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. Author manuscript; available in PMC: 2025 Aug 1.
Published in final edited form as: J Subst Use Addict Treat. 2024 May 15;163:209406. doi: 10.1016/j.josat.2024.209406

A Lifestyle Physical Activity Intervention for Women in Alcohol Treatment: A Pilot Randomized Controlled Trial

Ana M Abrantes 1,2, Julia Browne 3,2, Michael D Stein 1,4, Bradley Anderson 1, Sydney Iacoi 1, Sarah Barter 1, Zainab Shah 1, Jennifer Read 5, Cynthia Battle 1,2,6
PMCID: PMC11240883  NIHMSID: NIHMS1997110  PMID: 38759733

Abstract

Introduction:

Compared to men, women with alcohol use disorder (AUD) are more likely to drink to manage stress and negative affect. Given women’s risk for poor drinking outcomes, it is critical to develop and test interventions that target these affective factors. Physical activity improves negative affect and has emerged as a promising adjunct to AUD treatment and, thus, may be especially valuable for women.

Methods:

Fifty women with AUD (49.9 ± 12.0 years of age) participated in either a 12-week telephone-delivered lifestyle physical activity plus Fitbit (LPA+Fitbit) or a health education contact (HEC) control intervention following a partial hospital addictions treatment program. The study examined changes in drinking behaviors, mental health outcomes, and physical activity engagement post-intervention using both conventional test statistics and standard effect sizes.

Results:

Higher rates of continuous abstinence during the 12-week period were observed in the LPA+Fitbit condition (55.6%) than in the HEC condition (33.6%); odds ratio = 2.97. However, among women who drank any alcohol during the 12-weeks, slightly higher rates of heavy drinking and drinks/day were observed among women in the LPA+Fitbit condition. Significant differences for improved mental health outcomes (including depression, anxiety, negative affect, positive affect, perceived stress, and behavioral activation) and increased self-reported physical activity were consistently observed among participants in the LPA+Fitbit condition, relative to HEC.

Conclusions:

The LPA+Fitbit program had a positive impact on alcohol abstinence, mental health, and physical activity in adult women receiving treatment for AUD. Future research should continue to investigate the optimal implementation strategies, duration, and intensity of LPA interventions in the context of a fully-powered RCT.

Keywords: physical activity, alcohol craving, depression, drinking outcomes, women

1. Introduction

Alcohol use disorder (AUD) is a prevalent, undertreated, and costly condition (Carvalho et al., 2019; Rehm & Shield, 2019). The deleterious effects of AUD span physical health, mental health, employment, legal, and social domains, which contribute to major individual and societal costs (Bouchery et al., 2011; Friedmann, 2013). AUD affects men at a higher rate than women; however, rates of alcohol use and binge drinking have increased for women over the past two decades (Grant et al., 2017; Keyes et al., 2019; McCaul et al., 2019). Consequently, there has been a rise in alcohol-related harms, including emergency department visits, hospitalizations, and deaths for women (White, 2019). Compared to men, women have higher rates of depression (Guinle & Sinha, 2019; Lim et al., 2018), a consistent predictor of resuming alcohol use after a period of abstinence (Sliedrecht et al., 2019), and are more likely to drink to manage stress and negative affect (Peltier et al., 2019; Verplaetse et al., 2018). Taken together, targeted treatment efforts are needed to address the growing rates and negative outcomes of AUD in adult women.

Women are significantly less likely to engage in alcohol treatment than men (Alvanzo et al., 2014; Martin et al., 2022). Internal (e.g., mental health symptoms, low perceived need) and external (e.g., childcare responsibilities, financial or insurance disparities) barriers, along with the societal stigma of AUD, contribute to the low initiation of treatment among women (McCrady et al., 2019). However, understanding the reasons that women do present for AUD treatment can be helpful in informing effective interventions. For example, in a study of 180 women with AUD who participated in individual or couple-based therapy targeting alcohol use, the most common reasons for treatment-seeking included concerns about their AUD becoming more severe, as well as their physical health and mental health worsening (Grosso et al., 2013). Therefore, interventions that can directly address these priorities and preferences may be most effective for women seeking AUD treatment.

