consumption questions from Alcohol Use Disorder Identification Test , AUDIT-C, range
0-12, αT0/1=.59) (39) and quality of life (Assessment of Quality of Life-8D, AQoL-8D, range 0-100,
αT0=.92; αT1=.94) (40).
Work-related, the subjective capacity to work (SPE, Guttmanscale categories 0-3, repT0/1=.95) (41) as
well as burnout symptomology (Maslach Burnout Inventory, MBI-GS, subscales: “Emotional
Exhaustion” , EE, range 0-30; “Cynicism”, CY, range 0-30; “Professional Efficacy”, PE, range 0-36;
αT0=.70 - .89, αT1=.77 - .91) (42,43) was assessed.
A context-adapted version of the cost questionnaire “Trimbos Institute and Institute of Medical
Technology Questionnaire for Costs Associated with Psychiatric Illness” (TiC-P) (27,28) was used to
monitor TAU.
Intervention-related Outcomes
Intervention-related outcomes included satisfaction with the coaching or information material (Client
Satisfaction Questionnaire for internet interventions , CSQ-I, range 8-32, αCG=.95 - αIG=.96) (4446),
working alliance (Working Alliance Inventory, participants: WAI-SR, range 12-60, α=.93; coaches:
WAI-SRT, range 10-50, α=.90) (47), and negative effects (Inventory for the Assessment of Negative
Effects of Psychotherapy, INEP) (48).
Data on the coaching process (e.g. duration, sessions, topics) were retrieved from IVPnet.
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Data Analyses
Based on a power of 80%, an alpha of 0.05 (two-sided), and an attrition rate of 20% (21,22), 312
participants were needed to be able to detect an effect of d=0.35 (49) using an independent t-test
(G*Power Version 3.1.9.2).
Results are reported according to the Consolidated Standards of Reporting Trials (CONSORT) 2010
Statement and its extension for reporting pragmatic trials (50). Analyses were performed in R (51)
based on intention-to-treat principles with a two-sided significance level of .05. Missing data were
imputed using the R-package MICE (52) to apply Multiple Imputation by Chained Equations assuming
data to be missing at random. Representing the percentage of incomplete cases at post-treatment,
21 imputed datasets were generated (53). Intervention-related outcomes and health service use
were not imputed. We used analysis of covariance (ANCOVA) to compare primary and secondary
outcomes between study groups, adjusting for baseline scores. All analyses were run in each imputed
data set and estimates were pooled using Rubin’s Rule (54,55). Results were reported as mean
within- and between-group differences and as Cohen’s d effect sizes with corresponding 95%-CIs
according to Hedges and Olkin (56).
Treatment response, reliable deterioration & close-to-symptom-free status
To assess improvements in the primary outcome at an individual level, we examined the number of
participants who showed a treatment response as defined by a reliable change from baseline to post-
treatment according to the reliable change index (RCI) of Jacobson and Truax (57). Participants were
defined as reliably improved if their QIDS-SR16 score declined from baseline to post-treatment with
more than 1.96 standard units corresponding to a point change of at least 6, while taking into
account the reliability of the measurement instrument. The same method was used to assess an
increase in depressive symptoms to indicate reliable deterioration.
Close-to-symptom-free status was a priori defined as a score of ≤ 5 on the QIDS-SR16 (29). Individuals
below this score at baseline were excluded from this analysis.
Differences in treatment response and close-to-symptom-free status between study groups were
assessed using χ² tests. Numbers-needed-to-treat (NNT; with 95%-CI) to achieve one treatment
response and close-to-symptom-free status, respectively, were calculated as the inverse of the risk
difference (58,59).
Onset and remission of potential MDD
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Group differences in depression onset were assessed with Poisson regression in the subsamples of
individuals without self-reported MDE/MDD at baseline based on CIDI and QIDS-SR16<13,
respectively, estimating incidence rate ratios (IRR) with 95%-CIs. For individuals with potential MDD
at baseline (QIDS-SR16≥13) group differences in remission were assessed using logistic regression.
Odds Ratios (OR) and 95%-CIs were reported.
Sensitivity analyses
To test the robustness of the findings, subgroup analyses were conducted with study completers. The
influence of intervention (e.g. coaching duration, coaches’ background) and individual characteristics
(e.g. treatment preferences) on the primary outcome was analyzed using linear regression.
Results
Participants
A total of 1347 individuals completed the screening. Two individuals who completed the baseline
assessment after study N was reached were randomized by coin-flip, resulting in a total sample of
314 participants, with 160 participants in IG and 154 in CG. In IG, 24 participants (15.0%) and in CG 43
participants (27.9%) were lost to follow-up. Study completers and study dropouts did not differ in
any sociodemographic or clinical baseline characteristics.
The majority of participants were male (n=165, 52.5%), lived in partnership (n=281, 89.5%), had
middle education (n=197, 62.7%) and were entrepreneurs (n=197, 62.7%) with an average age of 52
(SD=9.98, range: 25-87). Average depressive symptom severity was mild to moderate (M=10.03,
SD=4.26), with 88 participants (28.0%) scoring ≥13 on the QIDS-SR16 (Table 1).
Intervention-related outcomes
Coaches and Intervention Use
During the study, 34 coaches (n=31 female, 91.2%) worked with 1 20 participants (M=4.71,
SD=2.26). According to IVPNetworks, coaches had a background in systemic counselling (n=18,
53.0%), cognitive behavioral methods (n=11, 32.0%), hypnotherapy (n=8, 24.0%), gestalt- or analytic
therapy (n=7, 20.5%) or other coaching and counseling methods (n=19, 55.9%). Additional
sociodemographic and work-related information was assessed via online questionnaire from coaches
(17/34, 50%; Supplement 1). Analyses of semi-structured interviews with eight coaches revealed
psychoeducation, conjoint goalsetting, and a three-phase model (“introduction and alliance
building”-, “working”- and “stabilizing”-phase) as common elements for the individualized coaching.
Intervention use is displayed in Table 2.
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Satisfaction and working alliance
Satisfaction in IG was higher (n=135; M=28.17, SD=5.58) compared to CG (n=109; M=16.11, SD=6.46,
t(214.64)=-15.39, p<.001).
Working alliance between coach and participant was rated as good by participants (n=135, 84.9%,
M=4.05, SD=0.66) and by the 17 coaches who rated a total of 86/159 (54.1%) coachings (M=3.94,
SD=0.68).
Primary Outcome
Both groups showed statistically significant reductions in depressive symptom severity indicated by
changes in baseline to post-treatment scores on the QIDS-SR16 (IG: 3.37 points, SD=4.21, T(3597)=-
7.17, p<.001, d=-0.92 [95%-CI: -0.67 - -1.17]; CG: 2.10 points, SD=5.01, T(1793)=-3.86, p<.001, d=-0.50
[95%-CI: -0.25 - -0.77]). Depressive symptom severity was statistically significantly reduced in IG
compared to CG while adjusting for baseline symptoms (F(1, 306.54) = 12.78, p<.001) corresponding
to a small to medium between-group effect size of Cohen’s d=-0.39 [95%-CI: -0.15 - -0.64].
