Digital Cell Phone Case Study

To understand how the phone was used for self-reflection and coping, we analyzed interview narratives and experience sampling data for indications of change in mood patterns. Accounts of mood changes from interviews were used to examine patterns in the experience sampling data during the same time ranges.

We examined changes that were described in interviews as occurring over the course of the one month study; characteristic patterns of change over the diurnal cycle; and changes during specific stressful incidents. These three categories emerged from qualitative analysis of interview data. Agreements and disagreements between experience sampling data and weekly interviews are highlighted in the case studies. A number of participants reported changes over the course of the study in their mood patterns and coping skills, and ascribed these changes to use of the application. These examples illustrate the potential of mobile tools not just for gathering data about mood patterns, but also for querying in a way that invites emotional awareness, self-regulation, and behavioral change. Five case studies are shared.

We examined changes over the one-month study in several ways. We used the Behrens-Fisher t-test to compare data from the first half of the study with data from the second half. We also examined standard and robust linear regression of the mood scales against time in the one-month study.The regression results largely corroborated the t-test results, but since the linear trends generally are not a good fit to these data, we report only the t-test results here.

To study diurnal patterns, we segmented each participant's data into time blocks. This segmentation was guided by the raw data and by mood patterns reported during the interviews. We used a two-way analysis of variance (ANOVA) for joint analysis of diurnal changes and changes over the course of the study. The diurnal time blocks just mentioned formed the first grouping variable for this analysis. Two groupings were explored for the second variable: (1) week number in the study, and (2) first versus second half of the study. This joint analysis of diurnal changes and changes over the course of the study allowed us to study interaction effects between the two. Bonferroni adjustments were made for multiple comparisons in the ANOVA. We adopted a significance level of P = .05 for reporting results and report the actual P values for significant test results, except when P < .001.

To examine stressful episodes, we segmented the participant’s experience sampling data according to the time intervals of incidents reported in interviews. We analyzed whether emotion ratings during time intervals corresponding to reported stressful episodes differed from the emotion ratings outside of those intervals, referred to as the background period. We compared the mood scale levels in the episodes with the background by one-way ANOVA. Names and identifying information of participants have been changed.

Case Studies

Tobias

Tobias, a man in his early thirties, enrolled in this study because he was eager to extend his self-improvement from weight loss to stress reduction. During the previous year he had lost close to 60 pounds by following a strict diet and exercise plan. His stress stemmed from conflict with his wife over childcare and household responsibilities.

Tobias described a clear diurnal pattern in his mood. Each day at 5PM, he raced back from work to immediately take over responsibility for the kids, pets, dinner, and general chaos of home life, as his wife, also exhausted, left the house to find time alone. He found the transition jarring and often remained irritated the entire evening.

Echoing this verbal account, Tobias’ phone entries show a decrease in energy upon coming home. In fact, his energy, as reflected in the Y values in the Mood Map, decreased continually throughout the day. As shown in Figure 6, his mean energy values were 6.01 before 11:30AM, dropped to 4.58 between 11:30AM and 5:00PM, and further dropped to 1.67 from 5:00PM until his last recording at 10:49PM (two-way ANOVA, P < .001).

Figure 6

Progressive drop in Tobias’ energy through the day. The circles show the mean values in the diurnal segments indicated on the abscissa. Error bars show the 95% confidence limits on the means. Note that the total Mood Y range available to the user...

Tobias’ mood and communication patterns shifted as the study progressed. He explored some of the mobile therapy concepts, using them to anticipate his negative reactions to coming home and curtail them so that they would not dominate the entire evening. Tobias applied a rhetorical question from the Mind Scan, “Might I be stabilizing?” by telling himself “Just be prepared for the next 15 to 30 minutes…It probably isn't going to be an ideal situation for you, but just get through the 15 to 30 minutes and then, you know, you’ll be fine.” He practiced this and other short exercises before he walked in the door. Perhaps more importantly, he spoke with his wife about alternative solutions and schedules for sharing responsibilities. He was pleased by the self-awareness and coping abilities he developed during the study.