Physical activity has emerged as a promising component of treatment for AUD (Cabé et al., 2021; Hallgren et al., 2018) and may be especially valuable for women. Systematic reviews and meta-analyses have consistently shown that physical activity significantly improves fitness and depression (Gür & Can Gür, 2020; Hallgren et al., 2017; Lardier et al., 2021), outcomes which align with women’s motivators for treatment. But most physical activity interventions have involved supervised facility-based exercise sessions (Gür & Can Gür, 2020; Hallgren et al., 2017; Lardier et al., 2021) that can be difficult for women to attend regularly due to competing demands (e.g., childcare responsibilities, employment), which limit their utility and potential for long-term change. Further, current physical activity programs have yielded mixed findings with respect to alcohol consumption and binge drinking (Gür & Can Gür, 2020; Hallgren et al., 2017; Lardier et al., 2021). Existing supervised, structured exercise programs may not be effective for improving alcohol outcomes because 1) sufficient adherence to these programs is lacking (Hallgren et al., 2017)and 2) they do not capitalize on using exercise when it is most needed (i.e., during times of heightened alcohol craving or negative affect). Rather, a lifestyle physical activity (LPA) approach, which involves accumulating shorter bouts of movement throughout the day in a manner that is integrated into one’s routine has been associated with increased adherence and is equally effective at improving fitness than structured, supervised programs (Dunn et al., 1999). Further, given that physical activity has acute effects (observed 10 to 40 minutes after completion) on reducing cravings and negative affect (Hallgren, Herring, et al., 2021; Hallgren, Vancampfort, et al., 2021), the LPA paradigm, unlike structured, scheduled physical activity sessions, can be used to manage these experiences in the moment to maximize impact. Therefore, through its flexible approach, an LPA intervention could improve physical activity and drinking outcomes while reducing barriers to structured exercise program engagement for women.

Our research group developed a Fitbit-supported LPA intervention (LPA+Fitbit) for adult women with elevated depressive symptoms who were receiving treatment in a partial hospitalization program for alcohol use (Abrantes et al., 2017). Given the high comorbidity between AUD and depression as well as the negative impact of depression on alcohol use recovery among women (Kathryn Mchugh & Weiss, 2019), the program was designed to improve alcohol and depression outcomes by having women learn to strategically use physical activity (e.g., walking) to manage cravings and negative affect. To address barriers to engagement in traditional programs and promote long-term adoption of exercise, our intervention emphasized accessibility and flexibility in terms of when, where, and for how long the physical activity bouts were completed. A 12-week pilot open trial of 20 women demonstrated strong feasibility and acceptability as well as increases in physical activity and reductions in alcohol cravings from baseline to end of treatment (Abrantes et al., 2017). To extend this formative work, we conducted a pilot randomized controlled trial (RCT) of the LPA+Fitbit compared to a health education contact (HEC) condition to evaluate group differences in drinking outcomes, mental health symptoms, and physical activity engagement.

2. Method

2.1. Study design

The present study was a two-arm parallel pilot RCT. All study procedures were approved by the local Institutional Review Board [1507-001] and the trial was registered on clinicaltrials.gov [NCT02705898]. In this study, we report on the pre-registered primary drinking outcome. Other outcomes examined, though not pre-registered, reflect relevant treatment-related effects. The first participant enrolled in [February 2017] and the final participant completed the end of treatment assessment in [January 2019]. Reporting was done in accordance with the CONSORT guidelines for pilot randomized trials (Eldridge et al., 2016).

2.2. Participants

Participants were eligible to participate if they were: female, between ages 18-65, met Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria for AUD, were currently enrolled in alcohol treatment, had access to a computer connected to the internet or a smartphone compatible with the Fitbit application, had at least mild depressive symptoms (defined as a total score of 5 or higher and a score of “1” on one of the first 2 items on the Patient Health Questionnaire-9 [PHQ-9]), and were considered to be low active (i.e. less than 150 minutes of moderate-intensity exercise per week for the past six months). Exclusion criteria included: current DSM-5 diagnosis of moderate/severe substance use disorder, or anorexia or bulimia nervosa, history of psychotic disorder or current psychotic symptoms, current suicidality or homicidally, current mania, marked organic impairment according to the medical record or responses to the diagnostic assessments (assessed using the DSM-5), physical or medical problems that would not allow safe participation in a program of moderate-intensity physical activity, and/or current pregnancy or intent to become pregnant during the next 12 weeks.