Treatment response, symptom deterioration & close-to-symptom-free status
Treatment response was observed in more participants in IG (58/160=36.3%) than in CG
(44/154=28.6%) but without statistical significance (χ²=2.11, p=.16; NNT=13.02, 95%-CI: -38.71
5.57). In IG, statistically significantly more participants achieved a close-to-symptom-free status
(61/140=43.6%) compared to CG (37/133=27.8%; χ²=7.35, p=.007; NTT=6.35, 95%-CI: 3.72 -21.70). A
significant difference was observed in reliable deterioration with one case (0.6%) in IG and nine cases
(5.8%) in CG (χ²=6.4, p=.02).
Secondary Outcomes
Onset and remission of depression
Based on CIDI (n=260), 15 cases (6.6%) of possible MDE onset were observed in CG compared to six
cases (2.7%) in IG (IRR=0.39, 95%-CI: 0.14 -1.09, p=.07). Likewise, no group difference was observed
in potential MDD cases based on QIDS-SR16 (n=226) with eight (3.5%) and four (1.8%) new cases of
potential MDD in CG and IG respectively (IRR=0.36, 95%-CI: 0.10 1.36, p=.13).
No statistically significant difference between groups in remission (IG: nbaseline=40, npost=11, 27.5%
remission, CG: nbaseline=48, npost=22, 45.8% remission; OR=1.84, 95%-CI: 0.66 5.42, p=.27) was
observed.
Other mental health outcomes
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Analyses yielded significant differences in favor of IG for perceived stress (d=-0.34, 95%-CI: -0.08 - -
0.59), generalized anxiety (d=-0.30, 95%-CI: -0.07 - -0.53), somatic symptoms (d=-0.39, 95%-CI: -0.16 -
-0.63), dimensions of the Maslach burnout inventory (EE: d=-0.28, 95%-CI: -0.05 - -0.51; PE: d=0.4,
95%-CI: 0.64 - 0.16; CY: d=-0.24, 95%-CI: 0 - -0.47), and quality of life (d=0.29, 95%-CI: 0.52 - 0.06)
(Table 3).
Negative Effects
Based on the INEP, one-third of IG participants (47/135, 34.8%) reported at least one negative effect
attributed to the intervention. In total, 82 negative effects were reported of which most were
“intrapersonal changes” (n=33, 40.2%, Supplement 2).
Use of TAU
Overall, more participants in IG (n=97, 71.3%) reported use of at least one health service (Table 4)
during the last three months compared to CG participants (n=63, 56.8%, χ²(1)=5.06, p=.02).
Sensitivity analyses
Analyses of study completer (n=247, 78.7%) showed that outcomes remained comparable with
greater effect sizes compared to the main analyses (Supplement 3). Additionally, small effects of
increased reduction in insomnia (d=-0.22 [95%-CI: -0.47 0.03]) and panic and agoraphobic
symptoms (d=-0.3 [95%-CI: -0.6 - 0.01]) were found in IG compared to CG.
Baseline depressive symptom severity and coaching duration were uncorrelated (r=0.12, p=.11) but
baseline symptom severity predicted depressive symptoms at post-treatment (β=0.51, t(111)=7.35,
p<0.0001, R²=0.29 [95%-CI: 0.17 0.42]; Supplement 3).
Discussion
We reported the post-treatment results from a pragmatic randomized controlled trial comparing a
personalized telephone coaching to a control group receiving brief psychoeducational material.
Coaching participants showed a significantly greater reduction in depressive symptom severity (d=-
0.39, 95%-CI: -0.64 - -0.15) compared to CG. No significant differences in reliable symptom
improvement and depression onset were observed, but coaching led to fewer reliable deteriorations
compared to CG. Positive effects in favor of IG were also shown for stress, anxiety, somatic
symptoms, burnout risk, and quality of life.
To our knowledge, this is the first trial for a personalized preventive telephone coaching targeting
depressive symptoms in farmers. The observed reduction in depressive symptoms is comparable to
TELEPHONE COACHING FOR PREVENTION IN FARMERS
previous findings for indicated depression prevention with effect sizes reported at post-treatment as
d=0.35 (95%-CI: -.23 to - .47) for face-to-face interventions (49), d=-0.39 (95%-CI: -0.25 to -0.53) for
general online interventions (60), and d=-0.28 (95%-CI: -0.50 to -0.07) for guided online-interventions
for farmers (61). The average intervention duration of 13 sessions over six months in this study is
higher compared to most online trainings (6-12 sessions, 3-12 weeks) (60) but similar to preventive
health coaching (1-20 sessions, 1-24 weeks) (62) and face-to-face interventions in subthreshold (5-16
session) (10) and major depression (3-24 sessions, 3-36 weeks) (63). Studies on telephone coaching
based on cognitive behavioral therapy or motivational interviewing for adults with MDD reported
slightly higher effect sizes for symptom reduction with d=-0.76 (95%-CI: -.40 to -.80) at 4-months (21)
and d=-0.45 (95%-CI: -.14 to -.75) at 12-months (22).
Regarding the non-significant group differences in MDE onset, three potential reasons should be
considered. First, overall onset rates at post-treatment were low in both groups (CG: 12.0%, IG: 4.4%
based on CIDI). Onset rates for non-active control groups are reported with incidence rates of 25%-
30% in (indicated) prevention over 12 months (10,60). As shown by Reins et al. (60) depression onset
in subclinical control conditions occurred on average 8 months after study enrolment. Thus, our
follow-up period might have been too short to observe group differences in depression onset.
Second, our control group was not completely inactive but received psychoeducational material
about stress, depression, and healthcare services. Yet, brief psychoeducational interventions can
result in depressive symptom reduction (64). Third, the study was powered to detect statistically
significant differences in depressive symptom severity, which might not have been sufficient to
detect differences in MDD onset.
The recent Covid-19 pandemic has increased the demand and use of telehealth services rapidly (65
67). While the increased exposure to telehealth options might have positively influenced attitudes of
practitioners an users (68,69), it also revealed that telehealth is still considered a more niche
intervention option in many countries resulting in a lack of training of heath care professionals and
reimbursement opportunities (7072). In line with other (pre-)pandemic studies, our results show
that telephone coaching can effectively promote mental health, not only as a necessity in a global
health crisis but also in easing access for remotely living communities (73), lowering socio-economic
costs (74,75) and offering alternatives for people with restricted (broadband) internet-access (75
77). Implementing a structure for telehealth in routine care can further strengthen and enable the
health care system to spontaneously and flexibly react to future large scale health crisis (67,71,72).