The improvements in mood and family relations that Tobias described are reflected in his mood entries. There was a lifting of energy (the Y value of the mood scale), and a decrease in negative emotions on the single dimension scales. Figure 7 shows his anger, anxiety, and sadness ratings throughout the study. His mean anger, anxiety, and sadness ratings all were lower in the second half of the study than in the first half. His mean anger ratings decreased from 0.49 during the first half of the study to zero in the second half (P = .01, Behrens-Fisher t-test). His anxiety ratings decreased from 0.37 to 0.04 (P = .006), and his sadness ratings from 0.61 to zero (P < .001). His energy ratings (y-axis of Mood Map) increased from 3.28 in the first half of the study to 6.58 in the second half (P < .001).

Figure 7

Anger, anxiety, and sadness mood ratings change across the study for Tobias, who described improved mood and better communication with his wife. Vertical lines mark the half-way point in the study.

Tobias’ pattern of decreased energy in the evenings abated to some degree as the study progressed. His energy continued to decrease throughout the day, but the decrease was less extreme in the second half of the study than in the first half. Two-way ANOVA comparing his energy before and after 4PM and between the first and second half of the study showed a significant interaction (P =.005). Specifically, in the first half of the study, Tobias’ energy ratings before 4PM averaged 5.09 and after 4PM averaged 2.22. In the second half of the study, his energy ratings before and after 4PM were more nearly equal (3.67 and 3.64, respectively). That is, Tobias showed less fatigue or burnout in the evening hours as the study progressed.

In addition to improved mood, Tobias described greater self-awareness throughout the study. He found the Mood Map useful as a way “to check in with myself.” At the beginning of the study, he was disappointed that the system wasn’t telling him his mood: “What I was hoping this device was going to be was something that told me how I was feeling, because that’s one of the things I struggle with,” he said. Later, though, he expressed a comfortable curiosity in his mood patterns, and “more confidence in my feelings.”

Theresa

Another participant whose mood improved over the course of the study was Theresa, a woman in her late thirties who had been chronically frustrated at home and in her work as a manager. Her frustration at home related to her niece, who recently had moved in with the participant but never helped with household chores and continually left the lights on when she left the house. At work, Theresa struggled with a technician on her team who failed to take ownership for finishing tasks. In an interview, she characterized her exasperation with both relationships: “It’s like [the movie] Groundhog Day...it’s the same thing...over and over again!”

Eventually, Theresa tried out a collaborative approach that worked well in both situations. At home, she devised a system that finally motivated her niece to turn off the lights: “I was like, ‘Okay, maybe we’ll have an energy conservation initiative with her, that if she turns off all the lights before she goes to school and turns down her heat, that’s a point. And we’ll keep points every day.’” And at work, she suggested a “priority list” for managing tasks, and this approach went over well with the technician: “He continued to work on the list, so it was working as expected...and it turns out he loves it!”

She described her satisfaction with her negotiation: “It was this neat experience for me…The conflict was done…and I didn’t grow up that way…It goes back to looking—okay, ‘What’s the priority?’ ‘What is the true goal?’ because we both have the same goal in mind, but we might get there different ways. So, I think that the questions that are on there, you know, helped to get to that, even if I didn’t look at them right before the meeting”. In these quotes, Theresa not only describes the skills that she developed during the study, but also her internalization of the concepts. She interweaves language and concepts from the mobile therapies, such as “What is the true goal?” with her self-reflection in the interview. She also makes it clear that she applies these concepts even if she isn’t looking at the phone.

The satisfaction that Theresa described is reflected in the positive change in her mood ratings recorded on the phone. The energy dimension of her Mood Map ratings rose from a mean 1.14 in the first half of the study to 1.8 in the second half (Behrens-Fisher t-test P = .01). As shown in Figure 8, her sadness decreased dramatically, from a mean of 3.15 to 0.875 (P < .001), the largest cross-study shift in a mood scale we observed among all participants. Surprisingly, this decided drop in sadness was not accompanied by a significant drop in anger. This discrepancy suggests that the label of anger on the specific scale did not resonate with Theresa’s frustration, and points to the need to tailor mood queries to an individual’s emotional signature, that is, the range and pattern of each person’s emotions.