The study screened a total of 119 individuals. Sixty-seven people were ineligible for the study (see Consort Figure 1 for specific reasons for ineligibility) n=38 of these participants because they declined participation. Fifty-two participants were randomized (n=27 to LPA+Fitbit and n=25 to HEC). However, n=2 participants in the LPA+Fitbit condition never received the intervention, leaving n=25 participants in each condition. Given the preliminary nature of this pilot study, 25 participants in each group would allow for stable group means to aid in determining the strength of effects between groups on outcomes of interest. Seventy-eight percent of the sample completed the end-of-treatment (EOT) follow-up assessment

Figure 1.

Figure 1.

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Consort Diagram

2.3. Intervention Description and Procedures

LPA+Fitbit Intervention.

Participants randomized to LPA+Fitbit received an initial hour-long orientation session during which a study clinician provided information regarding the acute and long-term psychological and physiological benefits of increasing physical activity and instructed about utilizing brief bouts of physical activity as a coping strategy for managing difficult emotions or alcohol cravings. Participants were given information on the public health guidelines for physical activity (PA) and how to determine if their physical activity is moderate intensity. The study clinician oriented the participant to all aspects of the LPA+Fitbit intervention, which included proper use of the Fitbit activity tracker, tips for self-monitoring step counts, and duration and frequency of PA goals. The clinician also engaged the participant in a discussion regarding various strategies for including physical activity into their everyday lives (e.g., using stairs rather than elevator, walking in place while watching TV, etc.). Lastly, participants were given a starting step-count goal of 4,500 steps/day with a plan for increasing 500 steps/day each week of the intervention, and were asked to wear the Fitbit Alta activity tracker daily.

The study clinicians called participants at weeks 1, 2, 4, 6, 8, and 10 of the intervention for a 30-minute telephone session to to review PA progress (including barriers and facilitators toward goal achievement) and engage participants in a brief topic discussion focused on increasing and maintaining physical activity. These topics included Goal Setting & Physical Activity to Cope with Negative Emotions and Cravings (weeks 1 & 2), Getting and Staying Motivated & Social Support (week 4), Identifying and Overcoming Barriers & Getting Back on Track (weeks 6 & 8); and Making Plans for Action & Maintenance of Physical Activity (week 10). At each telephone session, participants were asked to reflect on whether they engaged in any bouts of PA to manage negative affect or cravings to drink. Lastly, participants received brief text messages twice a week including their current step count goal, feedback, and encouragement for achieving increases in physical activity.

Health Education Contact Control.

The participants randomized to the HEC condition received the same contact time with research staff as those randomized to the LPA+Fitbit condition. Specifically, they were given a 30-minute orientation session to the program which included: a general discussion of the health-related consequences of alcohol use (with a particular focus on women’s health) and how alcohol has affected their personal health. Subsequent 30-minute telephone sessions followed the same schedule as the LPA+Fitbit intervention (i.e., at weeks 1, 2, 4, 6, 8, and 10). No information about the benefits of physical activity or making changes to current levels of physical activity were provided. Rather, a variety of health and lifestyle topics were addressed: Nutrition—What to Eat and What Not to Eat (week 1), Sleep Problems and Sleep Hygiene (week 2), Alcohol Use among Women (week 4), Relaxation Training (week 6): Time Management and Goal Setting (week 8), and Being a Smart Patient when Coordinating your Health (week 10). Participants also received text messages twice a week that provided supporting information on the effect of alcohol for health-related problems (e.g., nutrition and sleep).

2.4. Measures

2.4.1. Demographics

The study collected self-reported age, race, ethnicity, gender identity, and socioeconomic status.

2.4.2. Alcohol Outcomes

TLFB:

The Timeline Follow Back (TLFB) (Sobell & Sobell, 1992) is a calendar assessment used here to ask participants about their estimated daily alcohol consumption. At baseline, the study asked participants to recall the number of standard drinks consumed daily over past 90 days, and at the EOT follow-up assessment they reported drinking since the baseline assessment. Several memory aids assisted participants in recounting their alcohol use (e.g., key dates in the participant’s life, holidays). Alcohol outcomes included percent days abstinent (PDA; i.e., percentage of days on which the participant reported no use of alcohol), percent days of no heavy drinking (i.e., percentage of days on which they did not consume 4 or more drinks), and mean drinks/day observed in the TLFB.