Limitations
TELEPHONE COACHING FOR PREVENTION IN FARMERS
We neither applied a cut-off on self-reported depression nor conducted diagnostic interviews at
baseline in order to mimic routine care. Thus, around a third of included individuals were potential
MDD cases and about a quarter of participants reported lifetime MDE. The telephone coaching might
therefore have functioned as recurrence/relapse prevention or (early) treatment. Future studies
should clarify whether telephone coaching is equally effective in the prevention of first depression
onset, prevention of recurrence /relapse and early treatment, respectively.
Although we evaluated the coaching as a depression prevention intervention and included
participants with at least mild depressive symptoms, we did not exclude participants with comorbid
symptoms. Due to the personalized approach, coaches and participants might have agreed to focus
on other symptoms. This might partly explain why only 36% (n=113) of participants showed a
treatment response, a finding similar to other studies on online indicated prevention (60) and
preventive face-to-face interventions (49). Treatment response might be especially critical for this
target group in order to avoid an even greater reluctance to seek professional help (78). Non-
response can lead to (early) dropout (79). However, early termination of telephone coaching was low
in this study (n=17/160, 10.6%), which might be due to the low-threshold telephone delivery (80) or
because of the personalized approached.
Nevertheless, the highly personalized intervention and diverse coaches’ backgrounds restrict
generalizability of the intervention effects. More research on preventive (telephone) coaching on
mental health outcomes is needed, especially to determine effective methods and effects of
personalization (81).
Finally, the assumed advantage of telephone coaching making health care more accessible might
have been limited due to study specific inclusion criteria (e.g. internet access). The multi-stage
inclusion procedure for study participation might have resulted in inclusion of highly motivated
participants.
Conclusion
Personalized telephone coaching can effectively reduce depression symptoms and improve mental
health in farmers. It could play an important role in intervening at an early stage of mental health
problems and if needed facilitating access to further support (e.g. psychotherapy, farming specific
offers) which is especially crucial in rural areas and populations with low-help seeking behavior.
Long-term (cost)-effectiveness analyses and the evaluation of the implementation into routine care
will gain more insight into the potential of telephone coaching in this population.
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Declaration of Authorship contribution according to CRediT
Janika Thielecke: Conceptualization, Methodology, Project administration, Investigation, Data
Curation, Formal analysis, Visualization, Writing - Original Draft; Claudia Buntrock: Supervision,
Conceptualization, Methodology, Writing - Original Draft; Ingrid Titzler: Project administration,
Conceptualization, Methodology, Writing - Review & Editing; Lina Braun: Resources, Writing - Review
& Editing; Johanna Freund: Resources, Writing - Review & Editing; Harald Baumeister: Resources,
Conceptualization, Funding acquisition, Writing - Review & Editing; Matthias Berking: Funding
acquisition, Writing - Review & Editing; David Daniel Ebert: Funding acquisition, Conceptualization,
Methodology, Writing - Review & Editing. All authors provided critical revision of the article and
approved the final manuscript.
Declaration of Conflict of Interest
DDE has served as a consultant to/on the scientific advisory boards of Sanofi, Novartis, Minddistrict,
Lantern, Schoen Kliniken, Ideamed, and German health insurance companies (BARMER, Techniker
Krankenkasse) and a number of federal chambers for psychotherapy. DDE and MB are stakeholders
of the Institute for health training online (GET.ON/HelloBetter), which aims to implement scientific
findings related to digital health interventions into routine care. MB is scientific advisor of mentalis
GmbH, a provider for digital aftercare. HB reports to have received consultancy fees and fees for
lectures/workshops from chambers of psychotherapists and training institutes for psychotherapists
in the e-mental-health context. IT reports to have received fees for lectures/workshops in the e-
mental-health context from training institutes for psychotherapists. She was research and
implementation project lead of the trial site Institute for health training online (GET.ON) for the
European implementation research project ImpleMentAll (11/2017 -03/2021) funded by the
European Commission. The remaining authors report no conflicts of interest.
Declaration of Data availability
Access to the final pseudonomized trial dataset can be provided to fellow researchers upon request,
depending on to be specified data security and data exchange regulation agreements.
Declaration of the role of funding source
The German insurance company SVLFG provided a financial contribution to the Friedrich-Alexander
University Erlangen-Nürnberg and Ulm University as expense allowance. SVLFG had no role in study
design, data collection, analyses, interpretation or writing the manuscript and the decision to publish
it.
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Acknowledgements
Authors would like to thank Friederike Dietz and Annika Montag for programming the online surveys.
Further, authors thank Sarah Banellis, Merle Bloom, Hanna Böckeler, Albina Chafisuf, Doro Dressel,
Johanna Finitzer and Tomris Ohloff for their engagement in enrolling and supporting participants
throughout the study. Lukas Fuhrmann and Marvin Franke are thanked for carrying out the
randomization and Mathias Harrer for statistical advice. Authors thank the IVPNetworks staff who
provided details on their intervention and Madison Ehmann for proofreading the manuscript as well.