Figure 8

Sadness decreases dramatically in the second half of the study for Theresa, coinciding with her successful negotiation of conflict at home and work.

Forest

Personal stressors marked the interview and experience sampling data of Forest, a man in his mid thirties who had recently moved to the United States. He described frequent anger and frustration related to an overarching struggle to establish professional and financial stability.

Here we explore two stressful episodes described in Forest’s interviews and experience sampling data. The first stressor, which occurred early in the study, followed his wife’s selection of a physician who was not covered by his insurance. He spent days on the phone arguing with insurance companies and with the physician, trying to find a way to please his wife without incurring enormous expense. Although he and his wife eventually agreed on a doctor within the insurance network, he regretted the fruitless frustration he experienced along the way.

Forest’s mean anger ratings (but not other emotion ratings) during this episode were significantly higher than the background level (2.41 vs. 1.57, P = .03). This difference is reflected in the time series of his anger ratings, shown in Figure 9.

Figure 9

Snapshots of two comparably stressful episodes (1/21 to 1/29 and 2/4 to 2/11) identified by Forest during interviews. The solid and dotted vertical lines mark the beginning and end of the episodes respectively. In the second episode, he applied stress...

Several weeks later, Forest relayed a similarly stressful series of interactions as he tried to obtain a US passport for his daughter. He was turned away because of missing paper work on his first visit to the consulate, and on each subsequent visit he had to interact with a rude officer.

Although irritated, Forest mentally prepared for each follow-up interaction by repeating to himself some of the mobile therapy concepts about goal orientation and constructive confrontation. In an interview, he relayed his self-talk from the day of the incident, in which he combined text from the mobile therapies shown in Figure 10 (“Step back…expand perspective” ) with his own self-reflection ( “What is my goal here? So what if I don’t like this guy? Step back, expand perspective.”)

Figure 10

Forest and other participants quickly internalized the mobile therapies.

Even though securing the passport required a stressful series of interactions that took up far more time than he had anticipated, Forest felt good about the outcome and the way he handled the interactions. Unlike the first episode, his anger ratings associated with obtaining the passport were not significantly higher than the background level. This episode is reflected in the second segment of the anger time series (dated from 2/4 to 2/11) in Figure 9. The majority of Forest’s mobile therapy usages occurred during these two stressful episodes, suggesting that he reached out to the phone for help in moments of need.

Octavia

Octavia, a woman in her late thirties with an advanced technical job, described ongoing struggles with anxiety and procrastination. After several reorganizations in her division, she struggled to prioritize tasks and spent much of her day simply reacting to email or addressing small requests. She described the most difficulty focusing and the most anxiety in the morning. In keeping with this interview account, her mood phone entries showed more negativity in the morning hours than in the afternoon. As shown in Figure 11, her anxiety averaged 3.04 before 1:00PM, and dropped to 2.16 after (two-way ANOVA, P < .001). Her unhappiness dropped from a mean of 4.27 before 1:00PM to a mean of 3.94 after (P = .01).

Figure 11

Octavia’s interview accounts of morning procrastination are paralleled in her experience sampling data, which show elevated anxiety before 1:00 PM. The circles show the mean values in the diurnal segments indicated on the abscissa. Error bars...

In her closing interview, Octavia described notably better focus, productivity and clarity in presenting her work to others. She attributed these improvements to the phone application, particularly the prompts about prioritizing (an example prompt is shown in Figure 12):

Figure 12

Example Mind Scan prompt that helped Octavia prioritize and stop procrastinating

Not a whole lot else has changed other than usage of this (application) and just a refocusing…what it helped me say is, “What is the absolute most important thing I should accomplish?”…knowing there are other things out there that need to happen, that just are not quite as important…I was thinking about the visual…where it says, “Focus on what matters to me.”