Penn Alcohol Craving Scale:

The 5-item PACS was utilized to understand alcohol craving frequency, intensity, and duration in the past week (Flannery et al., 1999). Item responses ranged from 0 “Never” to 6 “Nearly all the time”. The composite score consisted of summed items. Internal consistency of the PACS items was excellent (Cronbach’s alpha = .93 at baseline and .96 at EOT).

2.4.3. Mental Health Outcomes

PHQ-9:

The Patient Health Questionnaire-9 (PHQ-9) assessed the nine DSM-5 criteria for depression from 0 “not at all” to 3 “nearly every day” (Kroenke et al., 2001). Participants reported if they had been bothered by any of the following symptoms in the past 2 weeks. PHQ-9 scores range from 0-27 with scores between 5-9 representing mild depression, 10-14 moderate depression, and scores greater than 15 reflecting severe depression. Internal consistency of the PHQ-9 items was questionable at baseline (Cronbach’s alpha = .60) and excellent at EOT (Cronbach’s alpha =.96).

PANAS:

The study assessed positive and negative emotionality using the Positive and Negative Affect Schedule (PANAS) (Watson et al., 1988). The PANAS consists of two 10-item question sets assessing a variety of different mood states. Participants ranked if they had experienced these states from “very slightly or not at all”, “a little”, “moderately”, “quite a bit”, to “extremely” in the past few days. Internal consistency of the PANAS positive items was excellent (Cronbach’s alpha = .92 at baseline and .93 at EOT). Internal consistency of the PANAS negative items was excellent (Cronbach’s alpha = .90 at baseline and .95 at EOT).

GAD:

The study measured anxiety symptoms using the Generalized Anxiety Disorder - 7 Item Scale (GAD-7) (Spitzer et al., 2006). Participants rated whether they experienced anxiety symptoms over the past 2 weeks from “not at all”, “several days”, “more than half the days”, to “nearly every day”. Total scores can range from 0-21; where 0-4: minimal anxiety, 5-9: mild anxiety, 10-14: moderate anxiety, and 15-21: severe anxiety. Internal consistency of the GAD-7 items was good at baseline (Cronbach’s alpha = .89) and excellent at EOT (Cronbach’s alpha =.92).

BADS:

The Behavioral Activation for Depression Scale (BADS) is a 25-item scale assessing levels of activation, avoidance/rumination, work/school impairment, and social impairment (Kanter et al., 2007). This study used the 9-item short form (BADS-SF) version of the measure where participants reported how true the statements were during the past week, including today on a scale ranging from 0 “not at all” to 6 “completely”. Internal consistency of the BADS items was good at baseline (Cronbach’s alpha = .82) and excellent at EOT (Cronbach’s alpha =.93).

Perceived Stress Scale:

The Perceived Stress Scale (PSS) measures the degree to which situations in one’s life are appraised as stressful (Roberti et al., 2006). This study utilized the 10-item version of this scale (PSS-10) with responses ranging from 0 “never” to 4 “very often” to assess perceived psychological stress in the past month. Scores range from 0 to 40 with higher composite scores indicative of greater perceived stress. Internal consistency of the PSS items was good (Cronbach’s alpha = .83 at baseline and .86 at EOT).

2.4.4. Physical Activity Outcomes

The TLFB-Exercise (Panza et al., 2012) assessed the type of exercise done, duration of exercise, and the intensity of the exercise (using Borg’s Rating of Perceived Exertion (RPE) Scale (Borg, 1982)) for each day of the last 90 days at baseline and end-of-treatment. The study calculated mean minutes of total self-reported physical activity (PA) per week. Mean minutes of moderate to vigorous physical activity (MVPA) were calculated by adding total minutes per week for those activities rated 12 and above on the RPE scale (i.e., of at least moderate intensity).