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TELEPHONE COACHING FOR PREVENTION IN FARMERS
Table 1 Baseline characteristics of study population (N=314)
Name
IG (n=160)
CG (n=154)
Total
M
SD
M
SD
M
SD
Age
53.12
9.25
50.10
10.49
51.64
9.98
QIDS-SR16 (sum score)
9.81
4.13
10.26
4.40
10.03
4.26
QIDS-SR16 (categorical)
n
%
n
%
n
%
0-5 none
20
12.5
21
13.6
41
13.1
6-10 mild
76
47.5
61
39.6
137
43.6
11-15 moderate
45
28.1
53
34.4
98
31.2
16-20 severe
17
10.6
17
11.0
34
10.8
21 very severe
2
1.3
2
1.3
4
1.3
Sex
Male
84
52.5
81
52.6
165
52.6
Female
76
47.5
73
47.4
149
47.5
Birthplace
Germany
158
98.8
154
100.0
312
99.4
Other
2
1.3
0
0.0
2
0.6
Ethnicity
Caucasian
158
98.8
153
99.4
311
99.0
Other
2
1.3
1
0.7
3
1.0
Relationship status
With partnership
148
92.5
133
86.4
281
89.5
No partnership
12
7.5
21
13.6
33
10.5
Education level a
Low
14
8.8
17
11.0
31
9.9
Middle
103
64.4
94
61.0
197
62.7
High
43
26.9
43
27.9
86
27.4
Employment status
Entrepreneur
94
58.8
103
66.9
197
62.7
Entrepreneurs spouse
35
21.9
25
16.2
60
19.1
Pensioner farmer
16
10.0
13
8.4
29
9.2
Family member of entrepreneur
14
8.8
12
7.8
26
8.3
Incapacitated for work
1
0.6
1
0.7
2
0.6
Second job off the farm b
No
42
85.7
33
89.2
75
87.2
Yes
7
14.3
4
10.8
11
12.8
Brutto Income c
Low (<1000€)
13
13.3
11
10.4
24
11.8
Middle (1000-5000€)
45
45.9
61
57.6
106
52.0
High (>5000€)
17
17.4
10
9.4
27
13.2
Not disclosed
23
23.5
24
22.6
47
23.0
Main farm branch
Dairy farming
34
23.1
35
24.1
69
23.6
Arable farming
27
18.4
30
20.7
57
19.5
Animal farming
33
22.5
18
12.4
51
17.5
Wine growing
19
12.9
25
17.2
44
15.1
Vegetable growing
9
6.1
11
7.6
20
6.9
Horticulture
11
7.5
7
4.8
18
6.2
Fruit growing
1
0.7
5
3.5
6
2.1
Direct marketing
3
2.0
3
2.1
6
2.1
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Other
10
6.8
11
7.6
21
7.2
Physical illness
Yes
94
58.8
85
55.2
179
57.0
No
66
41.3
69
44.8
135
43.0
Current antidepressant
medication
No
86
83.5
78
94.0
164
88.2
Yes
17
16.5
5
6.0
22
11.8
Experience with psychotherapy
No
129
80.6
132
85.7
261
83.1
Yes
31
19.4
22
14.3
53
16.9
CIDI - MDE
MDE in lifetime
32
20.0
44
28.6
76
24.2
MDE in past 12 month
25
15.6
29
18.8
54
17.2
CIDI - BPD
Broad mania in lifetime
10
6.3
24
15.6
34
10.8
Broad mania in past 12 month
8
5.0
15
9.7
23
7.3
CIDI - GAD
Anxiety in lifetime
33
20.6
33
21.4
66
21.0
Anxiety in past 12 month
28
17.5
22
14.3
50
15.9
Search for therapy
Not searching or waiting
152
95.0
148
96.1
300
95.5
Searching for therapy
7
4.4
6
3.9
13
4.1
On a waiting list for therapy
1
0.6
0
0.0
1
0.3
Intervention preference
Preference for telephone coaching
72
45.0
74
48.1
146
46.5
No preference
76
47.5
69
44.8
145
46.2
Preference for information
material
12
7.5
11
7.1
23
7.3
IG=Intervention group; CG=Control group; QIDS-SR16=Quick Inventory of Depressive Symptomatology; CIDI=Composite International
Diagnostic Interview; MDE=Major Depressive Episode; BPD=Bipolar Disorder; GAD=Generalized Anxiety Disorder. a low: no formal
education or lower secondary education, middle: finished upper secondary education, high: finished study or master's certificate; b only
applied to spouses and family members of entrepreneur (n=86); c only applied to entrepreneurs and spouses/family members with working
contract in the company (n=204).
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Table 2 Intervention use in the study period based on IVPNetworks reporting (N=160)
Coaching participants (N=160)
M
SD
Min
Max
13.43
6.02
1
32
14.09
5.87
0
42.25
48.57
14.69
1
120
6.14
1.99
0.36
11.11
652.52
325.76
50
1598
n
%
29
18.2
0
0.0
n
%
110
68.7
75
46.9
56
35.0
44
27.5
43
26.9
39
24.34
28
17.5
26
16.3
22
13.8
18
11.3
43
26.9
Documented discharge reason
n
%
Improved outcome (coach rating)
96
59.9
further support recommended or installed
47
29.4
withdrew consent
7
4.4
lacked compliance
5
3.2
agreed on different needs for support
3
1.9
physically too impaired to participate
1
0.6
Coaching not started
1
0.6
a data referring to n=159 participants who had at least one coaching session
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Table 3 Primary and secondary outcomes at 6 month post-treatment (N=314)
outcome
group
baseline
assessment
(T0)
6m post-
treatment (T1)
F
p
Between-group effect
size Cohen's d (95% -
CI)
M
SD
M
SD
QIDS-
SR16
IG
9.81
4.13
6.44
3.95
12.78
<.001
-0.39 (-0.64 - -0.15)
CG
10.26
4.40
8.16
4.62
PSS-10
IG
21.76
5.78
17.06
7.31
8.06
.01
-0.34 (-0.59 - -0.08)
CG
21.95
6.36
19.73
8.31
ISI
IG
10.95
4.98
7.91
5.21
3.47
.06
-0.20 (-0.44 - 0.05)
CG
10.72
5.05
8.97
5.55
GAD-7
IG
8.28
3.67
5.87
3.78
9.72
<.001
-0.30 (-0.53 - -0.07)
CG
8.70
4.05
7.07
4.11
PAS
IG
6.08
6.67
4.26
6.47
0.39
.53
-0.07 (-0.30 - 0.17)
CG
6.65
7.10
4.67
6.18
AUDIT-C
IG
3.04
1.77
2.89
1.77
1.28
.26
-0.07 (-0.30 - 0.15)
CG
3.19
2.05
3.02
1.96
SSS-8
IG
11.03
4.66
8.36
4.60
17.98
<.001
-0.39 (-0.63 - -0.16)
CG
11.74
5.50
10.46
5.84
MBI (EE)
IG
15.33
7.17
12.92
7.71
10.76
<.001
-0.28 (-0.51 - -0.05)
CG
16.44
7.58
15.14
8.07
MBY (CY)
IG
8.50
5.84
8.11
6.69
5.73
.02
-0.24 (-0.47 - 0.00)
CG
9.41
5.86
9.72
6.68
MBI (PE)
IG
26.69
7.19
28.33
6.56
13.96
<.001
0.40 (0.16 - 0.64)
CG
25.12
7.58
25.34
8.00
AQoL-8D
(total)
IG
66.89
9.38
72.17
10.39
13.71
<.001
0.29 (0.06 - 0.52)
CG
66.39
10.90
68.73
12.77
AQoL-8D
(PSD)
IG
77.67
10.34
80.67
10.45
9.06
<.001
0.25 (0.01 - 0.48)
CG
77.67
10.81
77.9
11.77
AQoL-8D
(MSD)
IG
62.47
10.73
68.81
11.88
11.04
<.001
0.28 (0.05 - 0.51)
CG
61.76
12.34
65.16
14.21
SPE a
SPE=0
SPE=1
SPE=2
SPE=3
IG
75
36
40
9
46.88
22.50
25.00
5.63
92
32
28
8
57.50
20.00
17.50
5.00
.51
SPE=0
SPE=1
SPE=2
SPE=3
CG
66
44
33
11
42.86
28.57
21.43
7.14
76
38
29
11
49.35
24.68
18.83
7.14
IG=Intervention group; CG=Control group; QIDS-SR16=Quick Inventory of Depressive Symptomatology; PSS-10=Perceived Stress Scale;
ISI=Insomnia Severity Index; GAD-7=Generalized Anxiety Disorder; PAS=Panic and Agoraphobia Scale; AUDIT-C=Alcohol Use Disorder
Identification Test Consumption Questions; SSS-8=Somatic Symptom Scale; MBI-GS=Maslach-Burnout-Inventory General Survey;
EE=Emotional Exhaustion; CY=Cynism; PE=Professional Efficacy; AQoL-8D=Assessment of Quality of Life; PSD=Physical Super Dimension;
MSD=Mental Super Dimension; SPE=Subjective Prognosis of Gainful Employment Scale. Data is imputed and based on ITT analyses. a
Reported are N and %. Tested with Fishers exact test.