Octavia’s verbal account of increased focus over as the month that she used the phone application was echoed in the experience sampling data: Her mean anxiety dropped from 2.88 to 2.16 from the first to the second half of the study (Behrens-Fisher t-test P = . 005). The drop in anxiety also is evident from the two-way ANOVA, which showed significantly higher anxiety in week one (3.06) than in weeks three or four (1.92 and 1.37, respectively, P = . 001). The time series of her anxiety ratings, with the weeks demarcated, are shown in Figure 13. Octavia’s sadness and unhappiness also declined through the course of the study; sadness dropped from a mean of 0.49 to 0.08 (Behrens-Fisher t-test P = . 001), and unhappiness dropped from 4.39 to 3.41 (P < .001). Her energy ratings on the Mood Map (y-axis) do not reflect the increased energy that she reported, however. As mentioned above, Octavia described improved focus and prioritization over the course of the study. While her anxiety and some other negative moods were lower in the second half of the study, no change in the diurnal pattern of anxiety across the study was revealed by the two-way ANOVA used to simultaneously study diurnal and across-study changes. That is, her anxiety was lower overall by the end of the study, but remained higher in the morning.

Figure 13

This time series visualization shows Octavia’s lowered anxiety ratings in weeks three and four of the study, a pattern that matches her interview account of decreased anxiety. The short vertical lines along the x-axis mark the beginning of each...

Eliza

A more complex trend in moods was exhibited by Eliza, a woman in her mid-forties who juggled a full-time job and close relationships with her two sons, husband and extended family. She managed a tight schedule, running each morning, arriving at work by 7:00 AM, picking up her children after school, and reviewing their homework–all before preparing dinner. Historically, Eliza dealt with anxiety and other negative emotions through constant busyness, but said that this coping style eventually left her exhausted. She worked hard to be positive and supportive at work and at home, and experienced deep regret when she let others down. She also described frequent waves of anxiety that “wipe out the joy” of positive moments with her family.

As the study progressed, Eliza expressed great interest in mapping her moods, describing better self-understanding, clearer communication and improved resolution of conflicts with her husband and eldest son. In light of these reported gains, initially it was surprising to see an increased negative affect in her experience sampling data across the study. Specifically, the mean of her energy ratings on the Mood Map decreased from 2.82 to 1.88 from the first to the second half of the study (Behrens-Fisher t-test P = .007) and her mean sadness ratings increased from 0.69 to 1.38 (P = .039). This change, although surprising in light of her reported increased insight and improved communication, made sense on closer analysis. The trend towards negativity in her experience sampling mirrored statements she made in interviews about learning to acknowledge different emotional states. She described calibrating herself on the Mood Map:

I allowed myself more freedom. It’s exploratory. I allowed myself more freedom and range of motion in there just to get myself rolling…I thought, “I’m going to explore what it feels like to put it right over here because that’s where I think I’m at” …[Before] I need[ed] everybody to be happy. This has allowed me to go, ”Oh, it’s okay, I’m not always happy either,”…something I’ve learned from this is, instead of always needing to be in that positive, happy quadrant, accepting that I can be in a negative quadrant, either with energy or with mood, and still be managing myself…that I can be okay even when I’m not in a positive energetic state, and that allows me to say for other people, oh, they can feel that way and still be—I don’t have to fix it.

For Eliza and others, it was difficult to disentangle mood changes from changes in self-awareness. That is, the experience sampling data could reflect either increased distress or acknowledgment of previously disavowed negative moods.