2.5. Procedure

The study reviewed patients’ clinical records from their addictions-focused partial hospital stay, including the results of a hospital-based limited physical examination and medical history to preliminarily assess the psychiatric and medical inclusion and exclusion criteria detailed above. Eligible participants were approached and provided with study information. Interested participants underwent a brief screening process to confirm final inclusion criteria, and the study physician evaluated them for medical clearance. If medically cleared, following informed consent, participants were randomized 1:1 to either the LPA+Fitbit or HEC 12-week intervention with age and body mass index as blocking variables. The study statistician generated randomization sequences. A research member who had no interaction with study participants uploaded these into REDCap. When a participant was ready to be randomized, study staff pressed a button in REDCap to learn to which arm the participant was allocated. Participants learned of their assigned condition at the orientation session. Participants completed end of treatment assessments at 3-months. Participants received $50 in gift cards to a local mall or supermarket for the baseline assessment and the EOT assessments. In addition, participants received $10 for attending each telephone counseling session. The study utilized all available methods of communication to retain participants, typically in the following order: texted 3 times, voicemail, email, and snail mail that included a self-addressed stamped envelope to facilitate the return of self-report questionnaires.

2.6. Analytical Methods

We report means, standard deviation, and counts describing baseline characteristics for the entire sample and each treatment arm. The sample size is relatively small and some outcomes exhibited marked skew, heteroskedasticity, and a small number of highly influential observations; this was especially observed with alcohol and physical activity outcomes. Therefore, we present a range of statistics including means, standard deviations and p-values for conventional test statistics (e.g., t-tests, ANCOVA, etc.) as well as effect size measures to facilitate comparisons. First we report the exact p-value for the Wilcoxon rank-sum test as a robust test of statistical significance. As a measure of effect size for rank ordered distributions we report the common language effect size (McGraw & Wong, 1992). This statistic gives the probability that a score randomly selected from 1 arm will be greater (or lower) than a randomly selected score from the comparison arm. Values of .50 indicate no between group difference in relative rank-order. Here we compare LPA to HEC; values < .50 indicate that participants randomized to LPA rank lower than participants randomized to HEC. Values > .50 indicate that participants randomized to LPA probably rank higher than those randomized to HEC. This statistic is not influenced by a small number of extreme values. This or similar statistics appear in a range of contexts with different nomenclatures; here we refer to this as the porder statistic.

We also report group means, standard deviations, and Cohen’s d estimating the standardized difference in means. These estimates may prove useful for investigators planning future investigations. In tables we also report the p-value for a t-test comparing means. As an additional measure of effect size, we report partial eta2 for an ANCOVA model estimating the effect of treatment adjusting for the value of the outcome at baseline. Finally, we report percentage differences and Fisher’s exact p-value to compare rates of complete alcohol abstinence during follow-up. We also estimated a logistic regression model to estimate the average marginal probability of abstinence adjusting for percent days abstinent assessed at baseline.

3. Results

3.1. Baseline Characteristics

The sample was comprised of 50 women enrolled in an alcohol and drug partial hospitalization program. Participants averaged 49.9 (± 12.0) years of age, 92.0% were White, and 6.0% were Latina (Table 1). They average 15.3 (± 3.09) years of education and 56% were employed part or full time. Means and standard deviations of the outcome variables assessed at baseline are also reported in Table 1. There were no significant differences at baseline in outcome variables for those participants who completed vs those who did not complete follow-up.

Table 1.

Baseline Characteristics by Treatment Arm

TREATMENT ARMa
Sample
(n = 50)		 HEC
(n = 25)		 LPA+Fitbit
(n = 25)
Years Age 40.9 (± 12.0) 44.9 (± 11.7) 37.0 (± 11.2)
Race (White) 46 (92.0%) 23 (92.0%) 23 (92.0%)
Ethnicity (Latina/o) 3 (6.0%) 2 (8.0%) 1 (4.0%)
Years Education 15.3 (± 3.09) 15.8 (± 2.96) 14.8 (± 3.18)
Employed Part/Full-Time 28 (56.0%) 12 (48.0%) 16 (64.0%)
Depressive Symptoms (PHQ-9) 13.6 (± 4.27) 13.9 (± 4.91) 13.2 (± 3.60)
PANAS Positive Affect 29.4 (± 9.22) 27.6 (± 9.83) 31.2 (± 8.42)
PANAS Negative Affect 26.7 (± 8.68) 26.8 (± 9.46) 26.5 (± 8.05)
Anxiety Symptoms (GAD-7) 18.3 (± 5.45) 19.1 (± 5.56) 17.5 (± 5.33)
Behavioral Activation (BADS) 28.3 (± 9.79) 26.5 (± 9.87) 30.1 (± 9.57)
Perceived Stress (PSS) 27.2 (± 5.50) 27.7 (± 6.38) 26.7 (± 4.59)
Penn Alcohol Craving 11.5 (± 7.86) 13.0 (± 7.37) 10.0 (± 8.19)
Total PA Min/Week 46.5 (± 71.1) 60.0 (± 89.7) 32.4 (± 71.1)
Total MVPA Min/Week 28.8 (± 38.9) 33.6 (± 47.3) 23.9 (± 27.6)
% Days Alcohol Abstinent 31.7 (± 27.7) 33.0 (± 32.1) 30.3 (± 22.9)
% Days Not Heavy Alcohol 35.1 (± 28.8) 39.0 (± 33.5) 31.1 (± 22.9)
Mean Drinks / Day 5.86 (± 4.88) 5.53 (± 5.93) 6.21 (± 3.58)
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Note. HEC = Health Education Contact Control Condition, LPA+Fitbit = Lifestyle Physical Activity + Fitbit Condition, PHQ-9 = Patient Health Questionnaire - 9, PANAS = Positive and Negative Affect Schedule, GAD-7 = Generalized Anxiety Disorder – 7 Item Scale, BADS = Behavioral Activation for Depression Scale, PSS = Perceived Stress Scale, PA = physical activity, MVPA = moderate-to-vigorous physical activity.