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Table 4 Use of psychosocial and (mental) health services in the study population according to
corresponding items of Tic-P (study-completer only, n=247 at post-assessment)
Service
Group
Assessment at study begin (T0)
Assessment at post-treatment
(T1)
n
%
Difference between
groups % (95%-CI)
n
%
Difference between
groups % (95%-CI)
Use of at least one
health service
IG
39
24.4
2.30 (0.64 3.95)
97
71.3
19.72 (15.31 24.12)
CG
34
22.1
63
56.8
General practitioner
IG
111
69.4
2.05 (0.48 - 3.62)
87
64.0
12.62 (8.48 - 16.76)
CG
110
71.4
57
51.4
Psychotherapist
IG
4
2.5
1.85 (0.36 - 3.34)
3
2.2
0.5 (-0.38 - 1.37)
CG
1
0.7
3
2.7
Specialist for
psychiatry, neurology
or psychosomatic
medicine
IG
9
5.6
3.03 (1.13 - 4.93)
5
3.7
0.83 (-0.30 - 1.96)
CG
4
2.6
5
4.5
Clinic for
psychiatry/psychoso
matic
IG
1
0.6
0.63 (-0.25 - 1.51)
2
1.5
2.13 (0.33 - 3.93)
CG
0
0.0
4
3.6
Prescribed
antidepressants
IG
17
10.6
7.38 (4.49 - 10.27)
13
9.6
5.96 (3.00 - 8.91)
CG
5
3.3
4
3.6
SVLFG psychosocial
health services
IG
2
1.3
2.65 (0.87 - 4.42)
3
2.2
4.1 (1.63 - 6.57)
CG
6
4.0
7
6.3
Other psychosocial
health services
IG
13
8.1
2.91 (0.81 - 5.01)
8
5.9
5.83 (2.91 - 8.75)
CG
17
11.0
13
11.7
IG=Intervention group; CG=Control group; TIC-P=Treatment Inventory of Costs in Patients with psychiatric disorders; SVLFG=social
insurance for farmers, foresters, and gardeners. Based on study completer answers (T0: N IG=160, N CG=154; T1: N IG=136, N CG=111).
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Caption: Figure 1 CONSORT study flow
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Supplement 1: Additional information on participating coaches
Table 1 Sociodemographic and occupational information of participating coaches (self-report, n=17)
Variable
M
SD
Min
Max
n
%
Age
47.87
9.66
30
64
Gender
Female
Male
15
2
88.2
11.8
Country of birth
Germany
Russia
16
1
94.1
5.9
State
Hamburg
North Rhine-Westphalia
Schleswig-Holstein
Bavaria
12
2
2
1
70.6
11.8
11.8
5.9
Subject of
degree a
Psychology
Education science
17
1
100.0
5.9
State-licenced
psychotherapist
No
Yes
In training
12
3
2
70.6
17.6
11.8
Description of
the trainings a
Systemic therapy
Hypnotherapy
Conflict Counselling/ Moderation
Supervision
Psychodynamic therapy
Traumatherapy/-counselling
Gestalt therapy
Behavioral therapy
Educational guidance
Other coaching or counseling training
6
5
4
4
4
3
3
3
2
5
35.3
29.4
23.5
23.5
23.5
17.7
17.7
17.7
11.8
29.5
Duration of the
trainings a
Indicated in hours
Indicated in years
622.36
2.75
942.28
0.83
40
2
3500
4
Type of
employment
Permanent employee
Freelancer
12
5
70.6
29.4
Clinical
experience
0-6 months
12-24 months
5-6 years
7-8 years
8-9 years
9-10 years
More than 10 years
1
2
2
3
1
1
7
5.9
11.8
11.8
17.6
5.9
5.9
41.2
Coaching
experience at
study begin
0-6 months
6-12 months
12-24 months
2-3 years
3-4 years
5-6 years
6-7 years
9-10 years
More than 10 years
4
1
1
1
2
1
2
1
4
23.5
5.9
5.9
5.9
11.8
5.9
11.8
5.9
23.5
a multiple answers possible
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Supplement 2: Negative effects of the personalized telephone coaching
Table 1 Negative effects of the intervention reported by participants (n=135) in the INEP
Item
N
%
Intrapersonal change
33
23.9
Longer periods of feeling bad
13
9.6
Depending on coach
9
6.7
Severity of suffering from past experiences/events
5
3.7
Neglect of hobbies and social contacts because of coaching
5
3.7
Worsening of symptoms
2
1.5
Experiencing new thinking and behavior patterns as harmful
2
1.5
Difficulties to make decisions alone
2
1.5
Less trusting others
1
0.7
Feeling more lonely
0
0.0
As a human being changed to the negative
0
0.0
Thoughts/plans to commit suicide for the first time
0
0.0
Relationship
13
9.6
Problem with partner's jealousy
10
7.4
More arguments/conflicts in relationship
5
3.7
Stigma
11
8.1
Financial worries
10
7.4
Worries about (potentially) increasing insurance fees
2
1.5
Fear of others discovering about the program usage
2
1.5
Therapeutic malpractice of the coach
10
7.4
Feeling of being forced to do exercises given by coach
8
5.9
Hurtful statements by coach
3
2.2
Feeling of data security not being ensured during the coaching
1
0.7
Feeling of being made fun of by the coach
0
0.0
Friends and Family
2
1.5
Worsened relationship with family
2
1.5
Worsened relationship with friends
0
0.0
INEP=Inventory for Assessing Negative Effects of Psychotherapy. Data based on n=135 study completer. Only negative effects attributed
directly to the intervention are reported.