Two stressful episodes, both family conflicts, stood out in Eliza’s interview narratives and experience sampling data (see Figure 14). The first incident occurred shortly before her birthday. Her mother, after trying unsuccessfully to arrange a birthday dinner for Eliza, sent a card, followed by a phone call and an email, all expressing sorrow that they were not able to see one another. Eliza felt a surge of anger after each message, resenting that her mother had manipulated her into feeling guilt. She explained that the mobile therapies helped her sympathize with her mother’s intent and decide to postpone a heated conversation. She also became more comfortable with her decision to decline the dinner invitation and reserve time for herself. Nonetheless, the event took its toll. Eliza described sadness that is mirrored in her experience sampling data: During this episode, her mean ratings for sadness, but no other emotion, rose above the background level (from 0.040 to 1.88, P < .001, one-way ANOVA).

Figure 14

The time series of mood ratings echoes Eliza’s interview account of two stressful episodes; both were family conflicts that stretched over multiple days. The first episode (between 2/6 and 2/8) was characterized in mobile entries as sadness; the...

The second conflict involved Eliza’s ten-year-old son. A call from his teacher about his disruption of a class triggered her anger: “I was definitely in a rage. I was really angry. I was, you know, I was already at my wit’s end, and I’d been trying to make the afternoon nice and then, you know, all the chemical elements came together.” Shortly after, she regretted lashing out at her son for simply having fun with his friend during class. That afternoon they sat down together, exploring the mobile therapies and scales to process the conflict. Her son started to understand not only his mother’s anger, but also his anger at the teacher. During this episode too, Eliza’s sadness rose significantly above the background level (0.40) to 1.87 (P < .001 one-way ANOVA). In addition, her anger, typically near zero, increased to 0.94 (P = .008), and her happiness fell from 5.53 to 4.47 (P = .002).

Eliza’s phone entries characterize the first event as more disappointing and the second as more infuriating. She used the mobile therapies for anger management and conflict resolution heavily during both episodes; in fact, one half of her usages of these therapies throughout the study fell on the dates of those episodes. For Eliza, negative affect may have been experienced primarily as sadness, but at a certain threshold developed to include anger.

Question

Digital Cell Phone, Inc.

Paul Jordan has just been hired as a management analyst at Digital Cell Phone, Inc. Digital Cell manufactures a broad line of phones for the consumer market. Paul’s boss, John Smithers, chief operations officer, has asked Paul to stop by his office this morning. After a brief exchange of pleasantries over a cup of coffee, he says he has a special assignment for Paul: “We’ve always just made an educated guess about how many phones we need to make each month. Usually we just look at how many we sold last month and plan to produce about the same number. This sometimes works fine. But most months we either have too many phones in inventory or we are out of stock. Neither situation is good.”

Handing Paul the table shown here, Smithers continues, “Here are our actual orders entered for the past 36 months. There are 144 phones per case. I was hoping that since you graduated recently from the University of Alaska, you might have studied some techniques that would help us plan better. It’s been awhile since I was in college—I think I forgot most of the details I learned then. I’d like you to analyze these data and give me an idea of what our business will look like over the next 6 to 12 months. Do you think you can handle this?”

“Of course,” Paul replies, sounding more confident than he really is. “How much time do I have?”

“I need your report on the Monday before Thanksgiving—that would be November 20th. I plan to take it home with me and read it during the holiday. Since I’m sure you will not be around during the holiday, be sure that you explain things carefully so that I can understand your recommendation without having to ask you any more questions. Since you are new to the company, you should know that I like to see all the details and complete justification for recommendations from my staff.”

With that, Paul was dismissed. Arriving back at his office, he began his analysis.

TABLE—Orders Received, by Month

Month

Cases
Year 1

Cases
Year 2

Cases
Year 3

January

480

575

608

February

436

527

597

March

482

540

612

April

448

502

603

May

458

508

628

June

489

573

605

July

498

508

627

August

430

498

578

September

444

485

585

October

496

526

581

November

487

552

632

December

525

587

656


Discuss


1. Prepare Paul Jordan’s report to John Smithers using regression analysis. Provide a summary of the cell phone industry outlook as part of Paul’s response.

2. Adding seasonality into your model, how does the analysis change?

Summary

This question belongs to statistics and discusses about regression analysis of Digital Cell Phone Inc.

Word count: NA

 

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