a

There were no significant differences between groups.

3.2. Intervention Adherence

Adherence to phone sessions was five out of six sessions in each condition. In the LPA+Fitbit intervention, the Fitbit was worn on 70.6% of days during the intervention period and 80% of the sample wore the Fitbit for at least 6 out of the 12 weeks of the intervention. Among participants who wore the Fitbit at least 6 weeks, there were significant increases in the number of steps/day (baseline=4,338 and 12-week intervention=8,600; p<.0001). The participants were highly engaged with the Fitbit tracker checking it an average of 6.5 times per day to monitor current steps and activity minutes. More than half the LPA+Fitbit sample also utilized the Fitbit mobile app each day (56.3%), 25% used the app several times per week, and 12.5% once/week. Overall, among the LPA+Fitbit participants, 93.6% reported being satisfied with the Fitbit tracker.

3.3. Alcohol Outcomes

Of participants who completed follow-up assessments, 56% (n=10) participants of those randomized to LPA+Fitbit compared to 33% (n=7) of participants randomized to HEC were abstinent from alcohol during the entire 12 weeks of the intervention (Fisher’s exact p = .206). Using logistic regression to adjust for the percent days using alcohol at baseline, the adjusted odds-ratio of continuous abstinence was 2.97 (p = .129), with the estimated average probabilities of abstinence during follow-up being .57 in LPA+Fitbit versus .34 in HEC. A detailed examination of the distributions (data not presented) suggested that persons randomized to LPA+Fitbit who were not completely abstinent drank more frequently and consumed heavier amounts of alcohol than HEC participants. See Table 2 for rank order distributions for the LPA+Fitbit and HEC conditions on drinking outcomes. Exact p-values for the rank-ordered distributions of % days abstinent, % days with no heavy alcohol use, and mean drinks/day were all > .800. Lastly, participants randomized to LPA+Fitbit rank lower (porder = .303, p = .046) than those randomized to HEC on alcohol craving assessed with the Penn Alcohol Craving Scale.

Table 2.