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Supplement 3: Sensitivity Analyses
Table 1 Primary and secondary outcomes at post-treatment (study completer only, n= 168)
Outcome
Group
n
Baseline
assessment
(T0)
6m post-
treatment
(T1)
F
p
Between-group
effect size Cohen's d
(95%-CI)
M
SD
M
SD
QIDS-SR16
IG
85
9.11
3.74
5.71
3.17
34.47
<0.001
-0.74 (-1.02 - -0.45)
CG
83
10.19
4.26
8.71
4.35
PSS-10
IG
85
21.69
5.46
16.98
6.50
12.2
<0.001
-0.44 (-0.73 - -0.14)
CG
83
21.70
6.20
20.23
7.97
ISI
IG
85
10.27
4.77
8.05
5.20
4.28
0.04
-0.24 (-0.54 - 0.06)
CG
83
10.89
5.64
9.30
5.36
GAD-7
IG
85
8.15
3.38
6.02
3.44
8.49
<0.001
-0.37 (-0.67 - -0.07)
CG
83
8.92
4.24
7.41
3.96
PAS
IG
85
5.18
5.21
3.19
4.91
5.46
0.02
-0.3 (-0.6 - 0.01)
CG
83
6.96
7.55
4.84
6.16
AUDIT-C
IG
85
3.01
1.59
2.86
1.53
0.62
0.43
0.07 (-0.23 - 0.37)
CG
83
2.87
1.83
2.75
1.74
SSS-8
IG
85
10.45
4.23
8.04
4.13
26.34
<0.001
-0.58 (-0.87 - -0.29)
CG
83
12.24
5.76
10.94
5.46
MBI (EE)
IG
85
14.81
6.49
12.34
7.14
21.74
<0.001
-0.48 (-0.78 - -0.19)
CG
83
16.46
7.39
15.95
7.47
MBY (CY)
IG
85
8.36
5.55
7.84
6.08
9.03
<0.001
-0.36 (-0.66 - -0.06)
CG
83
9.45
6.08
10.22
6.98
MBI (PE)
IG
85
27.86
5.59
27.92
6.67
8.48
<0.001
0.35 (0.05 - 0.65)
CG
83
24.17
7.96
25.30
7.95
AQoL-8D
(total)
IG
85
67.64
7.59
72.99
7.86
33.41
<0.001
0.51 (0.22 - 0.81)
CG
83
65.70
11.71
67.50
12.39
AQoL-8D
(PSD)
IG
85
78.45
8.78
81.61
9.54
27.79
<0.001
0.45 (0.15 - 0.74)
CG
83
76.37
11.38
76.61
12.09
AQoL-8D
(MSD)
IG
85
63.21
9.12
69.46
9.21
23.85
<0.001
0.48 (0.18 - 0.77)
CG
83
61.33
13.19
63.76
13.76
SPE a
SPE=0
SPE=1
SPE=2
SPE=3
IG
85
40
21
21
3
47.1
24.7
24.7
3.5
52
15
17
1
61.2
17.7
20.0
1.2
1.0
SPE=0
SPE=1
SPE=2
SPE=3
CG
83
32
22
22
7
38.6
26.5
26.5
8.4
36
18
20
9
43.4
21.7
24.1
10.8
Data is based on study completer (n=168). IG=Intervention group; CG=Control group; QIDS-SR16=Quick Inventory of Depressive
Symptomatology; PSS-10=Perceived Stress Scale; ISI=Insomnia Severity Index; GAD-7=Generalized Anxiety Disorder; PAS=Panic and
Agoraphobia Scale; AUDIT-C=Alcohol Use Disorder Identification Test Consumption Questions; SSS-8=Somatic Symptom Scale; MBI-
GS=Maslach-Burnout-Inventory General Survey; EE=Emotional Exhaustion; CY=Cynism; PE=Professional Efficacy; AQoL-8D=Assessment of
Quality of Life; PSD=Physical Super Dimension; MSD=Mental Super Dimension; SPE=Subjective Prognosis of Gainful Employment Scale. a
Reported are N and %. Tested with Fishers exact test.
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Table 2 Linear regression models to assess influence of intervention characteristics on primary outcome (QIDS-SR16) in IG (n=160)
Model
Term
β
SE
t
df
p
Nagelkerk
lower
95%-CI
upper
95%-CI
Model 1: symptom
severity baseline
Intercept
1.40
0.73
1.92
125.39
0.06
0.29
0.17
0.42
QIDS-SR16 (T0)
0.51
0.07
7.35
110.99
<.001
Model 2: symptom
severity baseline +
coaching durations
in hours
Intercept
4.10
1.75
2.34
77.03
0.02
0.31
0.19
0.44
QIDS-SR16 (T0)
0.24
0.19
1.28
48.19
0.21
Duration (h)
-0.25
0.14
-1.80
98.70
0.08
QIDS-SR16 (T0) * duration (h)
0.02
0.01
1.69
62.38
0.10
Model 3: symptom
severity baseline +
months
accompanied by
coach
Intercept
3.37
2.58
1.30
82.96
0.20
0.31
0.19
0.43
QIDS-SR16 (T0)
0.25
0.27
0.95
51.40
0.34
duration (month)
-0.32
0.40
-0.81
98.39
0.42
QIDS-SR16 (T0) * duration (month)
0.04
0.04
1.07
62.36
0.29
Model 4: symptom
severity baseline +
session count
Intercept
3.70
2.11
1.76
66.30
0.08
0.31
0.19
0.43
QIDS-SR16 (T0)
0.26
0.23
1.16
43.06
0.25
Session count
-0.17
0.14
-1.22
80.05
0.22
QIDS-SR16 (T0) * session count
0.02
0.01
1.25
52.23
0.22
Model 5: symptom
severity baseline +
mean frequency of
sessions
Intercept
0.91
2.31
0.40
110.80
0.69
0.30
0.18
0.43
QIDS-SR16 (T0)
0.57
0.22
2.57
102.59
0.01
Frequency
0.03
0.15
0.19
111.76
0.85
QIDS-SR16 (T0) * frequency
0.00
0.02
-0.23
90.42
0.82
IG=Intervention group; QIDS-SR16=Quick Inventory of Depressive Symptomology; T0=baseline measurement. Reported are regression analyses based on intervention group data (ITT analyses, n=160).
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Table 3 Linear regression models to assess influence of coaches background on primary outcome (QIDS-SR16) in IG (n = 160)
Model
Term
β
SE
t
df
p
Nagelkerk
lower
95%-CI
upper
95%-CI
Model 1: symptom severity
baseline
Intercept
1.40
0.73
1.92
125.39
0.06
0.29
0.17
0.42
QIDS-SR16 (T0)
0.51
0.07
7.35
110.99
<.001
Model 2: symptom severity
baseline + cognitive-behavioral-
trained
Intercept
1.64
0.90
1.83
109.18
0.07
0.29
0.17
0.42
QIDS-SR16 (T0)
0.49
0.09
5.69
99.59
0.00
cognitive-behavioral-trained
-0.82
1.55
-0.53
136.16
0.60
QIDS-SR16 (T0) * cognitive-behavioral-trained
0.09
0.15
0.59
129.34
0.55
Model 3: symptom severity
baseline + systemic trained coach
Intercept
0.85
0.93
0.92
133.85
0.36
0.30
0.17
0.42
QIDS-SR16 (T0)
0.56
0.08
6.62
129.41
0.00
Systemic-trained
1.37
1.54
0.89
110.42
0.37
QIDS-SR16 (T0) * systemic-trained
-0.12
0.15
-0.78
89.04
0.43
QIDS-SR16=Quick Inventory of Depressive Symptomology; T0=baseline measurement. Reported are regression analyses based on intervention group data (ITT analyses, n=160)). Reference category is “other coaching or
counselling training.