Hypothesized Outcomes by Treatment Arme

Outcome Exact
p = a 		porderb LPA+Fitbit HEC Cohens d
p = c 		Partia
eta2d
PHQ-9 .059 .314 2.56
(2.66)		 6.26
(1.44)		 −.746
(.035)		 .115
PANAS Positive .043 .701 36.4
(9.19)		 30.9
(8.02)		 .641
(.068)		 .035
PANAS Negative .027 .281 16.2
(5.39)		 23.8
(10.7)		 −.871
(.015)		 .164
GAD-7 .013 .258 11.4
(3.16)		 15.7
(6.14)		 −.859
(.016)		 .046
BADS .033 .711 36.5
(7.28)		 27.0
(12.2)		 .926
(.010)		 .105
PSS .029 .285 14.3
(5.10)		 19.8
(7.92)		 −.807
(.023)		 .106
Penn Alcohol Craving .046 .303 5.68
(6.99)		 10.4
(8.76)		 −.591
(.091)		 .029
Mean Total PA min/wk .028 .705 209.1
(±331.2)		 82.0
(±127.4)		 .522
(.113)		 .100
Mean MVPA min/wk .255 .607 180.9
(±328.0)		 75.7
(±128.6)		 .435
(.184)		 .049
% Days Abstinent .891 .513 89.8
(±17.1)		 94.1
(±10.4)		 −.310
(.340)		 .005
% Days No Heavy Alc. .838 .480 89.9
(±15.1)		 94.2
(±5.7)		 −.395
(.227)		 .024
Mean Drinks / Day .874 .484 0.70
(±1.50)		 0.21
(±0.33)		 0.452
(.168)		 .034
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Note. LPA+Fitbit = Lifestyle Physical Activity+ Fitbit Condition, HEC = Health Education Contact Control Condition, PHQ-9 = Patient Health Questionnaire - 9, PANAS = Positive and Negative Affect Schedule, GAD-7 = Generalized Anxiety Disorder – 7 Item Scale, BADS = Behavioral Activation for Depression Scale, PSS = Perceived Stress Scale, PA = physical activity, MVPA = moderate-to-vigorous physical activity

a

Exact probability for the Wilcoxon rank-sum test comparing the equality of rank-ordered distributions.

b

The porder statistic gives the probability that a randomly selected participant in the LPA arm will rank higher than a randomly selected participant from the HEC arm. Values of .50 indicate no difference in relative rank order between the arms. This statistic is sometimes called a common language effect size (McGraw & Wong, 1992).

c

T-test for differences in means.

d

Partial eta2 for treatment controlling for the baseline value of the outcome (ANCOVA model). Values of .01, .06, and .14 are frequently referenced values representing small, medium, and large effects, respectively.

e

Values in the table above were derived participants with data from both baseline and follow-up timepoints (n=18 for LPA+Fitbit; n=21 for HEC).

3.4. Mental Health Outcomes

Participants randomized to LPA+Fitbit rank lower (porder = .314, p = .059) than those randomized the HEC on depressive symptoms, rank higher (porder = .701, p = .043) on the positive affect, rank lower on the negative affect (porder = .281, p = .027), lower on anxiety symptoms (porder = .258, p = .013), higher on behavioral activation (porder = .711, p = .013), and lower on perceived stress (porder = .285, p = .029) (Table 2). An examination of values for Cohen’s d comparing unadjusted means and partial eta2 adjusting for baseline value of the outcome are generally consistent with the porder statistic.

3.5. Physical Activity Outcomes

The LPA+Fitbit arm ranked higher on self-reported mean total PA minutes/week (porder = .705, p=.028) and mean MVPA minutes week (porder = .607, p = .255). Cohen’s d and partial eta2 support the inference that participants randomized to LPA+Fitbit had higher levels of total PA than those randomized to HEC.

4. Discussion

This pilot RCT evaluated the impact of a 12-week LPA+Fitbit program on alcohol, mental health, and physical activity outcomes compared to HEC in a sample of 50 women receiving alcohol treatment. At end of treatment, a greater proportion of women randomized to LPA+Fitbit had sustained abstinence compared to the HEC condition; differences in mean drinks/day, days abstinent, and days without heavy alcohol were minimally different between the two groups. Women in LPA+Fitbit reported significantly lower anxiety symptoms, stress, cravings, and negative affect, higher positive affect, and more exercise minutes per week than those randomized to HEC. These findings suggest that the LPA+Fitbit program had a substantive positive impact on alcohol abstinence, mental health, and physical activity in adult women that cannot be attributed to general health information and staff contact.

The greater abstinence rate for LPA+Fitbit (56%) compared to HEC (33%) suggests that this intervention, which aimed to promote use of physical activity to manage cravings and negative affect in the moment, may have had a protective effect against resuming alcohol use for some participants. Indeed, the ability of PA to acutely decrease negative affect is well documented in both clinical and nonclinical populations, particularly with moderate-intensity exercise(Elkington et al., 2017; Ensari et al., 2015; Reed & Buck, 2009). Similarly, acute bouts of PA significantly decrease alcohol cravings in individuals with AUD (Hallgren et al., 2017; Brown et al., 2016) even with bouts as brief as 10 minutes (Ussher et al., 2004). These findings contribute to the growing evidence supporting the integration of physical activity interventions in substance use disorder treatment programs and the promise of the LPA approach in particular given its flexibility and capacity to mitigate at-risk periods for returning to drinking following abstinence (i.e., increased cravings and/or negative affect) (Abrantes & Blevins, 2019).