Table 4 Overview over number of participants treated by coach with specific therapeutic background (n = 160)
Coach trained in …
Participants treated
N
%
Syctemic counselling
74
46.3
Cognitive behavioral therapy
51
31.88
not specified coaching and counselling methods
29
18.13
Hypnotherapeuty
28
17.5
Analytic therapy
16
10.0
Gestalttherapy
12
7.5
Not specified additional methods
58
36.3
TELEPHONE COACHING FOR PREVENTION IN FARMERS
Table 5 Linear regression models to assess influence of participant characteristic on primary outcome (QIDS-SR16) (N = 314)
Model
Term
β
SE
t
df
p
Nagelkerk
lower
95%-CI
upper
95%-CI
Model 1: group
Intercept
8.16
0.39
21.02
160.82
<.001
0.04
0.01
0.1
Group: IG
-1.72
0.54
-3.18
166
.001
Model 2: group + treatment
preferencea
Intercept
7.96
0.58
13.81
169.72
<.001
0.05
0.01
0.11
Group: IG
-1.73
0.78
-2.22
193.21
0.03
Preference for telephone coaching
0.25
0.79
0.32
182.73
0.75
Preference for information material
1.21
1.51
0.8
207.89
0.42
Group: IG * Preference for telephone coaching
-0.02
1.07
-0.02
226.81
0.98
Group: IG * Preference for information material
0.33
2.06
0.16
227.19
0.87
Model 3: group + psychotherapy
experienceb
Intercept
7.91
0.42
18.8
149.38
<.001
0.06
0.02
0.13
Group: IG
-1.77
0.58
-3.07
190.09
<.001
Experience with psychotherapy
1.8
1.14
1.58
127.66
0.12
Group: IG * Experience with psychotherapy
-0.22
1.43
-0.15
179.76
0.88
QIDS-SR16=Quick Inventory of Depressive Symptomology. Reported are regression analyses based on imputed data (ITT analyses, n=314)). aReference group model 2: no preference. bReference group model 3: no
experience with psychotherap
1
2
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Background COVID-19 has profoundly affected the work of mental health professionals with many transitioning to telehealth to comply with public health measures. This large international study examined the impact of the pandemic on mental health clinicians’ telehealth use. Methods This survey study was conducted with mental health professionals, primarily psychiatrists and psychologists, registered with WHO's Global Clinical Practice Network (GCPN). 1206 clinicians from 100 countries completed the telehealth section of the online survey in one of six languages between June 4 and July 7, 2020. Participants were asked about their use, training (i.e., aspects of telehealth addressed), perceptions, and concerns. Outcomes Since the pandemic onset, 1092 (90.5%) clinicians reported to have started or increased their telehealth services. Telephone and videoconferencing were the most common modalities. 592 (49.1%) participants indicated that they had not received any training. Clinicians with no training or that only addressed a single aspect of telehealth practice were more likely to perceive their services as somewhat ineffective than those with training that addressed two or more aspects. Most clinicians indicated positive perceptions of effectiveness and patient satisfaction. Quality of care compared to in-person services and technical issues were the most common concerns. Findings varied by WHO region, country income level, and profession. Interpretation Findings suggest a global practice change with providers perceiving telehealth as a viable option for mental health care. Increasing local training opportunities and efforts to address clinical and technological concerns is important for meeting ongoing demands.
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Background: The COVID-19 pandemic has highlighted telemedicine use for mental illness (telemental health). Objective: In the scoping review, we describe the scope and domains of telemental health during the COVID-19 pandemic from the published literature and discuss associated challenges. Methods: PubMed, EMBASE, and the World Health Organization's Global COVID-19 Database were searched up to August 23, 2020 with no restrictions on study design, language, or geographical, following an a priori protocol ( https://osf.io/4dxms/ ). Data were synthesized using descriptive statistics from the peer-reviewed literature and the National Quality Forum's (NQF) framework for telemental health. Sentiment analysis was also used to gauge patient and healthcare provider opinion toward telemental health. Results: After screening, we identified 196 articles, predominantly from high-income countries (36.22%). Most articles were classified as commentaries (51.53%) and discussed telemental health from a management standpoint (86.22%). Conditions commonly treated with telemental health were depression, anxiety, and eating disorders. Where data were available, most articles described telemental health in a home-based setting (use of telemental health at home by patients). Overall sentiment was neutral-to-positive for the individual domains of the NQF framework. Conclusions: Our findings suggest that there was a marked growth in the uptake of telemental health during the pandemic and that telemental health is effective, safe, and will remain in use for the foreseeable future. However, more needs to be done to better understand these findings. Greater investment into human and financial resources, and research should be made by governments, global funding agencies, academia, and other stakeholders, especially in low- and middle- income countries. Uniform guidelines for licensing and credentialing, payment and insurance, and standards of care need to be developed to ensure safe and optimal telemental health delivery. Telemental health education should be incorporated into health professions curricula globally. With rapidly advancing technology and increasing acceptance of interactive online platforms amongst patients and healthcare providers, telemental health can provide sustainable mental healthcare across patient populations. Systematic Review Registration: https://osf.io/4dxms/ .
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Telehealth is an important source of health care during the COVID-19 pandemic. Evidence is scarce regarding disparities in telehealth utilization in the United States. We aimed to investigate the prevalence and factors associated with telehealth utilization among US adults. Our data came from the Health, Ethnicity, and Pandemic Study, a nationally representative survey conducted in October 2020, with 2554 adults ≥ 18 and an oversample of racial/ethnic minorities. Telehealth utilization was measured as self-reported teleconsultation with providers via email, text message, phone, video, and remote patient monitoring during the pandemic. Logistic regressions were performed to examine the association between telehealth use and factors at the individual, household, and community levels. Overall, 43% of the sample reported having used telehealth, representing 114.5 million adults in the nation. East and Southeast Asians used telehealth less than non-Hispanic Whites (OR = 0.5, 95% CI: 0.3-0.8). Being uninsured (compared with private insurance: OR = 0.4, 95% CI: 0.2-0.8), and those with limited broadband coverage in the community (OR = 0.5, 95% CI: 0.3-0.8) were less likely to use telehealth. There is a need to develop and implement more equitable policies and interventions at both the individual and community levels to improve access to telehealth services and reduce related disparities.
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Background The prominence of telemental health, including providing care by video call and telephone, has greatly increased during the COVID-19 pandemic. However, there are clear variations in uptake and acceptability, and concerns that digital exclusion may exacerbate previous inequalities in access to good quality care. Greater understanding is needed of how service users experience telemental health, and what determines whether they engage and find it acceptable. Methods We conducted a collaborative framework analysis of data from semi-structured interviews with a sample of people already experiencing mental health problems prior to the pandemic. Data relevant to participants’ experiences and views regarding telemental health during the pandemic were identified and extracted. Data collection and analysis used a participatory, coproduction approach where researchers with relevant lived experience, contributed to all stages of data collection, analysis and interpretation of findings alongside clinical and academic researchers. Findings The experiences and preferences regarding telemental health care of the forty-four participants were dynamic and varied across time and settings, as well as between individuals. Participants’ preferences were shaped by reasons for contacting services, their relationship with care providers, and both parties’ access to technology and their individual preferences. While face-to-face care tended to be the preferred option, participants identified benefits of remote care including making care more accessible for some populations and improved efficiency for functional appointments such as prescription reviews. Participants highlighted important challenges related to safety and privacy in online settings, and gave examples of good remote care strategies they had experienced, including services scheduling regular phone calls and developing guidelines about how to access remote care tools. Discussion Participants in our study have highlighted advantages of telemental health care, as well as significant limitations that risk hindering mental health support and exacerbate inequalities in access to services. Some of these limitations are seen as potentially removable, for example through staff training or better digital access for staff or service users. Others indicate a need to maintain traditional face-to-face contact at least for some appointments. There is a clear need for care to be flexible and individualised to service user circumstances and preferences. Further research is needed on ways of minimising digital exclusion and of supporting staff in making effective and collaborative use of relevant technologies.
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Purpose of Review The concept of telehealth has been around since the early twentieth century and has been used in different healthcare specialties. However, with the recent COVID-19 pandemic necessitating physical distancing, there has been an increased emphasis and utilization of this mode of healthcare delivery. With increasing reliance on telehealth services, data from investigator groups have brought to light several merits as well as failings of telehealth. Recent Findings Telehealth services have been associated with improved healthcare outcomes while remaining a cost-effective mode of healthcare delivery. Improving access and timeliness of care has also been observed by multiple telehealth-related studies. Finally, telehealth services are also anticipated to serve as part of emergency preparedness protocol and have shown to reduce provider-patient supply-demand mismatch, prevalent in certain subspecialties. With these benefits come certain challenges that have been highlighted in the literature. Indiscriminate utilization of telehealth services may widen public health disparities among minority groups and may increase overall healthcare expenditure due to overutilization of care, and the digital platform may jeopardize security of patient data. Summary COVID-19 has been a catalyst in increasing utilization of telehealth services. As we move forward from the current pandemic, lessons learned from the studies demonstrating benefits and challenges associated with telehealth should be taken into account when drafting post-pandemic telehealth policies. Special attention should be paid to ensure that telehealth narrows, and not widens, the currently existing disparities in access to healthcare.
Article
Purpose Telehealth plays an important role on the combat of COVID-19. In this context, the SAS Brasil telemedicine program became a viable option in Brazil, where the population faced challenging access to healthcare services during the pandemics. In this study, we describe the sociodemographic profile, reasons for enrollment, outcomes of consultation, and satisfaction of participants who received telemedicine consultations through the SAS program. Methods A retrospective cross-sectional study was conducted with data from the SAS Telemedicine program including consultations performed from July 15, 2020, to April 15, 2021. The study describes the SAS Brasil experience and data collected in the period. Patients’ satisfaction perception was evaluated through the Net Promoter Score (NPS). Results A total of 6490 participants were evaluated, 69.5% of them were female and 40.8% with age from 21 to 40 years. In the period, 22,664 teleconsultations were performed, mainly due to Mental health (40.4%), Respiratory (35.8%), and Nutritional (4.5%) disorders. Out of the 6312 patients with a defined outcome along the period, 96.0% were discharged and 4.0% were referred to presential care. The calculated NPS was + 95.77 and most patients answered that they would use the service again if needed (99.21%) and had their issue resolved (89.76%). Conclusion During the period of the COVID-19 pandemic, telehealth has been consolidated as a tool that offers access to specialized healthcare with wide acceptance by users and can be implemented in populations in vulnerability situations.
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Introduction Worldwide, it is estimated that 264 million people meet the diagnostic criteria for anxiety conditions. Effective treatment regimens consist of cognitive and behavioural therapies. During the COVID-19 pandemic, treatment delivery relied heavily on telemedicine technologies which enabled remote consultation with patients via phone or video platforms. We aim to identify, appraise and synthesise randomised controlled trials comparing telehealth to face-to-face delivery of care to individuals of any age or gender, diagnosed with anxiety disorders, and disorders with anxiety features. Methods To conduct this systematic review and meta-analysis, we searched three electronic databases, clinical trial registries and citing-cited references of included studies. Results A total of five small randomised controlled trials were includable; telehealth was conducted by video in three studies, and by telephone in two. The risk of bias for the 5 studies was low to moderate for most domains. Outcomes related to anxiety, depression symptom severity, obsessive-compulsive disorder, function, working alliance, and satisfaction were comparable between the two modes of delivery at each follow-up time point (immediately post-intervention, 3 months, 6 months and 12 months), with no significant differences reported ( p > 0.05). None of the trials reported on the costs of telehealth compared to face-to-face care. Discussion For effectively treating anxiety and related conditions, interventions delivered by telehealth appear to be as effective as the same therapy delivered in-person. However, further high-quality trials are warranted to determine the effectiveness, acceptability, feasibility, and cost-effectiveness of telehealth interventions for the management of a wider range of anxiety disorders and treatments.
Article
Introduction: This study investigated how mental health providers' use of telemedicine has changed since the coronavirus disease (COVID) 2019 pandemic and their expectations for continuing to use it once the pandemic ends. Methods: A 15-min online survey was completed by 175 practicing and licensed telemental health providers who use telemedicine. In addition to personal and professional demographic items, the survey included items about the frequency of telemedicine use, proportion of caseload served by telemedicine, comfort using telemedicine before and during the COVID-19 pandemic, and expectations to use telemedicine after the pandemic ends. A series of χ2 analyses, an independent samples t-test, and analyses of variance were conducted. Results: The pandemic resulted in a greater proportion of telemental health providers using telemedicine on a daily basis (17% before and 40% during the pandemic; p < 0.01) and serving more than half of their caseload remotely (9.1% before and 57.7% during the pandemic; p < 0.05). Also, there was a statistically significant increase in their comfort using telemedicine before and during the pandemic (p < 0.001). Providers reported expecting to use telemedicine more often after the pandemic ends (M = 3.35; SD = 0.99). Expectations to provide telemental health services after the pandemic were greater for mental health counselors, providers who practiced in rural regions, and providers who served patients through out-of-pocket payments. Discussion: Telemental health providers use telemedicine daily as a result of the COVID-19 pandemic, with expectations of continuing to use telemedicine in practice after the pandemic. This expectation is more prominent in certain segments of providers and warrants further investigation.