Alcohol use among those who did not achieve abstinence was greater for LPA+Fitbit participants relative to HEC participants on non-abstinent days. Yet, relative to baseline, alcohol use was quite low in both conditions at EOT (i.e., drinks/day decreased from ~6 at baseline to <1 at EOT). It is possible that improved mental health of those in LPA+Fitbit may have resulted in different motives for drinking than those that were present at the start of the program. For example, it could be that women in LPA+Fitbit initially engaged in alcohol use to minimize negative affect but as they developed skills to manage affect with PA, their mental health improved and alcohol use may have occurred for different reasons (e.g., socializing with others). Given the differences between abstinence and other alcohol use outcomes, future research on the impact of LPA program across a variety of recovery measures is needed.

The increases in exercise observed in the LPA+Fitbit condition and associated mental health improvements align with the literature documenting that engaging in regular physical activity can reduce symptoms of depression, anxiety, and stress, while enhancing well-being and quality of life, including in people with substance use disorders (Giménez-Meseguer et al., 2020; Schuch et al., 2016). In fact, the effect size measures of mental health improvements indicate that the LPA+Fitbit arm exhibited consistently better outcomes on measures of psychological well-being; and the magnitude of these differences would generally be described as “medium” to “large” with respect to standardized effect size measures. In addition, women randomized to the LPA+Fitbit group, on average, achieved exercise levels within the recommended national physical activity guidelines at 12 weeks, which is especially promising given relationships between meeting these guidelines and reduced risk of mortality and cardiovascular disease (Bull et al., 2020; Kraus et al., 2019). Sedentary and low active individuals (such as those in this study) who increase their exercise are found to experience the largest physical health benefits (Powell et al., 2019). Implementing physical activity as an adjunct to traditional alcohol treatment can facilitate a more comprehensive and holistic approach to recovery (Hagman et al., 2022).

The study was primarily limited by its modest sample size and reliance on self-report measures, although PA was objectively measured. Moderate participant attrition (22% over 3 months) further limited analyses. In addition, as this study focused entirely on women receiving alcohol use treatment in a partial hospital, the results cannot be generalized to persons of other genders or who were not enrolled in comparable alcohol treatment.

Our study both reinforces the well-established benefits of PA for mental health and highlights the benefits of incorporating LPA interventions into alcohol treatment programs for women with AUD. Future research should continue to investigate the optimal implementation strategies, duration, and intensity of LPA interventions in the context of a fully-powered RCT. Further, given the initial promise of this work for women in alcohol use treatment, future studies should consider investigating the applicability of LPA programs on promoting recovery in other populations with substance use disorders.

Highlights.

  • Lifestyle physical activity (LPA) may be an effective complementary treatment for AUD for treatment-seeking women.

  • LPA+Fitbit was associated with higher rates of abstinence following a partial hospital alcohol treatment program.

  • LPA+Fitbit resulted in significant improvements in mental health outcomes, as well as self-reported physical activity, among women with AUD.

Acknowledgments:

This research was supported by the National Institutes on Alcohol Abuse and Alcoholism (NIAAA; R34 AA024038; PI: Ana M. Abrantes, Ph.D.). Dr. Browne is supported by a VA Rehabilitation Research and Development Career Development Award (IK1RX003904). The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the United States Government or Department of Veterans Affairs.

Footnotes

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Author Statement

Ana M. Abrantes: Conceptualization, Methodology, Writing – Review & Editing, Supervision, Project Administration, Funding Acquisition; Julia Browne: Conceptualization, Writing – Original Draft, Writing – Review & Editing; Michael D. Stein: Conceptualization, Methodology, Writing – Review & Editing; Bradley Anderson: Formal Analysis; Sydney lacoi: Investigation, Writing – Review & Editing; Sarah Barter: Investigation, Writing – Review & Editing; Zainab Shah: Investigation, Writing – Review & Editing; Jennifer Read: Conceptualization, Methodology, Writing – Review & Editing; Cynthia Battle: Conceptualization, Methodology, Writing – Review & Editing, Supervision, Project Administration

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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